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Study on the Geographical Traceability of Boletus Tomentipes Using Multi-Spectra Data Fusion

文献类型： 外文期刊

作者： Zhang Yu 1 ; Li Jie-qing 1 ; Li Tao 3 ; Liu Hong-gao 1 ; Wang Yuan-zhong 2 ;

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

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

3.Yuxi Normal Univ, Coll Resources & Environm, Yuxi 653100, Peoples R China

关键词： The geographical traceability; Data fusion; Boletus tomentipes; UV-Vis; FTIR

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2018 年 38 卷 8 期

页码：

收录情况： SCI

摘要： Currently, since the domestic and international food marketing on the safety supervision and traceability system is defective, as well as false labels used by agency, situation on the food safety is becoming more and more serious. In order to enhance food safety, it's essential to establish a fast and efficient geographical traceability method to protect the agricultural brand of Yunnan plateau. A total of 77 fruit bodies of Boletus tomentipes were collected from 8 geographical origins. Raw of ultraviolet-visible (UV-Vis) and Fourier transform infrared (FTIR) spectra were preprocessed by multiplicative scatter correction (MSC), standard normal variate (SNV), second derivative (2D), Savitzky-Golay (SG) smoothing. Based on pretreatment of UV and FUR spectra, low-level and mid-level data fusion strategy combined with partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to identify Boletus in different regions. The results indicated that: (1) that the best pretreatment, was SNV+2D with highest (RY)-Y-2 (61. 58%) and Q(2) (95. 09%) for UV-Vis spectra, and MSC+2D with highest (RY)-Y-2 (50. 85%) and Q(2) (82. 16%) for FTIR spectra; (2) For UV-Vis, FTIR spectra, low-level and mid-level data fusion, the number of error samples in the classification of PLS-DA and SVM analysis were 24, 6, 2, 2, and 6, 1, 1, 0, respectively; (3) In the mid-level data fusion, the best classification of SVM with none error sample was better than that of the PLSDA with 2 error samples; (4) The classification of HCA analysis in the mid-level data fusion with 4 error samples had the better performance than that in the low-level data fusion with 1 error sample. In addition, HCA analysis of mid-level data fusion showed that the distance of samples collected from same area were longer than that collected from different sites. It indicated that the differences of samples collected from different sites in the same area were less than that collected from different regions. Those results indicated that mid-level data fusion combined with SVM model using UV-Vis and FTIR spectroscopy can accurately identify Boletus collected from different geographical origins. It will provide a new strategy on the research of geographical traceability of wild edible fungus.

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