Feasibility study on discrimination of Polygonatum kingianum origins by NIR and MIR spectra data

文献类型： 外文期刊

第一作者： Wang, Yue

作者： Wang, Yue;Wang, Yue;Wang, Yuanzhong

作者机构：

关键词： data fusion; infrared spectroscopy; machine learning; origins discrimination; Polygonatum kingianum

期刊名称：JOURNAL OF FOOD SCIENCE （ 影响因子：3.4； 五年影响因子：4.1 ）

ISSN： 0022-1147

年卷期： 2024 年 89 卷 11 期

页码：

收录情况： SCI

摘要： Most existing studies have focused on identifying the origin of species with protected geographical indications while neglecting to determine the proximate geographical origin of different species. In this study, we investigated the feasibility of using near- and mid-infrared spectroscopy to identify the origin of 156 Polygonatum kingianum samples from six regions in Yunnan, China. In this work, spectral images of different modes reveal more information about the P. kingianum. Comparing the performance of traditional machine learning models according to single spectrum and data fusion, the middle-level data fusion-principal component model has the best performance, and its sensitivity, specificity, and accuracy are all 1, and the model has the least number of variables. The residual convolutional neural network (ResNet) model constructed in the 1050-850 cm(-1) band confirms that fewer variables are beneficial in improving the accuracy of the model. In conclusion, this study verifies the feasibility of the proposed strategy and establishes a practical model to determine the source of P. kingianum.

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