ArticleCurrent research in food science2026
Synergistic optimization of thawing efficiency and water-holding capacity in chicken breast via magneto-electric coupling: A MISO-based predictive modeling approach.
Article in Current research in food science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Traditional chicken breast thawing suffers from severe phase-transition instability and quality degradation. To overcome limitations of standalone fields, a novel magneto-electric coupling thawing (MECT) system integrating pulsed magnetic fields (PMF) and high-voltage electrostatic fields (HVEF) was developed. A decoupled multi-input single-output back-propagation neural network (MISO-BPNN) framework precisely mapped the nonlinear kinetics. Featuring a "Slope-Forced Alignment" penalty mechanism, this MISO architecture eliminated systematic bias and task interference, demonstrating exceptional predictive fidelity (R > 0.97) for thawing time and moisture loss. To resolve the inherent efficiency-quality trade-off, a multi-objective Desirability function was globally optimized via parallel heuristic algorithms (GA, PSO, SA). The industrial-adapted optimum (4.0 mT, 0.2 Hz, 2.0 kV/cm) synergistically yielded a reduced thawing time (8.37 h), thawing loss (3.93%), and cooking loss (16.82%). With experimental relative errors strictly below 5%, MECT proved highly effective in enhancing both efficiency and water-holding capacity. This MISO-based predictive modeling framework provides a robust paradigm for intelligent and adaptive control of complex multi-field coupled food processing systems.
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