The growing consumer demand for minimally processed, “clean-label” foods is increasing interest in innovative technologies that maintain quality while ensuring microbial safety. This study sheds light on how the protein:lipid ratio in meat-like model matrices modulates the effectiveness of combined high-intensity ultrasound (20 kHz) and carvacrol treatments applied against Escherichia coli ATCC 25922. Three emulsified systems with geometrically spaced protein:lipid ratios (0.33, 1.0, 3.0) were subjected to combinations of ultrasound and carvacrol (0–1200 ppm) at 30±2 °C. To address the rheological non-linearity, the matrix index was log-transformed, and the process was modeled using both Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). While both models achieved high predictive accuracy (R2>0.96), lack-of-fit analysis revealed that the reduced polynomial RSM model provided a more robust and statistically valid representation of the process compared to the ANN, which exhibited significant overfitting to experimental noise (p<10−9). The results highlighted a distinct matrix dependency: ultrasound alone provided the fastest inactivation in the high-lipid matrix, while the high-protein matrix exhibited much slower kinetics due to viscous damping. Consequently, the explicit mathematical relationships derived from the RSM model are proposed as the preferred, transparent kernel for future digital twins and autonomous process-control systems in smart food-processing lines.
Baghirov et al. (Sat,) studied this question.