Study On Application Of Raman And Near Infrared Spectroscopy Technology For Rapid Detection Of Minced Pork Freshness

Minced pork is the main raw material for traditional food such as dumplings,steamed buns,wontons,and the most basic raw material for meat products.It has a great and broad market in China.The freshness of minced pork is an important part of its quality.Traditional freshness testing methods are time-consuming,cumbersome,and destroy samples.A rapid and non-destructive testing method for the freshness of minced pork plays an important role in ensuring quality and safety of minced pork.In this study,minced fat pork,minced lean pork,and mixed minced pork(fat-to-thin ratio 2:8)were used as the research objects to explore a rapid detection method for the minced pork freshness by Raman spectroscopy combined with chemometrics and and compared with near-infrared spectroscopy.This paper mainly includes the following three parts:1.The potential of Raman spectroscopy and Near Infrared spectroscopy to detect the freshness of minced pork fat rapidly was investigated.The Raman spectrum acquisition conditions were optimized,and the optimal conditions were determined as laser power 70 m W,integration time 5 s,and accumulation times 2 times.detection distance 6 mm.The results of Pearson correlation analysis showed that Raman spectroscopy can characterize the freshness of pork minced fat.Raman full-spectrum partial least squares regression(PLSR)modeling results showed that the best full-spectrum prediction models for thiobarbituric acid value(TBARS)and acid value are aseline-corrected-PLSR and detrended-PLSR,respectively,Compared with the near-infrared full-spectrum modeling results,the Raman spectrum is better.Then,PCA loading was used to analyze the main spectral contribution variables of the optimal prediction model of the full spectrum of Raman spectrum.Based on this,The PLSR,multiple linear regression(MLR),and back-propagation artificial neural network(BP-ANN)models were established,and their effects were compared.The results showed that the BP-ANN model had the best prediction effect.The calibration set correlation coefficient(R_C)of TBARS and acid value models was 0.937 and 0.957,respectively,and the predicted set correlation coefficient(R_P)was 0.865 and 0.838,respectively,and the TBARS-BP-ANN model was more robust than the acid value-BP-ANN model so that Raman spectroscopy has the potential to rapidly detect the freshness of minced pork fat.2.The effect of Raman spectroscopy and near-infrared spectroscopy on the rapid detection of the freshness of pork lean minced meat was compared.The rapid detection of minced pork freshness by Raman spectroscopy was studied with near infrared spectroscopy as the control:Raman full-spectrum PLSR prediction models were established for 5 kinds of freshness indicators based on different spectral preprocessing methods,and the results showed that the predictive ability of the PLSR model for each freshness index was TVB-N>p H>b*>L*>a*,and the TVB-N optimal PLSR model prediction performance deviation ratio(RPD)was 3.265(greater than 2.5);the effect of successive projections algorithm(SPA)and principal component algorithm(PCA)to select the main spectral contribution variables of the optimal full-spectrum model was compared,and the results showed that PCA loading was better than SPA,the R_P of TVB-N and p H simplified PCA-PLSR model were 0.951 and 0.939,respectively,and the RPD were 3.803 and 2.335,respectively.The Near infrared spectroscopy technology was studied to rapidly detect the freshness of pork minced meat:TVB-N and p H Near infrared full-spectrum PLSR prediction models based on different spectral preprocessing methods were established respectively,and the results showed that the R_P of their optimal models based on full spectroscopy were 0.937 and 0.895,respectively,and RPD were 2.618 and 2.563,respectively;the RPD of the two freshness indicators PLSR prediction models based on the 1390-1669 nm band were both greater than 2.The above results showed that both Raman spectroscopy and near-infrared spectroscopy can rapidly detect the freshness of minced lean pork,and the Raman spectroscopy based on the PLSR model had a better rapid detection effect.3.Taking TVB-N and TBARS as freshness evaluation indicators,and comparing with near-infrared spectroscopy,the feasibility of Raman spectroscopy to rapidly detect the freshness of minced pork(fat to lean ratio 2:8)was studied.The Raman and Near infrared spectral PLSR prediction models based on ten spectral preprocessing methods for TVB-N were established respectively.The results showed that the optimal Raman and Near infrared full-spectrum models were the moving average smoothing-PLSR and convolution smoothing-PLSR models respectively,the R_P were 0.926 and 0.923,respectively,and the RPD were 2.618 and 2.563,respectively;the RPD of the simplified models established based on the preferred variables of Raman and Near infrared spectroscopy were all greater than 2,and the accuracy was slightly reduced,but they could still be predicted quantitatively;It was proved that both Raman and Near infrared spectroscopy could rapidly detect the TVB-N content in minced pork,and it was found that the prediction effect of Raman spectroscopy was better than that of Near infrared spectroscopy.The TBARS-BP-ANN prediction models based on ten spectral preprocessing methods were established.The results showed that the optimal prediction models of TBARS by Raman and near-infrared spectroscopy were both the optimal variable orthogonal signal(OSC)-BP-ANN model with R_P of 0.8780 and 0.8290,respectively,which proved that Raman spectroscopy and near-infrared spectroscopy had high feasibility for rapid quantitative prediction of TBARS content in minced pork,and the Raman spectroscopy was better.