Science, Irving K. Barber Faculty of (Okanagan)
Chemistry, Department of (Okanagan)
DSpace
UBCO
Greene, Alisha Lasko
Master of Science - MSc
University of British Columbia
The wine industry in British Columbia is an important sector, generating approximately $3.75 billion annually to the provincial economy and contributing over $440 million in tax revenues. Unfortunately, the industry is challenged by the effects of climate change, such as wildfires. It is accepted that compounds present in wildfire smoke can affect wine grapes and the resulting wine. Artemisia tridentata Nutt. is an indigenous plant that often grows near vineyards in the South Okanagan wine growing region. I hypothesized that volatile compounds from growing and burning A. tridentata accumulated in commercial grapevines (Vitis vinifera L.) and that wildfire smoke influenced the terroir of finished wines. To investigate this hypothesis, my specific objectives were: (a) to develop and validate a method for detection and quantification of terpenes in Vitis vinifera L. grapevine leaves and wine; (b) to determine Artemisia tridentata derived terpenes in Vitis vinifera leaves after different passive and extracted exposures to Artemisia tridentata; and (c) to characterize smoky wine and award-winning wines from the South Okanagan wine growing region using metabolomics. My data show that terpenes released in burning A. tridentata accumulated in Vitis vinifera leaves. Both terpenes detected in the A. tridentata extracts and their degradation products were also detected in the treated leaves. After a metabolomic investigation of very smoky wine and unaffected wine produced by the same winery a year later, two hypotheses were generated: 1) wildfire stresses and smoke exposure increase the accumulation of nitrogen through the arginine and proline pathways, potentially resulting in unpalatable flavour metabolites such as ornithine, putrescine, and others; and 2) wildfire and smoke exposure alter the biosynthesis of tannins and the metabolism of quercetins during grape development thereby altering the character of wines. Together, these data improve our understanding of smoke events on grapevine chemistry and terroir.
INVESTIGATING THE EFFECTS OF SMOKE ON GRAPEVINE CHEMISTRY AND TERROIR USING ARTEMISIA TRIDENTATA NUTT. DERIVED TERPENES AND METABOLOMICS AS A HYPOTHESIS-GENERATING TOOL by Alisha Lasko Greene B.Sc., University of British Columbia Okanagan, 2022 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Chemistry) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) August 2024 © Alisha Greene, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: Investigating the Effects of Smoke on Grapevine Chemistry and Terroir Using Artemisia tridentata Nutt. Derived Terpenes and Metabolomics as a Hypothesis-Generating Tool submitted by Alisha Lasko Greene in partial fulfillment of the requirements of the degree of Master of Science. Dr. Susan Murch, Irving K. Barber Faculty of Science, UBC Okanagan Supervisor Dr. Lauren Erland, Agriculture, Faculty of Science, University of the Fraser Valley Supervisory Committee Member Dr. Paul Shipley, Irving K. Barber Faculty of Science, UBC Okanagan Supervisory Committee Member Dr. Sanjoy Ghosh, Irving K. Barber Faculty of Science, UBC Okanagan University Examiner ggg iii Abstract The wine industry in British Columbia is an important sector, generating approximately $3.75 billion annually to the provincial economy and contributing over $440 million in tax revenues. Unfortunately, the industry is challenged by the effects of climate change, such as wildfires. It is accepted that compounds present in wildfire smoke can affect wine grapes and the resulting wine. Artemisia tridentata Nutt. is an indigenous plant that often grows near vineyards in the South Okanagan wine growing region. I hypothesized that volatile compounds from growing and burning A. tridentata accumulated in commercial grapevines (Vitis vinifera L.) and that wildfire smoke influenced the terroir of finished wines. To investigate this hypothesis, my specific objectives were: (a) to develop and validate a method for detection and quantification of terpenes in Vitis vinifera L. grapevine leaves and wine; (b) to determine Artemisia tridentata derived terpenes in Vitis vinifera leaves after different passive and extracted exposures to Artemisia tridentata; and (c) to characterize smoky wine and award-winning wines from the South Okanagan wine growing region using metabolomics. My data show that terpenes released in burning A. tridentata accumulated in Vitis vinifera leaves. Both terpenes detected in the A. tridentata extracts and their degradation products were also detected in the treated leaves. After a metabolomic investigation of very smoky wine and unaffected wine produced by the same winery a year later, two hypotheses were generated: 1) wildfire stresses and smoke exposure increase the accumulation of nitrogen through the arginine and proline pathways, potentially resulting in unpalatable flavour metabolites such as ornithine, putrescine, and others; and 2) wildfire and smoke exposure alter the biosynthesis of tannins and the metabolism of quercetins during grape development thereby altering the character of wines. Together, these data improve our understanding of smoke events on grapevine chemistry and terroir. iv Lay Summary The wine industry is facing challenges due to wildfire smoke such as smoke tainted wine. Terroir is the idea that the environment grapevines are grown in affects the finished wines. I hypothesized that volatile compounds from growing and burning big sagebrush accumulate in commercial grapevines, and that wildfire smoke influences the terroir of finished wines from the South Okanagan wine growing region. I found that volatile compounds known as terpenes originating from Big Sagebrush accumulated in grapevine leaves. I also found differences in the wine chemistry of very smoky wine when it was compared with wine produced by the same winery a year later, but unaffected by wildfire smoke events. These data inform future research on the effects of smoke events on grapevine chemistry and terroir. v Preface Chapter 2: Chapter 2 outlines the development and single lab validation of a GC-MS method for detection and quantification of terpenes in Vitis vinifera L. leaves. I performed the experimental work and data analysis. I designed the experiment with Dr. Murch, who participated in manuscript preparation. Dr. Robert O’Brien provided resources and participated in manuscript preparation. The results of this work have been submitted for publication in: Greene, A., Riordan-Short, S., Hayward, R., O’Brien, R., Murch, S.J. Single-Laboratory Validation for the Determination of 31 Terpenes in Vitis Vinifera L. Leaves by Chromatography and Mass Spectrometry. Assoc. Off. Anal. Chem. 2024. Chapter 3: Chapter 3 is based on work I conducted in Dr. Susan Murch’s lab at UBC Okanagan and Supra Research and Development in Kelowna British Columbia. I performed the experimental work and data analysis. I designed the experiment with Dr. Susan Murch, who participated with manuscript preparation. Dr. Robert O’Brien provided resources and participated in manuscript preparation. Samples were collected with the help of Mr. Sam Baptiste. Corresponding vouchers are deposited at the Beaty Diversity Museum (UBC). The results of this work are being prepared for publication as: Greene, A., Murch, S.J., O’Brien, R. (2024) Smoke Signaling: Volatile terpenes released in burning Artemisia tridentata Nutt. Are accumulated in grapevines. Chapter 4: Chapter 4 is based on work I conducted in Dr. Susan Murch’s lab at UBC Okanagan and Supra Research and Development in Kelowna British Columbia. I performed the experimental work and data analysis. I designed the experiment with Dr. Susan Murch, who participated with vi manuscript preparation. Dr. Robert O’Brien provided resources and participated in manuscript preparation. Data from this work was presented as “Metabolomics to Investigate Smoke Tainted and Award-Winning Wines from the Okanagan” at the Canadian Metabolomics Conference (2024) in Vancouver, British Columbia, and at the Wine Production and Climate Change Symposium (2024) in Kelowna, British Columbia. The results of this work are being prepared for publication as: Greene, A., Sandhu, P.K., Hall, B., Hayward, R., Murch, S.J. (2024) Wine Metabolomics: Understanding the impacts of climate change on vineyards and wines. Generative artificial intelligence assisted in writing the Python code that was used to generate the data figures in chapters 3 and 4. vii Table of Contents Abstract .................................................................................................................................... iii Lay Summary ........................................................................................................................... iv Preface ....................................................................................................................................... v Table of Contents .................................................................................................................... vii List of Tables .......................................................................................................................... xiii List of Figures ........................................................................................................................ xiv List of Abbreviations ............................................................................................................. xxi Acknowledgements ............................................................................................................. xxiii Dedication ............................................................................................................................ xxiv Chapter 1: Introduction ............................................................................................................ 1 1.1 Wildfires in British Columbia ..................................................................................... 1 1.2 The Wine Industry in British Columbia ...................................................................... 3 1.3 Chemistry of Wildfire Smoke ..................................................................................... 4 1.4 Impacts of Smoke-Derived Volatiles on Grapes and Wine ........................................ 7 1.4.1 Terpenes and Other Aromatics ........................................................................... 8 1.4.2 Volatile compounds and smoke taint ..................................................................11 1.4.2.1 Biomarkers of Smoke Taint ........................................................................11 1.4.2.2 Glycoconjugation of Volatiles .....................................................................12 1.4.3 Thiophenols .......................................................................................................14 1.5 Methods of Measuring Terpenes .................................................................................14 1.5.1 Instruments Used to Measure Terpenes ............................................................14 1.5.2 GC-MS for Terpene Analysis .............................................................................16 1.5.3 Gas Chromatographic Column Types ................................................................17 1.6 Terroir ......................................................................................................................18 1.7 Metabolomics ..........................................................................................................19 viii 1.8 Sources of Natural Terpenes in the South Okanagan – Artemisia tridentata Nutt. ...19 1.9 Hypothesis and Objectives ......................................................................................20 Chapter 2: Development and Single-Laboratory Validation for the Determination of 31 Terpenes in Vitis vinifera L. Leaves by Gas-Chromatography and Mass Spectrometry .... 22 2.1 Synopsis of Chapter ................................................................................................22 2.2 Materials and Methodshhh .......................................................................................23 2.2.1 Plant Material .............................................................................................23 2.2.2 Reference Standards .................................................................................23 2.2.3 Preparation of Test Samples ......................................................................25 2.2.4 Preparation of Calibration Solutions ...........................................................26 2.2.5 Separation, Detection, and Quantification of Terpenes .....................................26 2.2.5.1 Gas Chromatography Separation ........................................................26 2.2.5.2 Mass Spectrometry Detection .............................................................27 2.2.6 Method Performance Characteristics .........................................................27 2.2.6.1 Selectivity ................................................................................................27 2.2.6.2 Linearity ..................................................................................................28 2.2.6.3 LOD and lower LOQ (LLOQ) ...................................................................28 2.2.6.4 Precision (Repeatability) .........................................................................28 2.2.6.5 Accuracy .................................................................................................29 2.2.6.6 Robustness .............................................................................................29 2.3 Results and Discussion ...........................................................................................30 2.3.1 Selectivity ...................................................................................................30 2.3.2 Linearity .....................................................................................................32 2.3.3 LOD and LLOQ ..........................................................................................34 2.3.4 Precision (Repeatability) ............................................................................36 2.3.5 Accuracy ....................................................................................................38 ix 2.3.6 Robustness ................................................................................................40 2.4 Conclusions .............................................................................................................41 Chapter 3: Smoke Signaling: Volatile terpenes released in burning Artemisia tridentata Nutt. Are accumulated in grapevines .................................................................................... 42 3.1 Synopsis of Chapter ................................................................................................42 3.2 Materials and Methods ............................................................................................42 3.2.1 Chemicals and Standards ..........................................................................42 3.2.2 Field Collections of Artemisia tridentata Nutt. .............................................43 3.2.3 Chemical Extractions of Artemisia tridentata Nutt. ......................................43 3.2.3.1 Preparation of Supercritical Pentane Extract (SCPE) ..............................43 3.2.3.2 Smoke Capture Extract (SCE) .................................................................45 3.2.4 Grapevine Growing Conditions ...................................................................46 3.2.5 Experimental Procedure .............................................................................46 3.2.5.1 Co-culture of Grapevines and Artemisia tridentata ..................................46 3.2.5.2 Artemisia tridentata Controlled Burn Experiment .....................................46 3.2.5.3 Artemisia tridentata Extract Sprays .........................................................47 3.2.5.4 Natural Wildfire Event..............................................................................47 3.2.6 Chemical Analysis Methods .......................................................................47 3.2.6.1 Determination of Terpene Content in Grapevine Leaves .........................47 3.2.6.2 Determination of Terpene Content in Artemisia tridentata Extract ...........48 3.2.6.3 Untargeted Analysis of Leaf Material .......................................................49 3.2.6.4 Multivariate Analysis ..................................................................................50 3.2.6.5 Identification of Karrikins .........................................................................50 3.3 Results ....................................................................................................................51 3.3.1 Terpenes in Artemisia tridentata Extracts ...................................................51 3.3.2 Co-culture of Artemisia tridentata and Vitis vinifera ....................................53 x 3.3.3 Smoke Capture Extract (SCE) Sprays ........................................................55 3.3.4 Supercritical Fluid Extract (SCPE) Sprays ..................................................55 3.3.5 Controlled Burn ..........................................................................................66 3.3.6 Wildfire Event .............................................................................................68 3.3.7 Comparison of the Wildfire and Controlled Burn .........................................71 3.4 Discussion ...............................................................................................................75 3.5 Conclusions .............................................................................................................76 Chapter 4: Untargeted metabolomics of smoky wines......................................................... 77 4.1 Synopsis of Chapter ................................................................................................77 4.2 Materials and Methods ............................................................................................77 4.2.1 Wine Samples ............................................................................................77 4.2.1.2 Smoky Rosé ............................................................................................77 4.2.1.3 Saignée Rosé .........................................................................................78 4.2.1.4 Positive Control Wines ............................................................................78 4.2.2 Volatile Phenol Analysis of Wines ..............................................................79 4.2.3 Metabolomics Analysis ...............................................................................80 4.2.3.1 Sample Preparation ................................................................................80 4.2.3.2 Chromatography and Mass Spectrometry ...............................................80 4.2.3.3 Data Processing and Annotation .............................................................81 4.2.3.4 Multivariate Analysis ...............................................................................81 4.2.3.5 Detection of Significant Ions ....................................................................81 4.2.3.6 Pathway Analysis ....................................................................................82 4.2.3.7 Compound Identification ..........................................................................82 4.3 Results ....................................................................................................................82 4.3.1 Volatile Phenols .................................................................................................82 4.3.2 Metabolomics of Wine ................................................................................83 xi 4.3.2.1 Data Quality and Characteristics .............................................................83 4.3.2.2 Multivariate Analyses .................................................................................84 4.3.2.3 Pathway Analysis ....................................................................................86 4.3.2.4 Arginine and Proline metabolism ................................................................91 4.3.2.5 Flavonoid Biosynthesis ............................................................................ 100 4.4 Conclusions ........................................................................................................... 104 Chapter 5: Conclusion .......................................................................................................... 105 5.1 Objective 1 ............................................................................................................ 105 5.2 Objective 2 ............................................................................................................ 105 5.3 Objective 3 ............................................................................................................ 106 5.4 Conclusion ............................................................................................................. 107 Bibliography .......................................................................................................................... 108 Appendices ........................................................................................................................... 118 Appendix A: Supplemental Figures for Chapter 2 ............................................................ 118 xii xiii List of Tables Table 1. Key compounds in grapes and wine. ...........................................................................10 Table 2. Purity and reference information for analytes. .............................................................24 Table 3. Calibrator concentrations used to determine concentrations of terpenes. ....................26 Table 4. Experimental design of robustness test. ......................................................................30 Table 5. Equation of the line and R2 values. .............................................................................33 Table 6. LOD and LOQ determinations by USP and AOAC. .....................................................35 Table 7. Within-day, between-day, and HorRat precision measurements. .................................37 Table 8. Analyte recovery at three concentrations. ....................................................................39 Table 9. Features detected in the wine metabolomics analysis. ................................................84 Table 10. Pathways identified with significantly different features using MetaboAnalyst's algorithm. ..................................................................................................................................87 Table 11. Pathway analysis results generated with Pattern Hunter's results. ............................89 Table 12. Matched compounds in the arginine and proline metabolism pathway. .....................92 Table 13. Matched compounds in the flavonoid biosynthesis pathway. ................................... 101 xiv List of Figures Figure 1.Total hectares and number of fires burned in British Columbia between 2010 and 2023. .................................................................................................................................................. 2 Figure 2. A map of Southern British Columbia showing the overlap of historical fires up to 2022 and vineyard locations.10 ............................................................................................................ 4 Figure 3. Various representative guaiacol glycoconjugates, generated using ChemDraw.59 ......13 Figure 4. A Web of Science™ search performed on August 30, 2023, returned 237 results for the search terms "Vitis vinifera L." and "terpenes" in indexed journals since 2000. ...................15 Figure 5. Instruments used between 2000 and 2023 to detect and quantify terpenes in Vitis vinifera L. species in published literature. ..................................................................................16 Figure 6. Extraction methods used in published literature between 2000 and 2023 to analyze terpenes in Vitis vinifera L. species. ..........................................................................................17 Figure 7. GC columns used between 2000 and 2023 to detect and quantify terpenes in Vitis vinifera L. berries and wine via GC-MS. ....................................................................................18 Figure 8. Total ion chromatogram of grapevine leaf samples fortified at 500 (top), 1000 (middle), and 5000 ng/g (bottom). ............................................................................................................31 Figure 9. Surface plot of 31 terpenes and their responses to the seven factors.........................41 Figure 10: Bespoke supercritical pentane extraction (SCPE) apparatus used to prepare a concentrated extract from dried leaves of A. tridentata. (A) Apparatus design. (B) Set-up and extraction. (C) Pentane extract prior to rotary evaporation. (D) Final extraction. ........................44 Figure 11: Dried Artemisia tridentata leaves were volatilized with a commercial Volcano system. Volatiles were collected in a Rotary evaporator apparatus with plumbing reversed and a chilled acetone bath. ............................................................................................................................45 Figure 12: Detection of terpenes in Artemisia tridentata Nutt. dried plants and extracts. (A) Dried leaves harvested from wild plants. (B) Supercritical pentane extract (SCPE). (C) Smoke capture in ethanol (SCE). .......................................................................................................................52 xv Figure 13. Concentrations of monoterpenes in grapevines co-cultured with Artemisia tridentata in a controlled environmental chamber for 28 days (n=9). Missing boxes indicate the terpene was not detected. Statistically significant differences by unpaired t-test comparing to Day 0 (α=0.05) indicated with: (*) p ≤ 0.01. .........................................................................................54 Figure 14. Concentrations of monoterpenes detected in grapevines sprayed with an Artemisia tridentata supercritical fluid extract (n=3). The absence of a box means the terpene was not detected. Statistically significant differences by unpaired t-test (α=0.05) comparing to the control indicated with (*) p ≤ 0.05, (**) p ≤ 0.01, and p ≤ 0.001. ............................................................55 Figure 15. Degradation metabolites of camphor detected in A. tridentata-exposed Vitis vinifera (n=9). Camphor was quantified using a targeted method with an authentic standard, while all other metabolites are putatively identified terpenes using an untargeted method. The absence of a box means the metabolite was not detected. Statistically significant differences comparing to the control by Mann-Whitney U test (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.001. ...........57 Figure 16. Degradation metabolites of α-pinene detected in A. tridentata-exposed Vitis vinifera (n=9). Black arrows indicate all four treatments are detected in that part of the metabolic pathway. The absence of a box means the metabolite was not detected. Statistically significant differences comparing to the control by Mann-Whitney U test (α=0.05) indicated with: (*) p ≤ 0.05. ..........................................................................................................................................59 Figure 17. Monoterpenes detected in A. tridentata-exposed Vitis vinifera (n=9). The absence of a box means the metabolite was not detected. Statistically significant differences comparing to the control by Mann-Whitney U test (α=0.05) indicated with: (*) p ≤ 0.05. .................................61 Figure 18. Degradation metabolites of (6E)-oxogeraniol detected in A. tridentata-exposed Vitis vinifera (n=9). The absence of a box means the metabolite was not detected. Statistically significant differences comparing to the control by Mann-Whitney U test (α=0.05) indicated with: (*) p ≤ 0.05. ...............................................................................................................................63 Figure 19. Degradation metabolites of iridotriol detected in A. tridentata-exposed Vitis .............65 xvi Figure 20. Concentrations of monoterpenes detected in grapevines exposed to wildfire smoke in the Plant Growth Facility (smoked) and co-cultured with Artemisia tridentata Nutt. (n=9). The absence of a box means the metabolite was not detected. Statistically significant differences comparing to the control by t-test (α=0.05) indicated with: (*) p ≤ 0.005. .... Error! Bookmark not defined. Figure 21. Climate data in the Plant Growth Facility during the 2023 UBC Okanagan campus wildfire evacuation, noted in orange. (A) Temperature (°C). (B) Roof vent opening (% of day). (C) Photosynthetic Active Radiation (PAR). (D) Humidity (%). ..................................................69 Figure 22. Concentration of piperitone and eucalyptol detected in grapevines exposed to wildfire smoke, (n=3). The absence of a bar means the metabolite was not detected. Statistically significant differences by t-test (α=0.05) comparing to the control are indicated with: (*) p ≤ 0.05. .................................................................................................................................................70 Figure 23. Principal component analysis for the untargeted metabolomics data of the control, smoked leaves, and wildfire-exposed grapevine leaves. Contour lines depict the density contours of data points of higher density within the plot. ...........................................................72 Figure 24. Scree plot showing the proportion of variance explained by each principal component. ...............................................................................................................................73 Figure 25. Putatively identified M+H adducts of karrikins in grapevine leaves exposed to wildfire smoke and A. tridentata extract (n=9). (A) Karrikin 1. (B): Karrikin 2. (C): Karrikins 3 + 4. .........74 Figure 26. Free and bound volatile phenol profiles of smoke-exposed 2021 vintage and 2022 vintage. Error bars represent standard error. ............................................................................83 Figure 27. Principal component analysis of Saignée, smoky Rosé, and premium award-winning red wines. Contour lines depict the density contours of data points of higher density within the plot. ...........................................................................................................................................85 Figure 28. Scree plot showing the proportion of variance explained for each component. .........86 xvii Figure 29. Amino acids leading to L-glutamate detected in wines. The absence of a box indicates the metabolite was not detected. Statistically significant differences by Mann-Whitney U test comparing to the Saignée (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.01, and (***) p ≤ 0.005. ........................................................................................................................................94 Figure 30. Amino acids leading to 4-aminobutanoate detected in wines. Statistically significant differences by Mann-Whitney U test comparing to the Saignée (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.01, and (***) p ≤ 0.005. ......................................................................................95 Figure 31. Amino acids leading to proline detected in wines. Statistically significant differences by Mann-Whitney U test comparing to the Saignée (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.01, and (***) p ≤ 0.005. ...........................................................................................................97 Figure 32. Amino acids leading to N-carbamoyl putrescine detected in wines. Statistically significant differences by Mann-Whitney U test comparing to the Saignée (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.01, and (***) p ≤ 0.005. ...................................................................98 Figure 33. Amino acids leading to 4-aminobutanoate detected in wines. S Statistically significant differences by Mann-Whitney U test comparing to the Saignée (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.01, and (***) p ≤ 0.005. ......................................................................................99 Figure 34. Putatively identified flavonols leading to quercetin in the flavonoid biosynthesis pathway detected in wines. Statistically significant differences by Mann-Whitney U test comparing to the Saignée (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.01, and (***) p ≤ 0.005. ............................................................................................................................................... 102 Figure 35. Putatively identified flavonols in the flavonoid biosynthesis pathway detected in wines. Statistically significant differences by Mann-Whitney U test comparing to the Saignée (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.01, and (***) p ≤ 0.005. .................................... 103 Appendix Figure 1. Standard curve for camphene. ................................................................. 118 xviii Appendix Figure 2. Residual plot for camphene. ..................................................................... 118 Appendix Figure 3. Standard curve of limonene. ..................................................................... 119 Appendix Figure 4. Residual plot for limonene. ....................................................................... 119 Appendix Figure 5. Standard curve of sabinene. ..................................................................... 120 Appendix Figure 6. Residual plot for sabinene. ....................................................................... 120 Appendix Figure 7. Standard curve of p-cymene. .................................................................... 121 Appendix Figure 8. Residual plot for p-cymene. ...................................................................... 121 Appendix Figure 9. Standard curve of m-Cymene. .................................................................. 122 Appendix Figure 10. Residual plot for m-cymene. ................................................................... 122 Appendix Figure 11. Standard curve of eucalyptol. ................................................................. 123 Appendix Figure 12. Residual plot for eucalyptol. .................................................................... 123 Appendix Figure 13. Standard curve of o-Cymene. ................................................................. 124 Appendix Figure 14. Residual plot for o-cymene. .................................................................... 124 Appendix Figure 15. Standard curve of linalool. ...................................................................... 125 Appendix Figure 16. Residual plot for linalool. ........................................................................ 125 Appendix Figure 17. Standard curve of sabinene hydrate. ...................................................... 126 Appendix Figure 18. Residual plot for sabinene hydrate. ........................................................ 126 Appendix Figure 19. Standard curve of isopulegol. ................................................................. 127 Appendix Figure 20. Residual plot for isopulegol. .................................................................... 127 Appendix Figure 21. Standard curve of fenchol. ...................................................................... 128 Appendix Figure 22. Residual plot for fenchol. ........................................................................ 128 Appendix Figure 23. Standard curve of terpinolene. ................................................................ 129 Appendix Figure 24. Residual plot for terpinolene. .................................................................. 129 Appendix Figure 25. Standard curve of fenchone. ................................................................... 130 Appendix Figure 26. Residual plot for fenchone. ..................................................................... 130 Appendix Figure 27. Standard curve of isoborneol. ................................................................. 131 xix Appendix Figure 28. Residual plot for isoborneol. ................................................................... 131 Appendix Figure 29. Standard curve of pulegone. ................................................................... 132 Appendix Figure 30. Residual plot for pulegone. ..................................................................... 132 Appendix Figure 31. Standard curve of verbenone. ................................................................ 133 Appendix Figure 32. Residual plot for verbenone. ................................................................... 133 Appendix Figure 33. Standard curve of safranal. ..................................................................... 134 Appendix Figure 34. Residual plot for safranal. ....................................................................... 134 Appendix Figure 35. Standard curve of carvone. ..................................................................... 135 Appendix Figure 36. Residual plot for carvone. ....................................................................... 135 Appendix Figure 37. Standard curve of α-thujone. .................................................................. 136 Appendix Figure 38. Residual plot for α-thujone. ..................................................................... 136 Appendix Figure 39. Standard curve of γ-terpinene. ............................................................... 137 Appendix Figure 40. Residual plot for γ-terpinene. .................................................................. 137 Appendix Figure 41. Standard curve of α-terpineol. ................................................................ 138 Appendix Figure 42. Residual plot for α-terpineol. ................................................................... 138 Appendix Figure 43. Standard curve of α-phellandrene. ......................................................... 139 Appendix Figure 44. Residual plot for α-phellandrene. ............................................................ 139 Appendix Figure 45. Standard curve of α-terpinene. ............................................................... 140 Appendix Figure 46. Residual plot for α-terpinene. .................................................................. 140 Appendix Figure 47. Standard curve of α-cedrene. ................................................................. 141 Appendix Figure 48. Residual plot for α-cedrene..................................................................... 141 Appendix Figure 49. Standard curve of δ-3-carene. ................................................................ 142 Appendix Figure 50. Residual plot of δ-3-carene. .................................................................... 142 Appendix Figure 51. Standard curve of β-pinene. ................................................................... 143 Appendix Figure 52. Residual plot of β-pinene. ....................................................................... 143 Appendix Figure 53. Standard curve of α-pinene. ................................................................... 144 xx Appendix Figure 54. Residual plot of α-pinene. ....................................................................... 144 Appendix Figure 55. Standard curve of terpinen-4-ol. ............................................................. 145 Appendix Figure 56. Residual plot of terpinen-4-ol. ................................................................. 145 Appendix Figure 57. Standard curve of trans-β-ocimene. ........................................................ 146 Appendix Figure 58. Residual plot of trans-β-ocimene. ........................................................... 146 xxi List of Abbreviations 2D-GC-ToF-MS: Two-dimensional gas chromatography time-of-flight-mass spectrometry 4EG: 4-Ethylguaiacol 4MG: 4-Methylguaiacol 4MS: 4-methylsyringol AOAC: Association of Official Analytical Chemists BBOA: biomass burning organic aerosol BVOC: Biogenic volatile organic compound DAD: Diode array detectors FID: Flame ionization detector GC: Gas chromatography HPLC: High-performance liquid chromatography IPM: Integrated pest management KARs: Karrikins LC: Liquid chromatography LLE: Liquid-liquid extraction LOD: Limit of detection LLOQ: Lower limit of quantification LMW: Llower-molecular-weight MS: Mass spectrometry MPS: Methylpolysiloxane MS/MS: Tandem mass spectrometry OVOC: Oxygenated volatile organic compound PAH: Polycyclic aromatic hydrocarbons xxii PCA: Principal component analysis PLS-DA: Partial least squares discriminant analysis POP: Persistent organic pollutant PEG: Polyethylene glycol SBSE: Stir-bar sorptive extraction SCE: Smoke capture in ethanol SIM: Selected ion monitoring SLV: Single laboratory validation SOA: Secondary organic aerosol SCPE: Supercritical pentane extract SPE: Solid phase extraction SPME: Solid phase microextraction SVOC: Semi-volatile organic compound USP: United States Pharmacopheia UV: Ultraviolet VP: Volatile phenol YAN: Yeast assimilable nitrogen xxiii Acknowledgements I would like to thank everyone who was a part of my time at UBCO. I owe a major thank you to Dr. Susan Murch for her mentorship in both academia and life. Your guidance will have lasting impacts on my future and career. I would also like to acknowledge my committee members, Dr. Lauren Erland and Dr. Paul Shipley, for their support and direction. I am proud of the work presented here because the three members of my committee helped me to grow as a researcher. I am forever grateful to the former and current members of the PlantSMART lab. My presentations would have been quite sad if not for the feedback of Pawanjit Sandhu, Daniel Gaudet, Adeyemi Adebowale, Karine Jarzecki, Olivia Carlson, Barbara Hall, and Julia Solonenka. I could not have asked for more collaborative and fun lab mates to spend the last two years with. I would like to thank Dr. Robert O’Brien for his support, guidance, and collaboration. This project would not have even begun without you. The team at Supra helped me in any and every way they could, and I will always cherish the learning environment fostered in that lab. Funding for this project was made possible by Mitacs, Supra Research and Development, and UBC Okanagan. xxiv Dedication To my parents, thank you for fostering my eagerness to learn since I was a little girl. To my other half, your support and love over the past few years made this possible. My sisters, you two are my rocks. The support from the rest of my family kept me going when I didn’t think that I could. Thank you to all my friends for listening to my endless complaining, but also for letting me gush when I’m excited about the project even though you had no idea what I was talking about. 1 Chapter 1: Introduction Click or tap here to enter text. 1.1 Wildfires in British Columbia It is well accepted that wildfires are increasing in intensity and frequency due to climate change and management practices.1 The Government of Canada projects a potential doubling of the amount of area burned by the end of this century when compared with amounts burned in recent decades.1 In British Columbia, the 2017 and 2018 fire seasons burned over three times the hectares as the previous seven years, as seen in Figure 1.2 Weather was a major factor for the 2018 fire season as July saw record-breaking temperatures and severe lightning storms.2 More recently in 2023, the McDougall Creek Wildfire was the largest wildfire in the history of West Kelowna and caused the evacuation of over 10,000 residents.3 2 Figure 1.Total hectares and number of fires burned in British Columbia between 2010 and 2023. 3 1.2 The Wine Industry in British Columbia The wine industry in British Columbia is a major economic driver, generating an estimated $3.75 billion annually.4 The province is home to over 1,200 vineyards with more than 12,681 acres of planted land, attracting more than one million visitors each year.4,5 The Okanagan is the major wine producing region of the province, boasting 10,920 acres of vines.6 Despite being smaller than Old World wine producers such as France and Italy, British Columbia has made its mark globally, with over 1200 wines ranking as top finishers in prestigious competitions.5 The strength of the wine industry depends on consistent harvests with abundant and high-quality yields.7 Although vineyard acreage remained constant, there was a 33% decline in tons harvested between 2018 and 2021.7 Rising average temperatures, extreme heat, extreme cold, and lower levels of precipitation all play a part.7 One study found that the Okanagan has already transitioned from cool to intermediate climate viticulture, with the higher likelihood of more unseasonable weather patterns.8 The consequence of this finding is that heat stress will become a bigger hurdle for the industry, with the threat of freeze-damage slowly diminishing. Although B.C. is not the only province or wine region affected by these issues, the industry will undoubtedly take a hit due to climate change. Wildfires can affect both the product and the experience when it comes to the wine industry. Volatile phenols present in forest fire smoke can accumulate in Vitis vinifera L. berries, resulting in the wine fault known as smoke taint. One winemaker publicly admitted to not bottling their 2021 vintage, so as not to damage their reputation.9 The tourism experience is also affected, as one winery reported an 80% decrease in traffic at their popular Okanagan winery due to the 2021 smoke in the valley.9 Figure 2 shows the overlap between vineyards in the province and historical data for fires up to the year 2022. There are no regions in which vineyards are unaffected by wildfires. To make matters worse, wildfire smoke can often travel long distances. Even when a vineyard does not 4 have to worry about being evacuated, they still must worry about how the smoke will affect their vines. Figure 2. A map of Southern British Columbia showing the overlap of historical fires up to 2022 and vineyard locations.10 1.3 Chemistry of Wildfire Smoke A recent study sampled PM2.5 (particulate matter having a diameter equal to or smaller than 2.5 mm) in the 2017 Northern California wildfires.11 They used two-dimensional gas chromatography time-of-flight-mass spectrometry (2D-GC-ToF-MS) to characterize biomass burning organic aerosol (BBOA). Biomass burning refers to the combustion of organic materials. Sugars made up over one third of the organic aerosol, with acids and aromatics trailing behind (Figure 3). There were also oxygenated compounds, alcohols, alkanes, terpenoids, nitrogen-5 containing compounds, and polycyclic aromatic compounds (PAHs) present. Their estimated origin is also included in Figure 3. Most identified compounds could be traced to urban aerosol (particles suspended in air originating from urban sources like vehicle emissions) if not biomass burning. Figure 3. Number of putatively identified classes found in smoke from the 2017 Northern California fires with their most likely origin.11 Levoglucosan made up about 20% of the BBOA species measured.11 Levoglucosan and mannosan (Figure 4) are decomposition products of cellulose and hemicellulose that most plants emit during combustion.11 Levoglucosan will either further degrade to lower-molecular-weight (LMW) products or polymerize into polysaccharides which will eventually form char.12 The LMW products can be furfural, 5-hydroxymethylfurfural, glycoaldehyde, hydroxyacetone, acetic acid, and formic acid.13 0102030405060Number of putatively identified compoundsCompound ClassBiomass BurningOther Souce6 Figure 4. Structures of levoglucosan (left) and mannosan (right). Biogenic volatile organic compounds (BVOCs) are non-methane hydrocarbons made and emitted by plants.14 They are involved in plant growth, reproduction, and defense.15 Isoprene and monoterpenes are the most prominent group of BVOCs, followed by alcohols and carbonyls. They are also found in wildfire smoke, with isoprene being known as a major component from various types of biomass burning.16 Researchers in 2011 found that the pinenes (alpha and beta) contributed 20.7% of the BVOCs in Canadian smoke plumes, highlighting their importance.16 BVOC emissions are expected to increase due to increased wildfire activity.15 Terpenoids are a major class of secondary organic aerosol (SOA) precursors.17 Many plants, particularly conifers, produce and store resins that are rich in monoterpenes, sesquiterpenes, and di- or triterpenes.17 When the vegetation is heated, the resin constituents are released via distillation out of the storage ducts or glands, so vegetation fires of resinous fuels have the potential for high emissions of terpenoids.17 Diterpenoids (including resin acids) are unique markers in biomass burning emissions for conifer combustion.11 The nitrogen content of biomass is known to be small, and it is mostly in the form of proteins.18 When bark was pyrolyzed in laboratory studies, the main nitrogen-containing species were found to be hydrogen cyanide, ammonia, and isocyanic acid.19 A recent study in 2022 found that biomass burning contributes approximately 27% to oxygenated volatile organic compounds (OVOCs), and formaldehyde, acetaldehyde, and acetone account for about 60%.20 As for aromatic compounds, laboratory studies showed oxygenated aromatics such as phenols and 7 methoxyphenols accounted for just under 60% of the secondary organic aerosol (SOA) generated.21 Alkanes and polycyclic aromatic hydrocarbons (PAHs) are semi-volatile organic compounds (SVOCs).22 PAHs are persistent organic pollutants (POPs) commonly found in the atmosphere. They are generated by the incomplete combustion of biomass and fossil fuels.23 The PM2.5 data obtained in California in 2017 putatively identified C18-C34 alkanes, which are commonly found in aerosol.11,22 Laboratory studies have found n-alkanes in green vegetation smoke to range from C23-C35, with the odd number alkanes dominating.24 Gogou et al. reported that a Cmax in the range of 27 to 33 is characteristic of biogenic sources such as higher plant wax.22,25 Alcohols are known to be the second most predominant group in BVOCs, falling just behind terpenoids.26 The predominant alcohols found in wood smoke are methanol, ethanol, isopropanol, and furfuryl alcohol.27 As for acids, examples found in wood smoke are acetic, propionic, sorbic, butyric, salicylic, benzoic, and primaric.27 Biomass burning is thought to be a significant source of organic acids.26 1.4 Impacts of Smoke-Derived Volatiles on Grapes and Wine Many compounds present in smoke can affect plant growth. Non-volatile compounds like karrikins (KARs) which are derived from biomass burning have been shown to affect seed germination in a range of species.28 The volatile compounds in smoke such as terpenes and thiophenols can also affect plant health. Many gases in smoke have been shown to inhibit photosynthesis and cause leaf necrosis such as NO2, CO2, SO2, and O3.29,30 It is thought that the presence of smoke might affect the leaf-to-air vapor pressure difference, which has been shown to influence stomatal control and thus photosynthesis.29 Researchers in Australia noticed an immediate response to smoke exposure was the initialization of stomatal closure.29 Five varietals showed significant decreases in stomatal conductance, with Pinot Noir having nearly a 50% 8 reduction.29 Factors that contribute to the amount of smoke contamination are temperature gradients, proximity to the fire, wind vectors, and land topography.31 One study from 2009 showed Merlot berry weights to be significantly lower for smoke exposed grapes than for control, while a 2017 study found no significant differences between control and smoked grapes except for Shiraz and Pinot Noir.29,32 In the second study, any significant differences between bunch length, width, and density were seen between vineyards rather than varietals.29 Smoked vines from the 2017 study showed no observed difference in berry weight, degree of cell death or berry shrivel, and no correlation was found between stomatal conductance or gas exchange parameters and vegetative growth.29 No correlation was found for berry development and sugar accumulation either, leading the researchers to conclude that vine phenology and other plant stresses such as drought and high temperatures could impact the vine recovery after smoke exposure.29 1.4.1 Terpenes and Other Aromatics Terpenes are very important for plants, acting as secondary metabolites in plant physiological system and being involved in defense against herbivores, pathogens, biotic and abiotic stressors.33 Weather, soil type, sun exposure, and water stress are environmental factors that can all affect terpene content.33 Terpenes are very important for grapes and wine, and how their presence in smoke affects grapevine chemistry has yet to be investigated. The grape berry, yeast, and bacteria all produce glycosidase enzymes that facilitate the release of terpenes during winemaking.34 A higher Yeast Assimilable Nitrogen (YAN) content of grape must stimulates the presence of terpenes in wine.34 The characteristic floral, muscatel, and fruity aromas of Riesling, Muscat, and Gewürztraminer owe their fame to monoterpenes and sesquiterpenes.35,36 Terpene profiles can be used to authenticate true Riesling from others such as Kap or Emerald Riesling.37 These compounds are present in free and sugar-bound precursors, and were the first glycosidically-bound compounds identified in grapes and wine.34,38 Terpenes 9 found in Gewürztraminer grown in British Columbia had linalool, nerol, geraniol, citronellol, p-cymene, and terpinolene.39 Other important monoterpenes for wine aroma are alpha-terpineol and hotrienol.35 Many of the varietals’ aromas originating from terpenes also come from C13 norisoprenoids such as β-ionone or β-damascenone.35,40 Again, the majority of this class of compounds is present as glycosidic precursors.41 However, they have very low odour threshold so can have a large effect on wine at low concentrations.42 For Chardonnay, aromas such as honey, tea, lime, and grassy characteristics are derived from these compounds.40 Viticulture practices such as sun exposure, leaf removal, and cluster microclimate can affect norisoprenoid concentrations.42 It was also found that oak extract treatments done on Syrah significantly decreased the C13 concentration, while an inverse effect was seen for Chardonnay.41 Table 1 outlines terpenes important for wine’s aroma. 10 Table 1. Key compounds in grapes and wine. Compound Aroma Reference Monoterpenes Linalool Citrus, floral, lavender (Black et al., 2015) 1,4-Cineole Eucalyptus, minty, medicinal (Slaghenaufi et al., 2018) 1,8-Cineole Eucalyptus, cool (Slaghenaufi et al., 2018) Geraniol Rose, geranium (Black et al., 2015) Nerol Rose, fruity, floral (Black et al., 2015) α-Terpineol Floral, pine, fennel (Yang et al., 2019) Citronellol Green, lemon (Yang et al., 2019) Rose Oxide Floral, green (Black et al., 2015) (-)-Carvone Spearmint (Leitereg et al., 1971) (+)-Carvone Caraway oil (Leitereg et al., 1971) p-Cymene Minty, solvent (Slaghenaufi et al., 2018) C13-norisoprenoids β-Damascenone Fruity, floral, honey, balsamic (Slaghenaufi et al., 2018; Dunlevy et al., 2009) β-Ionone Sweet, violet (Dunlevy et al., 2009) 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN) Kerosene, petrol (Black et al., 2015)43,44 €-1-(2,3,6-trimethylphenyl)buta-1,3-diene (TPB) Cut-grass, tobacco, fly spray (Black et al., 2015) Sesquiterpenes Rotundone Black pepper (Black et al., 2015) 11 1.4.2 Volatile compounds and smoke taint During wildfire events, grapes are exposed to certain VPs originating from biomass burning. The wine fault known as smoke taint is associated with the concentration of VPs.45 Smoke tainted wine is said to have ashy, smoky, and medicinal characteristics.46 The main culprits are guaiacol, 4-methylguaiacol (4MG), p-,m-, and o-cresol, syringol, 4-methylsyringol (4MS), phenol, eugenol, and 4-ethylguaiacol (4EG).47 Research on these volatiles has been done extensively, and producers can have smoke taint testing done for grapes and wine, in which the aforementioned compounds are quantified. Smoke-derived volatile phenols (VPs) could enter vines through the berries via diffusion into the waxy cuticle, through the leaves, or be absorbed by the roots system.31 The evidence that VPs can enter the vines through the leaves originates from a denser canopy correlating with less smoke-derived volatiles being found in wine.31 It has been established that Vitis vinifera L. berries are most susceptible to the VPs in the seven days post-veraison, and repeated smoke exposures have a cumulative effect.48 1.4.2.1 Biomarkers of Smoke Taint Many of the volatile phenols are chemical indicators of smoke taint in grapes and wine. The burning of wood emits o-, m-, and p-cresol at significant levels.49 The often undesirable medicinal and smoky characteristics in wine are thought to be from o- and p-cresol, though their thresholds in red wine are three times that of m-cresol.50 Even when the individual volatile phenols are below threshold levels, they can have a cumulative effect and still contribute to the negative sensory characteristics.51 The biomarker m-cresol has also been found in grape botrytised samples.52 It was shown in 2009 that grape exposed to smoke for less than an hour produced wine with guaiacol concentrations ranging from 2 to 26 µg/mL; the detection threshold for guaiacol is 0.023 µg/L in red wine.32,50 However, guaiacol and 4MG are found in wine made from grapes with 12 no smoke exposure, their origin likely from oak barrel fermentation.32,48 Guaiacol and m-cresol are the most potent of the biomarkers, and have the lowest threshold.50 Although syringol is known as a good biomarker for smoke taint, its sensory threshold is much higher than guaiacol and the cresols. A baseline study done in 2022 with 13 non-smoked cultivars found the syringol and guaiacol to be highest across the red wines.53 Guaiacol and o-cresol were found to be the most abundant volatile phenols in the red grapes while syringol and o-cresol had the top concentrations in white grapes.53 Upon studying the content of sugar-bound VPs, it was found that only syringol and 4-methylsyringol were dominantly present as the conjugates.54 Brettanomyces/Dekkera spoilage can also cause the presence of 4-ethylguaiacol.55 This VP contributes notes of smoke, spice, toast, and bread.48 It has been found in much lower concentrations in smoked grape samples, nearly four times lower than 4MG.48 Another study reported high levels of 4EG in smoked strong acid hydrolysate compared to smoked finished wine, leading the authors to suggest incomplete hydrolysis of the VP during fermentation.56 The same study found 4EG to increase after 12 months post-bottling.56 Although eugenol is not a volatile phenol, the monoterpene is commonly associated with smoke taint. Eugenol has been found in trace amounts in unsmoked grapes, and nearly fifty times lower concentrations than guaiacol in smoked grapes.56 In a newer study, eugenol did not appear in different concentrations between oak treated and control grapes.57 1.4.2.2 Glycoconjugation of Volatiles Many of wine’s aroma-related volatile compounds (monoterpenes, C13-norisoprenoids, shikimates) are stored in grapes as sensorially inactive glycoconjugates.55 In 2010, a β-D-glucopyranoside precursor to guaiacol was discovered in extracts from grapes exposed to smoke, but was not present in the non-smoked extracts.55 This led to the observation that guaiacol taken up by grapevines during smoke exposure was glycosylated.55 Hayasaka et al. applied guaiacol to 13 grapevines and used stable isotope tracers to investigate the formation of conjugates. They found evidence to suggest very limited movement of the conjugates between leaf and berry bunch, leaf to leaf, or berry bunch to berry bunch.58 It was found in the same year that phenols, cresols, 4MG, syringol, and 4MS could be metabolized to glycoconjugate forms within grapes.54 This study showed that from about 20 mg/L of phenolic glycosides in smoke-affected grapes, there is the potential to release over 100 µg/L in total of volatile phenols during fermentation.54 The identified grape glycosides are comprised of a glycone directly linked to the aglycone (free VP).38 Disaccharide glycosides are the main aroma glycosides found in grapes.38 Figure 5 outlines various guaiacol glycoconjugates. To date, only guaiacol rutinoside has been quantified in grape juice or wine.47 Figure 3. Various representative guaiacol glycoconjugates, generated using ChemDraw.59 The smoke-derived volatiles can be released through more than fermentation. The enzymes in human saliva can cause the release of volatile phenols from their respective glycosides.60 This is also true for low pH and elevated ethanol conditions for experiments done in D DDD DD14 vitro and in vivo, thus enforcing that in-mouth breakdown is important in the off-flavour of smoke tainted wine.51 The location of glycosylation within the vine is still unknown, though it is hypothesized that the glycosylation step could be manipulated by vineyard management.60 It is thought that the plant chemical communication system involves low levels of volatile phenol glycoconjugates, explaining their natural presence in grapevine leaves and berries.60 However, during a smoke event vines metabolize potentially toxic amounts of foreign molecules (VPs), and the glycosylation serves as a form of detoxification.60 Higher plants are known to glycosylate foreign molecules such as phenols as a detoxification mechanism.61 In 2017, Hartl et al. demonstrated the glycosylation of VPs can occur via a glycosyltransferase (UGT72B27) that also recognizes trans-resveratrol as a substrate.60 1.4.3 Thiophenols Thiols are a major group of wine aroma compounds, responsible for the characteristic aromas of Sauvignon blanc.34 Researchers in Oregon hypothesized thiophenols as a novel class of biomarker compounds for smoke taint.62 This class of biomarkers would be extremely useful as they are not a breakdown product of lignin, and thus could not occur in the grapes from contact with oak. The work is not published at the time of this literature review, but there is a lot of excitement in the wine research community that remediation techniques could now be more successful if tailored to the thiophenols.63 1.5 Methods of Measuring Terpenes 1.5.1 Instruments Used to Measure Terpenes A Web of Science™ search for “Vitis vinifera L.” and “terpenes” conducted in August of 2023 returned 227 published articles between 2000 and 2023. Of those articles, 159 involved detecting and quantifying terpenes in Vitis vinifera L. leaves, berries, and wine (Figure 6). 15 Figure 4. A Web of Science™ search performed on August 30, 2023, returned 237 results for the search terms "Vitis vinifera L." and "terpenes" in indexed journals since 2000. Gas chromatography-mass spectrometry (GC-MS) was the predominant analytical method, making up 90% of the publications (Figure 7). In contrast, only a few studies used UV-visible spectrometry to assess monoterpenes in berries.64,65 High performance-liquid chromatography with mass spectrometry has also been used to detect the sugar-bound terpenes. The method can identify the glycosides but only some of the terpenes they were bound to.66 Moreover, HPLC, GC coupled to flame ionization detector (FID), and two-dimensional GC coupled to mass spectrometry each comprised only 3% of the published work. 16 Figure 5. Instruments used between 2000 and 2023 to detect and quantify terpenes in Vitis vinifera L. species in published literature. 1.5.2 GC-MS for Terpene Analysis GC-MS is the most popular choice for terpene analysis in grapevines, grapes, and wine and is widely used due to its superior sensitivity and selectivity compared to other detection methods (Figure 8).67 Although ultraviolet (UV) and diode array detectors (DAD) are easily accessible and economical, mass spectrometers provide better sensitivity and selectivity.67 It has lower detection limits than GC-FID, and benefits from mass spectral databases for efficient terpene identification.68 Solid phase microextraction (SPME) was the most used extraction method, followed by solid phase extraction (SPE), stir-bar sorptive extraction (SBSE), and liquid-liquid extraction (LLE), as seen in Figure 8. The fibers employed in SPME can be reused hundreds of times, offering a more sustainable method, whereas SPE generates a lot of solid waste from the cartridges.68 Additional sample preparation steps were needed to separate the bound terpenes from the sugars in the grape and wine matrices, which included enzymatic or acid hydrolysis. In studies focused on characterizing the bound terpenes in grapes and wine, acid hydrolysis was the more popular choice. 17 Figure 6. Extraction methods used in published literature between 2000 and 2023 to analyze terpenes in Vitis vinifera L. species. 1.5.3 Gas Chromatographic Column Types Wax columns are commonly used for terpene analysis in grapes and wine via GC-MS, as seen in Figure 9. These columns have a polar polyethylene glycol (PEG) stationary phase. Manufacturers tout these columns as being ideal for flavour and aroma compounds in food and beverages.69 Studies analyzing flavour compounds in grapes such as polyphenols, alcohols, esters, and terpenes, have shown that columns with a 5% Phenyl – 95% methylpolysiloxane (MPS) phase are better suited for metabolite profiling.70–72 Only 3% of studies used the high polarity nitroterephthalic acid modified PEG phase column (commercially known as FFAP).73 Two-dimensional chromatography employed a wax column connected to an MPS column.74–76 When leaf samples were analyzed, a 5-MPS column was consistently used. Finally, 3% of studies used a non-polar 100% dimethylpolysiloxane (PDMS) phase column, which separates analytes almost entirely based on their boiling points. No studies to date have employed a low 18 to mid-polarity (6% cyanopropyl-phenyl)-methylpolysiloxane column for terpene analysis in grapevine leaves, berries, or wine. Figure 7. GC columns used between 2000 and 2023 to detect and quantify terpenes in Vitis vinifera L. berries and wine via GC-MS. 1.6 Terroir Terroir is the concept that the environment the grapevines are grown in will define the unique characteristics of a finished wine.77 Climate, soil composition, and vineyard management practices can all affect terroir. Climate influences vine physiology through temperature, rainfall, wind, and sunshine hours.77 Soil composition, acidity, and nitrogen availability all affect vine growth and grape composition.77 Terroir is complex because it involves a combination of many factors, resulting in a wide range of variables.78 19 1.7 Metabolomics Metabolomics is the high-throughput analysis of small metabolites in a system.79 A search of Web of Science™ for the term “metabolomics” and “wine” finds 690 publications since 2003 but only 2 of these studies investigated the impacts of smoke.80,81 Targeted metabolomics is the detection and quantification of known metabolites and is used for authentication of wines and determination of varietal composition.82 This approach has also been used to authenticate label claims in Greek white wines by detection of specific phenolic compounds.83 Untargeted metabolomics is the detection and compound identification of unknown metabolites in biological samples and is a useful tool for understanding the effect of terroir in grapes and wine.84 For example, a metabolomics approach was used to discover the impacts of environmental conditions on sugars, amino acids, and plant hormones in wines.85 Researchers have used metabolomics to hypothesize that grape varietal influences more acidic compounds, while terroir affects more basic compounds.84 Furthermore, a metabolomics study led to the finding that volatile profiles, specifically terpene levels, were linked to geographical area.86 1.8 Sources of Natural Terpenes in the South Okanagan – Artemisia tridentata Nutt. Artemisia is a genus comprised of 50 species in North America.87 Contained within the subgenus Tridentatae, is Artemisia tridentata Nutt., with common names like Big Sagebrush and Common Sagebrush.87 It is a landscape dominant species found in Western North America.87 A. tridentata has a long list of documented medicinal uses.88 Indigenous cultures have used A. tridentata to cure tonsillitis and smallpox 89. A. tridentata is commonly found growing in the South Okanagan. The area sees low annual precipitation, hot summers, and mild winters, which create semi-arid habitats.90 Volatile compounds are regularly released by A. tridentata. It has been shown to communicate with its kin plants through volatile compounds. When the plant received volatile 20 cues released from other A. tridentata, it endured less physical damage than those that did not.91 Terpenes synthesized by A. tridentata are stored in glandular trichomes found on the surface of the plant.92 Another source of terpenes from A. tridentata is during biomass burning. Fuel from Artemisia tridentata Nutt. collected from the interior of British Columbia was dominated by oxygenated monoterpenoids such as camphor and eucalyptol.17 Some other monoterpenes found in A. tridentata are α- and β-pinene, p-cymene, α-phellandrene, and limonene. 1.9 Hypothesis and Objectives The primary objective of this research is to investigate the effect of smoke on grapevine chemistry and terroir. To achieve this, I first explored the chemical responses of grapevines when they are exposed to externally derived terpenes. For this objective, I validated a GC-MS method for detection and quantification of terpenes in wine grape leaves, fruit, and extracts. To simulate the South Okanagan ecosystem, I used Artemisia tridentata Nutt., a native plant that grows alongside Okanagan vineyards. Next, to explore the effects of smoke on terroir, I used a metabolomics workflow to investigate how wildfire smoke affects wine grape metabolism. Hypothesis: I hypothesized that volatile compounds released from Artemisia tridentata and its smoke accumulated in commercial grapevines (Vitis vinifera L.) and that wildfire smoke influenced the terroir of finished wines. To investigate this hypothesis, my specific objectives were: I) To develop and validate a method for detection and quantification of terpenes in Vitis vinifera L. grapevine leaves and wine. II) To determine terpenes from Artemisia tridentata in Vitis vinifera leaves after exposure to: a. Living A. tridentata in a controlled environment b. Smoke from a controlled burn of dried A. tridentata leaf 21 c. Smoke captured from controlled burn of A. tridentata leaf and dissolved in a solvent d. High-pressure solvent extraction of fresh A. tridentata leaves III) To use an untargeted metabolomics analysis to differentiate the terroir signature of wines from exposure to wildfire smoke. 22 Chapter 2: Development and Single-Laboratory Validation for the Determination of 31 Terpenes in Vitis vinifera L. Leaves by Gas-Chromatography and Mass Spectrometry x 2.1 Synopsis of Chapter Terpenes are important flavour and aroma compounds that contribute to the terroir of wines and the health of grapevines (Vitis vinifera L.). My objective was to develop and validate a fit for purpose method for the detection and quantification of 31 monoterpenes in grapevines and wine using a low to mid-polarity (6% cyanopropyl-phenyl, 94% methylpolysiloxane) column for improved separation and repeatability. I determined the selectivity, linearity, limit of detection (LOD), lower limit of quantification (LLOQ), accuracy, and robustness for optimized gradient separation and SIM detection. Authentic standards and quality control samples were used to determine intraday and interday precision and repeatability, expressed as relative standard deviation (RSD) for each of the analytes. The method was found to be precise, with intraday RSD values ranging from 1.55 to 4.48% and interday RSD values ranging from 7.24 to 10.3%. The accuracy of the method was determined by spiking samples with three concentrations of authentic standards and determining the %recovery. The robustness of the method was evaluated by changing seven factors in a Youden ruggedness test, finding that the method was not significantly affected by any of the factors. Overall, the method is fit for purpose for terpene determination in grapevines but is not suited for wine matrix. 23 2.2 Materials and Methodshhh 2.2.1 Plant Material Commercially propagated grapevines Vitis vinifera L. CV Cabernet Franc 327 on 3309 rootstocks (Mori Nurseries, ON) were grown from dormancy in a Controlled Environmental growth chamber (Conviron CMP, 6010, Winnipeg, MB). Plants were grown in Sunshine Mix #4 Professional Growing Mix soil (SunGro Horticulture, Brantford, ON) at a constant daytime (16 hours) temperature of 22 °C and night (8 hours) temperature of 20°C. The photosynthetic active radiation (PAR) was maintained by fluorescent lights active during 15 daylight hours at 334 µmol/m2/s at bench height, and halogen lights for 1 hour. Humidity was maintained around 80% by daily hand watering. Standard fertilizer (NPK 20:20:20 + Micro) was applied every 1-2 months. Integrated Pest Management (IPM) techniques were used to control pests with minimal pesticide sprays. Nine plants were selected for this study from the original population of 100 clones. Fully open mature leaves were excised from the plant and immediately flash frozen in liquid nitrogen. Samples were stored at -80 °C until analysis. Vitis vinifera L. grapes were acquired from a commercial vineyard near Naramata, BC (49.53942°N, -119.56481°W) and stored at -80 °C until analysis. Several commercial wines were purchased from vineyards in the Okanagan Valley and subsampled as blinded replicates. 2.2.2 Reference Standards Two reference standard mixes comprising 31 terpenes were purchased from Restek (Bellefont, PA), and d3-linalool was purchased from CDN Isotopes (Pointe-Claire, QC). Chemical formula and listed purity information are given in Table 2. 24 Table 2. Purity and reference information for analytes. Terpene Purity, % Chemical formula CAS No. Camphene 96 C10H16 72-95-5 Camphor 98 C10H16O 76-22-2 Carvone 99 C10H14O 2244-16-8 𝛿𝛿 3-Carene 99 C10H16 498-15-7 α-Cedrene 97 C15H24 469-61-4 m-Cymene 99 C10H14 535-77-3 o-Cymene 99 C10H14 527-84-4 p-Cymene 99 C10H14 99-87-6 Eucalyptol 99 C10H18O 470-82-6 Fenchol 97 C10H18O 2217-02-9 Fenchone 99 C10H16O 7787-20-4 Isoborneol 99 C10H18O 124-76-5 Isopulegol 99 C10H18O 89-79-2 Limonene 99 C10H16 5989-27-5 Linalool 97 C10H18O 78-70-6 d3-Linalool 99.5 C10H15D3O 1216673-02-7 trans-β-Ocimene 93 C10H16 13877-91-3 α-Phellandrene 91 C10H16 4221-98-1 α-Pinene 99 C10H16 80-56-8 β-Pinene 98 C10H16 18172-67-3 25 Terpene Purity, % Chemical formula CAS No. Piperitone 99 C10H16O 89-81-6 Pulegone 96 C10H16O 89-82-7 Sabinene 99 C10H16 3387-41-5 Sabinene Hydrate 97 C10H18O 546-79-2 Safranal 93 C10H14O 116-26-7 α-Terpinene 96 C10H16 99-86-5 γ-Terpinene 99 C10H16 99-85-4 α-Terpineol 96 C10H18O 98-55-5 Terpinen-4-ol 99 C10H16O 562-74-3 Terpinolene 99 C10H16 586-62-9 α-Thujone 99 C10H16O 546-80-5 Verbenone 99 C10H14O 1196-01-6 2.2.3 Preparation of Test Samples Approximately 100 mg of grapevine leaf material, grape tissue, or wine was weighed into a 50 mL centrifuge tube. Samples were extracted in 10 mL hexane (HPLC grade, Fisher, Toronto, ON). and an internal standard (10 μL of a 100 μg/mL solution d3-linalool) was added to give a final concentration of 0.10 μg/mL. Samples were vortexed (Fisherbrand, Ottawa, ON) for 10 seconds, sonicated (Fisherbrand) for 10 minutes, and centrifuged for 5 minutes (3,234 x g; Eppendorf, Hamburg, Germany). The resulting extract was transferred to an autosampler vial (Canadian Life Sciences, Peterborough, ON) for analysis. 26 2.2.4 Preparation of Calibration Solutions A terpene stock solution was prepared by mixing a 5 μL aliquot of both Terpenes MegaMix #1 (Restek #34142, 2,500 μg/mL in isopropanol) and Terpenes MegaMix #2 (Restek #34143 2,500 μg/mL in toluene) into 1240 μL hexane to achieve a 10 μg/mL solution of the terpenes (Table 2). This was then diluted 10-fold in hexane to give a 1000 ng/mL stock solution, which was used to create the calibrators. (Table 3). Table 3. Calibrator concentrations used to determine concentrations of terpenes. Calibrator Concentration (ng/mL) 1 0.004 2 0.018 3 0.032 4 0.046 5 0.060 6 0.075 7 0.100 2.2.5 Separation, Detection, and Quantification of Terpenes 2.2.5.1 Gas Chromatography Separation Using an AS 1310 autosampler and a Trace 1310 GC (Thermo Scientific, Ontario, Canada), 1.0 μL of sample was injected into the inlet at 220 °C with a split flow of 6 mL/min. The carrier gas was helium at a flow rate of 1.5 mL/min. A Thermo Scientific TG-642SilMS column (30 m x 0.25 mm ID x 1.4) was used to achieve separation. The column was held at 60 °C for 1 minute, then ramped to 130 °C at a rate of 50 °C/min and held for 2.5 minutes, then a 5 °C/min 27 ramp up to 140 °C, followed by a 15 °C/min ramp to 250 °C, and finally a 20 °C/min ramp to 300 °C, for a total run time of 16.5 minutes. MS detection (ISQ 7000 single quadrupole, Thermo Scientific) with electron ionization at 70 eV was used to collect spectra in selected ion monitoring mode. The transfer line was 250 °C and the ion source temperature was 300 °C. Chromeleon 7.3.2 was used for peak integration and analyte quantification. 2.2.5.2 Mass Spectrometry Detection MS detection (ISQ 7000 single quadrupole, Thermo Scientific) with electron ionization at 70 eV was used to collect spectra in selected ion monitoring mode. The transfer line was 250 °C and the ion source temperature was 300 °C. Chromeleon 7.3.2 was used for peak integration and analyte quantification. 2.2.6 Method Performance Characteristics The performance characteristics of the method were determined and evaluated on the Single-Laboratory Validation parameters,93 as follows: 2.2.6.1 Selectivity Selectivity was determined by calculation of a resolution value of at least 1.5, with 1.0 being the absolute minimum separation for analytes. The peak width was measured from half height. Reagent blanks demonstrated no interferences at SIM channels. 28 2.2.6.2 Linearity Linearity was determined using a seven-point calibration curve. Standards at each concentration on the calibration curve (Table 4) were prepared and analyzed as duplicates per day. A linear weighted (1/x) regression was determined for the linear range with an R2 value of 0.99 or better acceptable for linearity. Residuals were plotted to ensure random deviation around zero. 2.2.6.3 LOD and lower LOQ (LLOQ) Both the U.S. Pharmacopeia (USP) and the Association of Official Analytical Chemists (AOAC) definitions were used to determine the limits of detection and quantification for the method. The USP LOD is defined as three times the signal of the reagent blank, while the LOQ is ten times the signal of the reagent blank. The AOAC equations use the standard deviation of the blank signal,93 and are: 𝐿𝐿𝐿𝐿𝐿𝐿 = 𝑥𝑥 + (3 ∗ 𝑆𝑆𝐿𝐿𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏) (1) 𝐸𝐸𝐸𝐸𝐸𝐸 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = 𝑥𝑥 + (10 ∗ 𝑆𝑆𝐿𝐿𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏) (2) 2.2.6.4 Precision (Repeatability) Precision of the method was determined by analyzing five quality control spiked samples across 3 days. The authentic standard was spiked into a grapevine leaf sample matrix to give a final concentration of 5000 ng/g in the matrix. Both the inter- and intraday RSD values for recovery of the authentic standard were determined. The HorRat value was also determined to assess the overall precision of the method, and is given by equation 3: 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 = %𝑅𝑅𝑅𝑅𝐷𝐷𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎%𝑅𝑅𝑅𝑅𝐷𝐷𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐 (3) 29 2.2.6.5 Accuracy Recovery was determined by spiking authentic standards into grapevine leaf sample matrix. Three recovery concentrations were used: 500 ng/g (low), 1000 ng/g (mid), and 5000 ng/g (high). 2.2.6.6 Robustness An intra-laboratory experiment investigating the effects of minor changes to sample preparation factors was completed as described by Youden.94 By conducting eight experiments with 𝑛𝑛 = 3, seven factors were studied (Table 4). The factors were the sample grind size, sample temperature, vortex length, sonication length, sonication temperature, cold stabilization time, and extraction solvent. To determine the effects of each factor, equations 4-10 were used in which capital letters denote the normal value and higher values are shown with lowercase letters. A factor is significantly sensitive for changes if any of the values J to P are larger than √2*S.95,96 Effect of A and a: 4𝐴𝐴4−4𝑏𝑏4= 𝑋𝑋1+𝑋𝑋2+𝑋𝑋3+𝑋𝑋44−𝑋𝑋5+6+𝑋𝑋7+𝑋𝑋84= 𝐽𝐽 (4) Effect of B and b: 4𝐵𝐵4−4𝑏𝑏4= 𝑋𝑋1+𝑋𝑋2+𝑋𝑋3+𝑋𝑋44−𝑋𝑋5+6+𝑋𝑋7+𝑋𝑋84= 𝐾𝐾 (5) Effect of C and c: 4𝐶𝐶4−4𝑐𝑐4= 𝑋𝑋1+𝑋𝑋2+𝑋𝑋3+𝑋𝑋44−𝑋𝑋5+6+𝑋𝑋7+𝑋𝑋84= 𝐿𝐿 (6) Effect of D and d: 4𝐷𝐷4−4𝑑𝑑4= 𝑋𝑋1+𝑋𝑋2+𝑋𝑋3+𝑋𝑋44−𝑋𝑋5+6+𝑋𝑋7+𝑋𝑋84= 𝑀𝑀 (7) Effect of E and e: 4𝐸𝐸4−4𝑒𝑒4= 𝑋𝑋1+𝑋𝑋2+𝑋𝑋3+𝑋𝑋44−𝑋𝑋5+6+𝑋𝑋7+𝑋𝑋84= 𝑁𝑁 (8) Effect of F and f: 4𝐹𝐹4−4𝑓𝑓4= 𝑋𝑋1+𝑋𝑋2+𝑋𝑋3+𝑋𝑋44−𝑋𝑋5+6+𝑋𝑋7+𝑋𝑋84= 𝐿𝐿 (9) Effect of G and g: 4𝐺𝐺4−4𝑔𝑔4= 𝑋𝑋1+𝑋𝑋2+𝑋𝑋3+𝑋𝑋44−𝑋𝑋5+6+𝑋𝑋7+𝑋𝑋84= 𝐸𝐸 (10) |𝐸𝐸 − 𝐻𝐻| … |𝐺𝐺 − 𝑔𝑔| > √2 ∗ 𝑆𝑆𝐿𝐿 (11) 30 Table 4. Experimental design of robustness test. Run number Factor combinations Measurement obtained 1 A B C D E F X1 2 a b c D E F X2 3 A b c D E F X3 4 a B c d E F X4 5 A b C d e F X5 6 a B C d e F X6 7 A B c d e f X7 2.3 Results and Discussion 2.3.1 Selectivity We have developed and optimized the chromatographic separation for 31 terpenes in grapevine leaves with satisfactory resolution in 16.5 minutes. Figure 10 shows the total ion chromatograms in an extract spiked at 5000 ng/g. Retention time variations between days was low (<0.2% RSD) for all terpenes, ensuring confidence in analyte peak identification and minimal retention time drift (Table 3). Single Ion Monitoring (SIM) was optimized for quantification and peak identity confirmation (Table 3). Grape tissue and wine sample matrices did not provide suitable resolution31 Figure 8. Total ion chromatogram of grapevine leaf samples fortified at 500 (top), 1000 (middle), and 5000 ng/g (bottom). 32 2.3.2 Linearity The method was found to be linear for standard concentrations of 400 to 10,000 ng/g in matrix, providing a suitable method to quantify the expected levels of terpenes in grapevine leaves.97 Calibration curves of the percent of internal standard and the concentration plotted on the y and x axes respectively. The R2 of the linear regression was determined for each analyte (Table 5). Each terpene had a value of 0.986 to 0.993. The sum of the residuals was between -1.95x10-12 and 3.05x10-12 for all terpenes, emphasizing randomness around zero. 33 Table 5. Equation of the line and R2 values. Analyte R2 Equation of linear regression (Quantification Ion) 𝛼𝛼-Pinene 0.9919 𝑦𝑦 = 352.1(𝑥𝑥) + 1080 Camphene 0.9923 𝑦𝑦 = 242.1(𝑥𝑥) + 400.3 Sabinene 0.9923 𝑦𝑦 = 28.46(𝑥𝑥) + 43.86 𝛽𝛽-Pinene 0.9919 𝑦𝑦 = 281.7(𝑥𝑥) + 471.2 𝛼𝛼-Phellandrene 0.9918 𝑦𝑦 = 337.5(𝑥𝑥) + 46.39 𝛿𝛿-3-Carene 0.9916 𝑦𝑦 = 46.39(𝑥𝑥) + 58.70 𝛼𝛼-Terpinene 0.9921 𝑦𝑦 = 205.8(𝑥𝑥) + 210.1 m-Cymene 0.9918 𝑦𝑦 = 474.7(𝑥𝑥) + 589.5 Limonene 0.9930 𝑦𝑦 = 185.9(𝑥𝑥) + 643.2 p-Cymene 0.9920 𝑦𝑦 = 525.4(𝑥𝑥) + 655.4 trans-𝛽𝛽-Ocimene 0.9916 𝑦𝑦 = 51.91(𝑥𝑥) + 52.35 Eucalyptol 0.9913 𝑦𝑦 = 33.44(𝑥𝑥) + 29.30 o-Cymene 0.9918 𝑦𝑦 = 467.5(𝑥𝑥) + 609.8 𝛾𝛾-Terpinene 0.9921 𝑦𝑦 = 251.8(𝑥𝑥) + 308.8 Terpinolene 0.9919 𝑦𝑦 = 113.1(𝑥𝑥) + 119.9 Sabinene Hydrate 0.9918 𝑦𝑦 = 70.10(𝑥𝑥) + 44.41 Linalool 0.9909 𝑦𝑦 = 14.64(𝑥𝑥) + 73.07 Fenchone 0.9917 𝑦𝑦 = 319.1(𝑥𝑥) + 354.4 𝛼𝛼-Thujone 0.9908 𝑦𝑦 = 81.73(𝑥𝑥) + 22.73 Fenchol 0.9919 𝑦𝑦 = 141.2(𝑥𝑥) + 131.0 Isopulegol 0.9900 𝑦𝑦 = 17.27(𝑥𝑥) + 4.899 34 Analyte R2 Equation of linear regression (Quantification Ion) Camphor 0.9908 𝑦𝑦 = 125.5(𝑥𝑥) + 51.37 Isoborneol 0.9905 𝑦𝑦 = 169.9(𝑥𝑥)− 56.65 Terpinen-4-ol 0.9903 𝑦𝑦 = 57.61(𝑥𝑥)− 14.89 α-Terpineol 0.9881 𝑦𝑦 = 47.79(𝑥𝑥)− 41.17 Safranal 0.9912 𝑦𝑦 = 15.56(𝑥𝑥)− 10.11 Verbenone 0.9884 𝑦𝑦 = 23.80(𝑥𝑥)− 18.24 Pulegone 0.9911 𝑦𝑦 = 30.03(𝑥𝑥)− 14.95 Carvone 0.9890 𝑦𝑦 = 85.60(𝑥𝑥)− 73.30 Piperitone 0.9890 𝑦𝑦 = 90.95(𝑥𝑥)− 120.2 α-Cedrene 0.9918 𝑦𝑦 = 161.2(𝑥𝑥) + 186.6 2.3.3 LOD and LLOQ Both methods used to calculate the LOD and LLOQ used the signal of the blank. However, when the LOQ was calculated using the USP formula, the values for each terpene were consistently lower than those determined using the AOAC definition for the LOQ (Table 6). The more conservative AOAC definition for the LOQ was adopted for terpene analyses. 35 Table 6. LOD and LOQ determinations by USP and AOAC. Analyte USPc AOACd LODa (ng/g) LOQb (ng/g) LOD (ng/g) LOQ (ng/g) 𝛼𝛼-Pinene 227 758 269 2770 Camphene 63.9 213 52.0 542 Sabinene 131 436 133 1380 𝛽𝛽-Pinene 105 349 71.0 745 𝛼𝛼-Phellandrene 36.2 121 41.2 424 𝛿𝛿-3-Carene 157 525 156 1615 𝛼𝛼-Terpinene 56.9 190 55.6 575 m-Cymene 48.4 161 40.5 421 Limonene 2700 900 176 1850 p-Cymene 38.9 130 27.1 284 trans-𝛽𝛽-Ocimene 72.5 242 56.1 585 Eucalyptol 192 641 176 1828 o-Cymene 53.6 179 53.7 555 𝛾𝛾-Terpinene 44.5 148 34.1 356 Terpinolene 69.2 231 64.4 667 Sabinene Hydrate 142 474 127 1320 Linalool 416 1387 284 2980 Fenchone 27.2 91.0 20.0 209 𝛼𝛼-Thujone 168 559 144 1494 36 Analyte USP AOAC LOD (ng/g) LOQ (ng/g) LOD (ng/g) LOQ (ng/g) Fenchol 75.7 252 62.7 652 Isopulegol 469 1560 287 3020 Camphor 144 480 112 1160 Isoborneol 104 345 89.1 926 Terpinen-4-ol 180 599 160 1660 α-Terpineol 236 786 173 1810 Safranal 214 714 147 1540 Verbenone 583 1940 268 2880 Pulegone 184 614 151 1570 Carvone 226 752 182 1890 Piperitone 259 862 226 2350 α-Cedrene 48.1 160 41.7 433 aLimit of detection bLimit of quantification cUnited States Pharmacopeia dAssociation of Offical Analytical Chemists 2.3.4 Precision (Repeatability) The repeatability was performed at one concentration level with 5 injections on three different days. The intraday RSD (%) was between 1.55 and 4.48% for all terpenes, with the interday RSD being 7.24 – 10.3 % (Table 7). The HorRat values were between 1.43 and 2.03, which are within the acceptable values range of 0.5 – 2.98 37 Table 7. Within-day, between-day, and HorRat precision measurements. Terpene Within-day RSD (%) Between-day RSD (%) HorRat α-Pinene 1.61 8.66 1.71 Camphene 2.26 8.32 1.64 Sabinene 2.18 8.88 1.63 β-Pinene 2.62 8.24 1.65 α-Phellandrene 1.96 8.37 1.65 𝛿𝛿 3-Carene 2.23 8.37 1.65 α-Terpinene 2.49 8.32 1.64 m-Cymene 2.20 7.44 1.47 Limonene 2.49 8.36 1.65 p-Cymene 2.78 8.38 1.65 trans-β-Ocimene 3.40 7.96 1.57 Eucalyptol 2.53 7.24 1.43 o-Cymene 2.44 7.52 1.48 γ-Terpinene 2.69 8.19 1.62 Terpinolene 2.44 7.89 1.56 Sabinene Hydrate 4.48 8.22 1.63 Linalool 2.79 7.87 1.55 Fenchone 2.70 7.74 1.53 α-Thujone 1.55 8.74 1.72 Fenchol 1.56 8.60 1.70 38 Terpene Within-day RSD (%) Between-day RSD (%) HorRat Isopulegol 2.70 9.12 1.80 Camphor 2.72 8.38 1.63 Isoborneol 2.79 7.91 1.56 Terpinen-4-ol 2.69 9.58 1.55 α-Terpineol 2.70 9.58 1.89 Safranal 3.28 8.59 1.70 Verbenone 3.14 10.3 2.03 Pulegone 2.94 9.00 1.78 Carvone 4.22 10.2 2.01 Piperitone 3.84 9.82 1.94 α-Cedrene 2.83 9.44 1.86 2.3.5 Accuracy The accuracy of the method was presented in percent recovery for three concentrations. When spiked at 500 ng/g, the recovery was 80.2 – 117% (Table 8). At 1000 ng/g it was 92.6 – 119%, and at 5000 ng/g it was between 85.9 and 120%. All terpenes were recovered within the AOAC parameters of 80 – 120%.98 39 Table 8. Analyte recovery at three concentrations. Terpene LOW Recovery (%) MID Recovery (%) HIGH Recovery (%) α-Pinene 96.8 100 118 Camphene 92.6 105 117 Sabinene 96.3 108 117 β-Pinene 113 108 114 α-Phellandrene 92.3 102 117 𝛿𝛿 3-Carene 93.1 93.8 109 α-Terpinene 92.0 96.2 114 m-Cymene 87.1 93.1 109 Limonene 114 109 108 p-Cymene 94.1 92.6 85.9 trans-β-Ocimene 106 96.0 108 Eucalyptol 95.5 95.7 118 o-Cymene 88.1 94.5 110 γ-Terpinene 90.5 97.3 111 Terpinolene 92.6 97.2 112 Sabinene Hydrate 86.8 94.1 104 Linalool 91.4 95.5 112 Fenchone 80.0 92.6 108 α-Thujone 117 108 105 40 Terpene LOW Recovery (%) MID Recovery (%) HIGH Recovery (%) Fenchol 94.7 106 108 Isopulegol 97.1 106 126 Camphor 96.0 104 116 Isoborneol 107 104 115 Terpinen-4-ol 108 112 118 α-Terpineol 101 103 120 Safranal 101 102 125 Verbenone 111 99.8 106 Pulegone 105 114 129 Carvone 112 126 129 Piperitone 100 98.2 118 α-Cedrene 98.0 93.6 103 2.3.6 Robustness The robustness of the method was evaluated by changing seven factors. Figure 11 shows data for robustness of all terpenes in the method. For all 31 terpenes, values for J to P were within the conditions of Equation 11. 41 Figure 9. Surface plot of 31 terpenes and their responses to the seven factors. 2.4 Conclusions Based on the results of this SLV, the developed method demonstrated accuracy, precision, and robustness in grapevine leaves. Retention time variations between days was below 0.2% RSD for all terpenes, emphasizing the stability of the method. The recovery averaged 103.8% across all analyte concentrations, indicating negligible losses during both sample preparation and analysis. This method is valuable for Vitis vinifera L. grapevine growers and wine producers, as it provides the unique profile of compounds contributing to grapevine health and the aroma and flavour of resulting wines. 42 Chapter 3: Smoke Signaling: Volatile terpenes released in burning Artemisia tridentata Nutt. Are accumulated in grapevines 3.1 Synopsis of Chapter Indigenous plants growing near vineyards emit volatile organic compounds (VOCs) that can influence grapevines and wine characteristics. Like humans, plants communicate with their relatives, and Artemisia tridentata, commonly known as Big Sagebrush, communicates by releasing terpenes that trigger physiological responses in related and kin plants.91,99 Furthermore, A. tridentata has been shown to emit volatile compounds that induced changes in tomato plants.100 I hypothesized that volatiles released by Artemisia tridentata accumulate in grapevines and affect wine terroir. To investigate this hypothesis, grapevines were subjected to cohabitation with A. tridentata and exposed to both atmospheric and collected smoke from the plant. Using the previously validated method, terpene accumulation in the grapevines was monitored. Additionally, an untargeted metabolomics approach was used to putatively identify the degradation metabolites of these terpenes in the grapevines. 3.2 Materials and Methods 3.2.1 Chemicals and Standards Hexane (HPLC Grade, Fisher Scientific, Mississauga ON), ethanol (95%; (Greenfield Global Inc., Mississauga ON) authentic terpene standards (Restek, Bellefont, PA) and d3-linalool (CDN Isotopes, Pointe-Claire, QC) were obtained from commercial sources. 43 3.2.2 Field Collections of Artemisia tridentata Nutt. Artemisia tridentata Nutt was collected from public lands along a drainage ditch adjacent to a vineyard in the South Okanagan near Oliver, British Columbia on August 1, 2023 (49°12’34”N, 119°32’5”W). Vouchers were deposited at the Beaty Biodiversity Center herbarium for confirmation of plant identity. 3.2.3 Chemical Extractions of Artemisia tridentata Nutt. Two types of chemical extraction processes were developed to control concentration and application area of A. tridentata terpenes viz. supercritical pentane extract (SCPE) and a smoke capture in ethanol (SCE). 3.2.3.1 Preparation of Supercritical Pentane Extract (SCPE) A custom-made supercritical fluid extraction apparatus was used to prepare a concentrated extract of organic volatiles from dried leaves of A. tridentata. In brief, the apparatus consisted of a solvent reservoir and an extractor connected by a sample holder and wrapped in a thermal water jacket (Figure 12). Approximately 42 grams of dried leaf material was ground to a fine powder (Magic Bullet) and placed onto the sample holder. The reservoir cylinder was filled with ~1.0 L of pentane (Fisher). Hot water was pumped through the thermal jacket to increase the temperature inside of the extraction cylinder to ~36°C causing the extraction chamber to fill with supercritical pentane. The apparatus was left sealed for 1 hour to complete the extraction process after which time a valve at the base of the reservoir was opened and the pentane was released. Pentane was evaporated from the A. tridentata extraction using a rotary evaporator (Buchi, VWR Avantor, Mississauga, ON) resulting in a concentrated sample with the consistency of molasses (Figure 12d). 44 Figure 10: Bespoke supercritical pentane extraction (SCPE) apparatus used to prepare a concentrated extract from dried leaves of A. tridentata. (A) Apparatus design. (B) Set-up and extraction. (C) Pentane extract prior to rotary evaporation. (D) Final extraction. Pressure gaugePressure release valveWater jacket heating coilExtraction vesselSolvent vesselSample holderRelease valveExtractA B CD45 3.2.3.2 Smoke Capture Extract (SCE) Terpenes released in burning A. tridentata were captured in ethanol to create a standardized Smoke Capture Extract (SCE). Dried A. tridentata was ground using a mortar and pestle and placed in a vaporizer (Volcano Digit, Storz & Beckel). The vaporizer was attached to a rotary evaporator (Buchi, Ontario, Canada), connected to a vacuum flask containing 95% ethanol immersed in an acetone and dry ice bath (-60 °C) (Figure 13). With the Volcano vaporizer set to 185 °C, air was pushed through the vaporizer for 5 minutes at a time, replacing the plant material afterwards, until the smoke of 10 grams was collected in 100 mL of ethanol. Figure 11: Dried Artemisia tridentata leaves were volatilized with a commercial Volcano system. Volatiles were collected in a Rotary evaporator apparatus with plumbing reversed and a chilled acetone bath. 46 3.2.4 Grapevine Growing Conditions Commercially propagated grapevines Vitis vinifera L. CV Cabernet Franc 327 on 3309 rootstocks (Mori Nurseries, Ontario, Canada) were grown from dormancy in a walk-in controlled environmental growth chamber (Bio Chambers, Manitoba, Canada) in a mycorrhiza-free soil mix (Sunshine Mix #4 Professional Growing Mix; SunGro Horticulture, Agawam, MA) at a constant daytime temperature of 22 °C for 16 hours and night temperature of 20 °C for 8 hours. The photosynthetic active radiation (PAPR) was maintained by fluorescent lights at 334 μmol/m2/s, and then the last hour of daylight used halogen. Humidity was maintained around 80% by daily hand watering. Integrated pest management (IPM) techniques were used to manage pests without pesticide application. 3.2.5 Experimental Procedure 3.2.5.1 Co-culture of Grapevines and Artemisia tridentata Six grapevines from the original population of 100 clones were transferred to two experimental growth chambers (Conviron CMP 6010, Winnipeg, MN) at a constant temperature of 25 °C, 50% humidity, and fluorescent lights at 52.53 μmol/m2/s. A mature A. tridentata wild-collected plant (approx. 2’ tall, unknown age) was co-incubated with 3 of the grapevines in one of the chambers. Three control plants were incubated in a second Conviron chamber with identical settings and both chambers were sealed. After 14, 21, and 28 days, leaf samples were collected from the grapevines, flash frozen and stored at -80 °C for analysis of terpene profiles. 3.2.5.2 Artemisia tridentata Controlled Burn Experiment After 29 days, the mature A. tridentata plant was removed from the Conviron chamber. A hand-tied bundle of dried A. tridentata prepared by a Syilx student in the traditional manner was placed into a heat-tolerant glass bowl and ignited. The hand-tied bundle was designed to burn slowly emitting a constant stream of A. tridentata smoke. The chamber was sealed for 4 hours to 47 immerse the grapevines in dense A. tridentata smoke. Grapevine leaf samples were collected, flash frozen and stored at -80 °C until extraction for terpene analysis. 3.2.5.3 Artemisia tridentata Extract Sprays SCPE and SCE extracts were formulated into foliar sprays by dilution in 70% ethanol at 0.0110 g/mL and 0.110 g/mL and applied to selected marked leaves or entire plants in the controlled environment chambers. Pentane sprays were applied to the entire plant 3 times, with 24 hours between each spray. Leaf samples were collected after 0, 6, 12 hours and on days 4, and 7. Leaves were lightly sprayed with 70% ethanol and gently wiped with cheesecloth to remove surface residue prior to collection. Leaves were excised and immediately flash frozen in liquid nitrogen. Samples were stored at -80 °C until extraction for analysis. 3.2.5.4 Natural Wildfire Event An unexpected wildfire close to the Plant Growth Facility caused campus to be evacuated on days 3 to 11 of the co-culture experiment. At the closest point, the wildfire was approximately 800 m from the growth facility for 6 days. The grapevines in the greenhouse were wildfire exposed for 6 days and became an interesting but difficult to replicate set of observations. 3.2.6 Chemical Analysis Methods 3.2.6.1 Determination of Terpene Content in Grapevine Leaves The method for the detection and quantification of 31 terpenes was previously validated. In brief, ~25 mg of grape leaf material was weighed into a 15 mL centrifuge tube. Samples were hydrolyzed in 10 mL hexane and an internal standard (25 μL of a 10 μg/mL solution of d3-linalool) was added to give a final concentration of 0.10 μg/mL. Samples were vortexed for 10 seconds, sonicated for 10 minutes, and centrifuged for 5 minutes at 4,200 rpm. The resulting extract was transferred to an autosampler vial for analysis. Using an AS 1310 autosampler and a Trace 1310 48 GC (Thermo Scientific, Ontario, Canada), 1.0 μL of sample was injected into the inlet at 220 °C with a split flow of 6 mL/min. The carrier gas was helium at a flow rate of 1.5 mL/min. A Thermo Scientific TG-642SilMS column (30 m x 0.25 mm ID x 1.4) was used to achieve separation. The column was held at 60 °C for 1 minute, then ramped to 130 °C at a rate of 50 °C/min and held for 2.5 minutes, then a 5 °C/min ramp up to 140 °C, followed by a 15 °C/min ramp to 250 °C, and finally a 20 °C/min ramp to 300 °C, for a total run time of 16.5 minutes. MS detection (ISQ 7000 single quadrupole, Thermo Scientific) with electron ionization at 70 eV was used to collect spectra in selected ion monitoring mode. The transfer line was 250 °C and the ion source temperature was 300 °C. Chromeleon 7.3.2 was used for peak integration and analyte quantification. 3.2.6.2 Determination of Terpene Content in Artemisia tridentata Extract Approximately 200 mg of extract was weighed into a 50 mL centrifuge tube and hydrolyzed in 20 mL of hexane and spiked with internal standard to a concentration of 0.10 μg/mL. The extract was transferred to an autosampler vial and injected into the GCMS using the same instrument and parameters described above. 49 3.2.6.3 Untargeted Analysis of Leaf Material Metabolites were extracted from leaf tissue using previous methods.101–103 Approximately 50 mg of leaf material was weighed using an analytical balance (Ohaus EP114; Parsippany, New Jersey) into a 1.5 mL microcentrifuge tube (Eppendorf, Ontario, Canada ). The material was homogenized in 100 μL of 50% methanol (Fisher Scientific, Ontario, Canada) and 4% acetic acid (Fisher Scientific) with a disposable tissue grinder (Fisher Scientific, Ontario, Canada). Samples were then centrifuged at 3000 x g for 3 min (VWR High Speed; VWR, Ontario, Canada) and the supernatant was decanted, diluted 5 x with MilliQ water and centrifuge filtered using a 0.2 μm Ultrafree-MC centrifugal filter (Millipore-Sigma, Ontario, Canada) at 3000 x g for 3 min. The filtrate was transferred to an autosampler vial (12 x 32 mm screw-top vial; Canadian Life Sciences, Ontario, Canada) fitted with a conical spring insert (50 μL glass; Chromatographic Specialties, Ontario, Canada). Three replicates of each treatment were analyzed, plus five solvent blanks each for methanol and acetic acid, ethanol, acetonitrile, water, and 0.1% formic acid. Samples were injected into a Vanquish UHPLC (Thermo Scientific, Ontario, Canada) on a C18 column (150 mm, 1.7 µm, Phenomenex Kinetex). Table 2 shows the elution gradient for separation of metabolites by reverse phase chromatography. The needle wash solvent and purge solvent were both 10:90% water:acetonitrile (v/v). The column temperature was set to 40 °C and the injection volume was 10 µL. Injections were analyzed on a Q-Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer (Thermo Scientific) with a heated electrospray ionization (HESI) probe (Waltham, MA, USA) which was run in positive ionization mode. Data was obtained in full MS with a scan range between 100-1500 m/z, a resolution of 70,000, and a maximum injection time of 50 ms, and data dependent fragmentation. Data was collected in positive ionization and negative ionization. Thermo raw files were converted to mzML files using MSConvert.104 MZMine was used for peak detection and alignment.105 Mass detection was performed with a noise level of 1x104 for MS1 and MS2 scans in positive mode. The ADAP Chromatogram Builder was used to detect 50 features with a minimum group size of 4 and scan to scan accuracy of 0.002 m/z.106 Chromatogram resolving was done through ADAP resolver with a S/N of 7 and 1x106 minimum height, and 0.3-minute peak duration. The 13C Isotope Filter was used with a m/z tolerance of 0.001, and 0.4 second RT tolerance. Join Aligner was then used to align features into one list with a m/z tolerance of 0.002, and a RT tolerance of 6 seconds. Duplicate features were then removed. The mzML files were also uploaded to SIRIUS 5.8.5, using ZODIAC to improve ranking of formula candidates,107 CSI:FingerID to identify the structures of compounds,108 and COSMIC to assign a confidence score to CIS:FingerID structure identifications,109 and CANOPUS to predict compound classes.110 The peak list from MZMine was mined for monoisotopic masses present in the degradation pathways of camphor and p-cymene, as well as the monoterpenoid biosynthesis pathway. Only matches with a m/z difference of 0.002 or smaller were kept. 3.2.6.4 Multivariate Analysis Aligned data was submitted to MetaboAnalyst (v6.0), scaled by auto scaling (mean-centered and divided by the standard deviation of each variable), with missing values being replaced by 1/5 of the minimum positive value of their corresponding variable. Two features were removed that had a constant value across all samples. Principal component analysis (PCA) was applied. The number of principal components to retain was determined using a scree plot. 3.2.6.5 Identification of Karrikins The features with the highest intensities in the karrikin standards were matched with the formula and structure predictions of SIRIUS. MetaboAnalyst’s Pattern Search was applied to identify features which showed a linear increase (control < wildfire < smoked < Karrikin standard, 1-4-3-2) and decrease (control > wildfire > smoked > karrikin standard, 2-3-4-1). 51 3.3 Results 3.3.1 Terpenes in Artemisia tridentata Extracts The dried plant material and extracts were analyzed for their terpene content. The plant material was mostly camphor (50%), eucalyptol (28%), and camphene (10%) (Figure 14A). The main terpenes in the supercritical fluid extract were again camphor (79%), eucalyptol (13%), and camphene (4%) (Figure 14B). The smoke extract was only 18% camphor but had 36% eucalyptol (Figure 14C). Nerol made up 31% and trans-β-ocimene was 13%, while camphene was only 2%. 52 Figure 12: Detection of terpenes in Artemisia tridentata Nutt. dried plants and extracts. (A) Dried leaves harvested from wild plants. (B) Supercritical pentane extract (SCPE). (C) Smoke capture in ethanol (SCE). 0% 10% 1%1%1%28%50%3%6%α-PineneCampheneβ-Pinenem-Cymenep-CymeneEucalyptolCamphorBorneolβ-Caryophyllene2%13%36%18%31%Camphenetrans-𝛽𝛽-ocimeneEucalyptolCamphorNerol4%13%79%1% 1% 1% 1%CampheneEucalyptolCamphorBorenolCitronellolVerbenone𝛽𝛽-CaryophylleneCBA53 3.3.2 Co-culture of Artemisia tridentata and Vitis vinifera Grapevine leaves collected on days 14, 21, and 28 of co-incubation with Artemisia tridentata in a controlled environment had significantly higher levels of eucalyptol and piperitone over time (Figure 15). Camphor, the main terpene in the dried A. tridentata material, was not detected in the samples. 54 Figure 13. Concentrations of monoterpenes in grapevines co-cultured with Artemisia tridentata in a controlled environmental chamber for 28 days (n=9). Missing boxes indicate the terpene was not detected. Statistically significant differences by unpaired t-test comparing to Day 0 (α=0.05) indicated with: (*) p ≤ 0.01. 55 3.3.3 Smoke Capture Extract (SCE) Sprays The spray application containing SCE did not affect terpenes at levels above the detection limits of the method, thus no terpenes were detected. 3.3.4 Supercritical Fluid Extract (SCPE) Sprays The SCPE application significantly increased monoterpenes in grapevine leaves and differences were observed both between individual plants (Figure 16) and as a treatment effect (Figure 17). To my knowledge, p-cymene has not been previously reported in grapevine leaves. The terpene can be synthesized from γ-terpinene.111 Figure 14. Concentrations of monoterpenes detected in grapevines sprayed with an Artemisia tridentata supercritical fluid extract (n=3). The absence of a box means the 56 terpene was not detected. Statistically significant differences by unpaired t-test (α=0.05) comparing to the control indicated with (*) p ≤ 0.05, (**) p ≤ 0.01, and p ≤ 0.001. 57 Figure 15. Degradation metabolites of camphor detected in A. tridentata-exposed Vitis vinifera (n=9). Camphor was quantified using a targeted method with an authentic 5-exo-hydroxycamphorCamphorBornane-2,5-dione5-oxo-1,2-campholide[(1R)-2,2,3-trimethyl-5-oxocyclopent-3-enyl]acetate58 standard, while all other metabolites are putatively identified terpenes using an untargeted method. The absence of a box means the metabolite was not detected. Statistically significant differences comparing to the control by Mann-Whitney U test (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.001. Camphor was detected at significant concentrations in the SCPE and in the treated grapevine leaves (Figure 14 and 17). The other three monoterpenes, p-cymene, α-phellandrene, and γ-terpinene were not detected in the SCPE but were found in elevated levels in the treated leaves, potentially as products of metabolism of camphor (Figure 18). Figure 17 shows the degradation products of camphor, while the arrows indicate the observed metabolic pathway for the treatments. A treatment effect is observed, with the concentrated SCPE samples showing higher peak areas for the putatively identified metabolites. Although α-pinene was not detected in the extract or the samples, its degradation products were putatively identified. Like camphor, many of the metabolites show higher peak areas in the concentrated treatment spray (Figure 18). None of the metabolites were identified in the control treatment. 59 Figure 16. Degradation metabolites of α-pinene detected in A. tridentata-exposed Vitis vinifera (n=9). Black arrows indicate all four treatments are detected in that part of the metabolic pathway. The absence of a box means the metabolite was not detected. Myrtenol3-Isopropyl-3-butanoic acidα-PinenePinocarveolMyrtenalPinocarvone60 Statistically significant differences comparing to the control by Mann-Whitney U test (α=0.05) indicated with: (*) p ≤ 0.05. Camphene was detected in the extract, and putatively identified in the treatments (Figures 14B and 19). Eucalyptol, a main terpene in the extract, was not detected, but its degradation product 6-oxocineole was (Figure 19). Carvone, which is a degradation product of limonene, was also putatively identified in the treatments (Figure 19). The control treatment did not have these monoterpenes present. 61 Figure 17. Monoterpenes detected in A. tridentata-exposed Vitis vinifera (n=9). The absence of a box means the metabolite was not detected. Statistically significant 6-OxocineoleCampheneCarvone62 differences comparing to the control by Mann-Whitney U test (α=0.05) indicated with: (*) p ≤ 0.05. Unexpectedly, degradation products of geraniol in the monoterpenoid biosynthesis pathway were also putatively identified (Figures 20 and 21). The treated grapevine leaves had higher response values than the control plants for degradation products of geraniol (Figure 20), though after iridotriol this effect was not seen (Figure 21). This data and the presence of degradation metabolites of α-pinene suggest that terpenes not present in the extract were affected by the treatment. 63 Figure 18. Degradation metabolites of (6E)-oxogeraniol detected in A. tridentata-exposed Vitis vinifera (n=9). The absence of a box means the metabolite was not detected. (S)-8-Oxocitronellyl enolcis-trans-Nepetelalactol(6E)-8-OxogeraniolIridotriol64 Statistically significant differences comparing to the control by Mann-Whitney U test (α=0.05) indicated with: (*) p ≤ 0.05. 65 Figure 19. Degradation metabolites of iridotriol detected in A. tridentata-exposed Vitis IridotriolLoganin7-DeoxyloganetateLoganate7-Deoxyloganetin66 vinifera (n=9). Statistically significant differences comparing to the control by Mann-Whitney U test (α=0.05) indicated with: (*) p ≤ 0.05. 3.3.5 Controlled Burn Comparing grapevine leaves from day 29 of the co-culture experiment with leaves collected after 4 hours of continuous smoke from A. tridentata, elevated levels of camphor were observed, while there was no difference in eucalyptol or piperitone (Figure 22). This highlights the accumulation of terpenes in grapevines after a wildfire smoke event. After co-habitation with A. tridentata, there was no accumulation of camphor observed, although it was found to dominate the dried plant material. 67 Figure 20. Concentrations of monoterpenes detected in grapevines exposed to wildfire smoke in the Plant Growth Facility (smoked) and co-cultured with Artemisia tridentata Nutt. 68 (n=9). The absence of a box means the metabolite was not detected. Statistically significant differences comparing to the control by t-test (α=0.05) indicated with: (*) p ≤ 0.005. 3.3.6 Wildfire Event The McDougall Creek wildfire caused the evacuation of the University of British Columbia Okanagan campus from August 18th, 2023, to August 26th, 2023. The Plant Growth Facility on campus was completely full of smoke, as the wildfire came up to the edge of campus. Figure 23A shows the temperature data from the Plant Growth Facility where the grapevines were kept. The average temperature dropped 0.98 °C for the time campus was evacuated and dropped a further 0.08 °C after that. The vents were open for 38% of the time prior to the wildfire, 25% of the time during, and 22.1% afterwards (figure 23B). The photosynthetic active radiation (PAR) dropped by 47.55 µmoles during the wildfire, and back up by 8.04 µmoles afterwards (Figure 23C). The percent humidity jumped up by 0.15% during the wildfire, and then by another 2.72% afterwards (Figure 23D). 69 Figure 21. Climate data in the Plant Growth Facility during the 2023 UBC Okanagan campus wildfire evacuation, noted in orange. (A) Temperature (°C). (B) Roof vent opening (% of day). (C) Photosynthetic Active Radiation (PAR). (D) Humidity (%). 70 Figure 22. Concentration of piperitone and eucalyptol detected in grapevines exposed to wildfire smoke, (n=3). The absence of a bar means the metabolite was not detected. Statistically significant differences by t-test (α=0.05) comparing to the control are indicated with: (*) p ≤ 0.05. 71 3.3.7 Comparison of the Wildfire and Controlled Burn The variance of the untargeted metabolomics data of the wildfire-exposed samples and A. tridentata smoke-exposed samples were shown in principal component analysis (PCA) plots (Figure 25). The wildfire and smoked groups were clearly separated, indicating the unique metabolite profiles of the treatments. In the three PCA plots, the control samples clustered closer together than the other two treatments, suggesting greater similarity among the control samples. The scree plot (Figure 26) indicates that PC1 explains 29.4% of the variance, PC2 explains 21.6%, and PC3 explains 17.1%. Together, these PCs explain 68.1% of the variance. An elbow point is observed between PC3 and PC4, leading to the retention of only the first three PCs. The untargeted data was mined for karrikins using authentic standards. The samples exposed to the smoke of A. tridentata showed high levels of all karrikins (Figure 27). The wildfire-exposed samples had higher levels of Karrikin 2 and Karrikins 3 and 4. The samples not exposed to any smoke also had the putatively identified karrikins. Though it appears that the smoked leaves had higher levels of the karrikins, no statistically significant differences were found between treatments. 72 Figure 23. Principal component analysis for the untargeted metabolomics data of the control, smoked leaves, and wildfire-exposed grapevine leaves. Contour lines depict the density contours of data points of higher density within the plot. 73 Figure 24. Scree plot showing the proportion of variance explained by each principal component. 74 Figure 25. Putatively identified M+H adducts of karrikins in grapevine leaves exposed to wildfire smoke and A. tridentata extract (n=9). (A) Karrikin 1. (B): Karrikin 2. (C): Karrikins 3 + 4. 75 3.4 Discussion Changing climates and the increasing frequency and severity of wildfires are exposing vineyards to new complexities of volatile organic plant chemistry, plant communication mechanisms, and the terroir of wines. This chapter investigated whether Artemisia tridentata burning during wildfires impacts grapevines and wine production. The results showed that volatile organic compounds such as terpenes are released during the burning of A. tridentata and accumulate in grapevine leaves. The terpene composition in A. tridentata plant material and the SCPE agreed with existing literature, though m-cymene has not previously been reported. The smoke extract was almost one third nerol, which has been reported in headspace analysis of roots of A. tridentata, though not the leaves, and was suggested to be phytotoxic to neighbouring plants.112 The observed increase in camphor following the controlled burn is intriguing. Although this terpene is not a typical wine compound, it provides herbal, minty, and woody notes, and has been reported in wines stopped with natural cork.113 The potential agronomic benefits of camphor, such as increased bud fertility and yield, highlight an additional dimension of wildfire effects beyond sensory characteristics.114 Eucalyptol and piperitone’s accumulation in grapevines cohabitated with A. tridentata suggests that these compounds may be absorbed or synthesized as a response to the volatile environment created by neighbouring vegetation. While eucalyptol is known to provide a cool and minty aroma, piperitone’s presence, typically associated with red aged Bordeaux wines, emphasizes the interaction between local vegetation and vineyard terroir.115,116 The geographical origin effect seen with these terpenes supports the hypothesis that indigenous plants near vineyards contribute uniquely to the aromatic profile of wines.117 The detection of unexpected terpenes such as p-cymene, γ-terpinene, and α-phellandrene in the treated grapevine leaves, despite their absence in the extracts, suggests potential 76 limitations in the detection methods or the possibility of terpene transformation during exposure. The presence of these compounds, which are known components of A. tridentata extracts and smoke from vegetation fires, further emphasizes the influence of environmental factors on grapevine chemistry. 14,118–120 Geraniol and p-cymene have been detected in British Columbia wine previously, and provide rose-like and minty aromas, respectively.14,39,43,121,122 Finally, the presence of the degradation metabolites of α-pinene in the treated leaves indicates the terpene is undergoing metabolic transformation within the grapevine leaves. Detecting these metabolites indicates a possible enzymatic process in the plant, which highlights the complex interaction between plants and their environment in response to wildfire smoke. 3.5 Conclusions This chapter confirms that volatile compounds, particularly terpenes from A. tridentata, can accumulate in grapevine leaves, altering their chemical composition. The presence of camphor and its degradation products, along with 6-oxocineole, at elevated levels in treated leaves suggests that these compounds are effectively absorbed or synthesized in response to exposure. Interestingly, even terpenes not directly detected in the A. tridentata extracts, such as p-cymene, γ-terpinene, and α-phellandrene, were found in the treated leaves, indicating potential transformation or uptake from the environment. The lack of impact from the SCE treatment on terpene levels, contrasted with significant increases in eucalyptol and piperitone in cohabitated leaves, highlights the specific influence of A. tridentata volatiles. The increased camphor levels in wildfire-exposed plants emphasizes the role of environmental factors, particularly wildfires, in shaping grapevine chemistry. These findings highlight the impact of local vegetation and environmental conditions on grapevine metabolic profiles, potentially influencing wine terroir and sensory characteristics. 77 Chapter 4: Untargeted metabolomics of smoky wines 4.1 Synopsis of Chapter Wines can be damaged when they are made from grapes exposed to wildfire smoke. Previous research has partially characterized the chemistry of smoke taint in wines, but the full chemical profile of smoke-damaged wines and methods to remediate unpalatable smoke flavours are incomplete. I hypothesized that exposure to wildfire smoke creates a terroir signature that can be detected by metabolomics. This chapter study used a metabolomics approach to fully characterize the non-volatile metabolites that are accumulated in grapes exposed to wildfires and retained throughout the wine-making process. I analyzed a Rosé wine made from a single block at a single vineyard close to a wildfire in 2021 and a Saignée style Rosé made from grapes from the same winery in 2022. For a control, I selected 3 award winning wines from nearby vineyards. 4.2 Materials and Methods 4.2.1 Wine Samples All wines were sourced from commercial production facilities, estate vineyards and highly skilled winemakers. Details of the wine making process were collected through interviews with the winemakers. 4.2.1.2 Smoky Rosé Smoky wine samples were subsampled from 4 bottles of a single Rosé wine produced in 2021, purchased from a wineshop of an Estate vineyard in Oliver, British Columbia. The wine was marketed to consumers as a “smoky” wine with the aroma and taste of the Okanagan wildfires. The smoky wine was produced from 100% Estate-grown grapes made up of low-yielding vines of Merlot, Grenache and Mourvèdre grapes grown on the Black Sage Bench (49°7’44"N; 78 119°33'50"W). The Inkameep Creek wildfire burned across the street from July 19th, 2021, and the grapes were exposed to smoke until the wildfire was contained on September 4th, 2021. The time of skin contact was ~4 hours in an effort to reduce the smoky characteristics. Fermentation took place between 12 – 13 °C for about 3 weeks in a stainless-steel tank, and bentonite was added to the juice at about 50 g/hectolitre. 4.2.1.3 Saignée Rosé In 2022, the wildfires were not close to the Estate and the winemaker made the high-quality grapes from unaffected vines into a Saignée-style rosé.. The Saignée style of winemaking refers to the bleeding of juice from the tank, allowing the must on skins to be concentrated for a higher quality red wine, while the juice bled from the tank can be fermented as a Rosé wine with darker colour and more intense flavour and aromas. The 2022 Rosé vintage was predominantly Cabernet Franc, about a third Merlot, and had Cabernet Sauvignon, Chardonnay, and Pinot Blanc grapes. Bentonite was added post-fermentation in the range of 20-30 g/hectolitre, and the wine was left to settle on lees for about 30 days before transferring tanks to sit for another 23 days. Four bottles of this wine were sampled. 4.2.1.4 Positive Control Wines To eliminate any metabolites that could normally be present in high-quality wines from the smoky terroir, we compared our data with the metabolomics profiles of 3 award winning blended wines from nearby vineyards produced in 2018 to 2020. The 3 award winning wines were recognized with Bronze, Silver, and Gold Medals from the National Wine Awards of Canada (NWAC) and were blended from Cabernet Sauvignon, Merlot, Petit Verdot, Malbec, and Cabernet Franc grapes. 79 4.2.2 Volatile Phenol Analysis of Wines A modified version of a previously validated method using GC-MS/MS was used.123 For the quantitation of free volatile phenols, 2 mL of extraction solvent (hexane:ethyl acetate; Fisher Scientific. Ontario, Canada) was added to 5 mL of wine in a 15 mL centrifuge tube (Sarstedt, Quebec, Canada) and 50 µL of internal standard (d7-o-Cresol, d7-p-Cresol, d3-4-methylguaiacol, d3-guaiacol; CDN Isotopes, Quebec, Canada), was added. Approximately 2 g of NaCl (Fisher Scientific) was introduced and samples were vortexed and centrifuged (Eppendorf, Ontario, Canada) at 3,234 x g for 5 min. The supernatant was transferred to autosampler vials (12 x 32 mm screw-top vial; Canadian Life Sciences, Ontario, Canada) for injection on the GC. The samples to be analyzed for bound volatile phenols had 1 mL of 1 M HCl (Fisher Scientific) added to 5 mL of wine in a sleeve inside of a PTFE digestion tube. Samples were placed in an oven at 105 °C for 3.5 hours. They were cooled in an ice bath for 10 minutes, and the contents were poured into a 15 mL centrifuge tube with 2 mL extraction solvent and NaCl. The samples were vortexed and centrifuged prior to their transfer to an autosampler vial. Samples were analyzed using a TSQ 9000 AEI MS/MS coupled with a Trace 1310 GC (Thermo Scientific) equipped with a TriPlus RSH autosampler (Thermo Scientific). The column was a coupled restrictor column (5 m x 0.18 mm ID) and wide-bore Rtx-5ms analytical column (15 m x 0.53 mm ID x 1 µm film; Restek, Bellefont, PA). The inlet was 220 °C, with a split ratio of 3. The carrier gas was helium at 2.0 mL/min. The oven temperature was initially 70 °C for 0.5 min, the 25 °C/min up to 130 °C, and finally 125 °C/min up to 300 °C and held for 1.5 min. The transfer line was set to 250 °C and the ion source was 300 °C. The acquisition mode used was selected reaction monitoring. 80 4.2.3 Metabolomics Analysis 4.2.3.1 Sample Preparation Metabolites were extracted from wines using our previously published standard operating protocols and workflow 101–103. In brief, approximately 100 mg of wine was weighed using an analytical balance (Mettler Toledo, Columbus, OH) into a 1.5 mL microcentrifuge tube (Eppendorf, Ontario, Canada). To this, 200 µL of 50% methanol (Fisher Scientific, Ontario, Canada) and 4% acetic acid (Fisher Scientific) was added. Samples were then centrifuged at 20,817 x g for 3 min (Eppendorf, Ontario, Canada), decanted, and diluted 1.5 x with MilliQ water and centrifuge filtered using a 0.2 µm Ultrafree-MC centrifugal filter (Millipore-Sigma, Ontario, Canada) at 20,807 x g for 3 min. The filtrate was transferred to an autosampler vial (12 x 32 mm screw-top vial; Canadian Life Sciences, Ontario, Canada) for ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) analysis. 4.2.3.2 Chromatography and Mass Spectrometry Prepared samples were separated by reverse phase chromatography (Vanquish UHPLC; Thermo Scientific) with a C18 column (150 mm, 2.6 µm, Phenomenex Kinetex; Torrance, CA, USA). A gradient elution of 0.1% aqueous formic acid (Solvent A) and acetonitrile (Solvent B) with initial conditions of 95:5 reaching 5:95 at 15.00 min and 95:5 at 25.00 min, was used. It had curve 5 during the entire run. The column temperature was set to 40 °C and the injection volume was 10 µL. Metabolites were detected by a Q-Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer (Thermo Scientific) with a heated electrospray ionization (HESI) probe (Waltham, MA, USA) which was run in positive ionization mode. Data was obtained in full MS with a scan range between 100 and 1500 m/z, a resolution of 70,000, and a maximum injection time of 50 ms, and data dependent fragmentation. 81 4.2.3.3 Data Processing and Annotation Thermo raw files were converted to mzML files using MSConvert 104. MZMine 3.9.0 was used for peak detection and alignment 105. Mass detection was performed with a noise level of 5x104 for MS1 and 5x103 for MS2 scans in positive mode. The ADAP Chromatogram Builder was used to detect features with a minimum group size of 4 and scan to scan accuracy of 0.002 m/z.106 Minimum intensity for consecutive scans was 3x104 and minimum absolute height was 5x104. Chromatogram resolving was done through ADAP Resolver with a S/N of 7 and 1x105 minimum height, and 0.5-minute peak duration. The 13C Isotope filter was used with a m/z tolerance of 0.001, 4 second RT tolerance. Join Aligner was then used to align features into one list with a m/z tolerance of 0.003, and a RT tolerance of 3%. Duplicate features were then removed with a tolerance of 0.001 m/z and 0.2 minutes tolerance. 4.2.3.4 Multivariate Analysis Aligned data was submitted to MetaboAnalyst (v6.0), scaled by auto scaling (mean-centered and divided by the standard deviation of each variable), with missing values being replaced by 1/5 of the minimum positive value of their corresponding variable. Principal component analysis (PCA) was applied, and the number of principal components to retain was determined using a scree plot. 4.2.3.5 Detection of Significant Ions MetaboAnalyst’s student’s t-test was applied to the Saignée and Smoky wine. Pattern Hunter analysis was also applied to identify features which showed an increase in the smoky wine (Premium Reds > Saignée > Smoky Rose). 82 4.2.3.6 Pathway Analysis The results from MetaboAnalyst’s t-test comparing the Saignée and Smoky wine were ranked by t-value and input in Functional Analysis with a mass accuracy of 5 ppm, in positive ion mode, and subjected to Mummichog (p value cutoff of 0.1). Mapping was done to Arabidopsis thaliana, and only the [M+H], [M+Na], [M+K], and [M+NH4] adducts were kept. A second pathway analysis was done. The results from Pattern Hunter analysis were ranked by p-value and filtered to include only features identified as being unique to the smoky wine samples. Using MetaboAnalyst’s Functional Analysis, all of the parameters were kept as above. The results from this Functional Analysis were submitted to MetaboAnalyst’s Pathway Analysis, again with Arabidopsis thaliana as the KEGG pathway the results were mapped to. 4.2.3.7 Compound Identification Compound identification was carried out at Metabolomics Standard Initiative (MSI) level 2 by matching the accurate mass (<0.02 m/z) and fragmentation pattern of features with online libraries available in SIRIUS 5.8.5. ZODIAC was used to improve ranking of formula candidates,107 CSI:FingerID to identify the structures of compounds,108 and COSMIC to assign a confidence score to CIS:FingerID structure identifications,109 and CANOPUS to predict compound classes.110 This software was used to filter out adducts with the same mass in pathways that were mined. The peak areas of putatively identified compounds were plotted using Matplotlib in Python 3, in which the Mann-Whitney U test was used to compare treatments.124 4.3 Results 4.3.1 Volatile Phenols Previous researchers have hypothesized that the smoky flavour profile of wines was the result of accumulation of a small number of volatile phenols including guaiacol and its storage forms.125–127 We detected 4 volatile phenols in the smoky wine viz. o-cresol, p-cresol, guaiacol, 83 and 4-methylguiaicol (Figure 28). The smoky wine had more of the free and glycosidically-bound volatile phenols than the 2022 vintage (Figure 28). Figure 26. Free and bound volatile phenol profiles of smoke-exposed 2021 vintage and 2022 vintage. Error bars represent standard error. 4.3.2 Metabolomics of Wine 4.3.2.1 Data Quality and Characteristics Across all wines, 7,605 common features were detected. These represent the metabolic diversity of the wines and are most likely unrelated to the smoky flavour. The smoky wines had 108 features that were not detected in the award-winning wines, or the subsequent vintage 05101520253035402021 2022ng/mLo-Cresol0204060801001202021 2022ng/mLp-Cresol0204060801001201402021 2022ng/mLGuaiacol0204060801001201402021 2022ng/mL4-MethylguaiacolFree Bound84 produced from the same vines in the non-wildfire year (Table 9). Analysis of the 2021 smoky rosé detected 228 features that were not detected in the 2022 Saignée rosé (Table 9). These unique features were further investigated with statistical analysis. Table 9. Features detected in the wine metabolomics analysis. Smoky Rosé 2021 Vintage Saignée Rosé 2022 Vintage Award Winning Reds Total Features 8392 8967 8941 Average 2098 ± 115 2242 ± 457 2980 ± 161 %RSD 18.3 4.91 18.5 Compounds in All Replicates 5515 5515 6179 Unique to Each 108 420 617 Common to All 7605 Common Between Smoky Rosé Saignée Rosé Premium Saignée 451 491 Smoky Rosé 451 228 Premium 2228 491 4.3.2.2 Multivariate Analyses Figure 29 shows the PCA plot for the three wine treatments. Percent cumulative variance from PCA for PC1 (29.7%), PC2 (13.7%), and PC3 (10.8%) was 54.2%. There is clear separation between the different wines, indicating the unique metabolite profiles. The scree plot can be seen in Figure 30, which shows that the first three components explain most of the variance (54.2%). 85 Figure 27. Principal component analysis of Saignée, smoky Rosé, and premium award-winning red wines. Contour lines depict the density contours of data points of higher density within the plot. ‘‘86 Figure 28. Scree plot showing the proportion of variance explained for each component. 4.3.2.3 Pathway Analysis Functional Analysis using the MetaboAnalyst algorithm identified 9 pathways with a p-value of 0.5 or less (Table 10). A Pathway Analysis generated with Pattern Hunter’s output revealed 12 pathways with a p-value of 0.05 or less (Table 11). 87 Table 10. Pathways identified with significantly different features using MetaboAnalyst's algorithm. Pathway totala Hits totalb Hits sigc Expectedd P(Fisher)e P(EASE) f P(Gamma) g Emp.Hitsh Empiricali Arginine and proline metabolism 32 8 6 1.58 0.03 0.12 0.01 1 0.01 Biosynthesis of various plant secondary metabolites 29 6 4 1.43 0.13 0.38 0.01 2 0.02 Flavone and flavonol biosynthesis 10 4 3 0.49 0.14 0.46 0.01 1 0.01 Carotenoid biosynthesis 43 2 2 2.12 0.13 0.59 0.02 6 0.06 Lysine degradation 20 5 3 0.99 0.26 0.60 0.02 26 0.26 Nicotinate and nicotinamide metabolism 13 6 3 0.64 0.39 0.71 0.04 20 0.2 Arginine biosynthesis 18 4 2 0.89 0.47 0.84 0.07 16 0.16 Zeatin biosynthesis 21 4 2 1.04 0.47 0.84 0.07 36 0.36 Tropane, piperidine and pyridine alkaloid biosynthesis 9 4 2 0.44 0.47 0.84 0.07 23 0.23 aTotal number of compounds in Arabidopsis thaliana KEGG pathway 88 bNumber of matches in the data set based on m/z cNumber of significant matches based on the analysis eExpected number of hits based on the model fFisher’s exact test p-value tests for enrichment gadjusted p-values hEmpirical hits number of significant matches after adjustment 89 Table 11. Pathway analysis results generated with Pattern Hunter's results. Pathway totala Hits totalb Hits sigc Expectedd Raw pe Holm adjustf FDRg Impacth Flavonoid biosynthesis 47 20 4 47 5.0E-09 4.6E-07 4.6E-07 3.8E-01 Valine, leucine and isoleucine biosynthesis 22 13 5 22 2.1E-08 1.9E-06 9.4E-07 5.2E-01 Tyrosine metabolism 17 10 2 17 1.1E-06 9.6E-05 3.3E-05 5.0E-01 Isoquinoline alkaloid biosynthesis 6 5 3 6 6.1E-05 5.4E-03 1.4E-03 1 Phenylpropanoid biosynthesis 43 13 3 43 2.2E-04 1.9E-02 3.9E-03 3.8E-01 Phenylalanine metabolism 12 6 3 12 6.0E-04 5.2E-02 9.2E-03 7.7E-01 Phenylalanine, tyrosine and tryptophan biosynthesis 22 7 2 22 4.9E-03 4.2E-01 6.4E-02 2.0E-01 Biosynthesis of various plant secondary metabolites 29 8 4 29 7.0E-03 5.9E-01 8.0E-02 3.2E-01 Caffeine metabolism 10 4 1 10 1.4E-02 1 1.4E-01 0 Glucosinolate biosynthesis 65 12 1 65 3.0E-02 1 2.5E-01 1.7E-01 Valine, leucine and isoleucine degradation 37 8 1 37 3.1E-02 1 2.5E-01 4.9E-02 90 Pathway total Hits total Hits sig Expected Raw p Holm adjust FDR Impact alpha-Linolenic acid metabolism 26 6 2 26 4.4E-02 1 3.4E-01 3.8E-01 aTotal number of compounds in Arabidopsis thaliana KEGG pathway bNumber of matches in the data set based on m/z cNumber of significant matches based on the analysis eExpected number of hits based on the model fFisher’s exact test p-value gadjusted p-values using Holm’s method hFalse discovery rate iMeasure of relative importance of a pathway in the context of the entire metabolic network91 4.3.2.4 Arginine and Proline metabolism The Functional Analysis results indicated significant impacts of wildfire smoke on arginine and proline metabolism. The data was mined for metabolites of arginine and proline metabolism, using SIRIUS to confirm structure and formula (Table 12). Though the pathway can be traced to glutamate and 4-aminobutanoate (Figures 31 and 32) for all the wines, the premium red wines had higher levels of these metabolites. Glutamate is a precursor for many amino acids, and indirectly donates an amine nitrogen to all of the other amino acids.128 4-Aminobutanoate is the physiological form of γ-aminobutyric acid (GABA), which also has a role in nitrogen metabolism in plants.129 As for flavour associations, glutamate and N2-succinyl-arginine provide an umami flavour, which is one of the five basic tastes and is found in foods like seaweed and mushrooms.130 Arginine has been described as a bitter tasting amino acid.131 92 Table 12. Matched compounds in the arginine and proline metabolism pathway. KEGG ID Name Adduct Mass Matched Mass Diff Adduct Formula SIRIUS Confirmed C03415 N2-Succinyl-L-ornithine 233.113176 233.11349 0.0003 [M+H] C9H16N2O5 No C05932 N-Succinyl-L-glutamate 5-semialdehyde 254.0635 254.06559 0.0021 [M+Na] C9H13NO6 No C00025 L-Glutamate 148.060476 148.06084 0.0004 [M+H] C5H9NO4 Yes C00581 Guanidinoacetate 118.061076 118.06163 0.0006 [M+H] C3H7N3O2 No C01043 N-Carbamoylsarcosine 133.060776 133.06108 0.0003 [M+H] C4H8N2O3 No C00062 L-Arginine 175.118976 175.11927 0.0003 [M+H] C6H14N4O2 Yes C04137 Octopine 247.140076 247.14034 0.0003 [M+H] C9H18N4O4 No C01682 Nopaline 305.145576 305.14608 0.0005 [M+H] C11H20N4O6 No C05945 L-Arginine phosphate 277.0672 277.06892 0.0017 [M+NH4] C6H15N4O5P No C03166 Phosphoguanidinoacetate 198.027476 198.02877 0.0013 [M+H] C3H8N3O5P No C00077 L-Ornithine 133.097176 133.09746 0.0003 [M+H] C5H12N2O2 No C01165 L-Glutamate 5-semialdehyde 132.065476 132.06587 0.0004 [M+H] C5H9NO3 Yes C03912 (S)-1-Pyrroline-5-carboxylate 114.054976 114.0554 0.0004 [M+H] C5H7NO2 No C00148 L-Proline 116.070576 116.07101 0.0004 [M+H] C5H9NO2 Yes C04281 L-1-Pyrroline-3-hydroxy-5-carboxylate 130.049876 130.05031 0.0004 [M+H] C5H7NO3 No C01250 N-Acetyl-L-glutamate 5-semialdehyde 174.076076 174.07653 0.0005 [M+H] C7H11NO4 No C00179 Agmatine 131.129076 131.12955 0.0005 [M+H] C5H14N4 Yes C00436 N-Carbamoylputrescine 132.113176 132.11359 0.0004 [M+H] C5H13N3O No 93 KEGG ID Name Adduct Mass Matched Mass Diff Adduct Formula SIRIUS Confirmed C10497 Feruloylputrescine 265.154676 265.15533 0.0007 [M+H] C14H20N2O3 Yes C03771 5-Guanidino-2-oxopentanoate 196.06669 196.0692 0.0025 [M+Na] C6H11N3O3 No C02647 4-Guanidinobutanal 130.097476 130.09789 0.0004 [M+H] C5H11N3O No C03078 4-Guanidinobutanamide 183.06567 183.0643 0.0014 [M+K] C5H12N4O No C15699 gamma-L-Glutamylputrescine 218.149876 218.15032 0.0004 [M+H] C9H19N3O3 No C02714 N-Acetylputrescine 131.117876 131.11828 0.0004 [M+H] C6H14N2O No C05946 (4R)-4-Hydroxy-2-oxoglutarate 180.0502 180.05099 0.0008 [M+NH4] C5H6O6 No C00334 4-Aminobutanoate 104.070576 104.07123 0.0007 [M+H] C4H9NO2 No C01035 4-Guanidinobutanoate 146.092376 146.09263 0.0003 [M+H] C5H11N3O2 No C02946 4-Acetamidobutanoate 146.081176 146.08153 0.0004 [M+H] C6H11NO3 Yes C15700 gamma-Glutamyl-gamma-aminobutyraldehyde 217.118276 217.11866 0.0004 [M+H] C9H16N2O4 No C05936 N4-Acetylaminobutanal 130.086276 130.08665 0.0004 [M+H] C6H11NO2 Yes C00315 Spermidine 146.165176 146.16558 0.0004 [M+H] C7H19N3 Yes 94 Figure 29. Amino acids leading to L-glutamate detected in wines. The absence of a box indicates the metabolite was not detected. Statistically significant differences by Mann-Whitney U test comparing to the Saignée (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.01, and (***) p ≤ 0.005. L-ArginineN2-Succinyl-L-ArginineN2-Succinyl-L-OrnithineN-Succinyl-L-Glutamate-5-SemialdehydeL-Glutamate95 Figure 30. Amino acids leading to 4-aminobutanoate detected in wines. Statistically significant differences by Mann-Whitney U test comparing to the Saignée (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.01, and (***) p ≤ 0.005. 4-GuanadinobutanalL-Arginine5-Guanadino-2-oxopentanoate4-Guanadinobutanoate4-Aminobutanoate96 Figure 33 shows proline putatively detected at higher intensities in the smoky Rosé. This amino acid accumulates in plants under stress.132 Proline has also been shown to accumulate in two wheat varieties treated with plant-derived smoke.133 Furthermore, it has been discovered that karrikins, plant growth regulators found in biomass smoke, can enhance proline accumulation in creeping bentgrass.134 In rapeseed treated with wood vinegar, a byproduct of wood combustion, proline levels over two years increased by over 100%.135 Regarding flavour, proline contributes to the bitterness of peptides.136 The absence of ornithine in the smoky rose compared to the other two treatments suggests the substrate could be depleted as to synthesize more proline.137 Ornithine is also a precursor for putrescine, as is N-Carbamoylputrescine. The associated ions of both putatively identified metabolites were detected at lower intensities in the smoky rosé (Figures 33 and 34). Putrescine is one of the molecules associated with the smell of death.138 In Figure 35, spermidine is detected in the premium red wines but not the other two treatment groups. The precursor of spermidine, L-aspartate-4-semialdehyde, is not detected in the premium red wines but is seen in the Saignée and Rosé. Spermidine is synthesized from putrescine, and the two polyamines have been linked to more flower buds, more flowers, and a larger mean floral diameter in a few plant species.139 97 Figure 31. Amino acids leading to proline detected in wines. Statistically significant differences by Mann-Whitney U test comparing to the Saignée (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.01, and (***) p ≤ 0.005. L-ArginineOrnithineL-Glutamate-5 -semialdehydeProline98 Figure 32. Amino acids leading to N-carbamoyl putrescine detected in wines. Statistically significant differences by Mann-Whitney U test comparing to the Saignée (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.01, and (***) p ≤ 0.005. L-ArginineL-Arginine phosphateAgmatineN-Carbamoylputrescine99 Figure 33. Amino acids leading to 4-aminobutanoate detected in wines. S Statistically significant differences by Mann-Whitney U test comparing to the Saignée (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.01, and (***) p ≤ 0.005. 4-Guanidinobutanamide4-Guanadinobutanoate4-AminobutanoateSpermidineL-Aspartate-4-semialdehyde100 4.3.2.5 Flavonoid Biosynthesis The Pathway Analysis results generated various hypotheses involving metabolites from the flavonoid biosynthesis pathway (Table 13). Quercetin was putatively identified at higher intensities in the Smoky wine than in the Saignée and premium reds (Figure 36). As is indicated by the arrows, the metabolite was not detected in the premium red wines. Quercetin is generally not found in white wines, as they do not have skin contact, and so the flavanol does not have a chance to extract from the corresponding glycoside in the skins.140,141 Quercetin is thought to be responsible for the formation of undesirable deposits in certain wines.142 Figure 37 outlines a ratio of flavanols that agrees with the literature, where they are found to be more intense in the premium red wines.140,142 This is expected because these wines had more skin contact than the other wines, and so there was more time for these compounds to extract into the juice. 101 Table 13. Matched compounds in the flavonoid biosynthesis pathway. KEGG ID Name Adduct Mass Matched Mass Diff Adduct Formula SIRIUS Confirmed C02947 4-Coumaroylshikimate 321.095 321.0969 0.0019 [M+H] C16H16O7 No C05905 Cyanidin 288.0613 288.0629 0.0016 [M+H] C15H11O6 No C01617 Taxifolin 305.0653 305.0656 0.0003 [M+H] C15H12O7 No C09789 Naringenin 581.1879 581.1865 0.0014 [M+H] C27H32O14 Yes C01378 Fustin 289.0712 289.0707 0.0006 [M+H] C15H12O6 No C12136 (-)-Epigallocatechin 307.0818 307.0813 0.0005 [M+H] C15H14O8 Yes C05907 Luteoforol 291.0842 291.0863 0.0021 [M+H] C15H14O6 Yes C03648 cis-3,4-Leucopelargonidin 291.0869 291.0863 0.0006 [M+H] C15H14O6 Yes C09727 (-)-Epicatechin 291.0869 291.0863 0.0007 [M+H] C15H14O6 Yes C02906 Dihydromyricetin 321.0614 321.0605 0.0010 [M+H] C15H12O8 No C10107 Myricetin 319.0515 319.0449 0.0066 [M+H] C15H10O8 Yes C05909 Leucodelphinidin 323.0743 323.0762 0.0019 [M+H] C15H14O8 No C05904 Pelargonidin 272.0656 272.0679 0.0023 [M+H] C15H11O5 No C12208 p-Coumaroyl quinic acid 339.1058 339.1075 0.0017 [M+H] C16H18O8 No C16405 Homoeriodictyol chalcone 303.0928 303.0863 0.0066 [M+H] C16H14O6 No C10434 5-O-Caffeoylshikimic acid 337.0898 337.0918 0.0020 [M+H] C16H16O8 No C05908 Delphindin 304.0555 304.0578 0.0023 [M+H] C15H11O7 No C00389 Quercetin 303.0482 303.0499 0.0017 [M+H] C15H10O7 No 102 Figure 34. Putatively identified flavonols leading to quercetin in the flavonoid biosynthesis pathway detected in wines. Statistically significant differences by Mann-Whitney U test comparing to the Saignée (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.01, and (***) p ≤ 0.005. NaringeninLuteoforolTaxifolinQuercetin103 Figure 35. Putatively identified flavonols in the flavonoid biosynthesis pathway detected in wines. Statistically significant differences by Mann-Whitney U test comparing to the Saignée (α=0.05) indicated with: (*) p ≤ 0.05, (**) p ≤ 0.01, and (***) p ≤ 0.005. MyricetinDihydromyricetinPentahydroxyflavone(-)-EpigallocatechinDelphinidinLeucodelphinidin104 4.4 Conclusions This research has provided an untargeted metabolomics approach that can be used to characterize the non-volatile metabolites in wines. Through characterizing smoky and non-smoky wines, hypotheses regarding grapevines’ response to the stress of wildfire smoke were generated: 1) Wildfire smoke exposure increases the accumulation of nitrogen through the arginine and proline pathways, resulting in unpalatable flavour metabolites such as ornithine, putrescine, cadaverine and others. 2) Wildfire smoke exposure alters the biosynthesis of tannins and the metabolism of quercetins during grape development thereby altering the character of wines. 105 Chapter 5: Conclusion I hypothesized that volatile compounds released from Artemisia tridentata and its smoke accumulated in commercial grapevines (Vitis vinifera L.) and that wildfire smoke influenced the terroir of finished wines. 5.1 Objective 1 Based on previous literature stating that terpenes contribute to the terroir of wines, I hypothesized that terpenes released in burning and growing Artemisia tridentata accumulate in grapevines (Vitis vinifera). To investigate this hypothesis, I developed and validated a method to detect and quantify 31 terpenes in grapevine leaves and wine. This method was fit-for-purpose for grapevine leaves, with acceptable levels of selectivity and linearity. The AOAC definitions provided acceptable LOD and LOQ values. The accuracy was determined for grapevine leaf matrix. The method was precise, with acceptable values for both interday and intraday precision. The robustness of the method was evaluated by changing seven factors in a Youden ruggedness test, finding that the method was not significantly affected by any of the factors. Overall, the method is fit for purpose for terpene determination in grapevines. This method is useful for monitoring the compounds that contribute to the finished wine’s terroir. 5.2 Objective 2 To determine the accumulation of externally derived terpenes on grapevines, I used Artemisia tridentata as it is an indigenous plant in the South Okanagan wine-growing region that often grows near vineyards. My studies showed that terpenes released by A. tridentata, as well as its degradation products, accumulated in grapevine leaves. My data also suggest that the accumulation of the A. tridentata-derived terpenes affected other terpenes, as some analytes not detected in the leaf material or extracts were detected in the samples, and degradation products 106 of other terpenes were putatively identified. These data indicate that terpenes present in A. tridentata smoke extracts accumulate in grapevine leaves and that burning during wildfires impacts grapevine chemistry. The cohabitation experiment is evidence for interspecies communication, and how indigenous plants near vineyards can impact terroir. A challenge with this research was that the negative wildfire control was compromised by the McDougall Creek Wildfire. Future work should replicate the experiment with a negative control. Additionally, analyzing the volatiles released in burning the roots of A. tridentata should be investigated. 5.3 Objective 3 To determine the effect of smoke on the terroir of finished wine, I used a metabolomics approach to characterize the non-volatile metabolites in smoky wine. Together, the data from this experiment suggest several new avenues for future studies of the impacts of wildfires in vineyards and wines. The following hypothesis were generated from the data for future investigations: 1) Wildfire smoke exposure increases the accumulation of nitrogen through the arginine and proline pathways, resulting in unpalatable flavour metabolites such as ornithine, putrescine, cadaverine and others. 2) Wildfire smoke exposure alters the biosynthesis of tannins and the metabolism of quercetins during grape development thereby altering the character of wines. Future studies to investigate nitrogen cycling physiology and flavonoid metabolism in wine grapes will help wine makers and vineyard managers to mitigate the damage of climate events including wildfires on grapes and wines. Additionally, further studies could make their own positive and negative control wines through smoking vines, leaving others, and making two respective wines for analysis. 107 5.4 Conclusion Overall, the relationships between smoke exposure, grapevine chemistry, and terroir were investigated. Terpenes derived from living A. tridentata and its smoke were detected in grapevines. As terpenes play an important role in the aroma characteristics of wine, these plants in the vineyard could affect the terroir of finished wines. Wildfires are projected to increase due to climate change, and so the relationship between indigenous plants and terroir will become more pronounced, as the studies of this thesis saw wildfire smoke and atmospheric smoke to affect the terpene content of grapevines. The establishment of an untargeted metabolomics analysis for smoky wines resulted in hypotheses regarding grapevine metabolism that are worthy of further investigation. Understanding the grapevine’s response to wildfire smoke is important because it brings us closer to understanding how all of smoke’s components can affect the finished wines. This work can act as a basis for future studies with the overall goal of implementing novel remediation techniques for grapevines exposed to wildfire smoke and the finished wines. 108 Bibliography (1) Government of Canada. Climate change and fire. Natural Resources Canada. http://www.nrcan.gc.ca/forests/fire-insects-disturbances/fire/13155 (accessed 2022-12-08). (2) The Government of British Columbia. Wildfire Averages. https://www2.gov.bc.ca/gov/content/safety/wildfire-status/about-bcws/wildfire-statistics/wildfire-averages (accessed 2022-12-10). (3) City of West kelowna. McDougall Creek Wildfire Information. https://www.westkelownacity.ca/en/our-community/mcdougall-creek-wildfire-information.aspx (accessed 2024-04-02). (4) Wines of British Columbia. Quick Facts. https://winebc.com/industry/media/quick-facts/ (accessed 2022-12-06). (5) British Columbia’s Wine Industry. Government of British Columbia. https://news.gov.bc.ca/factsheets/factsheet-british-columbias-wine-industry (accessed 2022-12-06). (6) BC Wine Industry Quick Facts | Wine BC Indistry. Wine BC Industry. https://winebc.com/industry/media/quick-facts/ (accessed 2024-04-16). (7) Snoek, J.; Partner, M.; Elbourne, L. BC Vineyard Resilience Project; Cascadia Partners, 2022. (8) Hewer, M. J.; Gough, W. A. Climate Change Impact Assessment on Grape Growth and Wine Production in the Okanagan Valley (Canada). Clim. Risk Manag. 2021, 33 (January), 100343. https://doi.org/10.1016/j.crm.2021.100343. (9) Alsharif, G. B.C. wineries feel the burn after smoke from last year’s wildfires taints grape harvest. Toronto Star. https://www.thestar.com/news/canada/2022/05/21/bc-wineries-feel-the-burn-after-smoke-from-last-years-wildfires-taints-grape-harvest.html#:~:text=Record-breaking heat and smoke,30 per cent this year.&text=Gary Misson%2C owner of Montakarn,have gone up in s (accessed 2022-12-08). (10) GeoBC Branch. Landcover - TRIM Enhanced Base Map (EBM). Government of British Columbia. https://catalogue.data.gov.bc.ca/dataset/landcover-trim-enhanced-base-map-ebm (accessed 2022-12-12). (11) Liang, Y.; Jen, C. N.; Weber, R. J.; Misztal, P. K.; Goldstein, A. H. Chemical Composition of PM2.5 in October 2017 Northern California Wildfire Plumes. Atmospheric Chem. Phys. 2021, 21 (7), 5719–5737. https://doi.org/10.5194/acp-21-5719-2021. (12) Zhang, X.; Yang, W.; Blasiak, W. Thermal Decomposition Mechanism of Levoglucosan during Cellulose Pyrolysis. J. Anal. Appl. Pyrolysis 2012, 96, 110–119. https://doi.org/10.1016/j.jaap.2012.03.012. (13) Hosoya, T.; Kawamoto, H.; Saka, S. Different Pyrolytic Pathways of Levoglucosan in Vapor- and Liquid/Solid-Phases. J. Anal. Appl. Pyrolysis 2008, 83 (1), 64–70. https://doi.org/10.1016/j.jaap.2008.06.008. (14) Uddin, S. M. M.; Daniel, N. R. R.; Harper, R. J.; Henry, D. J. Why Do Biogenic Volatile Organic Compounds (BVOCs) Derived from Vegetation Fire Not Induce Soil Water Repellency? Biogeochemistry 2017, 134 (1–2), 147–161. https://doi.org/10.1007/s10533-017-0352-7. (15) Peñuelas, J.; Staudt, M. BVOCs and Global Change. Trends Plant Sci. 2010, 15 (3), 133–144. https://doi.org/10.1016/j.tplants.2009.12.005. (16) Simpson, I. J.; Akagi, S. K.; Barletta, B.; Blake, N. J.; Choi, Y.; Diskin, G. S.; Fried, A.; Fuelberg, H. E.; Meinardi, S.; Rowland, F. S.; Vay, S. A.; Weinheimer, A. J.; Wennberg, P. O.; Wiebring, P.; Wisthaler, A.; Yang, M.; Yokelson, R. J.; Blake, D. R. Boreal Forest Fire Emissions in Fresh Canadian Smoke Plumes: C1-C10 Volatile Organic Compounds 109 (VOCs), CO2, CO, NO2, NO, HCN and CH3CN. Atmospheric Chem. Phys. 2011, 11 (13), 6445–6463. https://doi.org/10.5194/acp-11-6445-2011. (17) Hatch, L. E.; Jen, C. N.; Kreisberg, N. M.; Selimovic, V.; Yokelson, R. J.; Stamatis, C.; York, R. A.; Foster, D.; Stephens, S. L.; Goldstein, A. H.; Barsanti, K. C. Highly Speciated Measurements of Terpenoids Emitted from Laboratory and Mixed-Conifer Forest Prescribed Fires. Environ. Sci. Technol. 2019, 53 (16), 9418–9428. https://doi.org/10.1021/acs.est.9b02612. (18) Darvell, L. I.; Brindley, C.; Baxter, X. C.; Jones, J. M.; Williams, A. Nitrogen in Biomass Char and Its Fate during Combustion: A Model Compound Approach. Energy Fuels 2012, 26 (11), 6482–6491. https://doi.org/10.1021/ef201676t. (19) Hansson, K. M.; Samuelsson, J.; Tullin, C.; Åmand, L. E. Formation of HNCO, HCN, and NH3 from the Pyrolysis of Bark and Nitrogen-Containing Model Compounds. Combust. Flame 2004, 137 (3), 265–277. https://doi.org/10.1016/j.combustflame.2004.01.005. (20) Cheng, P.; Liu, Z.; Feng, Y.; Han, Y.; Peng, Y.; Cai, J.; Chen, Y. Emission Characteristics and Formation Pathways of Carbonyl Compounds from the Combustion of Biomass and Their Cellulose, Hemicellulose, and Lignin at Different Temperatures and Oxygen Concentrations. Atmos. Environ. 2022, 291 (May), 119387. https://doi.org/10.1016/j.atmosenv.2022.119387. (21) Akherati, A.; He, Y.; Coggon, M. M.; Koss, A. R.; Hodshire, A. L.; Sekimoto, K.; Warneke, C.; De Gouw, J.; Yee, L.; Seinfeld, J. H.; Onasch, T. B.; Herndon, S. C.; Knighton, W. B.; Cappa, C. D.; Kleeman, M. J.; Lim, C. Y.; Kroll, J. H.; Pierce, J. R.; Jathar, S. H. Oxygenated Aromatic Compounds Are Important Precursors of Secondary Organic Aerosol in Biomass-Burning Emissions. Environ. Sci. Technol. 2020, 54 (14), 8568–8579. https://doi.org/10.1021/acs.est.0c01345. (22) He, J.; Zielinska, B.; Balasubramanian, R. Composition of Semi-Volatile Organic Compounds in the Urban Atmosphere of Singapore: Influence of Biomass Burning. Atmospheric Chem. Phys. 2010, 10 (23), 11401–11413. https://doi.org/10.5194/acp-10-11401-2010. (23) Mao, S.; Li, J.; Cheng, Z.; Zhong, G.; Li, K.; Liu, X.; Zhang, G. Contribution of Biomass Burning to Ambient Particulate Polycyclic Aromatic Hydrocarbons at a Regional Background Site in East China. Environ. Sci. Technol. Lett. 2018, 5 (2), 56–61. https://doi.org/10.1021/acs.estlett.8b00001. (24) Medeiros, P. M.; Simoneit, B. R. T. Source Profiles of Organic Compounds Emitted upon Combustion of Green Vegetation from Temperate Climate Forests. Environ. Sci. Technol. 2008, 42 (22), 8310–8316. https://doi.org/10.1021/es801533b. (25) Gogou, A.; Stratigakis, N.; Kanakidou, M.; Stephanou, E. G. Organic Aerosols in Eastern Mediterranean: Components Source Reconciliation by Using Molecular Markers and Atmospheric Back Trajectories. Org. Geochem. 1996, 25 (1–2), 79–96. https://doi.org/10.1016/S0146-6380(96)00105-2. (26) Kesselmeier, J.; Staudt, M. Biogenic Volatile Organic Compounds (VOC): An Overview on Emission, Physiology and Ecology. J. Atmospheric Chem. 1999, 33, 23–88. (27) Toledo, R. T. Wood Smoke Components and Functional Properties. 2008, 55–61. https://doi.org/10.4027/isscp.2008.12. (28) Chiwocha, S. D. S.; Dixon, K. W.; Flematti, G. R.; Ghisalberti, E. L.; Merritt, D. J.; Nelson, D. C.; Riseborough, J. A. M.; Smith, S. M.; Stevens, J. C. Karrikins: A New Family of Plant Growth Regulators in Smoke. Plant Sci. 2009, 177 (4), 252–256. https://doi.org/10.1016/j.plantsci.2009.06.007. (29) Ristic, R.; Fudge, A. L.; Pinchbeck, K. A.; De Bei, R.; Fuentes, S.; Hayasaka, Y.; Tyerman, S. D.; Wilkinson, K. L. Impact of Grapevine Exposure to Smoke on Vine Physiology and the Composition and Sensory Properties of Wine. Theor. Exp. Plant Physiol. 2016, 28 (1), 67–83. https://doi.org/10.1007/s40626-016-0054-x. 110 (30) Bell, T. L.; Stephens, S. L.; Moritz, M. A. Short-Term Physiological Effects of Smoke on Grapevine Leaves. Int. J. Wildland Fire 2013, 22 (7), 933–946. https://doi.org/10.1071/WF12140. (31) Summerson, V.; Viejo, C. G.; Pang, A.; Torrico, D. D.; Fuentes, S. Review of the Effects of Grapevine Smoke Exposure and Technologies to Assess Smoke Contamination and Taint in Grapes and Wine. Beverages 2021, 7 (1), 1–21. https://doi.org/10.3390/beverages7010007. (32) Sheppard, S. I.; Dhesi, M. K.; Eggers, N. J. Effect of Pre- And Postveraison Smoke Exposure on Guaiacol and 4-Methylguaiacol Concentration in Mature Grapes. Am. J. Enol. Vitic. 2009, 60 (1), 98–103. https://doi.org/10.5344/ajev.2009.60.1.98. (33) Mele, M. A.; Kang, H. M.; Lee, Y. T.; Islam, M. Z. Grape Terpenoids: Flavor Importance, Genetic Regulation, and Future Potential. Crit. Rev. Food Sci. Nutr. 2021, 61 (9), 1429–1447. https://doi.org/10.1080/10408398.2020.1760203. (34) Verdenal, T.; Dienes-Nagy, Á.; Spangenberg, J. E.; Zufferey, V.; Spring, J. L.; Viret, O.; Marin-Carbonne, J.; van Leeuwen, C. Understanding and Managing Nitrogen Nutrition in Grapevine: A Review. Oeno One 2021, 55 (1), 1–43. https://doi.org/10.20870/OENO-ONE.2021.55.1.3866. (35) Baron, M.; Prusova, B.; Tomaskova, L.; Kumsta, M.; Sochor, J. Terpene Content of Wine from the Aromatic Grape Variety “Irsai Oliver” (Vitis Vinifera L.) Depends on Maceration Time. Open Life Sci. 2017, 12 (1), 42–50. https://doi.org/10.1515/biol-2017-0005. (36) Gobert, A.; Tourdot-Maréchal, R.; Sparrow, C.; Morge, C.; Alexandre, H. Influence of Nitrogen Status in Wine Alcoholic Fermentation. Food Microbiol. 2019, 83 (April), 71–85. https://doi.org/10.1016/j.fm.2019.04.008. (37) Rapp, A.; Versini, G. Influence of Nitrogen Compounds in Grapes on Aroma Compounds of Wines. Dev. Food Sci. 1995, 37 (C), 1659–1694. https://doi.org/10.1016/S0167-4501(06)80257-8. (38) Liu, J.; Zhu, X. L.; Ullah, N.; Tao, Y. S. Aroma Glycosides in Grapes and Wine. J. Food Sci. 2017, 82 (2), 248–259. https://doi.org/10.1111/1750-3841.13598. (39) Girard, B.; Fukumoto, L.; Mazza, G.; Delaquis, P.; Ewert, B. Volatile Terpene Constituents in Maturing Gewürztraminer Grapes from British Columbia. Am. J. Enol. Vitic. 2002, 53 (2), 99–109. https://doi.org/10.5344/ajev.2002.53.2.99. (40) Crupi, P.; Coletta, A.; Antonacci, D. Analysis of Carotenoids in Grapes to Predict Norisoprenoid Varietal Aroma of Wines from Apulia. J. Agric. Food Chem. 2010, 58 (17), 9647–9656. https://doi.org/10.1021/jf100564v. (41) Martínez-Gil, A. M.; Angenieux, M.; Pardo-García, A. I.; Alonso, G. L.; Ojeda, H.; Rosario Salinas, M. Glycosidic Aroma Precursors of Syrah and Chardonnay Grapes after an Oak Extract Application to the Grapevines. Food Chem. 2013, 138 (2–3), 956–965. https://doi.org/10.1016/j.foodchem.2012.11.032. (42) Lee, S. H.; Seo, M. J.; Riu, M.; Cotta, J. P.; Block, D. E.; Dokoozlian, N. K.; Ebeler, S. E. Vine Microclimate and Norisoprenoid Concentration in Cabernet Sauvignon Grapes and Wines. Am. J. Enol. Vitic. 2007, 58 (3), 291–301. https://doi.org/10.5344/ajev.2007.58.3.291. (43) Black, C. A.; Parker, M.; Siebert, T. E.; Capone, D. L.; Francis, I. L. Terpenoids and Their Role in Wine Flavour: Recent Advances: Terpenoids: Role in Wine Flavour. Aust. J. Grape Wine Res. 2015, 21, 582–600. https://doi.org/10.1111/ajgw.12186. (44) Dunlevy, J. D.; Kalua, C. M.; Keyzers, R. A.; Boss, P. K. The Production of Flavour & Aroma Compounds in Grape Berries. In Grapevine Molecular Physiology & Biotechnology; Roubelakis-Angelakis, K. A., Ed.; Springer Netherlands: Dordrecht, 2009; pp 293–340. https://doi.org/10.1007/978-90-481-2305-6_11. 111 (45) Noestheden, M.; Noyovitz, B.; Riordan-Short, S.; Dennis, E. G.; Zandberg, W. F. Smoke from Simulated Forest Fire Alters Secondary Metabolites in Vitis Vinifera L. Berries and Wine. Planta 2018, 248 (6), 1537–1550. https://doi.org/10.1007/s00425-018-2994-7. (46) Ristic, R.; Van Der Hulst, L.; Capone, D. L.; Wilkinson, K. L. Impact of Bottle Aging on Smoke-Tainted Wines from Different Grape Cultivars. J. Agric. Food Chem. 2017, 65 (20), 4146–4152. https://doi.org/10.1021/acs.jafc.7b01233. (47) Crews, P.; Dorenbach, P.; Amberchan, G.; Keiffer, R. F.; Lizama-Chamu, I.; Ruthenburg, T. C.; McCauley, E. P.; McGourty, G. Natural Product Phenolic Diglycosides Created from Wildfires, Defining Their Impact on California and Oregon Grapes and Wines. J. Nat. Prod. 2022, 85 (3), 547–561. https://doi.org/10.1021/acs.jnatprod.2c00028. (48) Kennison, K. R.; Wilkinson, K. L.; Pollnitz, A. P.; Williams, H. G.; Gibberd, M. R. Effect of Timing and Duration of Grapevine Exposure to Smoke on the Composition and Sensory Properties of Wine. Aust. J. Grape Wine Res. 2009, 15 (3), 228–237. https://doi.org/10.1111/j.1755-0238.2009.00056.x. (49) Iinuma, Y.; Böge, O.; Herrmann, H. Methyl-Nitrocatechols: Atmospheric Tracer Compounds for Biomass Burning Secondary Organic Aerosols. Environ. Sci. Technol. 2010, 44 (22), 8453–8459. https://doi.org/10.1021/es102938a. (50) Australian Wine Research Institute. Sensory Impact of Smoke Exposure; Adelaide, 2016. (51) Mayr, C. M.; Parker, M.; Baldock, G. A.; Black, C. A.; Pardon, K. H.; Williamson, P. O.; Herderich, M. J.; Francis, I. L. Determination of the Importance of In-Mouth Release of Volatile Phenol Glycoconjugates to the Flavor of Smoke-Tainted Wines. J. Agric. Food Chem. 2014, 62 (11), 2327–2336. https://doi.org/10.1021/jf405327s. (52) Singleton, V. L.; Esau, P. Phenolic Substances in Grapes and Wine, and Their Significance; Chichester, C. O., Mrak, E. M., Stewart, G. F., Eds.; Academic Press Inc., 1969. (53) Coulter, A.; Baldock, G.; Parker, M.; Hayasaka, Y.; Francis, I. L.; Herderich, M. Concentration of Smoke Marker Compounds in Non-Smoke-Exposed Grapes and Wine in Australia. Aust. J. Grape Wine Res. 2022, 28 (3), 459–474. https://doi.org/10.1111/ajgw.12543. (54) Hayasaka, Y.; Baldock, G. A.; Parker, M.; Pardon, K. H.; Black, C. A.; Herderich, M. J.; Jeffery, D. W. Glycosylation of Smoke-Derived Volatile Phenols in Grapes as a Consequence of Grapevine Exposure to Bushfire Smoke. J. Agric. Food Chem. 2010, 58 (20), 10989–10998. https://doi.org/10.1021/jf103045t. (55) Hayasaka, Y.; Dungey, K. A.; Baldock, G. A.; Kennison, K. R.; Wilkinson, K. L. Identification of a β-d-Glucopyranoside Precursor to Guaiacol in Grape Juice Following Grapevine Exposure to Smoke. Anal. Chim. Acta 2010, 660 (1–2), 143–148. https://doi.org/10.1016/j.aca.2009.10.039. (56) Kennison, K. R.; Gibberd, M. R.; Pollnitz, A. P.; Wilkinson, K. L. Smoke-Derived Taint in Wine: The Release of Smoke-Derived Volatile Phenols during Fermentation of Merlot Juice Following Grapevine Exposure to Smoke. J. Agric. Food Chem. 2008, 56 (16), 7379–7383. https://doi.org/10.1021/jf800927e. (57) Martínez-Gil, A. M.; Garde-Cerdán, T.; Zalacain, A.; Pardo-García, A. I.; Salinas, M. R. Applications of an Oak Extract on Petit Verdot Grapevines. Influence on Grape and Wine Volatile Compounds. Food Chem. 2012, 132 (4), 1836–1845. https://doi.org/10.1016/j.foodchem.2011.12.016. (58) Hayasaka, Y.; Baldock, G. A.; Pardon, K. H.; Jeffery, D. W.; Herderich, M. J. Investigation into the Formation of Guaiacol Conjugates in Berries and Leaves of Grapevine Vitis Vinifera L. CV. Cabernet Sauvignon Using Stable Isotope Tracers Combined with HPLC-MS and MS/MS Analysis. J. Agric. Food Chem. 2010, 58 (4), 2076–2081. https://doi.org/10.1021/jf903732p. 112 (59) Noestheden, M.; Dennis, E. G.; Romero-Montalvo, E.; DiLabio, G. A.; Zandberg, W. F. Detailed Characterization of Glycosylated Sensory-Active Volatile Phenols in Smoke-Exposed Grapes and Wine. Food Chem. 2018, 259 (December 2017), 147–156. https://doi.org/10.1016/j.foodchem.2018.03.097. (60) Härtl, K.; Huang, F. C.; Giri, A. P.; Franz-Oberdorf, K.; Frotscher, J.; Shao, Y.; Hoffmann, T.; Schwab, W. Glucosylation of Smoke-Derived Volatiles in Grapevine (Vitis Vinifera) Is Catalyzed by a Promiscuous Resveratrol/Guaiacol Glucosyltransferase. J. Agric. Food Chem. 2017, 65 (28), 5681–5689. https://doi.org/10.1021/acs.jafc.7b01886. (61) Krstic, M. P.; Johnson, D. L.; Herderich, M. J. Review of Smoke Taint in Wine: Smoke-Derived Volatile Phenols and Their Glycosidic Metabolites in Grapes and Vines as Biomarkers for Smoke Exposure and Their Role in the Sensory Perception of Smoke Taint. Aust. J. Grape Wine Res. 2015, 21, 537–553. https://doi.org/10.1111/ajgw.12183. (62) Nealon, S. Oregon State researchers discover compounds contributing to smoke taint in wine and grapes. Oregon State University News Room. https://today.oregonstate.edu/news/oregon-state-researchers-discover-compounds-contributing-smoke-taint-wine-and-grapes (accessed 2022-12-10). (63) Danko, P. Oregon State scientists sniff out a wine smoke taint breakthrough. Portland Business Journal. https://www.bizjournals.com/portland/news/2022/06/23/smoke-taint-breakthrough.html (accessed 2022-12-10). (64) Reynolds, G.; Wardle, D. A. Impact of Various Canopy Manipulation Techniques on Growth, Yield, Fruit Composition, and Wine Quality of Gewiirztraminer. 1989, 40 (2). (65) Dimitriadis, E.; Williams, P. J. The Development and Use of a Rapid Analytical Technique for Estimation of Free and Potentially Volatile Monoterpene Flavorants of Grapes. Am. J. Enol. Vitic. 1984, 35 (2), 66–71. https://doi.org/10.5344/ajev.1984.35.2.66. (66) Schievano, E.; D’Ambrosio, M.; Mazzaretto, I.; Ferrarini, R.; Magno, F.; Mammi, S.; Favaro, G. Identification of Wine Aroma Precursors in Moscato Giallo Grape Juice: A Nuclear Magnetic Resonance and Liquid Chromatography–Mass Spectrometry Tandem Study. Talanta 2013, 116, 841–851. https://doi.org/10.1016/j.talanta.2013.07.049. (67) Micalizzi, G.; Vento, F.; Alibrando, F.; Donnarumma, D.; Dugo, P.; Mondello, L. Cannabis Sativa L.: A Comprehensive Review on the Analytical Methodologies for Cannabinoids and Terpenes Characterization. J. Chromatogr. A 2021, 1637, 461864. https://doi.org/10.1016/j.chroma.2020.461864. (68) Petronilho, S.; Coimbra, M. A.; Rocha, S. M. A Critical Review on Extraction Techniques and Gas Chromatography Based Determination of Grapevine Derived Sesquiterpenes. Anal. Chim. Acta 2014, 846, 8–35. https://doi.org/10.1016/j.aca.2014.05.049. (69) DB-WAX GC column | Agilent. Agilent. https://www.agilent.com/en/product/gc-columns/wax-gc-columns/db-wax-columns (accessed 2023-09-11). (70) Arbulu, M.; Sampedro, M. C.; Sanchez-Ortega, A.; Gómez-Caballero, A.; Unceta, N.; Goicolea, M. A.; Barrio, R. J. Characterisation of the Flavour Profile from Graciano Vitis Vinifera Wine Variety by a Novel Dual Stir Bar Sorptive Extraction Methodology Coupled to Thermal Desorption and Gas Chromatography–Mass Spectrometry. Anal. Chim. Acta 2013, 777, 41–48. https://doi.org/10.1016/j.aca.2013.03.024. (71) Wang, L.; Luo, Z.; Li, J.; Yang, M.; Yan, J.; Lu, H.; Li, D.; Chen, C.; Aghdam, M. S.; Wu, B.; Li, L. Morphological and Quality Characterization of Grape Berry and Rachis in Response to Postharvest 1-Methylcyclopropene and Elevated Oxygen and Carbon Dioxide Atmospheres. Postharvest Biol. Technol. 2019, 153 (April), 107–117. https://doi.org/10.1016/j.postharvbio.2019.04.001. (72) Song, J.; Smart, R.; Wang, H.; Dambergs, B.; Sparrow, A.; Qian, M. C. Effect of Grape Bunch Sunlight Exposure and UV Radiation on Phenolics and Volatile Composition of Vitis Vinifera L. Cv. Pinot Noir Wine. Food Chem. 2015, 173, 424–431. https://doi.org/10.1016/j.foodchem.2014.09.150. 113 (73) Souid, I.; Hassene, Z.; Palomo, E. S.; Pérez-Coello, M. S.; Ghorbel, A. VARIETAL AROMA COMPOUNDS OF VITIS VINIFERA CV. KHAMRI GROWN IN TUNISIA. J. Food Qual. 2007, 30 (5), 718–730. https://doi.org/10.1111/j.1745-4557.2007.00152.x. (74) Könen, P. P.; Stötzel, I.; Schwab, W.; Wüst, M. Qualitative Profiling of Mono- and Sesquiterpenols in Aglycon Libraries from Vitis Vinifera L. Gewürztraminer Using Multidimensional Gas Chromatography–Mass Spectrometry. Eur. Food Res. Technol. 2021, 247 (5), 1117–1124. https://doi.org/10.1007/s00217-021-03692-3. (75) Brillante, L.; Martínez-Lüscher, J.; Kurtural, S. K. Applied Water and Mechanical Canopy Management Affect Berry and Wine Phenolic and Aroma Composition of Grapevine ( Vitis Vinifera L., Cv. Syrah) in Central California. Sci. Hortic. 2018, 227, 261–271. https://doi.org/10.1016/j.scienta.2017.09.048. (76) Welke, J. E.; Manfroi, V.; Zanus, M.; Lazarotto, M.; Alcaraz Zini, C. Characterization of the Volatile Profile of Brazilian Merlot Wines through Comprehensive Two Dimensional Gas Chromatography Time-of-Flight Mass Spectrometric Detection. J. Chromatogr. A 2012, 1226, 124–139. https://doi.org/10.1016/j.chroma.2012.01.002. (77) Van Leeuwen, C. Terroir: The Effect of the Physical Environment on Vine Growth, Grape Ripening, and Wine Sensory Attributes. In Managing Wine Quality; Elsevier, 2022; pp 341–393. https://doi.org/10.1016/B978-0-08-102067-8.00005-1. (78) Anesi, A.; Stocchero, M.; Dal Santo, S.; Commisso, M.; Zenoni, S.; Ceoldo, S.; Tornielli, G. B.; Siebert, T. E.; Herderich, M.; Pezzotti, M.; Guzzo, F. Towards a Scientific Interpretation of the Terroir Concept: Plasticity of the Grape Berry Metabolome. BMC Plant Biol. 2015, 15 (1), 191. https://doi.org/10.1186/s12870-015-0584-4. (79) Fiehn, O. Metabolomics — the Link between Genotypes and Phenotypes. In Functional Genomics; Town, C., Ed.; Springer Netherlands: Dordrecht, 2002; pp 155–171. https://doi.org/10.1007/978-94-010-0448-0_11. (80) Parker, M.; Maddy Jiang, W.; Bilogrevic, E.; Likos, D.; Gledhill, J.; Coulter, A. D.; Cowey, G. D.; Simos, C. A.; Francis, I. L.; Herderich, M. J. Modelling Smoke Flavour in Wine from Chemical Composition of Smoke-Exposed Grapes and Wine. Aust. J. Grape Wine Res. 2023, 2023, 1–14. https://doi.org/10.1155/2023/4964850. (81) Szeto, C.; Lloyd, N.; Nicolotti, L.; Herderich, M. J.; Wilkinson, K. L. Beyond Volatile Phenols: An Untargeted Metabolomic Approach to Revealing Additional Markers of Smoke Taint in Grapevines ( Vitis Vinifera L.) Cv. Merlot. J. Agric. Food Chem. 2024, 72 (4), 2018–2033. https://doi.org/10.1021/acs.jafc.2c09013. (82) EU Regulation 2019/33; European Parliament and Council, 2019. (83) Tzachristas, A.; Dasenaki, M.; Aalizadeh, R.; Thomaidis, N. S.; Proestos, C. LC-MS Based Metabolomics for the Authentication of Selected Greek White Wines. Microchem. J. 2021, 169. https://doi.org/10.1016/j.microc.2021.106543. (84) Tarr, P. T.; Dreyer, M. L.; Athanas, M.; Shahgholi, M.; Saarloos, K.; Second, T. P. A Metabolomics Based Approach for Understanding the Influence of Terroir in Vitis Vinifera L. Metabolomics 2013, 9 (S1), 170–177. https://doi.org/10.1007/s11306-013-0497-x. (85) Son, H.-S.; Hwang, G.-S.; Kim, K. M.; Ahn, H.-J.; Park, W.-M.; Van Den Berg, F.; Hong, Y.-S.; Lee, C.-H. Metabolomic Studies on Geographical Grapes and Their Wines Using 1 H NMR Analysis Coupled with Multivariate Statistics. J. Agric. Food Chem. 2009, 57 (4), 1481–1490. https://doi.org/10.1021/jf803388w. (86) Li, S.; Blackman, J. W.; Schmidtke, L. M. Exploring the Regional Typicality of Australian Shiraz Wines Using Untargeted Metabolomics. Aust. J. Grape Wine Res. 2021, 27 (3), 378–391. https://doi.org/10.1111/ajgw.12493. (87) Turi, C. E.; Shipley, P. R.; Murch, S. J. North American Artemisia Species from the Subgenus Tridentatae (Sagebrush): A Phytochemical, Botanical and Pharmacological Review. Phytochemistry 2014, 98, 9–26. https://doi.org/10.1016/j.phytochem.2013.11.016. 114 (88) Kelley, B. D.; Appelt, J. M.; Appelt, G. D. Artemisia Tridentata (Basin Sagebrush) in the Southwestern United States of America: Medicinal Uses and Pharmacologic Implications. Int. J. Addict. 1992, 27 (3), 347–366. https://doi.org/10.3109/10826089209068747. (89) Turner, N. J.; Bouchard, R.; Kennedy, D. I. D. Ethnobotany of the Okanagan-Colville Indians of British Columbia and Washington; British Columbia Provincial Museum: Victoria, 1980. (90) Ministry of Environment - Okanagan Region. https://www.env.gov.bc.ca/okanagan/esd/atlas/ecosystems.html (accessed 2024-01-23). (91) Karban, R.; Shiojiri, K.; Ishizaki, S.; Wetzel, W. C.; Evans, R. Y. Kin Recognition Affects Plant Communication and Defence. Proc. R. Soc. B Biol. Sci. 2013, 280 (1756), 20123062. https://doi.org/10.1098/rspb.2012.3062. (92) Liu, Y.; Jing, S.-X.; Luo, S.-H.; Li, S.-H. Non-Volatile Natural Products in Plant Glandular Trichomes: Chemistry, Biological Activities and Biosynthesis. Nat. Prod. Rep. 2019, 36 (4), 626–665. https://doi.org/10.1039/C8NP00077H. (93) Official Methods of Analysis of AOAC INTERNATIONAL (22); Oxford University Press: Oxford, UK, 2023. (94) Youden, J. W. The Sample, the Procedure, and The Laboratory. Anal. Chem. 1960, 32 (13), 23A-37A. (95) Wongwaiwech, D.; Kamchonemenukool, S.; Ho, C.-T.; Detyothin, S.; Kasemweerasan, P.; Weerawatanakorn, M. Analytical Procedural Validation of Policosanol Compounds. Food Anal. Methods 2022, 15 (7), 2059–2068. https://doi.org/10.1007/s12161-022-02265-8. (96) Bedregal, P.; Torres, B.; Ubillús, M.; Mendoza, P.; Montoya, E. Robustness in NAA Evaluated by the Youden and Steiner Test. J. Radioanal. Nucl. Chem. 2008, 278 (3), 801–806. https://doi.org/10.1007/s10967-008-1704-5. (97) Gil, M.; Pontin, M.; Berli, F.; Bottini, R.; Piccoli, P. Metabolism of Terpenes in the Response of Grape (Vitis Vinifera L.) Leaf Tissues to UV-B Radiation. Phytochemistry 2012, 77, 89–98. https://doi.org/10.1016/j.phytochem.2011.12.011. (98) Guidelines for Single Laboratory Validation of Chemical Methods for Dietary Supplements and Botanicals. In Official Methods of Analysis of AOAC International (22); AOAC Int: New York, 2002; pp 1–38. (99) Ishizaki, S.; Shiojiri, K.; Karban, R.; Ohara, M. Seasonal Variation of Responses to Herbivory and Volatile Communication in Sagebrush (Artemisia Tridentata) (Asteraceae). J. Plant Res. 2016, 129 (4), 659–666. https://doi.org/10.1007/s10265-016-0818-z. (100) Long-Distance Systemic Signaling and Communication in Plants; Baluška, F., Ed.; Signaling and Communication in Plants; Springer Berlin Heidelberg: Berlin, Heidelberg, 2013; Vol. 19. https://doi.org/10.1007/978-3-642-36470-9. (101) Ye, J.; Erland, L. A. E.; Gill, S. K.; Bishop, S. L.; Verdugo-Meza, A.; Murch, S. J.; Gibson, D. L. Metabolomics-Guided Hypothesis Generation for Mechanisms of Intestinal Protection by Live Biotherapeutic Products. Biomolecules 2021, 11 (5), 738. https://doi.org/10.3390/biom11050738. (102) Turi, C. E.; Axwik, K. E.; Murch, S. J. In Vitro Conservation, Phytochemistry, and Medicinal Activity of Artemisia Tridentata Nutt.: Metabolomics as a Hypothesis-Generating Tool for Plant Tissue Culture. Plant Growth Regul. 2014, 74 (3), 239–250. https://doi.org/10.1007/s10725-014-9915-y. (103) Murch, S. J.; Hall, B. A.; Le, C. H.; Saxena, P. K. Changes in the Levels of Indoleamine Phytochemicals during Véraison and Ripening of Wine Grapes: Melatonin in Developing Wine Grapes. J. Pineal Res. 2010, no-no. https://doi.org/10.1111/j.1600-079X.2010.00774.x. (104) Chambers, M. C.; Maclean, B.; Burke, R.; Amodei, D.; Ruderman, D. L.; Neumann, S.; Gatto, L.; Fischer, B.; Pratt, B.; Egertson, J.; Hoff, K.; Kessner, D.; Tasman, N.; Shulman, 115 N.; Frewen, B.; Baker, T. A.; Brusniak, M.-Y.; Paulse, C.; Creasy, D.; Flashner, L.; Kani, K.; Moulding, C.; Seymour, S. L.; Nuwaysir, L. M.; Lefebvre, B.; Kuhlmann, F.; Roark, J.; Rainer, P.; Detlev, S.; Hemenway, T.; Huhmer, A.; Langridge, J.; Connolly, B.; Chadick, T.; Holly, K.; Eckels, J.; Deutsch, E. W.; Moritz, R. L.; Katz, J. E.; Agus, D. B.; MacCoss, M.; Tabb, D. L.; Mallick, P. A Cross-Platform Toolkit for Mass Spectrometry and Proteomics. Nat. Biotechnol. 2012, 30 (10), 918–920. https://doi.org/10.1038/nbt.2377. (105) Katajamaa, M.; Miettinen, J.; Orešič, M. MZmine: Toolbox for Processing and Visualization of Mass Spectrometry Based Molecular Profile Data. Bioinformatics 2006, 22 (5), 634–636. https://doi.org/10.1093/bioinformatics/btk039. (106) Du, X.; Smirnov, A.; Pluskal, T.; Jia, W.; Sumner, S. Metabolomics Data Preprocessing Using ADAP and MZmine 2. In Computational Methods and Data Analysis for Metabolomics; Li, S., Ed.; Methods in Molecular Biology; Springer US: New York, NY, 2020; Vol. 2104, pp 25–48. https://doi.org/10.1007/978-1-0716-0239-3_3. (107) Dührkop, K.; Fleischauer, M.; Ludwig, M.; Aksenov, A. A.; Melnik, A. V.; Meusel, M.; Dorrestein, P. C.; Rousu, J.; Böcker, S. SIRIUS 4: A Rapid Tool for Turning Tandem Mass Spectra into Metabolite Structure Information. Nat. Methods 2019, 16 (4), 299–302. https://doi.org/10.1038/s41592-019-0344-8. (108) Dührkop, K.; Shen, H.; Meusel, M.; Rousu, J.; Böcker, S. Searching Molecular Structure Databases with Tandem Mass Spectra Using CSI:FingerID. Proc. Natl. Acad. Sci. 2015, 112 (41), 12580–12585. https://doi.org/10.1073/pnas.1509788112. (109) Hoffmann, M. A.; Nothias, L.-F.; Ludwig, M.; Fleischauer, M.; Gentry, E. C.; Witting, M.; Dorrestein, P. C.; Dührkop, K.; Böcker, S. Assigning Confidence to Structural Annotations from Mass Spectra with COSMIC; preprint; Bioinformatics, 2021. https://doi.org/10.1101/2021.03.18.435634. (110) Djoumbou Feunang, Y.; Eisner, R.; Knox, C.; Chepelev, L.; Hastings, J.; Owen, G.; Fahy, E.; Steinbeck, C.; Subramanian, S.; Bolton, E.; Greiner, R.; Wishart, D. S. ClassyFire: Automated Chemical Classification with a Comprehensive, Computable Taxonomy. J. Cheminformatics 2016, 8 (1), 61. https://doi.org/10.1186/s13321-016-0174-y. (111) Poulose, A. J.; Croteau, R. Biosynthesis of Aromatic Monoterpenes. Arch. Biochem. Biophys. 1978, 187 (2), 307–314. https://doi.org/10.1016/0003-9861(78)90039-5. (112) Jassbi, A. R.; Zamanizadehnajari, S.; Baldwin, I. T. Phytotoxic Volatiles in the Roots and Shoots of Artemisia Tridentata as Detected by Headspace Solid-Phase Microextraction and Gas Chromatographic-Mass Spectrometry Analysis. J. Chem. Ecol. 2010, 36 (12), 1398–1407. https://doi.org/10.1007/s10886-010-9885-0. (113) Amaro, F.; Almeida, J.; Oliveira, A. S.; Furtado, I.; Bastos, M. D. L.; Guedes De Pinho, P.; Pinto, J. Impact of Cork Closures on the Volatile Profile of Sparkling Wines during Bottle Aging. Foods 2022, 11 (3), 293. https://doi.org/10.3390/foods11030293. (114) Effect of Garlic and Camphor Oils on Bud Fertility and Yield Components of Flame Seedless Grape Cultivar. Assiut J. Agric. Sci. 2015, 46 (3), 100–119. https://doi.org/10.21608/ajas.2015.552. (115) Pons, A.; Lavigne, V.; Darriet, P.; Dubourdieu, D. Identification and Analysis of Piperitone in Red Wines. Food Chem. 2016, 206, 191–196. https://doi.org/10.1016/j.foodchem.2016.03.064. (116) Picard, M.; Lytra, G.; Tempere, S.; Barbe, J.-C.; De Revel, G.; Marchand, S. Identification of Piperitone as an Aroma Compound Contributing to the Positive Mint Nuances Perceived in Aged Red Bordeaux Wines. J. Agric. Food Chem. 2016, 64 (2), 451–460. https://doi.org/10.1021/acs.jafc.5b04869. (117) Picard, M.; Tempere, S.; De Revel, G.; Marchand, S. Piperitone Profiling in Fine Red Bordeaux Wines: Geographical Influences in the Bordeaux Region and Enantiomeric Distribution. J. Agric. Food Chem. 2016, 64 (40), 7576–7584. https://doi.org/10.1021/acs.jafc.6b02835. 116 (118) Mota-Segantini, D.; Lombini, A.; Rodríguez Declet, A.; De Giorgio, R.; D’Onofrio, C.; Rombolà, A. D. Effects of Intercropping Medicinal and Aromatic Plants (MAPs) on Grapevine Cv. Sangiovese Berry Volatile Compounds. Agroecol. Sustain. Food Syst. 2022, 46 (3), 452–462. https://doi.org/10.1080/21683565.2022.2027841. (119) Rodríguez-Declet, A.; Castro-Marín, A.; Lombini, A.; Sevindik, O.; Selli, S.; Chinnici, F.; Rombolà, A. D. Characterization of Berry Aromatic Profile of Cv. Trebbiano Romagnolo Grapes and Effects of Intercropping with Salvia Officinalis L. Agronomy 2022, 12 (2). https://doi.org/10.3390/agronomy12020344. (120) Hochrein, J.; Irwin, A.; Borek, T. Composition of the Essential Oils from Rocky Mountain Juniper (Juniperus Scopulorum), Big Sagebrush (Artemisia Tridentata), and White Sage (Salvia Apiana).; SAND2003-3081, 918273; 2003; pp SAND2003-3081, 918273. https://doi.org/10.2172/918273. (121) Slaghenaufi, D.; Ugliano, M. Norisoprenoids, Sesquiterpenes and Terpenoids Content of Valpolicella Wines During Aging: Investigating Aroma Potential in Relationship to Evolution of Tobacco and Balsamic Aroma in Aged Wine. Front. Chem. 2018, 6, 66. https://doi.org/10.3389/fchem.2018.00066. (122) Swor, K.; Satyal, P.; Timsina, S.; Setzer, W. N. Chemical Composition and Terpenoid Enantiomeric Distribution of the Essential Oil of Artemisia Tridentata Subsp. Tridentata From Southwestern Idaho. Nat. Prod. Commun. 2022, 17 (7), 1934578X2211174. https://doi.org/10.1177/1934578X221117417. (123) Noestheden, M.; Thiessen, K.; Dennis, E. G.; Tiet, B.; Zandberg, W. F. Quantitating Organoleptic Volatile Phenols in Smoke-Exposed Vitis Vinifera Berries. J. Agric. Food Chem. 2017, 65 (38), 8418–8425. https://doi.org/10.1021/acs.jafc.7b03225. (124) Halvorsen, H.-P. Python Programming, 2019. https://www.halvorsen.blog/. (125) Favell, J. W.; Wilkinson, K. L.; Zigg, I.; Lyons, S. M.; Ristic, R.; Puglisi, C. J.; Wilkes, E.; Taylor, R.; Kelly, D.; Howell, G.; McKay, M.; Mokwena, L.; Plozza, T.; Zhang, P.; Bui, A.; Porter, I.; Frederick, O.; Karasek, J.; Szeto, C.; Pan, B. S.; Tallman, S.; McClure, B. A.; Feng, H.; Hervé, E.; Oberholster, A.; Zandberg, W. F.; Noestheden, M. Correlating Sensory Assessment of Smoke-Tainted Wines with Inter-Laboratory Study Consensus Values for Volatile Phenols. Molecules 2022, 27 (15), 4892. https://doi.org/10.3390/molecules27154892. (126) Whitmore, B. A.; McCann, S. E.; Noestheden, M.; Dennis, E. G.; Lyons, S. M.; Durall, D. M.; Zandberg, W. F. Glycosidically-Bound Volatile Phenols Linked to Smoke Taint: Stability during Fermentation with Different Yeasts and in Finished Wine. Molecules 2021, 26 (15), 4519. https://doi.org/10.3390/molecules26154519. (127) Kennison, K. R.; Gibberd, M. R.; Pollnitz, A. P.; Wilkinson, K. L. Smoke-Derived Taint in Wine: The Release of Smoke-Derived Volatile Phenols during Fermentation of Merlot Juice Following Grapevine Exposure to Smoke. J. Agric. Food Chem. 2008, 56 (16), 7379–7383. https://doi.org/10.1021/jf800927e. (128) Walker, M. C.; Van Der Donk, W. A. The Many Roles of Glutamate in Metabolism. J. Ind. Microbiol. Biotechnol. 2016, 43 (2–3), 419–430. https://doi.org/10.1007/s10295-015-1665-y. (129) Guo, Z.; Gong, J.; Luo, S.; Zuo, Y.; Shen, Y. Role of Gamma-Aminobutyric Acid in Plant Defense Response. Metabolites 2023, 13 (6), 741. https://doi.org/10.3390/metabo13060741. (130) Yamamoto, T.; Inui-Yamamoto, C. The Flavor-Enhancing Action of Glutamate and Its Mechanism Involving the Notion of Kokumi. Npj Sci. Food 2023, 7 (1), 3. https://doi.org/10.1038/s41538-023-00178-2. (131) Stoeger, V.; Holik, A.-K.; Hölz, K.; Dingjan, T.; Hans, J.; Ley, J. P.; Krammer, G. E.; Niv, M. Y.; Somoza, M. M.; Somoza, V. Bitter-Tasting Amino Acids L -Arginine and L -Isoleucine Differentially Regulate Proton Secretion via T2R1 Signaling in Human Parietal 117 Cells in Culture. J. Agric. Food Chem. 2020, 68 (11), 3434–3444. https://doi.org/10.1021/acs.jafc.9b06285. (132) Hayat, S.; Hayat, Q.; Alyemeni, M. N.; Wani, A. S.; Pichtel, J.; Ahmad, A. Role of Proline under Changing Environments: A Review. Plant Signal. Behav. 2012, 7 (11), 1456–1466. https://doi.org/10.4161/psb.21949. (133) Shabir, S.; Ilyas, N.; Asif, S.; Iqbal, M.; Kanwal, S.; Ali, Z. Deciphering the Role of Plant-Derived Smoke Solution in Ameliorating Saline Stress and Improving Physiological, Biochemical, and Growth Responses of Wheat. J. Plant Growth Regul. 2022, 41 (7), 2769–2786. https://doi.org/10.1007/s00344-021-10473-5. (134) Tan, Z.-Z.; Wang, Y.-T.; Zhang, X.-X.; Jiang, H.-Y.; Li, Y.; Zhuang, L.-L.; Yu, J.-J.; Yang, Z.-M. Karrikin1 Enhances Drought Tolerance in Creeping Bentgrass in Association with Antioxidative Protection and Regulation of Stress-Responsive Gene Expression. Agronomy 2023, 13 (3), 675. https://doi.org/10.3390/agronomy13030675. (135) Zhu, K.; Gu, S.; Liu, J.; Luo, T.; Khan, Z.; Zhang, K.; Hu, L. Wood Vinegar as a Complex Growth Regulator Promotes the Growth, Yield, and Quality of Rapeseed. Agronomy 2021, 11 (3), 510. https://doi.org/10.3390/agronomy11030510. (136) Zhao, C. J.; Schieber, A.; Gänzle, M. G. Formation of Taste-Active Amino Acids, Amino Acid Derivatives and Peptides in Food Fermentations – A Review. Food Res. Int. 2016, 89, 39–47. https://doi.org/10.1016/j.foodres.2016.08.042. (137) Mestichelli, L. J.; Gupta, R. N.; Spenser, I. D. The Biosynthetic Route from Ornithine to Proline. J. Biol. Chem. 1979, 254 (3), 640–647. https://doi.org/10.1016/S0021-9258(17)37853-5. (138) Izquierdo, C.; Gómez-Tamayo, J. C.; Nebel, J.-C.; Pardo, L.; Gonzalez, A. Identifying Human Diamine Sensors for Death Related Putrescine and Cadaverine Molecules. PLOS Comput. Biol. 2018, 14 (1), e1005945. https://doi.org/10.1371/journal.pcbi.1005945. (139) Chen, D.; Shao, Q.; Yin, L.; Younis, A.; Zheng, B. Polyamine Function in Plants: Metabolism, Regulation on Development, and Roles in Abiotic Stress Responses. Front. Plant Sci. 2019, 9, 1945. https://doi.org/10.3389/fpls.2018.01945. (140) Simonetti, G.; Buiarelli, F.; Bernardini, F.; Filippo, P. D.; Riccardi, C.; Pomata, D. Profile of Free and Conjugated Quercetin Content in Different Italian Wines. Food Chem. 2022, 382, 132377. https://doi.org/10.1016/j.foodchem.2022.132377. (141) Vendramin, V.; Pizzinato, D.; Sparrow, C.; Pagni, D.; Cascella, F.; Carapelli, C.; Vincenzi, S. Prevention of Quercetin Precipitation in Red Wines: A Promising Enzymatic Solution. OENO One 2022, 56 (1), 41–51. https://doi.org/10.20870/oeno-one.2022.56.1.4699. (142) Gambuti, A.; Picariello, L.; Rinaldi, A.; Forino, M.; Blaiotta, G.; Moine, V.; Moio, L. New Insights into the Formation of Precipitates of Quercetin in Sangiovese Wines. J. Food Sci. Technol. 2020, 57 (7), 2602–2611. https://doi.org/10.1007/s13197-020-04296-7. 118 Appendices Appendix A: Supplemental Figures for Chapter 2 Appendix Figure 1. Standard curve for camphene. Appendix Figure 2. Residual plot for camphene. 119 Appendix Figure 3. Standard curve of limonene. Appendix Figure 4. Residual plot for limonene. 120 Appendix Figure 5. Standard curve of sabinene. Appendix Figure 6. Residual plot for sabinene. 121 Appendix Figure 7. Standard curve of p-cymene. Appendix Figure 8. Residual plot for p-cymene. 122 Appendix Figure 9. Standard curve of m-Cymene. Appendix Figure 10. Residual plot for m-cymene. 123 Appendix Figure 11. Standard curve of eucalyptol. Appendix Figure 12. Residual plot for eucalyptol. 124 Appendix Figure 13. Standard curve of o-Cymene. Appendix Figure 14. Residual plot for o-cymene. 125 Appendix Figure 15. Standard curve of linalool. Appendix Figure 16. Residual plot for linalool. 126 Appendix Figure 17. Standard curve of sabinene hydrate. Appendix Figure 18. Residual plot for sabinene hydrate. 127 Appendix Figure 19. Standard curve of isopulegol. Appendix Figure 20. Residual plot for isopulegol. 128 Appendix Figure 21. Standard curve of fenchol. Appendix Figure 22. Residual plot for fenchol. 129 Appendix Figure 23. Standard curve of terpinolene. Appendix Figure 24. Residual plot for terpinolene. 130 Appendix Figure 25. Standard curve of fenchone. Appendix Figure 26. Residual plot for fenchone. 131 Appendix Figure 27. Standard curve of isoborneol. Appendix Figure 28. Residual plot for isoborneol. 132 Appendix Figure 29. Standard curve of pulegone. Appendix Figure 30. Residual plot for pulegone. 133 Appendix Figure 31. Standard curve of verbenone. Appendix Figure 32. Residual plot for verbenone. 134 Appendix Figure 33. Standard curve of safranal. Appendix Figure 34. Residual plot for safranal. 135 Appendix Figure 35. Standard curve of carvone. Appendix Figure 36. Residual plot for carvone. 136 Appendix Figure 37. Standard curve of α-thujone. Appendix Figure 38. Residual plot for α-thujone. 137 Appendix Figure 39. Standard curve of γ-terpinene. Appendix Figure 40. Residual plot for γ-terpinene. 138 Appendix Figure 41. Standard curve of α-terpineol. Appendix Figure 42. Residual plot for α-terpineol. 139 Appendix Figure 43. Standard curve of α-phellandrene. Appendix Figure 44. Residual plot for α-phellandrene. 140 Appendix Figure 45. Standard curve of α-terpinene. Appendix Figure 46. Residual plot for α-terpinene. 141 Appendix Figure 47. Standard curve of α-cedrene. Appendix Figure 48. Residual plot for α-cedrene. 142 Appendix Figure 49. Standard curve of δ-3-carene. Appendix Figure 50. Residual plot of δ-3-carene. 143 Appendix Figure 51. Standard curve of β-pinene. Appendix Figure 52. Residual plot of β-pinene. 144 Appendix Figure 53. Standard curve of α-pinene. Appendix Figure 54. Residual plot of α-pinene. 145 Appendix Figure 55. Standard curve of terpinen-4-ol. Appendix Figure 56. Residual plot of terpinen-4-ol. 146 Appendix Figure 57. Standard curve of trans-β-ocimene. Appendix Figure 58. Residual plot of trans-β-ocimene.
Thesis/Dissertation
eng
Chemistry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Murch, S. J. (Susan J.)
Investigating the effects of smoke on grapevine chemistry and terroir using Artemisia tridentata Nutt. derived terpenes and metabolomics as a hypothesis generating tool
Text