Prediction of Beef Fat Content Simultaneously under Static and Motion Conditions Using near Infrared Spectroscopy

Abstract

Fat content is one of the most important quality indicators for minced beef products. In this study, a multipoint near infrared (NIR) spectrophotometer system, based on a Fabry–Perot interferometer, combined with a four-point photodiode array detector and flexible collimator–probe arrangement, was used for real-time analysis of beef fat content. The system was employed to predict fat content of mixed minced beef samples concurrently under two different conditions: (a) static and slow motion and (b) static and fast motion. Additionally, a separate measurement was conducted to further test the independency of a collimator–probe arrangement by scanning two samples with different fat percentages concurrently under static and motion conditions. Partial least squares regression was employed, obtaining coefficients of determination in calibration (R2c) of 0.95, confirming a good fit for the three models. The fat contents of samples in the independent set were predicted with reasonable accuracy: r2 in the range 0.82–0.92 and standard error of prediction in the range 3.05–3.98%. Moreover, the spectral features observed for the probe independency test clearly illustrated the flexibility and independency of the collimator–probe arrangement. This study showed that the multipoint NIR spectroscopy system can predict beef fat content concurrently under static and motion conditions and illustrates its potential use as an in-line monitoring tool at various junctions in a meat processing plant.