Quantifying Total Viable Count in Pork Meat Using Combined Hyperspectral Imaging and Artificial Olfaction Techniques

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• 作者：Huanhuan Li ; Felix Kutsanedzie ; Jiewen Zhao ; Quansheng Chen
• 关键词：Pork meat ; Total viable count (TVC) ; Data fusion ; Multivariate analysis ; SVMR
• 刊名：Food Analytical Methods
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：9
• 期：11
• 页码：3015-3024
• 全文大小：1,575 KB
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Chemistry
  Food Science
  Chemistry
  Microbiology
  Analytical Chemistry
  
• 出版者：Springer New York
• ISSN：1936-976X
• 卷排序：9

文摘

Total viable count (TVC) of bacteria is one of the most important indexes in evaluation of quality and safety of meat. This study attempts to quantify the TVC content in pork by combining two nondestructive sensing tools of hyperspectral imaging (HSI) and artificial olfaction system based on the colorimetric sensor array. First, data were acquired using HSI system and colorimetric sensors array, respectively. Then, the individual characteristic variables were extracted from each sensor. Next, principal component analysis (PCA) was used to achieve data fusion based on these characteristic variables from two different sensor data for further multivariate analysis. In developing the models, linear (PLS and stepwise MLR) and nonlinear (BPANN and SVMR) pattern recognition methods were comparatively employed, and they were optimized by cross-validation. Compared with other models, the SVMR model achieved the best result, and the optimum results were achieved with the root mean square error of prediction (RMSEP) = 2.9913 and the determination coefficient (R_p) = 0.9055 in the prediction set. The overall results showed that it has the potential in nondestructive detection of TVC content in pork meat by integrating two nondestructive sensing tools of HSI and colorimetric sensors with SVMR pattern recognition tool.