Traceability of Boletaceae mushrooms using data fusion of UV-visible and FTIR combined with chemometrics methods
文献类型： 外文期刊
作者： Yao, Sen 1 ; Li, Tao 3 ; Liu, HongGao 1 ; Li, JieQing 1 ; Wang, YuanZhong 1 ;
作者机构： 1.Yunnan Agr Univ, Coll Agron & Biotechnol, 452 Fengyuan Rd, Kunming 650201, Yunnan, Peoples R China
2.Yunnan Acad Agr Sci, Inst Med Plants, 2238 Beijing Rd, Kunming 650200, Yunnan, Peoples R China
3.Yuxi Normal Univ, Coll Resources & Environm, Yuxi, Peoples R China
关键词： data fusion; traceability; Boletaceae mushrooms; ultraviolet-visible (UV-visible) spectroscopy; Fourier transform infrared (FTIR) spectroscopy
期刊名称：JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE （ 影响因子：3.638； 五年影响因子：3.802 ）
ISSN： 0022-5142
年卷期： 2018 年 98 卷 6 期
页码：
收录情况： SCI
摘要： BACKGROUNDBoletaceae mushrooms are wild-grown edible mushrooms that have high nutrition, delicious flavor and large economic value distributing in Yunnan Province, China. Traceability is important for the authentication and quality assessment of Boletaceae mushrooms. In this study, UV-visible and Fourier transform infrared (FTIR) spectroscopies were applied for traceability of 247 Boletaceae mushroom samples in combination with chemometrics. RESULTSCompared with a single spectroscopy technique, data fusion strategy can obviously improve the classification performance in partial least square discriminant analysis (PLS-DA) and grid-search support vector machine (GS-SVM) models, for both species and geographical origin traceability. In addition, PLS-DA and GS-SVM models can provide 100.00% accuracy for species traceability and have reliable evaluation parameters. For geographical origin traceability, the accuracy of prediction in the PLS-DA model by data fusion was just 64.63%, but the GS-SVM model based on data fusion was 100.00%. CONCLUSIONThe results demonstrated that the data fusion strategy of UV-visible and FTIR combined with GS-SVM could provide a higher synergic effect for traceability of Boletaceae mushrooms and have a good generalization ability for the comprehensive quality control and evaluation of similar foods. (c) 2017 Society of Chemical Industry
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