Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging

Abstract

Many subjective assessment methods for fresh meat quality are still widely used in the meat industry, making the development of an objective and non-destructive technique for assessing meat quality traits a vital need. In this study, a hyperspectral imaging technique was investigated for objective determination of pork quality attributes. Hyperspectral images in the near infrared region (900-1700 nm) were acquired for pork samples from the longissimus dorsi muscle, and the representative spectral information was extracted from the loin eye area. Several mathematical pre-treatments including first and second derivatives, standard normal variate (SNV) and multiplicative scatter correction (MSC) were applied to examine the influence of spectral variations in predicting pork quality characteristics. Spectral information was used for predicting color features (L, a, b, chroma and hue angle), drip loss, pH and sensory characteristics by partial least-squares regression (PLS-R) models. Independent sets of feature-related wavelengths were selected for predicting each quality attribute. The results showed that color reflectance (L), pH and drip loss of pork meat could be predicted with determination coefficients (R(CV)(2)) of 0.93, 0.87 and 0.83, respectively. The regression coefficients from the PLS-R models at the selected optimal wavelengths were applied in a pixel-wise manner to convert spectral images to prediction maps that display the distribution of attributes within the sample. Results indicated that this technique is a potential tool for rapid assessment of pork quality.