Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Ravishankara, Adithya
Master of Applied Science - MASc
University of British Columbia
Industrial cannabis production has been steadily growing over the past decade due to the legalization of recreational cannabis in many regions around the world. For both regulatory and quality purposes, companies must ensure that their products meet industry standards, generally quantified as concentration of cannabinoids. A separate group that faces similar requirements are farmer of cannabinoids as they seek to optimize yield during harvest. The gold standard for quantification of cannabinoids is done using an HPLC/LCMS and other chromatographic methods which are expensive. In this thesis a low-cost, automated, and field-portable quantitative chemical analyzer is developed based on thin-layer-chromatography (TLC). The working principle of the device involves using TLC to separate the various molecules in a mixture. Once separated two visualization methods are employed to selectively visualize the compounds. Analysis is performed by taking HDR pictures of the strip under both visualization methods and performing densitometric analysis using computer vision techniques (multi-spectral analysis). The output of this system provides compositional analysis of a complex mixture. The device is used to detect THC, THCA, CBD and CBDA. The performance of the device was evaluated and an overall accuracy of ±0.5 mg/mL is given in the range of 0.5 mg/mL to 5 mg/mL, which meets the error tolerance for industrial standards. A vaporization chamber is developed as well to measure the total concentration of the sample to ensure the sample concentration meets the device requirements. The vaporization chamber has a measurement accuracy of ±0.27 mg/mL. Due to the versatile nature of the device, other industries such as cosmetics, pharmaceutical and food are proposed as possible future targets.
Development of a field-portable thin-layer chromatography based chemical analyzer for cannabinoid sample analysis by Adithya Ravishankara B.Sc., University of Alberta, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in The College of Graduate Studies (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) December 2021 © Adithya Ravishankara, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Development of a field-portable thin-layer chromatography based chemical analyzer for cannabinoid sample analysis submitted by Adithya Ravishankara in partial fulfillment of the requirements of the degree of Master of Applied Science in Mechanical Engineering . Dr. Mina Hoorfar, School of Engineering Supervisor Dr. Homayoun Najjaran, School of Engineering Supervisory Committee Member Dr. Jonathan Holzman, School of Engineering Supervisory Committee Member Dr. Shahria Alam, School of Engineering University Examiner iii Abstract Industrial cannabis production has been steadily growing over the past decade due to the legalization of recreational cannabis in many regions around the world. For both regulatory and quality purposes, companies must ensure that their products meet industry standards, generally quantified as concentration of cannabinoids. A separate group that faces similar requirements are farmer of cannabinoids as they seek to optimize yield during harvest. The gold standard for quantification of cannabinoids is done using an HPLC/LCMS and other chromatographic methods which are expensive. In this thesis a low-cost, automated, and field-portable quantitative chemical analyzer is developed based on thin-layer-chromatography (TLC). The working principle of the device involves using TLC to separate the various molecules in a mixture. Once separated two visualization methods are employed to selectively visualize the compounds. Analysis is performed by taking HDR pictures of the strip under both visualization methods and performing densitometric analysis using computer vision techniques (multi-spectral analysis). The output of this system provides compositional analysis of a complex mixture. The device is used to detect THC, THCA, CBD and CBDA. The performance of the device was evaluated and an overall accuracy of ±0.5 mg/mL is given in the range of 0.5 mg/mL to 5 mg/mL, which meets the error tolerance for industrial standards. A vaporization chamber is developed as well to measure the total concentration of the sample to ensure the sample concentration meets the device requirements. The vaporization chamber has a measurement accuracy of ±0.27 mg/mL. Due to the versatile nature of the device, other industries such as cosmetics, pharmaceutical and food are proposed as possible future targets. iv Lay Summary Quality control is crucial to ensure the viability of the cannabis industry, for both growers and companies. Current quality control methods are expensive and require extensive training to operate and analyze. To reduce costs, a thin-layer chromatography (TLC) based system that outputs quantitative data is developed, that meets a reasonable level of accuracy. Working principles of the device involve visualizing a developed TLC strip both under UV and using a staining reagent. The visualization technique is used as a method to selectively detect molecules. The software to handle the analysis and the device development are described. The performance of the device is characterized and an accuracy of ±0.5 mg/mL in the range of 0.5 mg/mL to 5 mg/mL is given. Due to the restrictive concentration requirements, a vaporization chamber is included to make sure samples fall into the range. The vaporization chamber has an accuracy of ±0.27 mg/mL. v Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Table Of Contents ......................................................................................................................... v LIst Of Tables ................................................................................................................................ x List Of Equations ....................................................................................................................... xiv Acknowledgements .................................................................................................................... xv Dedication ................................................................................................................................... xvi 1. Introduction ........................................................................................................................... 1 1.1. Cannabinoids ...................................................................................................................... 1 1.2. Quality Control of Cannabinoid Extraction ......................................................................... 2 1.3. Cannabis Measurement ...................................................................................................... 3 1.4. Motivation ............................................................................................................................ 4 1.5. Objective ............................................................................................................................. 4 1.6. Thesis Organization ............................................................................................................ 4 2. Literature Review .................................................................................................................. 6 vi 2.1. Cannabis ............................................................................................................................. 6 2.1.1. History of Cannabis..................................................................................................... 6 2.1.2. Endocannabinoid System ........................................................................................... 6 2.1.3. Cannabinoids .............................................................................................................. 7 2.2. Cannabinoid Detection and Analysis .................................................................................. 8 2.2.1. Spectroscopic Methods .............................................................................................. 9 2.2.2. Chromatographic Methods ....................................................................................... 10 2.2.3. Alternate Methods ..................................................................................................... 12 2.3. Thin Layer Chromatography ............................................................................................. 13 2.3.1. TLC Overview ........................................................................................................... 13 2.3.2. TLC With Cannabis ................................................................................................... 16 2.3.3. TLC Visualization ...................................................................................................... 20 UV Visualization .................................................................................................................... 20 Derivatization ........................................................................................................................ 20 2.3.4. TLC Analysis ............................................................................................................. 22 HDR Imaging......................................................................................................................... 22 Chromatogram Generation ................................................................................................... 24 Baseline Correction............................................................................................................... 26 vii Peak Analysis........................................................................................................................ 27 3. Concept and Methods ........................................................................................................ 29 3.1. Concept ............................................................................................................................. 29 3.2. Hardware Design .............................................................................................................. 29 3.2.1. Vaporization Chamber .............................................................................................. 29 3.2.2. TLC Development and Imaging (Hardware) ............................................................ 30 3.3. Materials and Procedures ................................................................................................. 32 3.3.1. Cannabinoid Sample Preparation............................................................................. 33 3.3.2. Staining Reagent Preparation .................................................................................. 33 3.3.3. Vaporization Chamber Testing Procedure ............................................................... 34 3.3.4. TLC Development Procedure ................................................................................... 34 3.3.5. TLC Derivatization Procedure .................................................................................. 35 3.4. Thin Layer Chromatography Analysis Software (TLC-AS) .............................................. 35 3.4.1. Vaporization Chamber Characterization .................................................................. 35 3.4.2. Imaging Pipeline Development ................................................................................. 37 Derivatized Image Illumination ............................................................................................. 39 HDR Imaging......................................................................................................................... 40 viii Chromatogram Generation ................................................................................................... 41 Baseline Correction............................................................................................................... 41 Peak Analysis........................................................................................................................ 41 3.4.3. Imaging Pipeline Characterization ............................................................................ 42 3.5. Detection and quantification of target molecules ............................................................. 43 4. Results and Discussion ..................................................................................................... 45 4.1. Prototype Chemical Analyzer ........................................................................................... 45 4.1.1. Vaporization Chamber .............................................................................................. 45 4.1.2. TLC Development and Imaging (Hardware) ............................................................ 47 4.2. Thin Layer Chromatography Analysis Software (TLC-AS) .............................................. 49 4.2.1. TLC Development and Imaging (Software) .............................................................. 49 4.2.2. Vaporization Chamber Characterization .................................................................. 51 4.2.3. Imaging Pipeline ....................................................................................................... 53 Derivatized Image Illumination ............................................................................................. 53 HDR Imaging......................................................................................................................... 55 Chromatogram Generation Method ...................................................................................... 58 Baseline Correction............................................................................................................... 59 ix Peak Analysis........................................................................................................................ 61 4.3. Imaging Pipeline Characterization .................................................................................... 62 4.3.1. LOD, LOQ and Bias .................................................................................................. 65 4.4. Cannabinoid Separation and Detection............................................................................ 65 4.4.1. Visualization of Cannabinoids .................................................................................. 65 5. Conclusion........................................................................................................................... 69 5.1. Summary ........................................................................................................................... 69 5.2. Contributions ..................................................................................................................... 70 5.3. Future Works..................................................................................................................... 70 References ................................................................................................................................... 71 x List of Tables Table 1: Cannabinoid Properties .................................................................................................... 8 Table 2: Comparison of major analytical methodologies. ............................................................ 13 Table 3: List of prior works for separation of cannabinoids with TLC .......................................... 18 Table 4: HDR merging and tone-mapping algorithms.................................................................. 24 Table 5: Vaporization test concentrations. Series A is for calibrating the system. Series B is for testing precision. Series C is for confirmation tests. ............................................................ 37 Table 6: Default Image Analysis Pipeline Settings ...................................................................... 39 Table 7: List of colours for 2.2 ...................................................................................................... 40 Table 8: Imaging pipeline characterization - calibration. .............................................................. 43 Table 9: Evaluation metrics for derivatized strips ........................................................................ 54 Table 10: Properties of HDR system for each strip ...................................................................... 57 Table 11: Results from chromatogram generation technique optimization ................................. 59 Table 12: Baseline correction algorithm results ........................................................................... 61 Table 13: Peak analysis optimization parameters for UV and derivatization visualization ......... 62 Table 14: Comparison of expected values to calculated values for precision tests in Table 8 ... 65 Table 15: Analysis of cannabinoid development. The cells with “X” represent regions where no spot was detectable. ............................................................................................................. 68 xi List of Figures Figure 1: Popular forms of cannabis. a) Plant Matter, b) wax extracts, c) oils .............................. 2 Figure 2: FTIR Block Diagram ...................................................................................................... 10 Figure 3: Mechanism of TLC function. a) TLC chamber in thermodynamic equilibrium. Blue spots are eluent molecules. b) sample spot and travel direction. c) finished TLC .............. 15 Figure 4: Mechanism for interaction between THC and FBBB. a) THC, b) addition of FBBB, c) chromophore. Image reproduced with permission from [85]. .............................................. 22 Figure 5: TLC analysis. A chromatogram is generated from a plate. Features of the chromatogram are shown. .................................................................................................... 25 Figure 6: Flow chart for generating chromatogram from image. Also referred to as the image pipeline .................................................................................................................................. 26 Figure 7: Concept drawing of vaporization chamber ................................................................... 30 Figure 8: Concept drawing of development chamber (left) and strip holder (right). .................... 31 Figure 9: Concept drawing of imaging chamber. ......................................................................... 32 Figure 10: Custom designed PCB to power and measure PID sensor ....................................... 46 Figure 11: Vaporization chamber. a) computational and control unit, b) vaporization chamber . 47 Figure 12: TLC development and imaging device. A) TLC strip holder, b) development chamber, c) TLC strip holder inserted into development chamber, d) lid ............................................ 48 Figure 13: Imaging Chamber ........................................................................................................ 49 xii Figure 14: The three windows of the TLC-AS. A) Vaporization Chamber Window, b) Imaging window, c) analysis window .................................................................................................. 51 Figure 15: Graph of relation between voltage and concentration. ............................................... 52 Figure 16: Derivatized strip illumination optimization. a) Illuminated derivatized strips, b) all four channels shown, c) top four illumination-channel pairings showing orange, yellow, green and white illumination colour-channels................................................................................. 54 Figure 17: LDR images at different shutter speeds. Images a, b, c are of strip 1, while d, e, f are of strip 3 a) 1/100 s, b) 1/75 s, c) 1/30 s, d) 1/100 s, e) 1/75 s, c) 1/30 s. ........................... 55 Figure 18: Parametric sweep of HDR images generated from LDR images of a) strip 1, b) strip 3............................................................................................................................................... 56 Figure 19: Chromatograms generated from parametric sweep of HDR algorithms. Only the best performing colour channel is plotted. a) strip 1, b) strip 2, c) strip 3, d) strip 4. e) shows each of the strips with the selected HDR algorithm. ............................................................ 57 Figure 20: Chromatogram generation method optimization. a) column summing method, b) column summing with averaging window ............................................................................. 59 Figure 21: Baseline correction optimization results. a) low concentration THCA - UV , b) high concentration THCA - UV, c) low concentration THCA - derivatized, d) high - concentration UV - derivatized. ................................................................................................................... 60 Figure 22: Chromatograms of a) THCA (UV) and b) THC (derivatized) strips following Table 8 64 Figure 23: Relationship between area of peak and concentration. a) UV visualized spots, b) derivatized spots. .................................................................................................................. 64 xiii Figure 24: Visualization of the cannabinoids through UV (a) and derivatization (b). a) THC, b) THCA, c) CBD, d) CBDA. ..................................................................................................... 67 Figure 25: Resulting chromatograms based on images from figure 18 ....................................... 67 xiv List of Equations Equation 1: Relationship between transmittance and absorbance in spectroscopy ..................... 9 Equation 2: Calculation of the Rf .................................................................................................. 28 Equation 3: Mean absolute error .................................................................................................. 38 Equation 4: Calibration equation for vaporization chamber ......................................................... 51 Equation 5: Calibration equation for spot area vs concentration. a) UV, b) derivatized .............. 63 xv Acknowledgements I would like to give my utmost gratitude to Dr. Mina Hoorfar for giving me a chance to work on my master’s and develop my skills. Dr. Hoorfar has been an amazing mentor to help my development. I would like to thank James Seabrook at Vitalis for providing me with support throughout my master’s and providing advice whenever necessary. I would like to thank Nishat Tasnim, the lab manager at ATFL for her support throughout the entire process. Finally, I would like to thank Emily Earl for being someone I could share ideas with and help me maintain my sanity. xvi Dedication To my family, for their endless support, love, and strength. Thank you. 1 1. Introduction In the past decade products derived from cannabis and hemp have become increasingly available to the public due to legalization, providing the space for industry to gain a foothold [2]. According to the Grandview Market Analysis Report (GMAR, 2021), in 2020 the legal cannabis market had a global valuation of 9.7 billion USD. The GMAR expects the value to continue growing as more products become available and more regions relax regulations. Unsurprisingly, the growth seen in the cannabis industry comes with regulations regarding sale, possession, production, and distribution, as cannabis still maintains the designation of controlled substance [3]. From an industrial standpoint, the production of cannabis products, whether its plant matter, oils, or extracts, involves mandated rigorous testing to ensure compliance with the guidelines placed by governing bodies. Specifically, regulations state the concentrations of specific active compounds available in a product. For example, in Canada, products containing cannabis cannot have more than 10 mg of THC per dose and consider the ability of THCA to decarboxylate into THC [4]. Hence, companies need to perform rigorous quality check/control (QC) on their products to meet standards. From an agricultural standpoint, growers require similar testing to determine when crops are ready for harvesting to maximize yield [5]. Additionally, Hammersvik et al. (2012) have stated how one of the limiting factors for cannabis farmers to increase their business is the high initial cost required for the operation, which is partly due to the cost of analytical testing equipment [5]. 1.1. Cannabinoids Of the nearly 80 cannabinoids that have been discovered in cannabis plants, the two main compounds focused on both in industry and by consumers are Δ 9-Tetrahydrocannabinol (THC) and Cannabidiol (CBD) [6]. Both these molecules are widely available in multiple forms (i.e., dried leaf, oil, extracts, edibles, topicals, pills, (see Figure 1)) and in various concentrations and 2 ratios for targeted effects [7]. There are two main strains of cannabis, Sativa and Indica, where the concentrations of THC and CBD are at differing ratios [8]. Industry and consumers alike have mixed the two strains at varying ratios to create different blends of products, leading to complex mixtures of cannabinoids. As research shows more medicinal effects of cannabinoids, other cannabinoids, such as Cannabinol (CBN), Canaigre (CBG), along with the acidic forms of THC and CBD (Δ 9-Tetrahydrocannabinolic acid (THCA) and Cannabidiolic Acid (CBDA)) are being introduced to the mainstream consumer markets [9]. The number of commercial cannabis products is rapidly diversifying to provide consumers with an ever-expanding range of choices. However, these products are required to regulatory and quality standards. Figure 1: Popular forms of cannabis. a) Plant Matter, b) wax extracts, c) oils 1.2. Quality Control of Cannabinoid Extraction Focusing on industrial operations, there are numerous cannabinoid extraction processes[10], [11]. Extraction methods such as supercritical-fluid-extraction (SFE) are non-specific extractors that excel in high efficiency bulk extractions; however, this method is non-specific. The benefits of SFE include a more efficient run compared to other methods, with its short run time and 3 minimal solvent requirements, resulting in the one of the most economical method for extraction [10], [12]. The disadvantage of SFE is that the protocol development requires significant process-tuning to optimize the extraction [12]. The common process optimization technique involves repetitive sample monitoring for extraction efficiency. Sample monitoring is regularly performed on expensive analytical tools such as LCMS or HPLC, where device costs can reach upwards of $100,000 CAD to well over $1M CAD [13], [14]. This only accounts for a portion of the total cost as the cost of each test, maintenance costs, and the salary of a trained operator should be considered. A study based on the cannabis market in California estimated the average testing cost for one pound of dried cannabis plant material is approximately $136 USD [15]. This value is then compounded into the cost of consumer products, making cannabis products more expensive. This high cost for regular QC is a barrier for most companies to perform tests in-house, while leaving the customers to foot the bill for the high external testing costs. 1.3. Cannabis Measurement Composition analysis of cannabinoids is a popular field currently due to the regulatory push for fast and reliable cannabis detection methods for enforcement. Numerous companies are racing to develop a fast and reliable detector for the presence of THC or indicator of cannabinoid use. Unfortunately, this popularity does not extend into the development of chemical analyzers for industry or growers to manage their products. The systems available to these markets tend to be LC or GC based, which are highly accurate, but as mentioned above are prohibitively expensive to own and run. Recently, researchers have developed detectors that are generally cheaper to own and test. These technologies include molecularly imprinted polymers, FAIMS, and electronic noses [16], [17]. These technologies are generally selective towards one compound at a time, requiring 4 multiple sensors to achieve a holistic detection of a complex mixture. Technologies like electronic-noses are generally not selective inherently and rather find a feature-set that aims to detect cannabis. While promising, these technologies are still in their infancy and require significant development before they are ready for deployment. 1.4. Motivation Quality control/check Regulators and consumers alike demand a high degree of quality compliance by the cannabis industry for the products provided. Knowledgeable consumers are selective about their products to achieve a desired effect. In either case, producers must be able to assure the quality of a product, while maintaining a competitive pricing. As a way to reduce costs for cannabinoid chemical analysis while maintaining the quality integrity, a low-cost quality analytical method must be implemented. Additionally, the device should require minimal skill to use to allow both industries and consumers alike to validate the results. 1.5. Objective The objective of this thesis is to develop and validate a low-cost, automated, and field portable chemical analyzer based on TLC to output qualitative and quantitative data regarding the composition of a complex cannabinoid samples. Four cannabinoids are used as the target molecules: THC, THCA, CBD, CBDA. 1.6. Thesis Organization In chapter 2, a comprehensive literature review is performed. Included in this is information on the chemical nature of cannabinoids, common quality control methods and finally an in-depth review on the functionality and analysis of TLC. In chapter 3, the experimental methods are detailed including how the device is validated, and how the system is optimized for separating 5 the four target molecules. In chapter 4 the results are presented and discussed. And finally in chapter 5, a summary of the work is provided along with future works. 6 2. Literature Review 2.1. Cannabis 2.1.1. History of Cannabis Cannabinoids are a class of molecules found in the cannabis plant that contain a 21-carbon chain [6]. This also includes any analog and reaction products related to the molecules. So far, nearly 80 cannabinoids have been extracted from the cannabis plant [18]. Of the cannabinoids, THC and CBD play a key role in both the history and modern interest in cannabis [19],[20]. The history of cannabis stretches back thousands of years and is found to be used all over the world [18]. It is considered to be one of the oldest crops cultivated by humanity, one that has evolved with humanity and influenced our development as a species [21]. Cannabis has played a significant role in both medicinal/therapeutic and spiritual aspects of many societies around the world [18]. In order to understand the importance of cannabis in society, it is important to understand how it effects humans. 2.1.2. Endocannabinoid System The endocannabinoid system (ECS) is a set of neurotransmitters and receptors in our brain that function to maintain processes such as memory, sleep, pain, arousal, immune system among others [22], [23]. The receptors in the ECS are labelled CB1 and CB2. Normally, ECS agonists (neurotransmitters) produced internally bind to the ECS. When cannabis is consumed, THC can bind to CB1 and CB2, enhancing their functionality [23]. Similarly, CBD plays a role in modulating the ECS and effecting other neurotransmitters such as acetylcholine, dopamine among others [24]. THC is considered as the primary psychoactive component of cannabis, giving users a sense of high/euphoria [25]. In general regulation and enforcement, the regulations set the tolerance of THC in a system: usually in the ng/mL range [26]. Apart from the euphoric feeling, THC also effects memory (positively and negatively), causes sensory hallucinations, muscular spasms, and is responsible for the famous “munchies”[27], [28]. CBD 7 on the other hand, has limited to no psychoactive effects. In fact, some studies show that CBD helps mitigate or regulate some of the side-effects of THC [29]. Rather, CBD is shown to have numerous, positively perceived, effects such as pain/stress relief, reducing inflammation, helping with anxiety and depression, reducing schizophrenic symptoms, reducing symptoms of neuromuscular disease, reducing effects of Diabetes Mellitus I, and more [11], [19], [22], [30]. 2.1.3. Cannabinoids Cannabis plants contain relatively little THC and CBD naturally. In raw plant matter, THC and CBD are mainly stored as acidic analogs, Δ 9-Tetrahydrocannabinolic acid (THCA) and Cannabidiolic Acid (CBDA) [20]. A decarboxylation reaction must occur for the acidic analogs to be converted down to THC and CBD [31]. Decarboxylation can occur through a few different processes. The preferred method is addition of heat [32]. Alternate methods include light, or chemical based decarboxylation [20]. Additionally, there is a pathway for the degradation of cannabinoids. THCA, CBDA are converted into THC and CBD respectively. An oxidation reaction converts THC and CBD into CBN [35]. The ratio of the acidic analogs to THC and CBD is considered a good indicator of freshness of the material [32]. The acidic analogs are generally considered to not have any significant effect, but recent studies have put more attention towards CBDA for its role in treating chronic pain treatment and detecting consumption of cannabis [25], [33], [34]. For the purpose of analytical chemical analysis, there are several physical characteristics of the four target molecules that are of interest, shown in Table 1. These values effect how chromatographic systems work to separate the molecules. In the next section, common chromatographic techniques used to perform QC on cannabis will be explored. 8 Table 1: Cannabinoid Properties Name Abbreviation Molar Mass (g/mol) Polarity (Å²) Vaporization Temperature (℃) Decarboxylation Temperature (℃) Δ 9-Tetrahydrocannabinol THC 314.5 [36] 29.5 [36] 157 [37] N/A Δ 9-Tetrahydrocannabinolic acid THCA 358.5 [38] 66.8 [37] N/A 75-150 [38] Cannabidiol CBD 314.5 [39] 40.5 [39] 160-180 [36] N/A Cannabidiolic Acid CBDA 358.5 [40] 77.8 [40] N/A 100-175 [38] 2.2. Cannabinoid Detection and Analysis Due to the rigorous standards required by regulators and customers alike, products in the cannabis industry require significant testing and quality control [41], [42]. Currently, the most popular techniques are based around liquid chromatographic (LC) detection methods [43]. Other commonly used methods are HPLC, GC, and FTIR [10], [43], [44]. Each of these analysis methods are well developed and obtain a high degree of precision. The following section will elaborate on techniques considered the gold standard for chemical analysis: FTIR, LC and GC. Figure 2 shows the breakdown of analytical methods. Figure 2: Chemical analysis diagram. 9 2.2.1. Spectroscopic Methods Fourier Transform Infrared Spectroscopy (FTIR), a subset of Raman spectroscopy, is a common method of detection for organic molecules and compounds in the infrared range (12800 ~ 10 cm-1). When molecules are exposed to specific wavelengths in this range, molecular bonds selectively absorb the light based on the type of bond present. Contemporary FTIR devices use a Michaelson interferometer with a mobile mirror to generate the wavelengths in the spectrum range. Each wavelength is measured individually through a detector on the other end of the sample, which leads to the long testing times. There are two detection methods for FTIR: absorbance and transmittance. Transmittance and absorbance are related by the Equation 1. 𝑨 = 𝐥𝐨𝐠𝟏𝟎𝟏𝑻 Equation 1 Equation 1: Relationship between transmittance and absorbance in spectroscopy Commonly, results for FTIR tests are given in terms of %Transmittance as that relates the particular response of the molecule to the normal intensity of the light. Figure 3 shows the standard configuration of an FTIR device where the various wavelengths are generated by a Michaelson interferometer. The results of an FTIR sample test are passed through a Fourier transform to convert the time-domain results into the more informative frequency-domain. This operation yields the common unit for measurement used in FTIR, the wavenumber (cm -1). The intensity of transmittance/absorbance plotted against the wavenumber yields the normal plot seen throughout research. A catalog the various bonds and their peaks helps identify the molecules. If the sample is a mixture, FTIR is usually used to verify results after separation through GC or LC techniques. The benefit of FTIR for cannabinoid detection is that generally no chemicals or sample preparation is required for testing. The sample, whether it’s a flower, oil or wax can be tested directly. 10 Figure 3: FTIR Block Diagram 2.2.2. Chromatographic Methods All chromatographic methods follow the sample principle. A sample is exposed to a separating column which uses adsorbents, generally silica based, and through means of either a solvent or carrying gas the sample pushed along the length of the column. Due to the physical properties of the various compounds in a sample, the compounds separate. A detector is now required to analyze the separation. Gas chromatography is considered one of the gold standard methods in the field of compound analysis [45]. A sample is injected into the GC column which is heated to a temperature in which the sample will vaporize. A carrier gas carries the sample through a chromatographic column. This column can either be bare or might be coated or filled with a stationary phase [46]. As the sample travels through the column, physical properties such as mass, polarity or charge play a role in separating the individual compounds resulting in various travel times for the compounds through the column. At the other end of the column a detector is present to measure the molecules that arrive. There are multiple options for this detector, but the general principle is to 11 measure the concentration of the compounds that are output from the column using a sensitive detector. The most popular detection method used is a mass spectrometer (MS). This device ionizes the molecules output from a GC column and uses electric fields to control the flow of the ions [47]. Multiple variations of the MS exist including MALDI, MALDI-TOF, UV which mainly describe how the molecules are ionized, or how the molecules are detected. The results from a GC-MS test compare the mass to charge ratio of a particle and the abundance of it in a sample. A different option for detectors are flame-ionization-detectors (FIDs) or photo-ionization-detectors (PIDs). Both detectors ionize the particles output from the GC column, and then use a capacitor-based detector to measure the resulting charge. These systems tend to be less sensitive but are commonly used in devices considered portable GC. Specifically considering the detection of cannabinoids, GC is a popular option with thousands of articles and researchers working towards improving the detection ability of this method [45]. Due to the size and requirements of the technology, GC based cannabinoid detection rarely leaves the lab. Various companies are trying to develop micro-GC technologies to effectively bring the high reliability of the GC technology into the handheld world. None of the devices currently on the market are feasible for use [45], [48]. Liquid chromatography similarly is a popular and powerful tool for analytical detection. Like GC, LC uses a column packed with adsorbent to separate compounds based on characteristics. There are various methods that are commonly used for cannabinoid detection including High Performance/pressure Liquid Chromatography (HPLC) and Liquid-Chromatography Mass Spectrometry (LCMS) and its derivatives. There are numerous types of adsorbents developed (normal phase, reverse phase, carbon chain) that all effect the compounds in targeted ways. 12 Much like GC, LC needs a separate detector at the end of the column to detect the presence of compounds. Typically, a mass spectrometer is used for quantifying molecules but various other technologies (FID, PID, FTIR) exist. LC has garnered significant interest from the cannabis community for its fast and accurate results [44], [49]–[52]. Unfortunately, due to the scale of the device, portability is not currently viable and is therefore not available for field use. Additionally, this method is very expensive and requires skilled operators to collect and analyze samples. Recently many researchers have been focusing on the LC technique of thin layer chromatography for cannabinoid detection and analysis. Fischedick et al. (2009) and Citti et al. (2018) discusses the use of thin layer chromatography as a method for analytical detection of cannabinoids [59], [60]. This technique will be further discussed in section 2.3. 2.2.3. Alternate Methods In addition to the methodologies described above, a few other recent technologies should be noted. Molecularly imprinted polymers (MIPs) have gained a lot of traction over the recent years in the field of analytical detection of cannabinoids [53]–[55]. For this technique, a speciality polymer is generated which contains a selective binding site for a target [56]. When the target is bound to the polymer, a physical change occurs in the polymer and this change can be measured. Various measurement techniques exist including electrochemical, FTIR, and colorimetric [54], [57]. In addition to having a very small footprint, this method is highly sensitive and can be used to measure very low concentrations. Unfortunately, there is also a high occurrence of false positive detection, questioning the reliability of this method [51]. A separate method uses immunoassays for detection of cannabinoids [58], [59]. Hudson et al. (2011) use custom designed aptamers for the detection of THC and CBD built onto a FET platform. This technique shows promise as it is highly selective for the target molecules, but the 13 process is prohibitively expensive, and the aptamers have a short lifespan in standard environments. Table 2: Comparison of major analytical methodologies. Analytical method Test Time Test Cost FTIR 10-15 minutes $50 – 100 GC-MS 40 – 60 minutes $30 – 150 LC-MS/MS 40 – 60 minutes $50 – 200 2.3. Thin Layer Chromatography 2.3.1. TLC Overview Thin-Layer Chromatography (TLC), a subset of liquid and planar-chromatography, is a well-developed and useful tool in separating and identifying complex mixtures of compounds [61]. There are three main components: the compound that is being tested dissolved in a volatile liquid (solvent), the TLC plate, and the eluent [61]. Figure 4 is a diagram showing the TLC process. TLC involves spotting a dissolved sample onto a plate that is coated in some manner of adsorptive material (Figure 4b). In terms of chromatography, the adsorptive material is considered the stationary phase [61]. The plate is subsequently placed into a chamber containing an eluent. The eluent is the mobile phase which is composed of either a single or mixture of liquid solvents with specific properties such as vaporization pressure, polarity, and viscosity that causes the various compounds in the sample the separate [44]. In an optimal setting, the eluent reaches thermodynamic equilibrium within the chamber, at which point the TLC plate is inserted for development (Figure 4a). In conventional TLC, the major driving force for the eluent to travel along the plate is capillary action [61]. Capillary action causes the eluent to move along the length of the plate in a uniform fashion. In doing so, the compounds in the sample separate based on the physical characteristics of each compound (Figure 4c), a process 14 called broadening [62]. The amount of broadening is influenced by the size of the adsorbent material particles, also known as the height of the adsorptive layer and the overall length of the plate [62]. As a general rule, the smaller adsorptive layer height and longer the plate, the more effective the separation [61]. Specifically for the sample, the most important characteristics that effect broadening are molar mass, polarity, and reactivity with eluent, and reactivity with adsorptive layer [63]. Some materials such as acids are not compatible with conventional TLC plates and therefore need other adsorptive layers such as alumina or cellulose [44]. Other factors that are important for the performance of a TLC test are the relative humidity, vapor pressure of eluent in chamber, and the type of adsorbent (stationary phase) that is used [64]. There are several different adsorbents that TLC plates can use ranging from metal oxides to sugars, paper, starch, and talc to name a few [65]. The most prevalent in the world of chromatography is silica gel [66]. Silica gel itself can be divided into two main groups: Normal Phase (NP), and Reverse Phase (RP) [65]. The normal phase represents a system in which the stationary phase is more polar than the mobile phase, while reverse phase has a more polar mobile phase relative to the stationary phase [65]. Depending on the compounds being tested, and the eluents used, a particular phase is chosen. Another modification that can be performed on TLC plates is the addition of carbon chains [44]. C18 is the most widely used in TLC and HPTLC applications for its ability to separate molecules efficiently [67]. The addition of the carbon chains modifies the polarity of the stationary phase to enable a desired effect. One last variation between TLC plates is the addition of fluorescence. Certain adsorbents are mixed with fluorescent particles that can fluoresce when a particular light is applied [64]. Normally, the light is in the UV range (254 nm/365 nm) and enables the background of a plate to appear brighter than spots [61]. This phenomenon will be further discussed in section 2.3.3The reasoning for this being that organic molecules generally absorb the UV (specifically short-wave 254 nm) wavelengths, therefore appearing darker [68]. 15 Figure 4: Mechanism of TLC function. a) TLC chamber in thermodynamic equilibrium. Blue spots are eluent molecules. b) sample spot and travel direction. c) finished TLC A sample must be dissolved in a solvent that can either be separated or evaporates prior to testing [61]. TLC is sensitive to the concentration of sample. Too low, and the developed spot might not be visible. Too high of a concentration, and streaking might occur. A general range given as the sample concentration lies between 0.5 mg/mL and 10 mg/mL [65]. There are various methods for spotting a sample onto the plate. The most common include pipetting or using a capillary tube [65]. More specialized and automated methods involve using computer assisted spotting, either semi-automatic or fully automatic (i.e., CAMAG Linomat) [69]. These devices produce significantly better bands than spotting manually but are generally large and cost in the order of $1500 USD [69]. On a given TLC plate, barring that the plate is very narrow, multiple samples are developed simultaneously [61]. Each sample is spotted into a lane, which is made to be wide enough to prevent interference between neighbouring lanes. Spotting multiple samples allows for either repetition or testing multiple different samples simultaneously with a particular eluent. 16 Eluents are selected based on the relationship with the compounds in the sample, and the relationship with the TLC adsorbent. In an isocratic eluent system, the eluent remains the same throughout the test while in a gradient based system, the eluent can differ throughout the development process [61]. A general classification that can be made is polar vs. non-polar solvents [44]. Snyder (1968) developed the solvent elution strength chart for eluotropic series of solvents [70]. This chart lists the relative strength of common solvents with their ability to transport compounds along a TLC plate, based on polarity. While it is possible to optimize the eluent based on known physical characteristics, it is easier to narrow down the list of eluents based on broad classifications and testing the various mixtures. 2.3.2. TLC With Cannabis Cannabinoid detection with TLC is well developed field and has been proposed by numerous researchers as a tool for field testing in either an enforcement or production capacity [63], [68], [71], [72]. The majority of the work has been to separate THC, CBD, and Cannabinol (CBN), as these molecules tend to be the most available. Most researchers use normal phase silica gel plates as they are readily available and usually cheaper than their reverse-phase HPTLC counterparts. Table 3 is a compilation of the major works in the field. It includes the mobile phase, stationary phase, visualization method, and targeted compounds. It is important to note that many of the works cited in Table 3 are generally focused more on improving the detection technique rather than developing a method to make testing more accessible. Therefore, the methods described in the majority of the works are more suited for laboratory settings rather than something that could be feasible in the field. This is mainly due to the volatile and rather harmful combinations of chemicals used as eluents. Additionally, nearly all the papers cited use a CAMAG TLC Scanner for detection and identification of samples, which cost anywhere between $3000-8000 CAD depending on the model and options [73]. Satcher et al. (2012) developed a portable TLC kit that is meant for the detection and identification of explosive 17 compounds, however the analysis method in this work is a crude visual colour change check [74]. 18 Table 3: List of prior works for separation of cannabinoids with TLC Authors Citation TLC Dimension Mobile Phase Mobile Phase Ratio Plate Type Visualization Detected Components Grlic, L. [75] 1 isooctane:pyridine 8:02 Silica Gel silver nitrate d1-THC, CBN, CBD Grlic, L. [76] 1 toluene 1 Silica Gel silver nitrate d1-THC, CBN, CBD de Faubert Maunder, M. J. [77] 1 toluene or xylene 1 Silica Gel Fast Blue B d9-THC, CBD, CBN de Faubert Maunder, M. J. [77] 2 toluene-diethylamine 98:02:00 Silica Gel d9-THC, CBD, CBN Segelman, A. B. [78] 1 Benzene 1 Silica Gel G Fast Blue B d9-THC, CBD, CBN Krishnamurty, H. G.; Kaushal, R. [79] 1 Diethyl Ether: Petroleum Ether 1:04 Silica Gel G Fast Blue B d1-THC, CBN, CBD Poddar, M. K.; Ghosh, J. J.; Datta, J. [80] 1 benzene:n-hexane:diethylamine 50:10:01 Silica Gel Fast Blue B d9-THC, CBD, CBN Dahiya, M. S.; Jain, G. C. [81] 1 toluene:diethylamine 20:01 Silica Gel G Fast Blue B 17 Different Cannabinoids Tewari, S. N.; Sharma, J. D. [82] 1 n-hexane:acetone 87:13:00 Silica Gel G UV+FastBlue Debruyne, D.; Albessard, F.; Bigot, M. C.; Moulin, M. [83] 1 Hexane: Chloroform:dixoane 89:8.75:2.25 Silica Gel 60 F 200-300nm with Camag Scanner 2 Vollner, L.; Bieniek, D.; Korte, F. [84] 1 Benzene 1 Silica Gel G Fast Blue B [84] 1 benzene:n-hexane:diethylamine 70:25:05 Silica Gel G Fast Blue B [84] 1 benzene:n-hexane 6:04 Silica Gel G Fast Blue B [84] 2 n-heptane:dichloromethane:butan-2-one then n-hexane:acetone 83:5:12 then 86:14 Silica Gel G Fast Blue B 19 Authors Citation TLC Dimension Mobile Phase Mobile Phase Ratio Plate Type Visualization Detected Components [84] 1 Acetonitrile:Water 9:01 RP-18 Silica Gel Fast Blue B Hanus, L. [85] 1 methanol-dixoane-hexane 1:02:07 Silica Gel G Fast Blue B CBGM CBD CBDV CBDA CBGD CBL, d8-THC, d9THC, d9THCV, CBD, CBN [85] 1 hexane-ethyl acetate 4:01 Silica Gel G Fast Blue B [85] 1 petroleum ether: diethyl ether 4:01 Silica Gel G Fast Blue B [85] 1 hexane-diethyl ether 4:01 Silica Gel G Fast Blue B N. Galand1,*, D. Ernouf2, F. Montigny3, J. Dollet1, and J. Pothier1,* [86] 1 hexane-diethyl ether 80:20:00 Arno Hazekamp, Anja Peltenburg, and Rob Verpoorte [87] 1 Methanol-5%Acetic Acid 19:01 RP-18 Silica Gel 60 Fast Blue B CBGM CBD CBDV CBDA CBGD CBL, d8-THC, d9THC, d9THCV, CBD, CBN [87] 1 Cloroform-Methanol 19:01 Silica Gel G Fast Blue B CBGM CBD CBDV CBDA CBGD CBL, d8-THC, d9THC, d9THCV, CBD, CBN 20 2.3.3. TLC Visualization Visualizing a TLC plate after development is crucial to analyzing the results. There are two main methods for visualization: UV, and derivatization (staining reagents) [66]. There has been significant development in both visualization techniques over the past several decades with both the miniaturization of UV lamps, and discovery of novel reagents for selective visualization [66]. UV Visualization Ultraviolet visualization can be used in one of two ways. In the first, and most common method, the adsorbent of a TLC plate is doped with particles that fluoresce under UV light of particular spectrum (254 nm, 365 nm) [64]. This method is used in instances where compounds absorb the specific wavelengths of light. Many organic compounds fall into this category and hence is commonly used with organic molecules. Once a plate is developed, the UV light illuminates the background areas of the plate, leaving areas in which the compounds have broadened to darker than the surrounding. The second method using UV works opposite to this [72]. A wavelength of light that the target compounds reflect is selected, and a non-fluorescent plate is chosen. Once the plate is developed, the plate is illuminated with the UV light, resulting in bright spots in regions where the compounds are present. With the second method, multiple light sources can be used for differently reacting compounds to help differentiate and identify. These methods are verging on a bridge between chromatography and spectroscopy. Derivatization A different method for visualization is using dying or staining reagents through a process called derivatization. In this method developed plates are either dipped, coated, or sprayed with a reagent that reacts with the target compounds to form a chromophore allowing the spots to be differentiated form the background [61]. In most instances, the reagents make the spots visible in either the visible or UV spectrum. The derivatization method is performed after the plate is 21 fully developed and is therefore considered post-processing. In some instances, the stationary phase of a TLC plate is packed with reagents, that react with spotted compounds. These instances are quite rare as the reagent needs to be specifically chosen for the spotted compounds. The United Nations Office on Drug and Crime has tabulated a series of dyes that are particularly relevant for the use of cannabis detection [84]. Of this list some of the most popularly used in both forensics and industry for cannabis detection are azo dyes like Fast Blue B (Azoic Diazo no. 48) (FBB), Fast Blue BB (hemi(zinc chloride)) (FBBB) and Corinth V (zinc chloride). These azo dyes all come in the form of salts that are generally dissolved in water, or another polar solvent to be administered to the TLC plate. The mechanism by which the azo dyes interact with cannabinoids is shown in Figure 5. This is an example with THC, but other cannabinoids follow similar reactions. When an azo dye (FBBB is shown in Figure 5) interacts with a cannabinoid, a chromophore is created resulting in the colours that can be seen [85]. Depending on the cannabinoid and the dye used, various colours are formed. Additionally, different cannabinoids have different reaction rates to yield chromophores. THC and CBD readily form the chromophores starting under 20 seconds, while THCA and CBDA have a slower reaction rate, taking upwards of 30 minutes before spots are visible [72]. Some studies show that cannabinoid chromophores generated with FBB or FBBB are also fluorescent; the resulting compounds are called fluorphores [85]. In this thesis, FBBB is used as it yields a brighter spot than FBB. 22 Figure 5: Mechanism for interaction between THC and FBBB. a) THC, b) addition of FBBB, c) chromophore. Image reproduced with permission from [85]. 2.3.4. TLC Analysis Analysis of a TLC plate can be quite varied. In the simplest of cases, a qualitative check for presence/absence is enough for users. If a more detailed analysis is required, it involves evaluating features of the spots present. Performing a quantitative measurement on TLC is an aptly named term, quantitative TLC (qTLC) [86]. There are two types of output that qTLC can provide. The common, highly calibrated method outputs values as concentration (i.e., mg/mL, PPM, v/v). This method requires significant calibration for obtaining a reasonable precision. The second method outputs the ratio of the areas of each peak providing a ratiometric value. The ratio value is calculated by comparing the area of a peak to the total area above the baseline. In this thesis, a calibration is performed to correlate the size of a spot to the concentration. HDR Imaging One of the key factors to aid in the accuracy of qTLC is the quality of the image that is generated. While a simple image from a camera can obtain a reasonable amount of detail, for the precision required in this application, the contrast between the spots and background must be increased for optimal results. A common method to obtain an image with a high level of information is to create a High Dynamic Range (HDR) image. HDR imaging is a process in which the irradiance between bright and dark regions are tuned to return the highest possible range, yielding an image similar to what the human eye can see [87]. Normally, the pixels of a camera saturate when there are high amounts of both bright and dark spots present, resulting in 23 an image that is either washed out, or too dark. HDR images work around this by capturing multiple images in various exposures, thus obtaining information that would have been lost due to a single exposure image. The multiple images are merged using an irradiance map that translates the brightest and darkest values of the three images to the full range available in the image codex [88]. Additionally, regions that were previously washed out, are replaced with regions from the images where the light is appropriately exposed. OpenCV is a tool that is predominantly used by researchers to perform computer vision tasks and is used in this thesis as well. OpenCV has several algorithms that enable the generation of HDR images. These merging and tone-mapping images are listed in Table 4. It is important to understand the key differences between these algorithms. A pioneering researcher in the field of HDR imaging, Paul Debevec, created an algorithm for merging multiple images of varying exposures by first analyzing the sensor response function of the camera and tuning the multiple images based on this response. This transformation is performed a pixel level, where the three images are overlayed, and the colour space of the full image is mapped to the full colour space available [88]. Debevec and Malik (1997) developed their method on trying to map a pixel towards the middle of the camera sensor response function. To do this a least-squared error is performed on the pixel value at each exposure, and the resulting value is selected for the new HDR image. Robertson et al. (1999) developed a similar method which uses the camera response function to calculate the weighted HDR image values [89]. The main variance is the method for determining the camera response function. Robertson et al. (1999) use a Guass-Seidal relaxation method while Debevec et al. constrain the sensor reciprocity. 24 Mertens et al. (2007) similarly uses a multi-exposure model but skips the sensor response calibration that Debevec uses [90]. Instead Mertens performs quality measures on the multi-exposure image and returns weights that are used for the fusion of the multiple images. The qualities that are checked for are contrast, saturation, and well-exposedness. Tone-mapping is a little less complex in terms of computation. Tone-mapping takes the data generated from an HDR image and translate the colour map to a format that is applicable to screens and more importantly for this thesis, computer vision algorithms. Drago et al. (2003) developed a method that perform adaptive logarithmic compression of the colour values that enable the colour space to mimic what the human eye can see [91]. Mantiuk et al. (2006) developed a system that adjusts the contrast of the image relative to the brightness of the features in the image to produce something life-like [92]. Finally, Reinhard et al. (2005) is considered the best for reproducing a human eye like image [93]. Reinhard’s method is by far the simplest and compares the input image relative to the luminance to output a more even image. Table 4: HDR merging and tone-mapping algorithms Merging Algorithm Tone-mapping Algorithm Debevec Drago Mertens Mantiuk Robertson Reinhard Chromatogram Generation Due to the wide ranging importance of chromatography, densitometric analysis has garnered a lot of interest from the scientific community [78], [79]. Specifically for TLC, the majority of this 25 work has been conducted through companies like CAMAG that provide TLC scanner products and accompanying software for analysis [73]. Figure 6 is an example of the process used to convert an image of a TLC strip into a useable set of data that can be analyzed. The process of converting the spots on a TLC plate to the graph shown in Figure 6 is called densitometric analysis [79]. Densitometry quantifies the optical density along the length of the TLC, and by graphically representing the data, a chromatogram is generated [80]. Evaluating spots and their features is made easier if it is first converted into a chromatogram/densitograph [86]. Figure 7 shows a generalized process for moving from image to chromatogram and the analysis applied after. Figure 6: TLC analysis. A chromatogram is generated from a plate. Features of the chromatogram are shown. To generate a chromatogram, an image is first modified to be represented in a single channel. Normally this entails either splitting an image to the individual colour channels or converting the image to grayscale. The pixels are then tabulated in a column-wise fashion. One tabulation method simply adds the pixel values of each column to generate the chromatogram. This 26 method contains a great deal of information but is susceptible to errors in the form of noise. Another method averages the pixels values across a column. With this method, minimally dense regions lose information, but generated errors are suppressed. With either of these methods, a chromatogram is generated with the density of light present along a column indicating the value of each data point. The chromatogram at this point still has a high degree of error that must be corrected for prior to analysis. Figure 7: Flow chart for generating chromatogram from image. Also referred to as the image pipeline Baseline Correction One of the main problems to contend with in chromatographic analysis is baseline error [81]. Most spectrum-based imaging techniques (NIR, Raman, RDS, HPLC, LCMS) all face this problem [82]. One simple solution for eliminating baseline error is to employ baseline correction. Due to the popularity of chromatographic evaluation, there are numerous correction algorithms available, with a wide range of performance and target applications [81]. A Python library, Baseline Removal, has three algorithms available [83]. The three algorithms are: ModPoly, IModPoly, and ZhangFit [84]–[86]. ModPoly (Modified Polyfit) uses a polynomial fit based baseline estimation [84]. Once the baseline is estimated, the value is subtracted from the initial 27 dataset to produce a new dataset that is free from baseline error. IModPoly follows a similar approach but also includes statistical correction and peak dampening [85]. Finally, ZhangFit (airPLS) uses an “iteratively reweighted Penalized Least Squares” [86]. This method requires no prior information and performs generally faster (computation wise). With the addition of these techniques, the baseline error can be drastically reduced, to improve signal and improve analysis. Peak Analysis The eluent front is determined first, to set a reference for the Rf. CAMAG systems set the industry standard for TLC systems. Their methodology is commonly used for eluent front detection, which involves finding the first peak (with limitations such as minimum FWHM, minimum FWB, minimum PV) on the top side of the strip. Having found this value, CAMAG finds the trailing edge of the peak and sets that value as the eluent front. This method works very well to determine parameters of the system and allows for tolerance when the eluent does not repeatably travel to a given spot. With the eluent front determined, the retention factor can be calculated. Rf refers to the distance of a point compared to the distance of eluent travel [86]. This is calculated using equation Equation 2 where 𝐷𝑝 refers to the distance from the starting point to the point of interest along the x-axis and 𝐷𝑒 is the distance from the starting point to the eluent front [61]. This can be calculated using any units as this is a unitless measure. Conventionally, a person would use a ruler to measure. Using computer vision, this is easier to calculate and can be measured with the reference of pixel distance. Rf is a unitless measure. The benefit of using Rf is to enable a level of tolerance for changes due to environmental, testing, or purity conditions as a compound will travel a particular distance relative to the eluent [65],[68]. 28 𝑹𝒇 = 𝑫_𝒑 / 𝑫_𝒆 Equation 2 Equation 2: Calculation of the Rf After the Rf is analyzed, analytical features of the chromatogram must be determined. Due to the wide-ranging importance of chromatography, densitometric analysis has garnered a lot of interest from the scientific community [78], [79]. Specifically for TLC, the majority of this work has been conducted through companies like CAMAG that provide TLC scanner products and accompanying software for analysis [73]. The initial step is to perform peak detection. A simple method to detect peaks is to determine local maxima [87]. SciPy, a python library tuned towards science and engineering, has tools specifically for peak finding and analysis [88]. Peaks can be selectively determined based on features such as minimum space between peaks, minimum peak prominence, and minimum peak width. Once a peak is located, the area relative to the baseline can be calculated to determine the concentration [89]. A common method for determining the area under the curve is to use trapezoidal approximation [89]. Again, SciPy is used for the trapezoidal integration [90]. The rest of the parameters aid in determining the identity of the spot and validating the presence of a spot [91]. For example, the size of the spot is given by the FWB, while checking for a regular spot is given by FWHM. The SciPy peak tool again has the ability to determine these values. By analyzing if these values follow a regularized scheme, the presence of a spot is determined. Peak value serves two purposes, it enables lane separation when multiple tests are run, but also aids is detecting the presence of co-spotting: a phenomenon where multiple compounds have the same or similar Rfs [92], [93]. 29 3. Concept and Methods 3.1. Concept The proposed device uses thin-layer chromatographic techniques to separate the various cannabinoids in a mixture. Once separated, the compounds are visualized, imaged, and analyzed using custom made image analysis software. The position and size of the spots on the strip are evaluated via image analysis to extract information regarding the identity and quantity of the multiple target cannabinoids present. As TLC strips are sensitive to the concentration of the spotted sample a method to determine the concentration of a generated sample is also included. 3.2. Hardware Design The hardware design of the device is split into three distinct chambers. The vaporization chamber, development chamber and imaging chamber. To run a test a user would first develop a sample – methanol mixture. A small amount of that mixture will be vaporized in the vaporization chamber to determine the concentration of the mixture. If the concentration falls into the testing range, the user will then spot the sample onto the TLC plate for development and insert the strip into the development chamber. Once fully developed, the user would use the imaging chamber to take pictures of the strip. 3.2.1. Vaporization Chamber A vaporization chamber was designed to perform an accurate quantitative measurement of the total sample concentration (TSC). The main purpose for obtaining the TSC is to ensure that the samples generated fall within the optimal TLC testing range. A photoionization-detector (PID) was chosen as the appropriate sensor for detecting the sample concentration as the cannabinoids can be measured but the solvent, methanol cannot be and will therefore not interfere with measurement. A means of vaporization is also required, and for this an aluminium 30 PTC heater plate was chosen as it can reach the high temperatures required for vaporization. Finally, to control the heater plate, a method of measuring the temperature is required. A thermistor that can rapidly respond to temperature changes is chosen for this application. Figure 8 shows a conceptualization of this chamber. Figure 8: Concept drawing of vaporization chamber 3.2.2. TLC Development and Imaging (Hardware) For the repeatable and reliable development and analysis of the TLC plates, a process and accompanying components were designed. The first component is a holder for the TLC strips. The multiple functions of this component include guiding the spotting of samples, systematically exposing the TLC strip to the eluent, and carrying the TLC strip between the development and imaging chambers. It is comprised of two parts that go on either face of the TLC strip, effectively sandwiching the strip in place. The strip holder is designed in a way that when inserted into the development chamber, the chamber is sealed for optimal development and maintaining thermodynamic equilibrium. The concept of the strip holder is shown in Figure 9. The second component designed is the development chamber. This chamber will contain the eluent, and when the strip and holder are inserted, the strip will be developed. This chamber is 31 designed to be sealed as for proper development the eluent must reach thermodynamic equilibrium within the chamber. For this a lid is also designed with the chamber. During the initial development phase of this system, vertical and horizontal configurations were evaluated. A vertical configuration was chosen for further development as it resulted in reduced complexity and repeatable results. The dimensions of the chamber are such that when a loaded strip holder is inserted, the bottom 5 mm of a strip is exposed to the eluent at the bottom. The concepts for the system are shown in Figure 9. Figure 9: Concept drawing of development chamber (left) and strip holder (right). Finally, for imaging the TLC plates, a dark environment where the lighting can be controlled is designed. This is referred to as the imaging chamber. This chamber is designed to hold a camera, the strip holder, and the two light sources used in this thesis (UV and visible). To reduce complexity, the camera and lights are placed at a distance where the full strip falls under the field of view of the individual components. The last consideration for designing the imaging chamber is the material it is comprised of. The material should not interfere with the imaging process (i.e., reflecting light). The imaging chamber concept is shown in Figure 10. A shroud 32 accompanies this design, and covers the entirety of the chamber, to completely seal it from external light. Figure 10: Concept drawing of imaging chamber. 3.3. Materials and Procedures Aluminium backed RP-18 TLC strips with silica gel 60 and 254nm UV indicator, HPLC grade methanol and acetic acid, reagents Fast Blue BB and Sodium Hydroxide were obtained through Sigma-Aldrich. As the TLC arrives in sheets of 20 cm x 20 cm, the sheets were cut into strips of 1.8 cm x 10 cm. Ethanol, Acetonitrile, 1.0 mg/mL THC, 1.0 mg/mL THCA, 1.0 mg/mL CBD, and 1.0 mg/mL CBDA were obtained through VWR Canada. THC, THCA, and CBD distillates were obtained from Vitalis Extraction Technologies (Kelowna, BC, Canada). The vaporization chamber components include the red piD-TECH eVx PID sensor (Amtek-Mocon, US), Ultra-think 10K NTC Thermistor (Adafruit Industries, US), and a 12V aluminium PTC heater (Keen so, China). To control, a Sparkfun qwiic single relay (Sparkfun, US) was used and controlled by a Raspberry Pi 4 (8GB) (Raspberry Pi, UK). The components of the development chamber were 3D-printed using Veroclear RGD810 resin (Stratsys Inc., US) on 33 the Polyjet J4100 (Stratasys Inc., US) and coated with Parylene C (Speciality Coating Systems Inc., US). Finally, the imaging chamber components include a Raspberry Pi Camera v2 (Raspberry Pi, UK), Adafruit Neopixel Stick (8) (Adafruit Industries, US), and the Handheld Mini Ultraviolet Lamp (254 nm) (Cole-Parmer, US). The body of the imaging chamber was printed out of Black PLA (Creality Inc, China) on an Ender v3 Max (Creality Inc, China). 3.3.1. Cannabinoid Sample Preparation In this thesis, concentrations are referred to by the total sample concentration of the sample in order to standardize the results. However, the actual sample amount on either the TLC strip or the heater plate is smaller due to the volumes spotted being less 10 uL or less. Using the distillates obtained from Vitalis Extraction Technologies, a 20 mL sample of 5mg/mL was created by dissolving 100 mg of sample into 20 mL of methanol. This sample is then diluted down to achieve the required concentrations by creating new samples and mixing with methanol. This step was performed on all three of the distillates. As for the standards, samples were mainly extracted directly from the standard vial. To generate concentrations higher than the 1.0 mg/mL of the standards, samples were spotted multiple times to achieve the required concentrations. For example, to reach 3 mg/mL of THC on the TLC strip, the THC standard was spotted three times with a volume of 2 uL for a total of 6 uL spotted. To prevent the spot size from growing larger than a 2 uL spot, after each repetition, the solvent was given time to evaporate. 3.3.2. Staining Reagent Preparation Two solutions were created that together form the staining reagent used for derivatization, following the work of Hazekamp et al. (2007) [94]. A stock solution of 0.5% w/v Fast Blue BB in water was created inside a continuous spray bottle. When not in use, the FBBB solution was 34 kept in a fridge at 4℃ to prevent degradation. A separate stock solution of 0.1M w/v sodium hydroxide in water was created as well in a 50 mL spray bottle. 3.3.3. Vaporization Chamber Testing Procedure A total volume of 10 uL of will be pipetted onto the heater plate, the chamber sealed, and activated. Since the boiling temperature of CBD is 180℃ (the highest of the target compounds), 185℃ is chosen as an appropriate temperature to ensure vaporization. Once the temperature reaches 185 ℃, the temperature will be maintained for 60 seconds to ensure complete sample vaporization and allow for the PID sensor response to stabilize. At this point the final measurement is recorded. 3.3.4. TLC Development Procedure Various eluent combinations were tested to determine the ideal eluent for this thesis. One was selected as it performed reliably. The components of this eluent are considered proprietary and will therefore be referred to as “the eluent”. To develop a TLC strip, first 5 mL of the eluent is inserted into the development chamber. The development chamber lid is then used as a stopper to prevent the eluent from evaporating and allows for the eluent to reach thermodynamic equilibrium within the chamber. This process is given 60 seconds. Meanwhile, a TLC strip (1.8 cm x 10 cm) is inserted into the strip holder. 2 uL of a sample is spotted onto the adsorbent surface, using the guides for reference. At this point, the now spotted strip inside the strip holder is inserted into the development chamber, ensuring that the bottom of the strip is exposed to the eluent. The strip is left to develop until the eluent front reaches approximately 1 cm from the top of the strip, at which point the strip holder is removed from the development chamber stopping the development process and allowing for the eluent to evaporate off the TLC strip. The development process takes approximately 10 minutes, with an additional 5 minutes required for drying after. 35 3.3.5. TLC Derivatization Procedure Derivatization is always done after UV images have been captured. The strip is first sprayed with FBBB solution. Usually, two trigger presses are enough to coat the entire strip. Next the strip is sprayed with the NaOH solution, which usually takes about 2-3 trigger presses. The strip is allowed to rest for 3-4 minutes at which point spots develop if present. 3.4. Thin Layer Chromatography Analysis Software (TLC-AS) For analyzing the developed TLC strips, an imaging pipeline was created that works to convert the TLC strip from an image into a chromatogram, which can be analyzed to extract the relevant data. The main procedural steps the software can do are listed in Figure 7 following the common steps laid out in section 2.3.4. These steps can be performed in an autonomous fashion, moving from image capture to peak analysis. Additionally, the software is designed to easily switch between the various algorithms available to perform each of these transformations. A software was developed to handle all data collection and analysis steps for both the vaporization chamber and TLC imaging and analysis. The software was written in the Python programming language. The following section details the procedures used to tune and characterize the software for analysis of complex cannabinoid mixtures. 3.4.1. Vaporization Chamber Characterization The vaporization chamber is treated as a simple concentration measurement unit where the PID sensor output is correlated to the concentration. Therefore, the chamber needs to be characterized and a relationship drawn from the data. To characterize the vaporization chamber, the system will be calibrated using various concentrations of known samples, listed in Table 5. Series A of Table 5 is the main calibration concentrations, encompassing the full 36 optimal testing range between 0.5 mg/mL and 5 mg/mL. A linear regression will be performed on the data from series A to build a correlation between concentration and the PID sensor voltage response. To determine the efficacy of the regression, a goodness-of-fit test will be performed and an R2 value will be gathered. These calculations will be performed using tools in MATLAB (2021b) [95]. Series B of Table 5 will be used to determine the precision of the system by increasing the concentration in increments of 0.1 mg/mL. The PID sensor response from the series B tests will be converted into concentration using the values determined in the previous step, and the resulting values will be evaluated for their precision. Finally, series C of Table 5 will be used as a validation test, where the results from the system will be compared to the known values and the accuracy of the system is evaluated. Apart from series C, all tests will be performed in triplicates and the data collected will be averaged. 37 Table 5: Vaporization test concentrations. Series A is for calibrating the system. Series B is for testing precision. Series C is for confirmation tests. Test Number THC Concentration (mg/mL) CBD Concentration (mg/mL) Total Concentration (mg/mL) A1 0 0 0 A2 0 0.5 0.5 A3 0.5 0.5 1 A4 1 1 2 A5 2 0 2 A6 0 2 2 A7 2 2 4 A8 5 0 5 A9 0 5 5 B1 1 0 1.0 B2 0.55 0.55 1.1 B3 0.6 0.6 1.2 B4 0.65 0.65 1.3 B5 0.7 0.7 1.4 C1 1.5 1.5 3 C2 2 2 4 3.4.2. Imaging Pipeline Development The imaging pipeline, as shown in Figure 7, consists of multiple sequential steps that must be performed in order to obtain analytical information from a TLC strip. In this device, the imaging 38 pipeline was developed through sequentially testing and validating the steps. The development process is broken down into five discrete steps. Each step progressively builds upon prior steps to develop and tune the entire process. Due to the two visualization techniques, two pipelines were created and tuned towards the detection of cannabinoids. Two sample sets were generated for the pipeline development. Sample set A contained strips spotted with THCA for UV based visualization, while sample set B contained strips spotted with THC for derivatization based visualization. Sample set A strips contained 1.0 mg/mL THCA (strip 1), 4.0 mg/mL THCA (strip 2), while sample set B contained 1.0 mg/mL THC (strip 3), 4.0 mg/mL THC (strip 4). A default pipeline setting was created upon which the rest of the system was developed and tuned. These settings are listed in Table 6. The main parameters used for evaluation are peak area and baseline error. Peak area is calculated by performing trapezoidal integration in the region of a peak. The boundaries of the peak are derived from the Scipy find_peaks function. The baseline error evaluation occurs in a region of the chromatogram where no spots are expected. The region was selected by first determining the Rf of the spot on the plate, then finding a short region that does not interfere with the spot, starting point or eluent front. The baseline correction algorithm should ideally filter all baseline levels to near zero, therefore any variance from the mean is considered error. The mean absolute error (MAE) of this region is calculated using Equation 3 ). The error is optimized for a lower MAE. 𝑴𝑨𝑬 = ∑ |𝒙𝒊 −𝒏𝒊=𝟏 ?̅?|𝒏 Equation 3 Equation 3: Mean absolute error 39 Table 6: Default Image Analysis Pipeline Settings Parameter Value Shutter Speed 1/30 s Chromatogram Generation Algorithm Summing and Smoothing Baseline Correction ZhangFit Peak Identification Local Maxima Area Integration Trapezoidal Derivatized Image Illumination The first step was to determine the illumination of the derivatized strips. The colours that were tested are listed in Table 7, which are the colours of the rainbow and white light, representing the full visible spectrum. Violet is out of the range of producible light in the LED strip and therefore omitted. Once developed, strip 3 was sprayed with staining reagents until the adsorbent was evenly coated, then allowed to dry. Once dry, the strip was individually inserted into the imaging chamber and imaged using the default settings listed in Table 6 and illuminated by the colours listed in Table 7. Altogether, seven images were captured. Following the default settings, a chromatogram was generated from each of the images with baseline correction performed and the resulting chromatogram analyzed for baseline error and peak area. As there are four colour channels for each image (red, green, blue, gray), the best performing colour channel was chosen as well using similar criteria. Once all values were gathered and compared, the optimal illumination colour and the optimal colour channel for analysis were determined for derivatized strips. 40 Table 7: List of colours for 2.2 Colour RGB Value White 255, 255, 255 Red 255, 0, 0 Orange 255, 127, 0 Yellow 255, 255, 0 Green 0, 255, 0 Blue 0, 0, 255 Indigo 75, 0, 130 HDR Imaging Each of the four strips were analyzed individually in this step, creating the two streams: UV and derivatized. Three pictures with shutter speeds of 1/100 s, 1/75 s, and 1/30 s were captured sequentially under ideal illumination conditions. These shutter speeds were chosen as during preliminary trials, the resulting images from these speeds produced a low-exposure, normal-exposure, and high-exposure images, respectively, which are the requirements for optimal generation of HDR images. The images and their respective exposures were processed generating a parametric sweep between the HDR and tone-mapping algorithms listed in Table 4. Altogether, nine HDR images will be generated for each strip to be analyzed. Each of the nine HDR images was converted into chromatograms following the default settings, providing a method to compare the performance of the HDR images. As UV visualized images do not have a set colour channel, one was chosen in this step. The result of this step yielded the best HDR merging and tone-mapping algorithm, which is required to extract the greatest amount of information from the TLC strips under both UV and derivatized visualization. Four 41 graphs, one for each strip, were plotted showcasing the chromatogram generated from each of the viable merging – tone-mapping pairings. Chromatogram Generation The next method that was developed is related to how the images are converted into chromatograms. The two available methods are discussed in section 2.3.4. At this stage, an ideal HDR image is producible for both UV and derivatized strips, and the ideal colour channel for chromatogram generation was chosen. To test the best chromatogram generation method, each of the four images were converted into chromatograms using both techniques and rendering a graph showcasing the response of each strip under the generation technique. The response of each strip was evaluated for peak area and baseline error. Baseline Correction With the now optimized chromatograms, baseline correction must be performed. Since there are three available correction methods, the performance of each method was evaluated to determine the response. A total of 12 corrected chromatograms resulted from this parametric assay. Once again, both the peak and baseline error of each correction algorithm were compared, and an optimal method chosen. Peak Analysis The last parameter to optimize in the image analysis pipeline is the parameters by which the Scipy find peaks determines if a value is a peak. Thankfully, the peaks within the concentration range generally have a large peak prominence relative to error, helping aid their identification. The values that were optimized in this step is minimum-prominence, minimum distance between peaks, and minimum peak width. Up until this point, the main peak, and the eluent front peak 42 were identified manually. However, with this optimization, both of those peaks can be selected for through thresholding and a more automated process can be performed. 3.4.3. Imaging Pipeline Characterization As the image analysis pipeline is now tuned and developed, the system was characterized. To characterize the system the relationship between concentration and area, the precision of the pipeline, and finally an estimate of the limit of detection were made. Since there are two visualization methods, two calibration constants were calculated. The concentrations listed in series A of Table 8 were generated from both THCA and THC for UV and derivatized visualization respectively. 2 uL samples are spotted onto individual TLC strips, and the strips will be developed using the eluent, following the protocol in section 3.3.4. Each concentration was tested in triplicates and averaged to ensure the validity of the resulting data. The THC spotted strips were derivatized and subsequently visualized, while the THCA strips were imaged under UV. The images were analyzed according to their respective pipelines, and the area of each strip collected. Linear regression was performed on the series A data, resulting in a relationship between concentration and the area of the spot. Two relationships were determined: one for the UV strips, and one for the derivatized strips. A goodness-of-fit test was performed on the linear relationship. 43 Table 8: Imaging pipeline characterization - calibration. Test Strip Cannabinoid Concentration (mg/mL) Sample Volume (uL) A1 0.5 1 A2 1 2 A3 2 4 A4 3 6 A5 4 8 A6 5 10 B1 1.25 2.5 B2 1.5 3 B3 1.75 3.5 Series B of Table 8 was similarly tested, with the exception of incrementing the values by 0.25 mg/mL. The purpose of this test was to determine the precision of this evaluation method. The series B samples were tested with the appropriate relationships that were previously developed. The relationship was also extrapolated to determine the approximate limit of detection/quantification. 3.5. Detection and quantification of target molecules As the prototype device is now complete, the device was applied towards the detection of the four target compounds selected for this thesis: THC, THCA, CBD, CBDA. THC and THCA have been well characterized in the previous set of experiments, hence the next set of tests focusing on identifying and separating CBD and CBDA. CBD and CBDA were expected to follow a similar visualization methodology as THC and THCA. To test this, the four cannabinoids were 44 spotted onto individual strips with concentrations of 1.0 mg/mL and developed following the protocol in 3.3.4. Each of these tests were performed twice to validate the results. The strips were imaged under both visualization techniques and the results were analyzed. 45 4. Results and Discussion 4.1. Prototype Chemical Analyzer 4.1.1. Vaporization Chamber The design of the vaporization chamber includes three components: PID, PTC heater plate, and thermistor. The red PID was selected as the stated sensing range encompassed the target range of this thesis [96]. A custom PCB was developed to power the PID sensor and obtain sensor readings. The PCB is shown in Figure 11. These components are either encased or embedded into a sealed acrylic box. In this configuration, the heater plate and thermistor work in conjunction to reach temperatures at which the sample will vaporize. As the main microcontroller, a Raspberry Pi 4 (RPi) is used. The RPi, along with the qwiic relay switch is used as the controller for the heater, which is activated once a user starts the software. The thermistor works to measure the temperature of the heater and feeds the information back to the RPi. Once the temperature reaches the target of 185 ℃, the temperature is maintained using a simple control system for a set time to ensure that the total sample is vaporized. As the chamber is sealed, once the sample is fully vaporized, the total concentration of vaporized sample in the chamber must correlate with the TSC. Additionally, the PID cannot detect methanol due to the ionization potential of the UV lamp (10.0 eV) being lower than that of methanol (10.85 eV) [97]. Therefore, the solvent of the sample will not interfere in detection. 46 Figure 11: Custom designed PCB to power and measure PID sensor From the beginning of the heating process, the PID concentration data is logged at a rate of 100 Hz. Once the sample is fully vaporized, the PID response plateaus and it is at this point, the TSC is measured. In order to obtain a low-noise signal, 100 data points (1 second of data) is averaged to obtain the TSC. This value is used to ensure the concentration of the sample falls within the optimal testing range (0.5 – 5 mg/mL). Figure 12 shows the current iteration of the vaporization chamber. The white box on the right (Figure 12b) houses the computational and electrical components required to run the system. On the left (Figure 12a) is the chamber itself with the PID sensor on the top and the heater and temperature sensor within the chamber. The PID is situated directly over the sample to ensure maximal exposure. The thermistor is attached to the side of the heater plate to prevent any contamination from the sample. The heater plate is situated on top of a ceramic block to prevent the heater from damaging the acrylic box walls. 47 Figure 12: Vaporization chamber. a) computational and control unit, b) vaporization chamber 4.1.2. TLC Development and Imaging (Hardware) The strip holder (Figure 13a), the development chamber (Figure 13b), and the lid (Figure 13d) were 3D – printed out of Veroclear, which has been shown to be resistant to acetonitrile [98], [99]. Additionally, Veroclear is not fluorescent under UV (254nm) and hence not noisy during imaging. The Parylene C coating both prevents chemical attack and prevents adhesive forces between Veroclear and the eluent. Without the Parylene C coating, the quality of the development process in the Veroclear chamber was poor. Testing showed that the eluent would climb along the non-contacting surface of the strip holder at a faster rate than the TLC strip, and subsequently expose the TLC strip to the eluent in regions above the eluent front. This process would result in the spotted sample being eluted in multiple directions simultaneously, impairing the test. Parylene C prevented the eluent from climbing on the strip holder surfaces. As shown in Figure 13c, the loaded strip holder can fit inside the development chamber, sealing the 48 chamber, and maintains the level of the strip 5 mm from the bottom of the chamber, allowing for a known quantity of eluent to be used in the chamber. Figure 13: TLC development and imaging device. A) TLC strip holder, b) development chamber, c) TLC strip holder inserted into development chamber, d) lid The imaging chamber is shown in Figure 14. Much like the development chamber, the strip holder is inserted into the imaging chamber, albeit in a horizontal configuration. On the roof of the chamber there is a window from which the UV lamp illuminates the developed strip causing a fluorescence reaction when UV images are being taken. For derivatized tests, there is an LED strip that is attached to the lid to illuminate the strip in the visible spectrum. Finally, a Raspberry Pi Camera v2 is attached for visualization. The camera is capable of various shutter speeds which is a necessary feature for HDR imaging. The imaging chamber was 3d-printed from black PLA to prevent interference with the imaging process. The camera and light sources are placed at a distance of 11 cm from the strip to meet the field of view requirements. The shroud used to isolate the imaging chamber from external light is currently a black plastic box that covers the entire imaging chamber. In the near future, the system will be built into a Pelican case and 49 therefore, no shroud is necessary; rather when imaging is required, the lid of the Pelican case can be closed. Figure 14: Imaging Chamber 4.2. Thin Layer Chromatography Analysis Software (TLC-AS) 4.2.1. TLC Development and Imaging (Software) In the vaporization chamber window (Figure 15a), the user has control over relay that controls the heater. The user can start the automated heating process and monitor both the temperature of the heater and the response of the PID sensor. Additionally, the user can manually control the heater if necessary. In the imaging window (Figure 15b), the user has control over all aspects related to capturing the necessary images. A user can activate both the UV and LED light sources and specifically for the LED strip, control the colour output. As HDR images are used in this thesis, the user can also select the shutter speeds of the multiple LDR images that are later combined. When a user clicks “Take Picture”, the software takes four pictures, one 50 baseline image, and three images based on the shutter speeds set. Additionally, a CSV file that contains information regarding the images is saved. The software sets a default rotation and crop adjustment on the images to reduce the image to the strip only. If necessary, the user can also modify those settings. One additional function the software in this window provides is determination of the eluent front. This is performed by identifying the right-most peak (based on the orientation of the strip) and determining the trailing edge of the peak. The value of this point is set as the eluent front, which is used to calculate the Rf. This task is performed automatically but can be changed if necessary. Finally, in the analysis window (Figure 15c), the user can generate the HDR image and chromatogram based on various algorithms available. When a user clicks the “Update Image” button, the software takes the various inputs (HDR merging and tone-mapping algorithm, baseline correction algorithm, chromatogram generating algorithm), generates the HDR image, chromatogram, performs baseline correction, and performs peak analysis. The output of this analysis returns several files including the HDR image, the chromatogram, and a CSV file that yields information of the peaks detected. 51 Figure 15: The three windows of the TLC-AS. A) Vaporization Chamber Window, b) Imaging window, c) analysis window 4.2.2. Vaporization Chamber Characterization A linear regression was performed and is represented in Figure 16 by the black dashed line. The following equation was derived to relate sensor voltage and concentration, where 𝐶 is the concentration, and 𝑉 is the sensor response. 𝑪 =𝑽 − 𝟎. 𝟒𝟐𝟑𝟏𝟎. 𝟎𝟔𝟎𝟒 Equation 4 Equation 4: Calibration equation for vaporization chamber Additionally, the quality or goodness-of-fit of the linear regression was evaluated using R2. Again, MATLAB tools were used for this calculation, that led to a value of 0.985 or 98.5% correlation between the PID sensor voltage and concentration. Of course, in the scope of this 52 thesis, the range was limited to between 0.5 mg/mL and 5 mg/mL, therefore there is a possibility that measurements outside of this range have less accuracy. As this is a high correlation, this model was accepted and further evaluated. Figure 16: Graph of relation between voltage and concentration. The red data points in Figure 16 shows the precision tests performed with concentrations at the lower end of the sensor range. The largest divergence is seen by the 1.4 mg/mL samples. This value has a divergence of 0.0165V or 0.284 mg/mL. As this value falls within the acceptable range of error, the system is shown to perform well. Additional tests for confirmation of the system were performed, represented by the C-series of tests in Figure 16. These tests were only run once to simulate how a normal user would run a sample. Using the relationship developed in previous steps, the concentrations of these tests were calculated. The known concentration of C1 is 3 mg/mL, while the sensor returned a value 0 1 2 3 4 5Concentration (mg/mL)0.40.450.50.550.60.650.70.75Voltage (V)A SeriesB SeriesC SeriesLinear Regression 53 of 3.14 mg/mL. This value falls within the previously calculated ±0.27 mg/mL. The known concentration of C2 was 4 mg/mL while the sensor response returned a value of 3.58 mg/mL. As this value is greater than the previously stated error, this suggests that in the higher range of sample concentrations, the sensor response strays from the calibrated relationship. This is something to be accounted for in future development. As previously stated, the main purpose of this portion of the sensor is to ensure that users develop samples that are within range for TLC testing. In the following sections, most tests will be performed with known concentrations, resulting in vaporization tests being unnecessary for the characterization and software development tests. 4.2.3. Imaging Pipeline Derivatized Image Illumination The initial step was to determine the ideal illumination for derivatized strips. Figure 17a shows the images obtained from the low-concentration THC (strip 3) test illuminated by the various colours. As can be seen, certain illumination colours result in better visibility of the spots. Specifically, orange, yellow, green, and white have the best response. Figure 17b shows the resulting chromatograms generated from each of the images in Figure 17a. As each image outputs four chromatograms (one for each colour channel), the best-performing channel was chosen. Figure 17c shows a graph of the four best illumination colours with their best performing colour channel converted into chromatograms to compare. The Rf of the spot in the image was determined to be 0.64, therefore the baseline region chosen is between 0.25 and 0.35 Rf. This region will be used in future tests as well. The peak area and baseline error from each of the four chromatograms for strip three are analyzed and the results are displayed in Table 9. White was chosen as the optimal illumination colour and the green channel of the image was chosen as the best performer based on the peak size and the baseline error. Yellow performed similarly but was not reliable at higher concentrations. 54 Figure 17: Derivatized strip illumination optimization. a) Illuminated derivatized strips, b) all four channels shown, c) top four illumination-channel pairings showing orange, yellow, green, and white illumination colour-channels. Table 9: Evaluation metrics for derivatized strips Illumination Colour Channel Colour Peak Area Baseline MAE Orange Green 853 19 Yellow Green 1010 45 Green Red 1026 37 White Green 1145 35 55 HDR Imaging Figure 18 shows three low-dynamic-range (LDR) images taken at different shutter speeds for HDR image generation. Images a, b, and c are taken from strip 1 which has THCA and is imaged under UV, while d, e, and f are taken from strip 3 which has THC and is derivatized and imaged under white light. Figure 19 shows the various images generated by combining the LDR images into HDR using the merging and tone-mapping algorithms. It can be seen that the Robertson merging algorithm is not compatible with either the Mantiuk or Reinhard tone-mapping algorithms. Without tone-mapping, the Robertson merging algorithm yields an almost black image. The tone-mapping algorithms, Mantiuk or Reinhard must not be able to accurately modify the illuminance of the merged HDR image and therefore no data is able to be collected. However, during testing the Robertson-Mantiuk pairing was shown to work, when the concentration was in the higher end of the testing range. This was excluded from the results as a single solution that could work for both low and high concentrations was sought after. Figure 18: LDR images at different shutter speeds. Images a, b, c are of strip 1, while d, e, f are of strip 3 a) 1/100 s, b) 1/75 s, c) 1/30 s, d) 1/100 s, e) 1/75 s, c) 1/30 s. 56 Figure 19: Parametric sweep of HDR images generated from LDR images of a) strip 1, b) strip 3 Figure 20 portrays the chromatograms generated from the HDR images generated in Figure 19. Additionally, chromatograms are generated for strip 2 and 4. The best performing colour channel is chosen by once again analyzing the peak area and baseline error. For the Debevec merging algorithm, the red colour channel usually performed the best, while for either Mertens or Robertson, the green colour channel performed the best. The results of the optimal HDR system are shown in Table 10 for the ideal HDR algorithm and colour channels. The Mertens merging algorithm with Mantiuk tone-mapping using the green colour channel performed the best in this application. Both low and high concentration samples for UV and derivatized strips yielded a large area while minimizing error. The Reinhard tone-mapping algorithm performed similarly but tended to have large errors near either ends of the strip, which disqualified it. 57 Figure 20: Chromatograms generated from parametric sweep of HDR algorithms. Only the best performing colour channel is plotted. a) strip 1, b) strip 2, c) strip 3, d) strip 4. e) shows each of the strips with the selected HDR algorithm. Table 10: Properties of HDR system for each strip Strip Merging Algorithm Tone-mapping Algorithm Colour Channel Rf of Peak Peak Area Baseline MAE 1 Mertens Reinhard Green 0.5268 1217 46 2 Mertens Reinhard Green 0.5006 2528 23 3 Mertens Reinhard Green 0.6602 3018 28 4 Mertens Reinhard Green 0.6490 6745 35 58 The key observation from these tests is the distinct difference in Rf between THC and THCA. This is encouraging as the hope is to separate the spots as much as possible. Looking at the peak areas and the baseline MAE, while there were other algorithm pairings that performed better in either metric, Mertens and Reinhard had the most repeatable results. Chromatogram Generation Method The two methods described in section 2.3.4 were evaluated on each of the four test images processed using the techniques developed in previous sections. The results are displayed in Figure 21, where the different graphs show the results from the different methods. Figure 21a is generated from data being processed by the simple column-summing method, while Figure 21b is generated by the column-summing method with the averaging window. While both methods are quite similar, from the area and baseline error calculations it can be seen that the values differ. The peak area in the summing and smoothing plots are lower compared to the simple summing plots, but the error is reduced as well. It can be inferred that since a moving window is used for smoothing, some information is lost, but it is clearly not detrimental to the data. Therefore, the summing and smoothing method is chosen for future tests. Additionally, comparing the resulting area between the low and high concentration tests for each imaging type, the area values for the high concentration tests are consistently ~2 times larger giving an indication of the relationship between area and concentration. This relationship was further explored in section 4.3. 59 Figure 21: Chromatogram generation method optimization. a) column summing method, b) column summing with averaging window Table 11: Results from chromatogram generation technique optimization Column Summing Column Summing and Smoothing Strip Number Peak Area Baseline Error Peak Area Baseline Error 1 835 40 756 35 2 2375 35 2528 28 3 3149 36 3018 27 4 5594 45 6744 40 Baseline Correction To compare the baseline correction algorithms, once again the TLC-AS was used with the four chromatograms generated and optimized through the previous sections of 4.2.3. In the previous 60 set of optimizations, the area of the spot was the key determining factor while the error of the system was a secondary deciding factor. In this optimization, both factors play an equal role in determining the best algorithm. Figure 22 focuses the region prior to the peak and is used to evaluate the optimal baseline correction algorithm. As seen in Figure 22b, ZhangFit performs the best as it yields the lowest error in the baseline relative to the averaged baseline. Figure 22: Baseline correction optimization results. a) low concentration THCA - UV , b) high concentration THCA - UV, c) low concentration THCA - derivatized, d) high - concentration UV - derivatized. Each of the chromatograms were corrected using the three algorithms: ModPoly, IModPoly, ZhangFit. The results of each correction algorithm are plotted in Figure 22, where each plot represents a particular strip (1, 2, 3, 4) and the results of processing the chromatogram though one of the algorithms. Table 12 shows a tabulated result of the analysis metrics, peak area, and 61 baseline MAE. For each strip, ZhangFit performs the best by maintaining a high peak area value, while having a relatively low error value. Additionally, ZhangFit tends to have a more flatter baseline relative to the other two algorithms, as seen in Figure 22, and usually a quicker return to baseline upon the end of the peak. Table 12: Baseline correction algorithm results ModPoly IModPoly ZhangFit Strip Peak Area Baseline MAE Peak Area Baseline MAE Peak Area Baseline MAE 1 3127 406 1830 187 756 36 2 4412 390 4404 158 2528 28 3 5204 243 5227 96 3018 28 4 12523 698 12547 258 6744 39 Peak Analysis Peak Analysis parameters for the minimum distance between peaks, minimum peak width, minimum peak height, and area measurement method were tested. Different parameters were obtained from both UV visualized data and derivatization visualized data. The data is presented in Table 13 with distance and width values given in units of Rf, and the height given as a normalized value. The parameter optimization criteria ensured that peaks for both low-concentration and high-concentration samples would be identifiable with the software, and any error would be filtered out. With these optimizations performed, future tests build on this work to characterize the image pipeline as a means of detecting the presence of cannabinoids. 62 Table 13: Peak analysis optimization parameters for UV and derivatization visualization Parameter UV Derivatization Minimum Peak Distance (Rf) 0.05 0.05 Minimum Peak Width (Rf) 0.015 0.025 Minimum Peak Height (Intensity) 300 300 Area Calculation Method Trapezoidal Trapezoidal 4.3. Imaging Pipeline Characterization From the results obtained during the imaging pipeline development tests, a clear difference between the resulting areas of the two concentrations was observed. Further concentrations were also developed to generate a relationship between area and concentration. The resulting chromatograms from these tests are shown in Figure 23. The UV chromatograms are a little more variable in terms of the Rf of the peak. The suspected cause of this is probably the low resolution of image that comes from the UV images. The low-resolution leads to the automated eluent front measurement being noisy, which manipulates the location of the peak. Comparing this to the derivatized strips which are generally much higher resolution, and the eluent front can be easily determined. Yet, there is still a clear difference between the Rf of THCA compared to THC. Figure 24 compares the calculated area with the known concentrations, where Figure 24a relates to the UV tests using THCA and Figure 24b relates to the derivatized tests with THC. A relationship was generated with this dataset, represented by the dashed black line in Figure 24. MATLAB tools were once again used to generate this relationship and obtain the values of this relationship. Equation 5a describes the relationship for UV tests, while Equation 5b describes the relationship for derivatized tests. Again, a goodness-of-fit test was performed to obtain R2 values for the relationship. This resulted in the UV tests having a goodness-of-fit of 97.66% while the derivatized relationship had 93.75%. These values are sufficient to determine a 63 correlation between area and concentration for both visualization techniques. Additionally, the error bounds for these values are ±0.3 mg/mL for the UV calculations and ±0.5 mg/mL for the derivatized calculations. These values fall within the acceptable error for this application. 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 = 1.997𝑒−4 ∗ 𝑎𝑟𝑒𝑎 − 0.03564 Equation 5a 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 = 2.913𝑒−5 ∗ 𝑎𝑟𝑒𝑎 − 0.4594 Equation 5b Equation 5: Calibration equation for spot area vs concentration. a) UV, b) derivatized In Figure 24, the precision tests performed (Series B in Table 8), are represented by the red circles. Table 14 shows the calculated values of the concentration from the precision tests. Comparing the calculated values to the expected values, it can be seen that all the calculations fall within the error bounds stated above. From this, a precision of 0.5 mg/mL can be stated with this system. With better cameras and lighting, the precision of this system can drastically be improved. To add to the consideration, due to the spotting method described in 3.3.4, 0.5 mg/mL translates to a difference of 1 µg/mL of sampling difference which falls into the error of spotting. 64 Figure 23: Chromatograms of a) THCA (UV) and b) THC (derivatized) strips following Table 8 Figure 24: Relationship between area of peak and concentration. a) UV visualized spots, b) derivatized spots. 65 Table 14: Comparison of expected values to calculated values for precision tests in Table 8 B-Series Test Expected Concentration (mg/mL) Calculated Concentration (mg/mL) UV-1 1.25 1.517 UV-2 1.5 1.650 UV-3 1.75 1.941 Derivatized-1 1.25 1.136 Derivatized-2 1.5 1.397 Derivatized-3 1.75 1.889 4.3.1. LOD, LOQ and Bias Continuing with analysis of the data represented in Figure 24, the linear regression could be extrapolated to determine a LOD. As stated previously, a value of less than 0.5 mg/mL is generally considered unnecessary as it falls at the limit of what is perceivable through one’s eyes. As for what a camera might be able to discern, values such as 0.25 mg/mL might be possible, especially with derivatized samples as it seems to have more visible spots. Assuming a limit for the area value of 500 for the UV relationship, a value of 0.060mg/mL is calculated. Similarly, an area limit of 3500 yields a concentration of 0.056 mg/mL. The area limits are chosen by finding the area of a peak that would match the minimum parameters set in Imaging Pipeline- peak analysis. 4.4. Cannabinoid Separation and Detection 4.4.1. Visualization of Cannabinoids As a first step, each of the cannabinoids are visualized using both UV and derivatization. The images are converted into chromatogram using the pipelines optimized in section 4.2.3 and the 66 peaks analyzed following the results of characterization in section 4.3. The results from the analysis are given in Table 15. THC and CBD are both not visible using UV but are visible after derivatization. Conversely, THCA and CBDA are not visible directly after derivatization, but are visible under UV illumination. It was noticed however that when the samples were left for long periods of time (30 - 40 minutes for derivatized strips, 1 - 2 days for UV), spots would appear in the expected regions for the compound spotted. This is counter to the work by Acosta et al. (2021), where the researchers indicated the immediate detectable presence of THCA and CBDA are visible after derivatization [85]. The probable reason for this difference is the concentration of FBBB used by their research. While in this thesis a 0.5% solution of FBBB is used, Acosta et al. (2021) use a 2% solution. This visualization method separation for the four cannabinoids is very helpful for detection and identification of the target cannabinoids. Essentially, once a strip is developed, the UV and the derivatized images are gathered. Each image provides information on their visible compounds, and the data from both visualization techniques are combined to understand total data. This method is especially helpful as CBD and CBDA have very similar Rfs, which could lead to co-spotting, a phenomenon where two compounds have similar Rfs leading to a larger circle than two individual ones. This would have made the separation and identification of the molecules more complicated. This method of using multiple images (illumination techniques) and combining the data is called multispectral analysis. 67 Figure 25: Visualization of the cannabinoids through UV (a) and derivatization (b). a) THC, b) THCA, c) CBD, d) CBDA. Figure 26: Resulting chromatograms based on images from figure 18 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Rf-100001000200030004000IntensityTHCCBD0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Rf-1000100200300400IntensityTHCACBDA 68 Table 15: Analysis of cannabinoid development. The cells with “X” represent regions where no spot was detectable. Sample Rf Estimated Concentration from UV (mg/mL) Estimated Concentration from Derivatization (mg/mL) THC 0.65 X 1.119 THCA 0.50 1.124 X CBD 0.79 X 1.305 CBDA 0.71 1.183 X As seen from Table 15, the four cannabinoid compounds are individually quantifiable. Comparing the results from peak analysis to the known concentrations, calculated concentrations fall within the error bounds described previously. This value meets the error tolerance required by industries to validate their extraction procedures and would be appropriate for a grower to identify if crops are ready for harvest. The goal of this device is to provide an alternative to regular analytical tools in a cheaper and more user-friendly fashion while maintaining a reasonable level of accuracy. This method is not meant to replace the high level of accuracy and precision that an HPLC provides. Using the methodology described throughout this thesis, along with the device built, one could separate and quantify the four target compounds in a complex mixture. It also stands to reason that if any other molecules are present, as long as they do not have similar Rfs or interfere with the detection process, the system can work similarly and out values relevant to the other molecule. 69 5. Conclusion 5.1. Summary A low-cost portable TLC-based chemical analyzer is developed for the selective detection and quantification of the target cannabinoid compounds THC, THCA, CBD and CBDA. The working principle of the device is based on a low-cost TLC technique for separating various cannabinoids in a mixture. Once separated, two visualization techniques are used to selectively detect and measure the compounds. An image analysis software is used to generate chromatograms from images of the TLC strip and peak analysis is performed to extract information. Additionally, TLC tests are sensitive to the concentrations of sample spotted on the plate. To ensure that samples comply with an optimal concentration range, a method for detecting the sample concentration is included. For performing the TLC test, a development chamber is devised. An imaging chamber is developed where the strip can be visualized under UV or visible light and a camera to capture images. Finally, a vaporization chamber is developed to determine the total sample concentration. The bulk of this thesis focuses on developing the image analysis technique used to detect and quantify the cannabinoids. THCA and CBDA can be detected by developing a fluorescent dyed RP-18 TLC strip with the proprietary eluent and visualizing under UV. THC and CBD however are not readily visible under UV and require a derivatization step to be visible. Staining reagent FBBB and NaOH are used for this application where THC and CBD are readily visible after application, but THCA and CBDA have a slower reaction with the reagents. HDR images are gathered from the strips to maximize the amount of information that can be extracted. Densitometric analysis is performed on the HDR images to obtain qualitative and quantitative data on the samples. A relationship is shown between the area of a spot and the concentration, which is used to determine the concentration of a component. 70 The entire device cost totals to approximately $500 CAD, though with improved design and sourcing of material, the manufacturing costs can fall to anywhere from $50 – 100 CAD. Currently, the majority of the cost of the device comes from the material used to build the development chamber and strip holder. Each test, including reagents and eluents and strip, costs less than $5 CAD. 5.2. Contributions • Developed low-cost quantitative TLC based chemical analyzer • Simplified testing procedure and automated data collection and analysis • Developed protocol for separating and identifying THC, THCA, CBD, CBDA 5.3. Future Works There are numerous pathways to improve the performance of this device that will be explored in the future. The first and foremost method is improving the camera system. The higher end systems use a scanner like system that captures multiple high-resolution images along the length of the strip and stitches them together. This might allow for a much high quality of image, especially since lighting in this format will be more uniform. Another method for improvement includes an automated method for spraying the staining reagents, as that will result in more standardized results. There are multiple image analysis updates that could be performed, such as improving the HDR algorithms used, performing more optimized baseline correction, and finding ways to reject error. Lastly a more precise and non-destructive method for determining the total sample concentration might be to use spectroscopic analysis rather than the current vaporization-based method. Other future tasks include expanding the library of cannabinoids that can be detected and include terpenes. The other aspect that will be explored in the future is expanding the use of this device and detection technique to other industries such as wine, food, pharmaceuticals, oil & gas, and cosmetics. 71 2. References [1] Grand View Research, “Legal Marijuana Market Size | Industry Report, 2021-2028,” Market Analysis Report, 2021. https://www.grandviewresearch.com/industry-analysis/legal-marijuana-market (accessed Oct. 18, 2021). 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Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hoorfar, Mina
Development of a field-portable thin-layer chromatography based chemical analyzer for cannabinoid sample analysis
Text