光谱数据融合对绒柄牛肝菌产地溯源研究

doi:10.3964/j.issn.1000-0593(2018)08-2529-07

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Abstract 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 FTIR 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 R²Y (61.58%) and Q² (95.09%) for UV-Vis spectra, and MSC+2D with highest R²Y (50.85%) and Q² (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 PLS-DA 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.

Key words：The geographical traceability; Data fusion; Boletus tomentipes; UV-Vis; FTIR

Received: 2017-09-01 Accepted: 2018-02-15

ZTFLH:

TS227

Corresponding Authors: LIU Hong-gao, WANG Yuan-zhong E-mail: honggaoliu@126.com; boletus@126.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)08-2529-07 OR https://www.gpxygpfx.com/EN/Y2018/V38/I08/2529

[1] WEI Yi-min, GUO Bo-li, WEI Shuai, et al(魏益民, 郭波莉, 魏帅, 等). Scientia Agricultura Sinica(中国农业科学), 2012, 45(24): 5073.
[2] Nerín C, Aznar M, Carrizo D. Trends in Food Science & Technology, 2016, 48: 63.
[3] ZHANG Xiao-yan, SU Xue-su, JIAO Bi-ning, et al(张晓焱, 苏学素, 焦必宁, 等). Food Science(食品科学), 2010, 31(3): 271.
[4] Gambetta J M, Cozzolino D, Bastian S E P, et al. Food Analytical Methods, 2016, 9(10): 2842.
[5] Aung M M, Chang Y S. Food Control, 2014, 39: 172.
[6] Danezis G P, Tsagkaris A S, Camin F, et al. TrAC Trends in Analytical Chemistry, 2016, 85: 123.
[7] Ruthes A C, Smiderle F R, Iacomini M. Carbohydrate Polymers, 2015, 117: 753.
[8] Valverde M E, Hernández-Pérez T, Paredes-López O. International Journal of Microbiology, 2015, 2015: 376387.
[9] ZHANG Hai-xiang(张海翔). Social Sciences in Yunnan(云南社会科学), 2013，(4): 83.
[10] HUANG Chen-yang, CHEN Qiang, ZHAO Yong-chang, et al(黄晨阳, 陈强, 赵永昌, 等). Scientia Agricultura Sinica(中国农业科学), 2010, 43(6): 1198.
[11] Stefanovic′ V, Trifkovic′ J, Djurdjic′ S, et al. Environmental Science and Pollution Research, 2016, 23(21): 22084.
[12] Rashmi D, Shree P, Singh D K. Food Control, 2017, 79: 169.
[13] Carrera M, Gallardo J M. Journal of Agricultural and Food Chemistry, 2017, 65(5): 1070.
[14] Luo D, Dong H, Luo H, et al. Food Analytical Methods, 2016, 9(2): 437.
[15] Zhao H, Yu C, Li M. Journal of Food Composition and Analysis, 2017, 63: 15.
[16] Habte G, Hwang I M, Kim J S, et al. Food Chemistry, 2016, 212: 512.
[17] Paneque P, Morales M L, Burgos P, et al. Food Control, 2017, 75: 203.
[18] Xiong C, Zheng Y, Xing Y, et al. Food Analytical Methods, 2016, 9(3): 768.
[19] Nikolaou C, Karabagias I K, Gatzias I, et al. Food Analytical Methods, 2017: 1.
[20] Acierno V, Yener S, Alewijn M, et al. Food Research International, 2016, 84: 86.
[21] Yun Z, Sun Z, Xu H, et al. Food Science and Biotechnology, 2017, 26(2): 357.
[22] Prins T W, Scholtens I M J, Bak A W, et al. Food Chemistry, 2016, 213: 536.
[23] Qin O, Zhao J, Chen Q. Analytica Chimica Acta, 2014, 841(23): 68.
[24] Spiteri M, Dubin E, Cotton J, et al. Analytical & Bioanalytical Chemistry, 2016, 408(16): 4389.
[25] Biancolillo A, Bucci R, Magrì A L, et al. Analytica Chimica Acta, 2014, 820: 23.
[26] Lu Y, Du C, Yu C, et al. Computers & Electronics in Agriculture, 2014, 107(3): 58.
[27] Gad H A, Elahmady S H, Aboushoer M I, et al. Phytochemical Analysis, 2013, 24(6): 520.
[28] Ballabio D, Consonni V. Analytical Methods, 2013, 5(16): 3790.
[29] SUN Su-qin(孙素琴). Analysis of Traditional Chinese Medicine by Infrared Spectroscopy(中药红外光谱分析与鉴定). Beijing: Chemical Industry Press(北京：化学工业出版社), 2010.
[30] Silva S D, Feliciano R P, Boas L V, et al. Food Chemistry, 2014, 150: 489.
[31] Jaiswal P, Jha S N, Borah A, et al. Food Chemistry, 2015, 168(1): 41.
[32] Sobek S, Tranvik L J, Prairie Y T, et al. Limnology & Oceanography, 2007, 52(3): 1208.
[33] Casian T, Reznek A, Vonicagligor A L, et al. Talanta, 2017, 167: 333.
[34] Blanco M, Cueva-Mestanza R, Peguero A. Talanta, 2011, 85(4): 2218.
[35] Vapnik V N. The Nature of Statistical Learning Theory. Springer, 1995.
[36] Srbu C, Na瘙塂cu-Briciu R D, Kot-Wasik A, et al. Food Chemistry, 2012, 130(4): 994.