Rapid Construction Method for a Precision Pork Color Scoring Model Based on Standard Color Board Images

Pork color is one of the most important attributeswhich reflect meat quality and affect consumer perception and purchase decisions.However, the rapid and objectivepork color scoring method is still lack. The traditional methodsfor the construction of pork color scoring models depend on a large sample sizeand face the problem of sample imbalance,and the slope and intercept calibration significantly rely on the source of the samples and device.To addressed the limitations,a rapid method for constructing a precision pork color scoring models based on six standard color boardimages was proposed in this study, and performanceswere compared with the models constructed using traditional methodsbased on 525 actual pork images from seven pig herds. The results show the performances of CS_1models constructed by the novel methodwith a R ² of 0.96 to 0.97 are similar to the traditional CS_2 models with a R ² of 0.97 to 0.99, but are superior to the traditional CS_3 models with is a R ² of 0.50-0.81, while theoverallclassification accuraciesof theCS_1 models are similar to the traditional models at different scores by the intercept calibration using the pork images from the mixed pig herd, exhibiting thatthe overall classification accuracies of CS_1_L*, CS_1_L*a*, CS_1_L*a*b*models reach 91.43%, 95.62% and 94.10%within a scoring scale ≤± 0.50, respectively. Moreover, the results indicate that the classification accuracies of the models vary considerably among the pig herdsandsignificantly improved by the intercept calibration using the pork imagesfromthe individual pig herd, exhibiting that the overall classification accuracies are enhanced from 91.60% to 93.75%, 95.70% to 95.90%, 93.75% to 96.10% for the CS_1_L*, CS_1_L*a*, CS_1_L*a*b*modelswithin a scoring scale ≤± 0.50, respectively.Taken together, this study provides a fast method for constructing the pork color scoring model, whilethe novel method exhibits several advantages, including full range coverage ranged from 1.0 to 6.0, equivalent small samples with one score for six standard color board images, and rapid intercept calibration with five actual pork images. Moreover, this study demonstrates that the intercept calibration of models is a fast and effective method to enhance the classification accuracy for pork color scoring, which provides new theoretical support for the rapid and objective assessment of pork color.