Work

Rapid identification and quality evaluation of Gastrodia elata variants using chemometrics to integrate GC-MS and NIRS technologies

Year: 2026

Type: article

Abstract: Zhaotong Gastrodia elata Blume ( G. elata ) is a distinctive Chinese product used for food and medicine, yet there have been insufficient systematic comparisons of the metabolic components of sugar in different G. elata variants. This study employed near-infrared (NIR) spectroscopy combined with multivariate analysis methods to analyze and predict the accumulation of carbohydrate metabolites in various G. elata variants. The results indicate that G. elata Bl. f. glauca (WTM) shows a higher accumulation trend for the main carbohydrate metabolites and that D-fructose is a key differential metabolite. The constructed partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) models effectively distinguish G. elata subspecies and accurately predict D-fructose content. This provides a rapid, non-destructive analytical strategy for evaluating G. elata quality. • The study systematically analyzed the sugar metabolites of different varieties of G. elata for the first time, filling a gap in current research on this topic • There are significant differences in sugar metabolite content between different varieties of G. elata • Research finds D-fructose to be a key differential metabolite among G. elata varieties • Using NIRS technology, the study provided an efficient and accurate method for predicting G. elata varieties and sugar metabolite content

Source: Journal of Food Composition and Analysis

DOI: 10.1016/j.jfca.2026.108963

Authors: Guangyao Li, Jieqing Li, Honggao Liu, Yuanzhong Wang

Institutions: Yunnan Agricultural University, Yunnan Academy of Agricultural Sciences, Zhaotong University

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $3,450