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SVM classification method of meat freshness based on PSO |
1.College of Information Engineering, Shanghai Maritime University, Shanghai 200135, China; 2.School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China |
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Abstract TVB-N content, total bacterial count, pH value and sensory scores of four meat samples of pork, beef, mutton and shrimp were determined. According to support vector machine (SVM) method, the experimental data were trained to optimize the model parameters by particle swarm optimization (PSO). Based on the proposed method, the rapid and correct classification of meat freshness was realized. The experimental results show that it is difficult to obtain ideal classification accuracy by any single physicochemical or sensory property. The SVM model with RBF kernel function and default parameters can improve classification accuracy to some extent. The SVM model optimized by PSO can improve classification accuracy of meat freshness to 100% with high stability.
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