| 光谱学与光谱分析 |
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| Producing Area Identification of Letinus Edodes Using Mid-Infrared Spectroscopy |
| ZHU Zhe-yan1, 2, ZHANG Chu2, LIU Fei2, KONG Wen-wen2, HE Yong2* |
1. Zhejiang Technical Institute of Economics, Hangzhou 310018, China
2. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China |
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Abstract In the present study, Mid-infrared spectroscopy was used to identify the producing area of Letinus edodes, and relevance vector machine (RVM) was put forward to build classification models as a novel classification technique, and they obtained good performances. The head and the tail of the acquired mid-infrared spectra with the absolute noise were cut off, and the remaining spectra in the range of 3 581～689 cm-1 (full spectra) of Letinus edodes were preprocessed by multiplicative scatter correction (MSC). Five classification techniques, including partial least Squares-discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbor algorithm (KNN), support vector machine (SVM) and RVM, were applied to build classification models based on the preprocessed full spectra. All classification models obtained classification accuracy over 80%, KNN, SVM and RVM models based on full spectra obtained similar and good performances with classification accuracy over 90% in both the calibration set and the prediction set. The weighted regression coefficients (Bw) were used to select effective wave numbers of mid-infrared spectra and 6 effective wave numbers in total were selected on the basis of the weighted regression coefficients of PLS-DA model based on full spectra. PLS-DA, KNN, SVM and RVM models were built using these effective wave numbers. Compared with the classification models based on full spectra, PLS-DA models based on effective wave numbers obtained relatively worse results with classification accuracy less than 80%, and KNN, SVM and RVM obtained similar results in both calibration set and prediction set with classification accuracy over 90%. RVM performed well with classification rate over 90% based on full spectra and effective wave numbers. The overall results indicated that producing area of Letinus edodes could be identified by mid-infrared spectroscopy, while wave number selection and the RVM algorithm could be effectively used in mid-infrared spectroscopy analysis. In this study, mid-infrared spectroscopy was successfully applied to identify the producing area of Letinus edodes, which could provide a new concept for quality analysis of Letinus edodes and other agricultural products, and the application of mid-infrared spectroscopy had practical significance.
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Received: 2013-05-13
Accepted: 2013-09-02
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Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
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[1] LIU Chun-ru, YI Cheng(刘春如，易 诚). Quarterly of Forest By-Product and Speciality in China(中国林副特产), 2002, (1): 52.
[2] XU Xiao-fei, ZHANG Bing-qing, LUO Zhen, et al(徐晓飞, 张丙青, 罗 珍, 等). Edible Fungi(食用菌), 2012, (2): 57.
[3] Barron C. Journal of Agricultural and Food Chemistry, 2011, 59(19): 10442.
[4] Wu D, Nie P C, He Y, et al. Food and Bioprocess Technology, 2011, 5(4): 1402.
[5] Clegg I M, Daly A M, Donnelly C, et al. Applied Spectroscopy, 2012, 66(5): 574.
[6] Yoshida S, Yoshida M, Yamamoto M, et al. Journal of Pharmaceutical and Biomedical Analysis, 2013, 76: 169.
[7] Fudge A L, Wilkinson K L, Ristic R, et al. Journal of Agricultural and Food Chemistry, 2012, 60(1): 52.
[8] Kanakis C D, Petrakis E A, Kimbaris A C, et al. Phytochemical Analysis, 2012, 23(1): 34.
[9] He Y, Li X L, Deng X F. Journal of Food Engineering, 2007, 79(4): 1238.
[10] Liu F, Ye X J, He Y, et al. Journal of Food Engineering, 2009, 93(2): 127.
[11] Liu F, He Y. Food and Bioprocess Technology, 2011, 4(3): 387.
[12] Gori A, Maggio R M, Cerretani L, et al. International Dairy Journal, 2012, 23(2): 115.
[13] Balabin R M, Safieva R Z, Lomakina E I. Analytica Chimica Acta, 2010, 671(1-2): 27.
[14] Mireei S A, Sadeghi M. Journal of Food Engineering, 2013, 114(3): 397.
[15] Langeron Y, Doussot M, Hewson D J, et al. Engineering Applications of Artificial Intelligence, 2007, 20(3): 415.
[16] Liu F, He Y. Food Research International, 2008, 41(5): 562.
[17] Tipping M E. Journal of Machine Learning Research, 2001, 1(3): 211.
[18] Caesarendra W, Widodo A, Yang B S. Mechanical Systems and Signal Processing, 2010, 24(4): 1161. |
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