Matan Birenboim, Nimrod Brikenstein, David Kenigsbuch, Jakob A. Shimshoni
{"title":"Comparative chemometric modeling of fresh and dry cannabis inflorescences using FT‐NIR spectroscopy: Quantification and classification insights","authors":"Matan Birenboim, Nimrod Brikenstein, David Kenigsbuch, Jakob A. Shimshoni","doi":"10.1002/pca.3449","DOIUrl":null,"url":null,"abstract":"Introduction<jats:styled-content style=\"fixed-case\"><jats:italic>Cannabis sativa</jats:italic></jats:styled-content> L. inflorescences are rich in cannabinoids and terpenes. Traditional chemical analysis methods for cannabinoids and terpenes, such as liquid and gas chromatography (using UV or MS detectors), are expensive and time‐consuming.ObjectivesThis study explores the use of Fourier transform near‐infrared (FT‐NIR) spectroscopy combined with chemometric approaches for classifying cannabis chemovars and predicting cannabinoid and terpene concentrations for the first time in freshly harvested (wet) cannabis inflorescence. The study also compares the performance of FT‐NIR spectroscopy on wet versus dry cannabis inflorescences.Materials and methodsSpectral data from 187 samples across seven cannabis chemovars were analyzed using partial least squares‐discriminant analysis (PLS‐DA) and partial least squares‐regression (PLS‐R) models.ResultsThe PLS‐DA models effectively classified chemovars and major classes using only two latent variables (LVs) with minimal overfitting risk, with sensitivity, specificity, and accuracy values approaching 1. Despite the high water content in wet cannabis inflorescence, the PLS‐R models demonstrated good to excellent predictive capabilities for nine cannabinoids and eight terpenes using FT‐NIR spectra for the first time, achieving cross‐validation and prediction <jats:italic>R</jats:italic>‐squared values greater than 0.7, ratio of performance to interquartile range (RPIQ) exceeding 2, and a RMSECV/RMSEC ratio below 1.24. However, the low‐cannabidiolic acid submodel and (−)‐Δ9‐trans‐tetrahydrocannabinol model showed poor predictive performance. Some cannabinoid and terpene prediction models in wet cannabis inflorescence exhibited lower predictive capabilities compared with previously published models for dry cannabis inflorescence.ConclusionsThese findings suggest that FT‐NIR spectroscopy can be a viable rapid on‐site analytical tool for growers during the inflorescence flowering stage.","PeriodicalId":20095,"journal":{"name":"Phytochemical Analysis","volume":"29 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Phytochemical Analysis","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1002/pca.3449","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
IntroductionCannabis sativa L. inflorescences are rich in cannabinoids and terpenes. Traditional chemical analysis methods for cannabinoids and terpenes, such as liquid and gas chromatography (using UV or MS detectors), are expensive and time‐consuming.ObjectivesThis study explores the use of Fourier transform near‐infrared (FT‐NIR) spectroscopy combined with chemometric approaches for classifying cannabis chemovars and predicting cannabinoid and terpene concentrations for the first time in freshly harvested (wet) cannabis inflorescence. The study also compares the performance of FT‐NIR spectroscopy on wet versus dry cannabis inflorescences.Materials and methodsSpectral data from 187 samples across seven cannabis chemovars were analyzed using partial least squares‐discriminant analysis (PLS‐DA) and partial least squares‐regression (PLS‐R) models.ResultsThe PLS‐DA models effectively classified chemovars and major classes using only two latent variables (LVs) with minimal overfitting risk, with sensitivity, specificity, and accuracy values approaching 1. Despite the high water content in wet cannabis inflorescence, the PLS‐R models demonstrated good to excellent predictive capabilities for nine cannabinoids and eight terpenes using FT‐NIR spectra for the first time, achieving cross‐validation and prediction R‐squared values greater than 0.7, ratio of performance to interquartile range (RPIQ) exceeding 2, and a RMSECV/RMSEC ratio below 1.24. However, the low‐cannabidiolic acid submodel and (−)‐Δ9‐trans‐tetrahydrocannabinol model showed poor predictive performance. Some cannabinoid and terpene prediction models in wet cannabis inflorescence exhibited lower predictive capabilities compared with previously published models for dry cannabis inflorescence.ConclusionsThese findings suggest that FT‐NIR spectroscopy can be a viable rapid on‐site analytical tool for growers during the inflorescence flowering stage.
期刊介绍:
Phytochemical Analysis is devoted to the publication of original articles concerning the development, improvement, validation and/or extension of application of analytical methodology in the plant sciences. The spectrum of coverage is broad, encompassing methods and techniques relevant to the detection (including bio-screening), extraction, separation, purification, identification and quantification of compounds in plant biochemistry, plant cellular and molecular biology, plant biotechnology, the food sciences, agriculture and horticulture. The Journal publishes papers describing significant novelty in the analysis of whole plants (including algae), plant cells, tissues and organs, plant-derived extracts and plant products (including those which have been partially or completely refined for use in the food, agrochemical, pharmaceutical and related industries). All forms of physical, chemical, biochemical, spectroscopic, radiometric, electrometric, chromatographic, metabolomic and chemometric investigations of plant products (monomeric species as well as polymeric molecules such as nucleic acids, proteins, lipids and carbohydrates) are included within the remit of the Journal. Papers dealing with novel methods relating to areas such as data handling/ data mining in plant sciences will also be welcomed.