Multi‐Modal Bioimpedance Integrating Machine Learning for Rapid Grouper Quality Evaluation

ABSTRACT Grouper faces quality inspection challenges during cold chain logistics transportation, with traditional inspection methods suffering from labor‐intensive and damaging drawbacks. This study employs bioimpedance and bioimpedance imaging technology (multimodal detection) combined with machine learning algorithms to construct a quality classification model for chilled grouper, aiming to achieve rapid and precise monitoring. Research design of a multimodal bioimpedance detection system for grouper. Analysis of multimodal bioimpedance parameters and quality parameters. Construction of multimodal bioimpedance detection models using four machine learning classification models: K‐Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Average classification accuracies were 1.00, 0.96, and 0.96, respectively, and compared the performance of the models. The developed system achieves efficient classification of chilled grouper quality with an average accuracy ≥ 96%, establishing a new paradigm for non‐destructive testing and intelligent classification. This approach is scalable to other perishable foods, enhancing supply chain traceability and sustainability.