Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM)
文献类型： 外文期刊
作者： Li, Yun 1 ; Zhang, Ji 1 ; Li, Tao 5 ; Liu, Honggao 4 ; Li, Jieqing 4 ; Wang, Yuanzhong 1 ;
作者机构： 1.Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Peoples R China
2.Yunnan Tech Ctr Qual Chinese Mat Med, Kunming 650200, Peoples R China
3.Yunnan Univ Tradit Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Peoples R China
4.Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Peoples R China
5.Yuxi Normal Univ, Coll Resources & Environm, Yuxi 653100, Peoples R China
关键词： Boletus edulis;Geographical traceability;Fourier transform mid infrared (FT-MIR) spectroscopy;Inductively coupled plasma-atomic emission spectrometry (ICP-AES);Data fusion;Support vector machine (SVM);Quality control
期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.098； 五年影响因子：3.464 ）
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收录情况： SCI
摘要： In this work, the data fusion strategy of Fourier transform mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES) was used in combination with Support Vector Machine (SVM) to determine the geographic origin of Boletus edulis collected from nine regions of Yunnan Province in China. Firstly, competitive adaptive reweighted sampling (CARS) was used for selecting an optimal combination of key wavenumbers of second derivative FT-MIR spectra, and thirteen elements were sorted with variable importance in projection (VIP) scores. Secondly, thirteen subsets of multi-elements with the best VIP score were generated and each subset was used to fuse with FT-MIR Finally, the classification models were established by SVM, and the combination of parameter C and gamma (gamma) of SVM models was calculated by the approaches of grid search (GS) and genetic algorithm (GA). The results showed that both GS-SVM and GA-SVM models achieved good performances based on the #9 subset and the prediction accuracy in calibration and validation sets of the two models were 81.40% and 90.91%, correspondingly. In conclusion, it indicated that the data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms. (C) 2017 Published by Elsevier B.V.
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