1. Nondestructive prediction of physicochemical properties of kimchi sauce with artificial and convolutional neural networks
- Author
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Hae-Il Yang, Sung-Gi Min, Hye-Young Seo, Seong Youl Lee, Ji-Hee Yang, Mi-Ai Lee, Sung-Hee Park, Jong-Bang Eun, and Young Bae Chung
- Subjects
Kimchi sauce ,artificial neural network ,convolutional neural network ,nondestructive prediction ,physicochemical properties ,Nutrition. Foods and food supply ,TX341-641 ,Food processing and manufacture ,TP368-456 - Abstract
ABSTRACTThis 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.
- Published
- 2023
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