| 光谱学与光谱分析 |
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| Recognition of Water-Injected Meat Based on Visible/Near-Infrared Spectrum and Sparse Representation |
| HAO Dong-mei1, ZHOU Ya-nan1, WANG Yu1 , ZHANG Song1, YANG Yi-min1, LIN Ling2, LI Gang2, WANG Xiu-li3 |
1. College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
2. State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, China
3. Institute of Health Sciences, Anhui University, Hefei 230601, China |
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Abstract The present paper proposed a new nondestructive method based on visible/near infrared spectrum (Vis/NIRS) and sparse representation to rapidly and accurately discriminate between raw meat and water-injected meat. Water-injected meat model was built by injecting water into non-destructed meat samples comprising pigskin, fat layer and muscle layer. Vis/NIRS data were collected from raw meat and six scales of water-injected meat with spectrometers. To reduce the redundant information in the spectrum and improve the difference between the samples, some preprocessing steps were performed for the spectral data, including light modulation and normalization. Effective spectral bands were extracted from the preprocessed spectral data. The meat samples were classified as raw meat and water-injected meat, and further, water-injected meat with different water injection rates. All the training samples were used to compose an atom dictionary, and test samples were represented by the sparsest linear combinations of these atoms via l1-minimization. Projection errors of test samples with respect to each category were calculated. A test sample was classified to the category with the minimum projection error, and leave-one-out cross-validation was conducted. The recognition performance from sparse representation was compared with that from support vector machine (SVM). Experimental results showed that the overall recognition accuracy of sparse representation for raw meat and water-injected meat was more than 90%, which was higher than that of SVM. For water-injected meat samples with different water injection rates, the recognition accuracy presented a positive correlation with the water injection rate difference. Spare representation-based classifier eliminates the need for the training and feature extraction steps required by conventional pattern recognition models, and is suitable for processing data of high dimensionality and small sample size. Furthermore, it has a low computational cost. In this paper, spare representation is employed for the first time to identify water-injected meat based on Vis/NIRS, with a promising recognition accuracy. The experimental results demonstrate that the proposed method can be effectively used for discriminating water-injected meat from raw meat.
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Received: 2013-12-12
Accepted: 2014-04-07
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Corresponding Authors:
HAO Dong-mei
E-mail: haodongmei@bjut.edu.cn
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