Nondestructive prediction of physicochemical properties of kimchi sauce with artificial and convolutional neural networks

This study presents a comparison of prediction performances by an artificial neural network (ANN), well-known deep convolutional neural network (D-CNN) models, and four proposed shallow convolutional neural network (S-CNN) models to forecast three key physicochemical properties (PCPs): salinity, °Brix, and moisture content of kimchi sauce (KS). The S-CNN models effectively minimized underfitting issues found in D-CNN models, predicting PCPs with a low error rate even with small image datasets. Furthermore, the ANN model using color values also allowed for competitive predictions. We used two nondestructive prediction strategies: (i) using color values with ANNs for immediate application in small-scale enterprises and (ii) using photographs as input for S-CNN models, allowing for faster and more accurate quality prediction. These results highlight the potential for image-based quality prediction in food science, possibly enhancing the efficiency and accuracy of real-time quality control. Future enhancements could incorporate additional data sources for improved predictive performance.