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Solving the identification problems of Bolete origins based on multiple data processing: Take Boletus bainiugan as an example

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 Resources & Environm, Kunming 650201, Peoples R China

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

3.Zhaotong Univ, Yunnan Key Lab Gastrodia & Fungi Symbiot Biol, Zhaotong 657000, Yunnan, Peoples R China

关键词： Infrared spectroscopy; Boletus bainiugan; Data fusion; Chemometrics; Deep learning; Authentication

期刊名称：JOURNAL OF FOOD COMPOSITION AND ANALYSIS （ 影响因子：4.3； 五年影响因子：4.6 ）

ISSN： 0889-1575

年卷期： 2023 年 124 卷

页码：

收录情况： SCI

摘要： Wild porcini mushrooms have high nutritional value and are a kind of medicinal food. It is rich in protein and micronutrients needed by the human body, which is popular among consumers. In this paper, we take the problem of origin traceability of Boletus bainiugan as an example and use multiple data processing methods (preprocessing, feature extraction, and data fusion strategies) to establish partial least squares discriminant analysis and support vector machine models. In addition, a deep learning model (3DCOS-ResNet: threedimensional correlation spectra combined with residual convolutional neural network model) is built to compare with the above two models. It was found that 3DCOS-ResNet was the best model to solve the Boletus baniugan origin traceability problem. Compared to the chemometrics model, it does not require complex processing of the data. The ideal identification effect can be achieved by directly utilizing the raw data, and it saves a lot of time and cost.

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