Study On Rapid Detection Of Fresh Beef Tenderness Based On Sepctral And Imaging Technology

Beef, as one of the main meat ware for human daily consumption, is favored by consumers for its high protein, low fat, vitamin and mineral content. With the improvement of people’s living standards, the quality of beef has received the unprecedented attention. The traditional methods for beef detection which are time consuming, low efficiency and destructive already fail to satisfy the need of modern production. With wide use and development of spectral analysis in the field of agricultural products detection, there have been growing interests in the fast nondestructive method for assessing beef quality attributes.Therefore, in this paper, fresh beef tenderness and related quality parameters detecting and evaluating models were researched applying visible near infrared spectral and hyperspectral imaging technology in order to realize rapid nondestructive prediction of beef tenderness. The specific research contents and results are as follows:(1) Research on different spectral system for beef tenderness predicting, analysed the feasibility and evaluate the prediction effect. The difference between two systems, having different light source and resolution, and the results of beef tenderness prediction were discussed. The results indicated that original signal requires different preprocessing methods due to different light structure and characteristic. The spectral using of high-power lights need standard normal variates (SNV) method leaded to high precision for model. For other system with high resolution and low SNR spectrograph, the signal with Savitzky-Golay smoothing (S_G) had improved the accuracy of model. Both the two system can predict beef tenderness well enough using full range wave, the400-1700nm spectral system showed superior and stable performance based wavelength selecting methods, the prediction accuracy of which is higher than dual channel spectral system. Considering the cost, system structure and requirements for fast detection simultaneously, the system in range of400-1700nm were finally chosen for beef tenderness prediction, the correlation coefficient and root mean square prediction error is0.9085and7.5212and RPD value of2.16.(2) The study of pre-treatment methods, prediction models and model validation of fresh beef tenderness, L*, α*and cooking loss. The effects on PLSR prediction models of different preprocessing methods such as savitzky-golay smoothing (SG), multiplication scatter correction (MSC), standard normalized variate (SNV) and first derivative (FD) were studied. The best pretreatment method for tenderness and cooking loss are SNV+SG methods; the best method for L*is MSC+SG; and the original spectral for α*prediction model is best. Then effective variable for each parameters were selected with interval partial least-squares (iPLS), genetic algorithm (GA) to establish synergy interval partial least square regression model (si-PLSR), genetic algorithm-partial least squares regression (GA-PLSR) model. The results showed that the best prediction model for shear-force value, α*were si-PLSR model, with the correlation coefficient and standard deviation0.9085,0.9027and7.5212,1.4878and model RPD value of2.16and2.65, respectively. The best prediction model for L*was GA-PLSR, with correlation coefficient and standard deviation o of prediction set of f0.9457and1.7250, RPD is3.24. The prediction effect for cooking loss is poorer, the optimal prediction model was PLSR full range wavelength, prediction correlation coefficient and standard deviation of0.8453and2.5054, RPD value of1.84. Linear Discriminant Analysis (LDA) and support vector machine (SVM) methods were applied for beef tender level classification. The grading threshold was set as45N and60N according to the beef parts and eating mode, results showed that the LDA is better than SVM model, two types of beef classification accuracy of prediction set were92.85%and91.66%, respectively. The prediction and classification models of all parameters were verified, respectively. The correlation coefficient and standard deviation in validation set were0.8875,0.9060,0.8972,0.8217and10.16,2.319,1.055,2.493, respectively. The beef tenderness classification accuracy of validation set was up to100%.(3) Implanting and correction of models for fast detection system were studied. According to the characteristics of on-line detection and actual problems, prediction model after implanting need correction, the raw data with standardized correction variable (SNV) were most reasonable, and then si-PLSR model for shear force value, color a*, L*and cooking loss using PLSR model full range wavelength, correlation coefficient and the root mean square prediction error were0.9068,0.9031,0.9049,0.8276and7.1963N,1.8246,1.4931,1.8246, RPD all above2. Detection system was verified finally, which basically achieved rapid detection of beef tenderness, have met production demands. Detection software system and background database were designed and developed, Improved the detection software system, and establish the system of background database. Based on which, invocation of the model was flexible and real-time prediction and results display for beef quality parameters, and at the same time prediction results can be carried out for storage and data query statistics, which improved the universality of prediction models.(4) In view of the complexity of beef structure of and the disadvantages of visible near infrared spectral like single point detection, this study used hyperspectral imaging system to get three-dimensional images and more information of samples. The stepwise regression and GA of the resulting PLSR models were used to identify the most important wavelengths and to reduce the high dimensionality of the hyperspectral data. By using these important wavelengths, beef images were developed to predict shear force value and color of every pixel in the images for visualizing in all portions of the sample. On the other hand, texture features were extracted from beef sample images for classification model, and then tender level of beef was calculated in every pixel of the images for visualizing distribution overall the sample.