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Proceedings Paper

Nondestructive detection of pork comprehensive quality based on spectroscopy and support vector machine
Author(s): Yuanyuan Liu; Yankun Peng; Leilei Zhang; Sagar Dhakal; Caiping Wang
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Paper Abstract

Pork is one of the highly consumed meat item in the world. With growing improvement of living standard, concerned stakeholders including consumers and regulatory body pay more attention to comprehensive quality of fresh pork. Different analytical-laboratory based technologies exist to determine quality attributes of pork. However, none of the technologies are able to meet industrial desire of rapid and non-destructive technological development. Current study used optical instrument as a rapid and non-destructive tool to classify 24 h-aged pork longissimus dorsi samples into three kinds of meat (PSE, Normal and DFD), on the basis of color L* and pH24. Total of 66 samples were used in the experiment. Optical system based on Vis/NIR spectral acquisition system (300-1100 nm) was self- developed in laboratory to acquire spectral signal of pork samples. Median smoothing filter (M-filter) and multiplication scatter correction (MSC) was used to remove spectral noise and signal drift. Support vector machine (SVM) prediction model was developed to classify the samples based on their comprehensive qualities. The results showed that the classification model is highly correlated with the actual quality parameters with classification accuracy more than 85%. The system developed in this study being simple and easy to use, results being promising, the system can be used in meat processing industry for real time, non-destructive and rapid detection of pork qualities in future.

Paper Details

Date Published: 28 May 2014
PDF: 5 pages
Proc. SPIE 9108, Sensing for Agriculture and Food Quality and Safety VI, 91080R (28 May 2014); doi: 10.1117/12.2050143
Show Author Affiliations
Yuanyuan Liu, China Agricultural Univ. (China)
Tarim Univ. (China)
Yankun Peng, China Agricultural Univ. (China)
Leilei Zhang, China Agricultural Univ. (China)
Sagar Dhakal, China Agricultural Univ. (China)
Caiping Wang, Xinjiang Yurun Food Group Ltd. (China)

Published in SPIE Proceedings Vol. 9108:
Sensing for Agriculture and Food Quality and Safety VI
Moon S. Kim; Kuanglin Chao, Editor(s)

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