Birenboim Matan, Brikenstein Nimrod, Kenigsbuch David, Shimshoni Jakob A
Department of Food Science, Institute for Postharvest and Food Sciences, Agricultural Research Organization, Rishon LeZion, Israel.
Department of Plant Science, The Robert H Smith Faculty of Agriculture, Food and Environment, The Hebrew University, Rehovot, Israel.
Phytochem Anal. 2025 Apr;36(3):537-555. doi: 10.1002/pca.3449. Epub 2024 Sep 10.
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.
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.
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.
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.
These findings suggest that FT-NIR spectroscopy can be a viable rapid on-site analytical tool for growers during the inflorescence flowering stage.
大麻(Cannabis sativa L.)的花序富含大麻素和萜类化合物。用于分析大麻素和萜类化合物的传统化学分析方法,如液相色谱和气相色谱(使用紫外或质谱检测器),成本高且耗时。
本研究首次探索了将傅里叶变换近红外(FT-NIR)光谱与化学计量学方法相结合,用于对大麻化学变种进行分类,并预测新鲜收获(湿)大麻花序中大麻素和萜类化合物的浓度。该研究还比较了FT-NIR光谱在湿大麻花序和干大麻花序上的性能。
使用偏最小二乘判别分析(PLS-DA)和偏最小二乘回归(PLS-R)模型分析了来自七个大麻化学变种的187个样品的光谱数据。
PLS-DA模型仅使用两个潜在变量(LVs)就能有效地对化学变种和主要类别进行分类,且过拟合风险最小,灵敏度、特异性和准确度值接近1。尽管湿大麻花序含水量高,但PLS-R模型首次使用FT-NIR光谱对九种大麻素和八种萜类化合物显示出良好到优异的预测能力,交叉验证和预测的决定系数(R平方)值大于0.7,性能与四分位间距之比(RPIQ)超过2,且均方根交叉验证误差与均方根误差之比(RMSECV/RMSEC)低于1.24。然而,低大麻二酚酸子模型和(-)-Δ9-反式-四氢大麻酚模型的预测性能较差。与先前发表的干大麻花序模型相比,湿大麻花序中的一些大麻素和萜类化合物预测模型的预测能力较低。
这些发现表明,FT-NIR光谱可以成为种植者在花序开花阶段可行的快速现场分析工具。