Study On Rapid Detection Of Freshness Of Raw Meat And Adulteration Meat By Near Infrared Spectroscopy Analysis

As a food with rich nutrient, meat and meat products has been consumed more and more by people. But there exist some new quality problems of raw meat, such as adulteration raw meat, deterioration raw meat. They are serious threat to the safety of China's meat industry. So it is very important to develop a quick and accurate technology to rapid detection of adulteration meat, freshness of raw meat for market management and ensure meat safety.At first, non-destructive freshness assessment of 56 raw meat was carried out by means of near infrared spectroscopy analysis combined with chemometric methods (cluster analysis, partial least squares). For all meat samples, hierarchical cluster analysis was conducted to classify meat samples according to the days of storage, and PLS (Partial Least Square) were carried out in order to set up models to predict the freshness parameters(TVB-N).Then, 272 samples of raw meat and adulteration meat were collected according to the adulteration concentration, these adulteration meat have five categories which were mixed with water, salt, carrageenan, starch, soy protein, separately, we use these raw meat and adulteration meat as study object to carried out the study of rapid detecting of adulteration meat through near-infrared (NIR) spectroscopy combined with chemometrics methods, the aim of this research was to establish NIR discriminant model between raw meat and five kinds of adulteration meat, NIR classification discriminant model of five kinds of adulteration meat and quantitative models of the content of adulteration material in adulteration meat.Specific results were as follows:The freshness of raw meat model by near infrared spectroscopy was developed: use of cluster analysis on the freshness of raw meat trends according to the days of storage, After repeated verification, the best parameters : the spectrum by vector normalization pretreatment, spectral range choose 4231.2～7282.1cm-1, calculated from the Euclidean distance spectrum. The results show that the 8 day spectrums of the sample set was divided into two categories, the first class is spectrum of the first four days, the second class is spectrum after four days. From the classification results, the first category belongs to the early changes of the freshness, change little in sensory quality , the second category of Fifth, sixth days change from fresh to the corruption of the transition period, the last two days accelerate corruption, the quality change significantly obvious; near infrared spectroscopy coupled with partial least squares to built the TVB-N content of the quantitative analysis model, result indicated that R2 value of quantitative calibration model are 81.48%, RMSECV are1.54%,;the R2 value of validation set are 86.28%,RMSEP are 1.41%.The adulteration of raw meat detection model by Near Infrared Spectroscopy was developed: two categories discriminant model for raw meat and adulterated meat by NIRS coupled with Fisher discriminant analysis, 78 samples was selected and the absorbance data of them were analyzed by principal component analysis, results show that the accumulative reliabilities of the first eight components was more than 99.84%. Then the first eight components were applied as the principal variables of the Fisher discriminant model and a Fisher discriminant model for discriminating raw meat and adulterated meat was build, result indicated the correct distinguishing rate of test samples is 96%; Near-infrared classification discriminant models of five kinds of adulterated meat were then build: 230 samples was selected and the absorbance data of them were analyzed by principal component analysis, results show that the accumulative reliabilities of the first six components was more than 99.73%.Then the first six components were applied as the principal variables of the SVM model ,then select the parameters of SVM: the kernel function is RBF, the penalty coefficient c= 1024,γ= 0.0078125 to built SVM classification discriminant model, the recognition rate of calibration set was 100%,and the recognition rate of validation set was 93.913%;Finally, quantitative analysis models of the content of adulteration material in adulteration meat were build by PLS, the R2 (The Coefficient of Determination) value of quantitative calibration model and the R2 value of validation set of meat adulterated with water, meat adulterated with salt, meat adulterated with carrageenan, meat adulterated with starch, meat adulterated with soy protein are above 80.65%,77% respectively, RMSECV (Root Mean Square Error of Calibration) and RMSEP (Root Mean Square Error of Prediction) are below 4% respectively.The results of this study indicated that NIRS coupled with chemometric methods was a feasible way to quantitative and qualitative detect raw meat rapidly and non-destructively.