The application provides a method for constructing an intelligent prediction model for the quality of frozen prawns based on a
random forest algorithm. The method combines
test data of ultrasonic-assisted immersion frozen prawns, ice
crystal morphology detection data, and quality index detection data, and establishes a multivariate characteristic quality prediction model based on the
random forest algorithm between the ultrasonic-assisted immersion frozen process parameters, the ice
crystal morphology parameters, and the quality evaluation indexes, so as to realize accurate and efficient prediction of the frozen quality indexes. In combination with SHAP explanation and analysis, the influence weight and the nonlinear
response characteristics of each parameter in the prediction model on the quality indexes are quantified, the interactive influence of each parameter characteristic on the quality is analyzed, the internal mechanism that the ultrasonic parameters control the ice
crystal morphology and then affect the quality is revealed, and a clear target is provided for
process optimization.