Visible and near-infrared spectral imaging combined with robust regression for predicting firmness, fatness, and compositional properties of fresh pork bellies

Abstract

Belly is a widely consumed pork product with very variable properties. Meat industry needs real-time quality assessment for maintaining superior pork quality throughout the production. This study explores the potential of using visible and near-infrared (VNIR,386-1015 nm) spectral imaging for predicting firmness, fatness and chemical compositional properties in pork belly samples, offering robust spectral calibrations. A total of 182 samples with wide variations in firmness and compositional properties were analysed using common laboratory analyses, whereas spectral images were acquired with a VNIR spectral imaging system. Exploratory analysis of the studied properties was performed, followed by a robust regression approach called iterative reweighted partial least-squares regression to model and predict these belly properties. The models were also used to generate spatial maps of predicted chemical compositional properties. Chemical properties such as fat, dry matter, protein, ashes, iodine value, along with firmness measures as flop distance and angle, were predicted with excellent, very good and fair models, with a ratio prediction of standard deviation (RPD) of 4.93, 3.91, 2.58, 2.54, 2.41, 2.53 and 2.51 respectively. The methodology developed in this study showed that a short wavelength spectral imaging system can yield promising results, being a potential benefit for the pork industry in automating the analysis of fresh pork belly samples. VNIR spectral imaging emerges as a non-destructive method for pork belly characterization, guiding process optimization and marketing strategies. Moreover, future research can explore advanced data analytics approaches such as deep learning to facilitate the integration of spectral and spatial information in joint modelling.