Rapid Detection Of Minced Pork Quality And Safety Based On Visible And Near Infrared Spectroscopy

Meat and meat products are one of the essential sources of human nutritional component, such as protein, vitamins and minerals. Pork is one of the main components of human diet, which is the largest production and consumption in China. Along with improvement of living standard and changing of meal structure, the demand for pork quality and safety should be higher than before. China is a major pork producer as well as a major pork consumer, but not powerful in international market. Testing technology and assessment method have complicated formalities, which reduce the efficiency of regulatory authority. Therefore, rapid detection and evaluation of pork quality and safety become one of important part in the development of pork industry and the assurance of food safety.The research object is fresh minced pork. Detection of pork quality and safety were carried out based on visible/near infrared (NIR) spectroscopy, chemometrics techniques combined with modern physical-chemical analysis techniques. Quantitative models were established based on visible/NIR spectra for fat, intramuscular fat, protein, moisture and fatty acids determination, and qualitative models were also established for muscle discriminant from different part of pig. In this dissertation, detection of adulteration and freshness using visible/NIR spectra were also studied for safe quality detection of pork.The main results and conclusions were:(1) Quantitative analysis of minced pork quality was studied. Intramuscular fat, protein, moisture content in minced pork form longissimus dorsi muscle were analyzed quantitatively based on visible/NIR spectra by a portable instrument. Comparison results of different calibration methods of stepwise multi linear regression (SMLR), partial least squares regression (PLSR), least squares support vector machines (LS-SVM) and different spectra pretreatments showed that the performance of PLSR and LS-SVM model was much better than SMLR model. The best model of PLSR was established based on the raw spectra, and the best models of LS-SVM was based on the spectra with orthogonal signals correction (OSC) pretreatment for intramuscular fat, protein and moisture content, which was closed to PLSR calibration performance. Further research for quantitative analysis of Intramuscular fat was studied. Comparison results of different calibration methods of PLSR, LS-SVM and different spectra pretreatments and different detectors showed that the performance of model based on the spectra acquired by InGaAs detector was much better. The performance of LS-SVM model was close to that of PLSR model, but the performance of PLSR model based on the spectra acquired by USB4000 was much better than the LS-SVM model. For the best PLSR model, the correlation coefficients of calibration and validation were 0.964 and 0.960, respectively; the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.249and 0.491, respectively. For the best LS-SVM model, the correlation coefficients of calibration and validation were 0.966 and 0.960, respectively; RMSEC and RMSEP were 0.249 and 0.444, respectively. In order to expanding the range of the content for quality index, the samples of minced pork collected from different muscle of pig were analyzed quantitatively. Comparison results of different calibration methods of PLSR, LS-SVM and different spectra pretreatments and different detectors showed that the performance of LS-SVM model was better than PLSR model for fat, protein and moisture content. For fat, the model based on the spectra acquired by InGaAs detector performed better. For protein and moisture, the model based on the spectra acquired by USB4000 performed better. The correlation coefficients for calibration and validation were above 0.9 for detection of fat and moisture, and the precision and stability were strong. But for the protein, the correlation coefficient for calibration and validation were above 0.7, the precision was relatively worse.(2) Qualitative analysis of minced pork from 4 different muscle was studied. Comparison results of different calibration methods of discriminant analysis (DA), partial least squares discriminant analysis (PLSDA), least squares support vector machines discriminant analysis (LS-SVMDA) and different spectra pretreatments and different detectors showed that the model based on the raw spectra or first derivative performed better. Visible/NIR spectra obtained by portable instrument were more suitable for discrimination of 4 different muscles. The discriminant results of DA model were a bit worse than PLSDA model and LA-SVMDA model. The discriminant accuracy of calibration and validation for the best PLSDA model were 100% and 94%, respectively and for LA-SVMDA were 99% and 98%, respectively.(3) Quantitative analysis of fatty acids was researched. Comparison results of different calibration methods of PLSR, LS-SVM and different spectra pretreatments and different detectors for detection of fatty acid content showed that there were no significant difference between PLSR and LS-SVM or two different detectors as a whole. The correlations, precision and stability were relatively better for C17:0, C18:3 and SFA, but worse for C20:4, C20:5 and MUFA. The precision of quantitative analysis for fatty acids should be improved.(4) Quantitative analysis of adulteration in minced pork were studied with different grade adulteration based on different detectors. Comparison results of different calibration methods and different wavebands and different type adulteration showed that the performance of multi linear regression (MLR) model based on visible/NIR spectra was much better, the correlation coefficients of calibration, validation and cross validation were 0.965,0.958 and 0.949, respectively; RMSEC, RMSEP and RMSECV were 0.083,0.092 and 0.100, respectively for adulterated with the same homologous material, and the correlation coefficients of calibration, validation and cross validation were 0.961,0.971 and 0.945, respectively; RMSEC, RMSEP and RMSECV were 0.087,0.078 and 0.104, respectively for adulterated with the nonhomologous material.(5) Quantitive analysis of freshness of pork were studied. The models established using SMLR, PCR and PLSR based on the visible NIR spectra. Comparison results of different calibration methods and different spectra pretreatment showed that the performance of PLSR model was much better and the SMLR model was much worse. The best model using first derivative spectra achieved the correlation coefficients of calibration, validation and cross validation were 0.970,0.956 and 0.887, respectively; RMSEC, RMSEP and RMSECV were 2.63 mg/100g,3.45 mg/100g and 5.01 mg/100g, respectively. This study demonstrated that visible/NIR spectroscopy can be successfully applied as a rapid method to determine the quality and safety of pork.