Building and Analysis of Hyperspectral Detection Models of Bacillus Cereus in Milk
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Abstract:
In this paper, a new quantitative detection method based on hyperspectral imaging technology was proposed and studied, which was applied to test Bacillus Cereus in liquid milk. The feasibility of the prediction models for detection the content of Bacillus Cereus in milk was explored by image processing technology, spectral analysis technology and chemical metrology technology, based on the detection of the contamination degree of Bacillus cereus in milk. The image processing technology was used to select the sample area, and the energy value (Energy) texture feature was applied to reduce the dimensions of hyperspectral data to obtain the characteristic value based on the texture feature analysis. A PLS model was built to predict the content of Bacillus Cereus in milk. The correlation coefficients between the calibration set and the prediction set in the PLS prediction model were 0.92 and 0.91, and RMSEC and RESEP were 0.73 and 0.81, respectively. The results showed that the PLS prediction model could only identified the high and low concentration of Bacillus Cereus in the milk. Therefore, the two-dimensional correlation technology combined with N-PLS method was proposed, and the N-PLS prediction model was built. The correlation coefficients between the calibration set and the prediction set in the N-PLS prediction model were 0.99 and 0.99 respectively, and RMSEC and RESEP were 0.02 and 0.09, respectively. The results showed that the N-PLS model had higher accuracy and was able to achieve quantitative analysis of Bacillus Cereus in milk.
