A novel simultaneous quantitative method for differential volatile components in herbs based on combined near-infrared and mid-infrared spectroscopy. (1st May 2023)

• Record Type: Journal Article
• Authors: Fan, Yao; Bai, Xiuyun; Chen, Hengye; Yang, Xiaolong; Yang, Jian; She, Yuanbin; Fu, Haiyan
• Is Part Of: Food chemistry. Volume 407(2023)
• Publication Date: 2023-05-01
• Subjects: Herbs -- NIR-MIR -- Differential volatile components -- Simultaneous quantitative analysis -- Chemometrics; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2022.135096
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Simultaneous quantitative method for volatile components in herbs was established. The method only relied on the selected NIR-MIR spectra sections obtained by O-PLSDA. Five terpenes and five alcohol compounds were screened as differential components. Each terpene or alcohol had a unique variable weight combination during analysis. 10 differential components were accurately quantified through PSO-VWLS-SVM model. Abstract: A novel method based on GC–MS, near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometrics was established to simultaneously analyze differential volatile components (DVCs) of herb samples. Herein, Florists Chrysanthemum was adopted as the representative sample. Through the introduction of Automatic data analysis workflow (AntDAS) and one-class partial least squares discriminant analysis (O-PLSDA) model, five kinds of terpenes and five kinds of alcohols were efficiently screened as DVCs. By using the selected NIR-MIR spectra sections combined with O-PLSDA, it could achieve the accurate identification of Florists Chrysanthemum from Chrysanthemum morifolium Ramat. What's more, since the selected spectra sections were closely related to the structural and content of DVCs, they could be further used for simultaneous quantitative analysis of DVCs combined with optimized variable-weighted least-squares support vector machine based on particle swarm optimization (PSO-VWLS-SVM). This method only adopted the same NIR-MIR sections for multiple component accurate quantification, highlighting its convenience.