Quantification and visualization of meat quality traits in pork using hyperspectral imaging

• Published in: Meat science Vol. 196; p. 109052
• Main Authors: Tang, Xi, Rao, Lin, Xie, Lei, Yan, Min, Chen, Zuoquan, Liu, Siyi, Chen, Liqing, Xiao, Shijun, Ding, Nengshui, Zhang, Zhiyan, Huang, Lusheng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2023
• Subjects: Hyperspectral imaging; Meat quality; Pigs; VIS/NIRS; Visual appraisal; Pigs; ANN; VIS/NIRS; GWAS; RPD; ROI; AOAC; GLCM; RMSEC; RMSECV; SWAS; R2cal; R2cv; Visual appraisal; Hyperspectral imaging; Meat quality; HSI
• ISSN: 0309-1740; 1873-4138
• DOI: 10.1016/j.meatsci.2022.109052

Accurate and rapid determination of meat quality traits plays key roles in food industry and pig breeding. Currently, most of the spectroscopic instruments developed for meat quality determination can only obtain the spectral average value of the sample, so it is difficult to evaluate the spatial variation of meat quality traits. In this study, we evaluated the predictive potential of 14 meat quality traits based on large-scale VIS/NIR hyperspectral images collected by SpecimIQ. When predictions were based solely on hyperspectral data, the prediction accuracy (R2cv) for the majority of meat qualities ranged from 0.60 to 0.70. After adding texture information, the prediction accuracy of all traits is improved by different magnitudes (R2cv increases from 1.5% to 16.4%). Finally, the best model was utilized to visualize the spatial distribution of Fat (%) and Moisture (%) to assess their homogeneity. These results suggest that hyperspectral imaging has great potential for predicting and visualizing various meat qualities, as well as industrial applications for automated measurements. •This is a meat measurement process more suited to an assembly line operation.•Extraction of texture feature from hyperspectral images based on GLCM.•Use SWAS to validate the outcomes of artificial neural network model's prediction.•Spectral information combined with texture features improves prediction accuracy.•Visualize the distribution of Fat(%) and Moisture(%) in the longissimus muscle.