Wen Hui, Yang Tianmei, Yang Weize, Yang Meiquan, Wang Yuanzhong, Zhang Jinyu
Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
School of Agriculture, Yunnan University, Kunming 650504, China.
Foods. 2023 Oct 10;12(20):3714. doi: 10.3390/foods12203714.
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.
由于大多数姜科香料具有相似的植物形态，在销售过程中经常发生替代和掺假现象。因此，分析不同姜科香料的代谢产物和物种分类非常重要。本研究通过非靶向气相色谱-质谱联用（GC-MS）和联合光谱法初步探索了13种姜科香料代谢产物的差异，建立了不同姜科香料的分类模型。一方面，通过GC-MS成功鉴定出81种代谢产物。使用投影变量重要性（VIP≥1）筛选出37种差异代谢产物。然而，使用GC-MS数据建立的正交偏最小二乘判别分析（OPLS-DA）模型仅解释了约30%的变异。另一方面，比较了具有三种光谱数据融合策略的偏最小二乘判别分析（PLS-DA）模型，其分类准确率达到100%。其中，基于潜变量的中级数据融合模型性能最佳。本研究为区分不同姜科香料提供了有力工具，并有助于减少替代和掺假现象的发生。
