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

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

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): salin...

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Bibliographic Details
Main Authors: Hae-Il Yang, Sung-Gi Min, Hye-Young Seo, Seong Youl Lee, Ji-Hee Yang, Mi-Ai Lee, Sung-Hee Park, Jong-Bang Eun, Young Bae Chung
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:International Journal of Food Properties
Subjects:
Kimchi sauce
artificial neural network
convolutional neural network
nondestructive prediction
physicochemical properties
Online Access:https://www.tandfonline.com/doi/10.1080/10942912.2023.2250577

Internet

https://www.tandfonline.com/doi/10.1080/10942912.2023.2250577

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