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

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Phytochemical Analysis Pub Date : 2024-09-10 DOI:10.1002/pca.3449
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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用傅立叶变换近红外光谱对新鲜和干燥大麻花序进行化学计量比较建模:定量和分类见解
导言大麻花序中含有丰富的大麻素和萜类化合物。本研究首次探索使用傅立叶变换近红外光谱(FT-NIR)结合化学计量学方法对大麻化学品种进行分类,并预测新收获(湿)大麻花序中的大麻素和萜烯浓度。材料和方法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品种 187 个样本的光谱数据。结果PLS-DA 模型仅使用两个潜变量(LV)就能有效地对化学品种和主要类别进行分类,过拟合风险极低,灵敏度、特异性和准确度值均接近 1。尽管湿大麻花序的含水量很高,但 PLS-R 模型首次使用傅立叶变换近红外光谱对九种大麻素和八种萜烯类化合物表现出良好至卓越的预测能力,交叉验证和预测 R 平方值均大于 0.7,性能与四分位数间范围之比(RPIQ)超过 2,RMSECV/RMSEC 比值低于 1.24。不过,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。与以前公布的干大麻花序预测模型相比,湿大麻花序中的一些大麻素和萜烯预测模型显示出较低的预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Phytochemical Analysis
Phytochemical Analysis 生物-分析化学
CiteScore
6.00
自引率
6.10%
发文量
88
审稿时长
1.7 months
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.
期刊最新文献
Isomer Differentiation by UHPLC-Q-Exactive-Orbitrap MS led to Enhanced Identification of Daphnane Diterpenoids in Daphne tangutica. Quality Markers of Fufang Shenqi Oral Liquid Based on Integrated Fingerprint Analysis, Chemical Pattern Recognition, and Quantification. The Multifaceted Applications of Seaweed and Its Derived Compounds in Biomedicine and Nutraceuticals: A Promising Resource for Future. Comparison of Pressurized Water Extraction With Ultrasound Assisted Extraction for Isolation of Phycobiliproteins From Arthrospira platensis (Spirulina). HPLC Fingerprint Combined With Chemometrics and Multicomponent Content Determination for Quality Evaluation and Control of Huangma Tincture.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1