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

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":"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","authors":"Matan Birenboim, Nimrod Brikenstein, David Kenigsbuch, Jakob A. Shimshoni","doi":"10.1002/pca.3449","DOIUrl":null,"url":null,"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.","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":"{\"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\":\"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.\",\"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}","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

摘要

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。

查看原文本刊更多论文

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

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Phytochemical Analysis 生物-分析化学

CiteScore

6.00

自引率

6.10%

发文量

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.