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Foods (Basel, Switzerland)
Oct 10, 2023;12 (20):

Comparison of Metabolites and Species Classification of Thirteen Zingiberaceae Spices Based on GC-MS and Multi-Spectral Fusion Technology.

Hui Wen, Tianmei Yang, Weize Yang, Meiquan Yang, Yuanzhong Wang, Jinyu Zhang
Author Information
  1. Hui Wen: Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
  2. Tianmei Yang: Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
  3. Weize Yang: Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
  4. Meiquan Yang: Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
  5. Yuanzhong Wang: Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China. ORCID
  6. Jinyu Zhang: Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
PMID: 37893607 DOI: 10.3390/foods12203714

Abstract

Due to a similar plant morphology in the majority of Zingiberaceae spices, substitution and adulteration frequently take place during the sales process. Therefore, it is important to analyze the metabolites and species classification of different Zingiberaceae spices. This study preliminarily explored the differences in the metabolites in thirteen Zingiberaceae spices through untargeted gas chromatography-mass spectrometry (GC-MS) and combined spectroscopy, establishing models for classifying different Zingiberaceae spices. On one hand, a total of 81 metabolites were successfully identified by GC-MS. Thirty-seven differential metabolites were screened using variable important in projection (VIP ≥ 1). However, the orthogonal partial least squares discriminant analysis (OPLS-DA) model established using GC-MS data only explained about 30% of the variation. On the other hand, the partial least squares discriminant analysis (PLS-DA) models with three spectral data fusion strategies were compared, and their classification accuracy reached 100%. Among them, the mid-level data fusion model based on latent variables had the best performance. This study provides a powerful tool for distinguishing different Zingiberaceae spices and assists in reducing the occurrence of substitution and adulteration phenomena.

Keywords

GC–MS Zingiberaceae spices metabolites multi-spectral fusion

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Grants

  1. 202202AE090035/Yunnan Province's major science and technology special plan project
  2. 202104BI090011/Yunnan Provincial Innovation Guidance and Scientific and Technological Enterprise Cultivation Plan
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