Research On Nondestructive Testing Method Of Lamb Tenderness Based On Hyperspectral And Polarization Imaging

Tenderness is one of the important indicators of lamb quality evaluation,it affects the taste and commercial value of meat.Traditional lamb tenderness detection methods have low efficiency and damage samples,which are difficult to meet the current needs for fast and non-destructive testing of meat quality.However,models based on single spectrum or image feature information cannot comprehensively and accurately evaluate meat quality.Therefore,look for more It is particularly important for precise and efficient methods.In recent years,hyperspectral imaging technology has been widely used in food inspection,and it is also a research hotspot.In addition,the polarization image contains rich texture information,so the polarization imaging technology provides a new method for fast and non-destructive inspection of meat quality.Therefore,in order to explore the rapid non-destructive testing method of cold fresh lamb tenderness,this thesis takes Xilin Gol lamb in Inner Mongolia as the research object,and establishes a cold fresh lamb tenderness prediction model based on the feature layer information fusion method,which realizes the rapid and non-destructive detection of cold fresh lamb tenderness.Detection.The specific research content and results are as follows:(1)Taking 120 pieces of cold fresh lamb as the research object,the hyperspectral imaging system was used to obtain hyperspectral image data of lamb stored for different days,the tenderness of lamb was measured according to the NY/T1180-2006 standard,and the reflectance spectrum curve of lamb image was extracted.The scatter correction method corrects the original spectral reflectance.On this basis,the characteristic wavelengths of lamb tenderness were determined by principal component analysis,which were 620.23nm,761.48nm,and 819.48nm,and the gray-scale images at the corresponding wavelengths were extracted.(2)Extract the gray-level co-occurrence matrix texture of the feature image,and build the BP neural network and support vector machine(SVM)prediction model of cold fresh lamb tenderness based on the feature wavelength information,feature image texture information and map feature layer fusion information.The results show that the accuracy of the BP neural network model is generally higher than that of the SVM prediction model.The determination coefficients R~2of the BP and SVM model prediction sets based on the fusion information of the map feature layer are 0.8527 and 0.7964,respectively,and the root mean square error RMSEP is 1.7623 and 2.1541,respectively.(3)Use the polarization imaging system to collect images of cold fresh lamb samples,and use 90 randomly selected samples to establish a prediction model.LBP algorithm is used for texture feature extraction.After 8 experiments,the optimal sampling pixel point P and radius parameter R are determined to be 16 and 2,respectively.The established BP neural network and SVM model determination coefficients R~2are 0.8212 and 0.7641,respectively,root mean square The error RMSEC are 2.6581 and 2.7821 respectively.The gray-level co-occurrence matrix is used to extract the texture features of the polarization image,and the BP neural network model and the SVM model are established.The determination coefficients R~2are 0.8099 and 0.7708,and the RMSEC are 2.7296 and2.7569,respectively.The CLCM and LBP information feature layers are fused to form new texture features,and the BP and SVM models are established.The determination coefficients R~2are 0.8318 and 0.7932,respectively,and the RMSEC are 2.5837 and 2.8223,respectively.Comparing the model prediction accuracy,it is found that the determination coefficients R~2of the verification set of the BP and SVM prediction models established based on the feature layer fusion are 0.8100 and 0.7685,respectively,and the RMSEP are2.0712 and 1.9093,respectively,indicating that the model accuracy after the fusion of CLCM and LBP features is higher than that of single information Model accuracy.In summary,the above results verify the effectiveness of hyperspectral imaging technology and polarization imaging technology in the quality detection of lamb tenderness,and provide a new idea for rapid non-destructive testing of lamb.