| 光谱学与光谱分析 |
|
|
|
|
|
| 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 |
|
|
|
|
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.
|
|
Received: 2013-05-13
Accepted: 2013-09-02
|
|
|
|
Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
|
|
[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. |
| [1] |
ZHANG Yan-ru1, 2, SHAO Peng-shuai1*. Study on the Effects of Planting Years of Vegetable Greenhouse on the
Cucumber Qualties Using Mid-IR Spectroscopoy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1816-1821. |
| [2] |
XIAO Shi-jie1, WANG Qiao-hua1, 2*, LI Chun-fang3, 4, DU Chao3, ZHOU Zeng-po4, LIANG Sheng-chao4, ZHANG Shu-jun3*. Nondestructive Testing and Grading of Milk Quality Based on Fourier Transform Mid-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1243-1249. |
| [3] |
ZHOU Jun1, 2, YANG Yang2, YAO Yao2, LI Zi-wen3, WANG Jian3, HOU Chang-jun1*. Application of Mid-Infrared Spectroscopy in the Analysis of Key Indexes of Strong Flavour Chinese Spirits Base Liquor[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 764-768. |
| [4] |
CHEN Feng-xia1, YANG Tian-wei2, LI Jie-qing1, LIU Hong-gao3, FAN Mao-pan1*, WANG Yuan-zhong4*. Identification of Boletus Species Based on Discriminant Analysis of Partial Least Squares and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 549-554. |
| [5] |
XIAO Shi-jie1, WANG Qiao-hua1, 2*, FAN Yi-kai3, LIU Rui3, RUAN Jian3, WEN Wan4, LI Ji-qi4, SHAO Huai-feng4, LIU Wei-hua5, ZHANG Shu-jun3*. Rapid Determination of αs1-Casein and κ-Casein in Milk Based on Fourier Transform Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3688-3694. |
| [6] |
LIN Yan1, XIA Bo-hou1, LI Chun2, LIN Li-mei1, LI Ya-mei1*. Rapid Identification of Crude and Processed Polygonui Multiflori Radix With Mid-IR and Pattern Recognition[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3708-3711. |
| [7] |
SUN Di1, 2, LI Meng-ting1, MU Mei-rui1, ZHAO Run1*, ZHANG Ke-qiang1*. Rapid Determination of Nitrogen and Phosphorus in Dairy Farm Slurry Via Near-Mid Infrared Fusion Spectroscopy Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3092-3098. |
| [8] |
SHA Yun-fei1, HUANG Wen1, WANG Liang1, LIU Tai-ang2，YUE Bao-hua2, LI Min-jie2, YOU Jing-lin2, GE Jiong1*, XIE Wen-yan1*. Merging MIR and NIR Spectral Data for Flavor Style Determination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 473-477. |
| [9] |
PANG Jia-feng1, TANG Chen1, LI Yan-kun1, 2*, XU Chong-ran1, BIAN Xi-hui3. Identification of Melamine in Milk Powder by Mid-Infrared Spectroscopy Combined With Pattern Recognition Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(10): 3235-3240. |
| [10] |
FENG Guo-hong, ZHU Yu-jie*, LI Yao-xiang. Identification Method of Imported Timber Species by Mid-Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(07): 2128-2132. |
| [11] |
HU Yi-ran1, LI Jie-qing1, LIU Hong-gao2, FAN Mao-pan1*, WANG Yuan-zhong3*. Infrared Spectral Study on the Origin Identification of Boletus Tomentipes Based on the Random Forest Algorithm and Data Fusion Strategy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(05): 1495-1502. |
| [12] |
HU Yi-ran1, LI Jie-qing1, LIU Hong-gao2, FAN Mao-pan1*, WANG Yuan-zhong3*. The Origin Identification Study of Boletus Edulis Based on the Infrared Spctrum Data Fusion Strategy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(04): 1276-1282. |
| [13] |
LIANG Kun1, 2, ZHANG Xia-xia1, 2, DING Jing1, 2, XU Jian-hong3, HAN Dong-shen1, 2, SHEN Ming-xia1, 2*. Discrimination of Wheat Scab Infection Level by Fourier Mid-Infrared Technology Combined with Sparse Representation Based Classification Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(10): 3251-3255. |
| [14] |
SU Hui1, 2, PAN Hao-ran1, 2, YAO Zhi-xiang1, 2, 3*, HUANG Xiao-cheng1, 2, LIU Liu1, 2, LIU Chun-shui1, 2. Mid-Infrared Spectral Combined Vectorial Angle Method for Direct Quantifying the Content of Sucralose[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1742-1747. |
| [15] |
LIU Jing-hua1, 2, CHEN Jun3, QIN Song3, QI Ze-ming4, HUANG Qing1, 2, 4*. Application of Infrared Spectroscopy in Microalgal Research[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(01): 79-86. |
|
|
|
|
