结合GC-MS和NIRS技术的化学计量学快速鉴定和质量评价天麻变种

IF 4.6 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI:10.1016/j.jfca.2026.108963

Guangyao Li , Jieqing Li , Honggao Liu , Yuanzhong Wang

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引用次数: 0

摘要

昭通天麻是一种独特的中国食品和医药产品，但对不同天麻变种中糖代谢成分的系统比较还不够。本研究采用近红外（NIR）光谱技术结合多变量分析方法，分析和预测了不同叶黄变异体碳水化合物代谢产物的积累。结果表明，青花草（G. elata Bl. f. glauca， WTM）的主要碳水化合物代谢物具有较高的积累趋势，d -果糖是关键的差异代谢物。建立的偏最小二乘判别分析（PLS-DA）和偏最小二乘回归（PLSR）模型能有效区分大叶果亚种，准确预测d -果糖含量。这提供了一种快速，非破坏性的分析策略，以评估elata质量。

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

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.

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来源期刊

Journal of Food Composition and Analysis 工程技术-食品科技

CiteScore

6.20

自引率

11.60%

发文量

601

审稿时长

53 days

期刊介绍： The Journal of Food Composition and Analysis publishes manuscripts on scientific aspects of data on the chemical composition of human foods, with particular emphasis on actual data on composition of foods; analytical methods; studies on the manipulation, storage, distribution and use of food composition data; and studies on the statistics, use and distribution of such data and data systems. The Journal''s basis is nutrient composition, with increasing emphasis on bioactive non-nutrient and anti-nutrient components. Papers must provide sufficient description of the food samples, analytical methods, quality control procedures and statistical treatments of the data to permit the end users of the food composition data to evaluate the appropriateness of such data in their projects. The Journal does not publish papers on: microbiological compounds; sensory quality; aromatics/volatiles in food and wine; essential oils; organoleptic characteristics of food; physical properties; or clinical papers and pharmacology-related papers.