Monitoring of moisture content in meat emulsion using an SWIR hyperspectral imaging system.

The quality of meat emulsions during mixing is difficult to control because rapid, objective, and non-destructive measurement methods remain limited. This study investigated the effects of lean meat-to-fat ratio and mixing time on moisture content in meat batters using short-wave infrared (1,000-1,700 nm) hyperspectral imaging combined with chemometric modeling. Meat batters were prepared at three lean meat-to-fat ratios: Group A (6:2), Group B (5:3), and Group C (4:4), and mixed for 0, 1, 2, 5, 7, 10, and 15 min. Moisture content differed significantly among formulations, with Group A showing the highest value (63.4%), followed by Group B (57.2%) and Group C (52.4%). Mixing time also had a significant effect, with the highest moisture content observed at 0 min (61.8%) and stabilization at approximately 56-58% after 1-15 min, indicating progressive homogenization. Two-way ANOVA revealed significant main effects of lean meat-to-fat ratio and mixing time (p < 0.001), but no significant interaction. Important wavelengths were identified at 1,000-1,124, 1,190-1,230, near 1,400, and 1,600-1,700 nm, mainly related to water, fat, and protein absorptions. PLSR and Elastic Net emphasized lower water-related wavelengths, whereas RF highlighted mid- and high-wavelength regions associated with fat and protein. Among the tested models, Elastic Net with raw spectra showed the best prediction performance (R²P = 0.76, RMSEP = 2.54%), while the ensemble model also showed strong performance. In addition, hyperspectral imaging visualized moisture distribution and reduced sample heterogeneity with increased mixing time. These results suggest that SWIR hyperspectral imaging is an effective non-destructive tool for predicting moisture content and monitoring mixing uniformity in meat batters.