The Prediction of the Total Viable Count on Sausage Based with Hyperspectral Imaging Technique and Data Visualization
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Abstract:
There are a lot of evaluation standard of the quality for sausage, one of which is the total viable count(TVC). Hyperspectral imaging technique has become an effective method to detect food rapidly and nondestructively. In this paper, Hyperspectral imaging technique has carried on the quantitative analysis to the total viable count (TVC) on the sausage. The data sets of sausage were assessed using the PCA method, and then the study found that the contribution rate of the first four principal component reaches 97.65% which can reflect the most information of the sausage. The SVM regression model based on Gaussian kernel function and the optimal interval the first four principal components is used to forecast TVC. In order to improve the accuracy of the regression model, the genetic algorithm (GA), grid search algorithm and particle swarm optimization (PSO) are compared to get the c and g parameters of the model. The correlation coefficient of regression prediction value and real value is 0.9777, and the root mean square error of interactive verification of PSO is 0.0823, which can accurately and quickly predict the TVC. Besides, Use python to realize visualization of regression prediction data which can show the change of TVC more intuitively and can achieve real-time watching.
