Linear discriminant analysis based on gas chromatographic measurements for geographical prediction of USA medical domestic cannabis

Abstract

<jats:title>Abstract</jats:title><jats:p>Fifty four domestically produced cannabis samples obtained from different USA states were quantitatively assayed by GC–FID to detect 22 active components: 15 terpenoids and 7 cannabinoids. The profiles of the selected compounds were used as inputs for samples grouping to their geographical origins and for building a geographical prediction model using Linear Discriminant Analysis. The proposed sample extraction and chromatographic separation was satisfactory to select 22 active ingredients with a wide analytical range between 5.0 and 1,000 µg/mL. Analysis of GC-profiles by Principle Component Analysis retained three significant variables for grouping job (Δ<jats:sup>9</jats:sup>-THC, CBN, and CBC) and the modest discrimination of samples based on their geographical origin was reported. PCA was able to separate many samples of Oregon and Vermont while a mixed classification was observed for the rest of samples. By using LDA as a supervised classification method, excellent separation of cannabis samples was attained leading to a classification of new samples not being included in the model. Using two principal components and LDA with GC–FID profiles correctly predict the geographical of 100% Washington cannabis, 86% of both Oregon and Vermont samples, and finally, 71% of Ohio samples.</jats:p>

Application of gas chromatography in the analysis of phytocannabinoids: An update (2020–2023)

“…A GC-MS approach has recently been adopted for studying the effect of nitrogen deficiency on phytocannabinoids biosynthesis in medical and industrial Cannabis by facilitating a metabolic shift towards the production of low-N metabolites. 102 Various GC-FID and GC-MS methods have been employed for the analyses of different samples of marijuana or hashish (cannabis) [92][93][94][95][96][97] and marijuana blunts. 98 Chemical characterisation of marijuana blunt smoke by nontargeted chemical analysis was achieved by GC-MS analyses, where a TOF-MS was used.…”

“…GC‐based analyses of cannabinoids in various consumer products, 61–103 published during 2020–2023, are summarised in Table 1. Phytocannabinoids, particularly CBD ( 3 ), have been claimed to offer some benefits to the human skin, and this beneficial property has recently generated a trend in the use of phytocannabinoids in various cosmetics and cosmeceutical products 70 .…”

“…Various GC‐FID and GC‐MS methods have been employed for the analyses of different samples of marijuana or hashish (cannabis) 92–97 and marijuana blunts 98 . Chemical characterisation of marijuana blunt smoke by nontargeted chemical analysis was achieved by GC‐MS analyses, where a TOF‐MS was used 98 .…”

Cocoa bean fingerprinting via correlation networks

“…Often however, the patterns of variances among samples arise from an overlay of contributions from multiple sources, by no means limited to the parcellation of samples into the subcategories of relevance to food industry. These limitations of PCA are, in part, responsible for its lack of success in food fingerprinting (e.g., the classification of cocoa samples into origin countries based on their LC-MS profile 6 , 8 , 9 , 40 – 42 ).…”

Cell Coding Arrays Based on Fluorescent Glycan Nanoparticles for Cell Line Identification and Cell Contamination Evaluation

“…These signals were collected via flow cytometry and then transformed into “fingerprints” using linear discriminant analysis (LDA) to cluster and classify various cell samples . LDA is developed as a multivariate classification method by generating a newly formed space by canonical discriminant factors. , With our FGN-based cell coding arrays, we first achieved the identification of six human cell lines, including one normal type and five cancer types. Moreover, cell contamination evaluation of different cell lines with HeLa cells as the contaminant in a semiquantitative analysis was successfully demonstrated.…”