红外光谱法对牛肝菌种类鉴别及镉含量预测研究

doi:10.3964/j.issn.1000-0593(2017)09-2730-07

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Abstract Fourier transform infrared (FTIR) spectroscopy was used to establish a rapid method for the identification of species of bolete mushrooms and content prediction of cadmium (Cd). The information of infrared spectra based on 98 fruiting bodies of 11 species of bolete mushrooms were collected and analyzed. The original infrared spectra were optimized by first derivative, standard normal variate (SNV) and multiplicative signal correction (MSC), and then identification of different species of bolete mushrooms was performed by partial least squares discriminant analysis (PLS-DA). The Cd contents were determined by inductively coupled plasma emission spectrometer (ICP-AES) and the accumulation regularity for Cd was analyzed in order to evaluate the food safety of boletes according to Chinese national food safety standards and limit contaminants in food (GB 2762—2012). Based on the accumulation mechanism of Cd in edible mushrooms, the infrared spectral and Cd content data of tested samples were integrated and PLS model was used to rapidly predict the Cd content in boletes. The results showed that: (1) The spectral data were analyzed by PLS-DA after appropriate pretreatments, and the cumulative contribution rate of the first three principal components was 79.3% as well as all the samples could be correctly classified according to the species in the three-dimensional score plot. (2) There were obvious differences in the accumulation of Cd contents in different samples and the Cd contents in the bolete mushrooms were ranged from 0.05 to 23.41 mg·kg^-1 dw. In addition, there were some health risks for eating the mushrooms because Cd contents in most samples were higher than the standard GB 2762—2012 except the mushrooms collected from Wuhua district in Kunming. (3) The integrated data of the infrared spectra and Cd content were optimized by orthogonal signal correction-wavelet compression (OSCW) and the prediction of Cd content in boletes was performed by PLS model. The R² of the training set and validation set were 0.851 9 and 0.882 4, respectively, while RMSEE and RMSEP were 2.59 and 2.67, respectively. The predictive values of Cd content in most boletes were approximate to the measured values which indicated that the model could be used for rapid prediction of Cd content in boletes. FTIR combined with chemometrics could be proposed to rapidly discriminate the species of bolete mushrooms and predict Cd content accurately. This study can provide a rapid and effective method for quality control and identification of wild-grown bolete mushrooms.

Received: 2015-10-11 Accepted: 2016-03-25

ZTFLH:

TS201.2

Corresponding Authors: WANG Yuan-zhong, LIU Hong-gao

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2017)09-2730-07 OR https://www.gpxygpfx.com/EN/Y2017/V37/I09/2730

[1] Zhang X, Zhong T, Liu L, et al. PloS One, 2015, 10(8): e0135182.
[2] McLaughlin M J, Parker D R, Clarke J M. Field Crops Research, 1999, 60(1): 143.
[3] Jrup L, Akesson A. Toxicology and Applied Pharmacology, 2009, 238(3): 201.
[4] Zhuang P, Zou B, Li N Y, et al. Environmental Geochemistry and Health, 2009, 31(6): 707.
[5] ZHAO Jing, SUN Hai-juan, FENG Xu-qiao(赵静, 孙海娟, 冯叙桥). Science and Technology of Food Industry(食品工业科技), 2014, 35(16): 371.
[6] Corguinha A P B, de Souza G A, Goncalves V C, et al. Journal of Food Composition and Analysis, 2015, 37: 143.
[7] Wang X M, Zhang J, Wu L H, et al. Food Chemistry, 2014, 151: 279.
[8] Kalac P. Food Chemistry, 2009, 113(1): 9.
[9] Kuldo E, Jarzyńska G, Gucia M, et al. Chemical Papers, 2014, 68(4): 484.
[10] LI Tai-hui, SONG Bin(李泰辉, 宋斌). Journal of Edible Fungi(食用菌学报), 2002, 9(2): 22.
[11] DAI Yu-cheng, ZHOU Li-wei, YANG Zhu-liang, et al(戴玉成, 周丽伟, 杨祝良, 等). Mycosystema(菌物学报), 2010, 29(1): 1.
[12] Gucia M, Jarzyńska G, Rafal E, et al. Environmental Science and Pollution Research, 2012, 19(2): 416.
[13] Kalac P, Svoboda L. Food Chemistry, 2000, 69(3): 273.
[14] Falandysz J, Zhang J, Wang Y, et al. Science of The Total Environment, 2015, 537: 470.
[15] Wiejak A, Wang Y, Zhang J, et al. Journal of Environmental Science and Health, Part B, 2014, 49(11): 811.
[16] 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.
[17] Kojta A K, Zhang J, Wang Y, et al. Journal of Environmental Science and Health, Part A, 2015, 50(13): 1342.
[18] LIU Gao-xiang, LIU Yang-ming, ZHUANG Yong-liang, et al(刘高翔, 刘洋铭, 庄永亮, 等). Science and Technology of Food Industry(食品工业科技), 2012, 33(13): 392.
[19] LIU Rui-xia, TANG Hong-xiao, LAO Wei-xiong(刘瑞霞, 汤鸿霄, 劳伟雄). Progress in Chemical(化学进展), 2002, 14(2): 87.
[20] Schlecht M T, Sumel I. Environmental Pollution, 2015, 204: 298.
[21] Sarikurkcu C, Tepe B, Kocak M S, et al. Food Chemistry, 2015, 175: 549.
[22] Liu B, Huang Q, Cai H, et al. Food Chemistry, 2015, 188: 294.
[23] Collin-Hansen C, Yttri K E, Andersen R A, et al. Geochemistry: Exploration, Environment, Analysis, 2002, 2(2): 121.
[24] Collin-Hansen C, Andersen R A, Steinnes E. Journal de Physique IV (Proceedings), 2003, 107: 311.
[25] Vimala R, Das N. Journal of Environmental Sciences, 2011, 23(2): 288.
[26] Alamprese C, Casale M, Sinelli N, et al. LWT-Food Science and Technology, 2013, 53(1): 225.
[27] SHAO Xue-guang, NING Yu, LIU Feng-xia, et al(邵学广, 宁宇, 刘凤霞, 等). Acta Chimica Sinica(化学学报), 2012, 70(20): 2109.
[28] Wu C Y, Jacobson A R, Laba M, et al. Water, Air, & Soil Pollution, 2010, 209(1-4): 377.
[29] Siebielec G, McCarty G W, Stuczynski T I, et al. Journal of Environmental Quality, 2004, 33(6): 2056.
[30] ZHU Xiang-rong, LI Gao-yang, HUAN Lü-hong, et al(朱向荣, 李高阳, 黄绿红, 等). Chinese Journal of Analytical Chemistry(分析化学), 2015, 4: 029.
[31] Wu D, He Y, Shi J, et al. Journal of Agricultural and Food Chemistry, 2009, 57(5): 1697.
[32] Li J, Zhang Y, Cai W, et al. Talanta, 2011, 84(3): 679.
[33] Chen T, Chang Q, Clevers J, et al. Environmental Pollution, 2015, 206: 217.