Comparative chemometric modeling of fresh and dry cannabis inflorescences using FT‐NIR spectroscopy: Quantification and classification insights

偏最小二乘回归 化学 萜烯 花序 大麻 色谱法 线性判别分析 大麻素 植物 数学 统计 立体化学 心理学 生物化学 生物 受体 精神科
作者
Matan Birenboim,Nimrod Brikenstein,David Kenigsbuch,Jakob A. Shimshoni
出处
期刊:Phytochemical Analysis [Wiley]
标识
DOI:10.1002/pca.3449
摘要

Abstract Introduction Cannabis 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. Objectives This 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 methods Spectral 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. Results The 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. Conclusions These findings suggest that FT‐NIR spectroscopy can be a viable rapid on‐site analytical tool for growers during the inflorescence flowering stage.
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