Abstract
Food safety is one of the important problems in the development of society. Conventional polymerase chain reaction and other detection methods were costly and complicated. The paper presented a method for the infrared spectroscopy identification for food (chicken, beef, mutton as examples). In the paper, we used the Markov distance singular point Identification method to remove the singular samples of meat, and improved the accuracy and robustness of the model. Six kinds of pretreatment methods were used to exclude spectral noise, distortion and eliminate sample production. In the process of modeling, the paper used partial least squares discriminant analysis and BP neural network. The optimal modeling method was determined by comparing decision coefficient and the root mean square error of the correction set and the detection set. The results showed that the Mahalanobis distance identification method can effectively eliminate the singular points, improve the model correlation coefficient and reduce the error. The effect of normalized pretreatment was the best. When the number of principal components was 7, both PLS-DA and BP neural network can effectively identify three kinds, and the prediction accuracy of the detection set was 100%. The correction set and detection set decision coefficient of the PLS-DA modeling method was up to 0.99. RMSEC, RMSECV and RMSEP were 0.06, 0.08 and 0.08. The model performance was superior to BP neural network modeling method. The infrared spectrum detection technique, including PLS-DA modeling method and Markov distance singular point discrimination method, solved effectively the adulteration problem of common livestock meat.
