Study on Modeling Method of Total Viable Count of Fresh Pork Meat Based on Hyperspectral Imaging System

被引:29
作者
Wang Wei [1 ]
Peng Yan-kun [1 ]
Zhang Xiao-li [2 ]
机构
[1] China Agr Univ, Coll Engn, Beijing 100083, Peoples R China
[2] Georgia State Univ, Dept Biol, Atlanta, GA USA
关键词
Fresh pork meat; Total viable count of bacteria; Hyperspectral imaging system; Least square support vector machines; TRANSFORM INFRARED-SPECTROSCOPY; QUANTITATIVE DETECTION; MICROBIAL SPOILAGE; PREDICTION;
D O I
10.3964/j.issn.1000-0593(2010)02-0411-05
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
Once the total viable count (TVC) of bacteria in fresh pork meat exceeds a certain number, it will become pathogenic bacteria. The present paper is to explore the feasibility of hyperspectral imaging technology combined with relevant modeling method for the prediction of TVC in fresh pork meat. For the certain kind of problem that has remarkable nonlinear characteristic and contains few samples, as well as the problem that has large amount of data used to express the information of spectrum and space dimension, it is crucial to choose a logical modeling method in order to achieve good prediction result. Based on the comparative result of partial least-squares regression (PLSR), artificial neural networks (ANNs) and least square support vector machines (LS-SVM), the authors found that the PLSR method was helpless for nonlinear regression problem, and the ANNs method couldn't get approving prediction result for few samples problem, however the prediction models based on LS-SVM can give attention to the little training error and the favorable generalization ability as soon as possible, and can make them well synchronously. Therefore LS-SVM was adopted as the modeling method to predict the TVC of pork meat. Then the TVC prediction model was constructed using all the 512 wavelength data acquired by the hyperspectral imaging system. The determination coefficient between the TVC obtained with the standard plate count for bacterial colonies method and the LS-SVM prediction result was 0. 987 2 and 0. 942 6 for the samples of calibration set and prediction set respectively, also the root mean square error of calibration (RMSEC) and the root mean square error of prediction (RMSEP) was 0. 207 1 and 0. 217 6 individually, and the result was considerably better than that of MLR, PLSR and ANNs method. This research demonstrates that using the hyperspectral imaging system coupled with the LS-SVM modeling method is a valid means for quick and nondestructive determination of TVC of pork meat.
引用
收藏
页码:411 / 415
页数:5
相关论文
共 22 条
[1]  
[Anonymous], NATURE STAT LEARNING
[2]  
Champiat D, 2001, LUMINESCENCE, V16, P193
[3]   Bacterial identification by near-infrared chemical imaging of food-specific cards [J].
Dubois, J ;
Lewis, EN ;
Fry, FS ;
Calvey, EM .
FOOD MICROBIOLOGY, 2005, 22 (06) :577-583
[4]   Detection and quantification of poultry probiotic bacteria in mixed culture using monoclonal antibodies in an enzyme-linked immunosorbent assay [J].
Durant, JA ;
Young, CR ;
Nisbet, DJ ;
Stanker, LH ;
Ricke, SC .
INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY, 1997, 38 (2-3) :181-189
[5]   Rapid and quantitative detection of the microbial spoilage of beef by Fourier transform infrared spectroscopy and machine learning [J].
Ellis, DI ;
Broadhurst, D ;
Goodacre, R .
ANALYTICA CHIMICA ACTA, 2004, 514 (02) :193-201
[6]   Rapid and quantitative detection of the microbial spoilage of meat by Fourier transform infrared spectroscopy and machine learning [J].
Ellis, DI ;
Broadhurst, D ;
Kell, DB ;
Rowland, JJ ;
Goodacre, R .
APPLIED AND ENVIRONMENTAL MICROBIOLOGY, 2002, 68 (06) :2822-2828
[7]  
HON H, 2007, T ASAE, V50, P963
[8]  
HOON HS, 2006, IMMUNOL LETT, V106, P191
[9]  
Kim MS, 2001, T ASAE, V44, P721
[10]   The evidence framework applied to support vector machines [J].
Kwok, JTY .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2000, 11 (05) :1162-1173