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Building deep learning and traditional chemometric models based on Fourier transform mid- infrared spectroscopy: Identification of wild and cultivated Gastrodia elata

文献类型： 外文期刊

作者： Liu, Shuai 1 ; Liu, Honggao 3 ; Li, Jieqing 1 ; Wang, Yuanzhong 2 ;

作者机构： 1.Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Peoples R China

2.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Peoples R China

3.Zhaotong Univ, Yunnan Key Lab Gastrodia & Fungi Symbiot Biol, Zhaotong, Peoples R China

关键词： authentication; chemometrics; deep learning; Fourier transform mid-infrared (FT-MIR) spectroscopy; Gastrodia elata; three-dimensional correlated spectral (3DCOS)

期刊名称：FOOD SCIENCE & NUTRITION （ 影响因子：3.9； 五年影响因子：4.1 ）

ISSN： 2048-7177

年卷期： 2023 年

页码：

收录情况： SCI

摘要： To identify wild and cultivated Gastrodia elata quickly and accurately, this study is the first to apply three-dimensional correlation spectroscopy (3DCOS) images combined with deep learning models to the identification of G. elata. The spectral data used for model building do not require any preprocessing, and the spectral data are converted into three-dimensional spectral images for model building. For large sample studies, the time cost is minimized. In addition, a partial least squares discriminant analysis (PLS-DA) model and a support vector machine (SVM) model are built for comparison with the deep learning model. The overall effect of the deep learning model is significantly better than that of the traditional chemometric models. The results show that the model achieves 100% accuracy in the training set, test set, and external validation set of the model built after 46 iterations without preprocessing the original spectral data. The sensitivity, specificity, and the effectiveness of the model are all 1. The results concluded that the deep learning model is more effective than the traditional chemometric model and has greater potential for application in the identification of wild and cultivated G. elata.

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