Challenges in Model Development for Meat Composition Using Multipoint NIR Spectroscopy from At-Line to In-Line Monitoring

This study evaluates the efficiency of multipoint near-infrared spectroscopy (NIRS) to predict the fat and moisture content of minced beef samples both in at-line and on-line modes. Additionally, it aims at identifying the obstacles that can be encountered in the path of performing in-line monitoring. Near-infrared (NIR) reflectance spectra of minced beef samples were collected using an NIR spectrophotometer, employing a Fabry-Perot interferometer. Partial least squares regression (PLSR) models based on reference values from proximate analysis yielded calibration coefficients of determination R²_c of 0.96 for both fat and moisture. For an independent batch of samples, fat was estimated with a prediction coefficient of determination R²_p of 0.87 and 0.82 for the samples in at-line and on-line modes, respectively. All the models were found to have good prediction accuracy; however, a higher bias was observed for predictions under on-line mode. Overall results from this study illustrate that multipoint NIR systems combined with multivariate analysis has potential as a process analytical technology (PAT) tool for monitoring process parameters such as fat and moisture in the meat industry, providing real-time spectral and spatial information.