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, °Br...

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Veröffentlicht in:	International journal of food properties 2023-12, Vol.26 (2), p.2924-2938
Hauptverfasser:	Yang, Hae-Il, Min, Sung-Gi, Seo, Hye-Young, Lee, Seong Youl, Yang, Ji-Hee, Lee, Mi-Ai, Park, Sung-Hee, Eun, Jong-Bang, Chung, Young Bae
Format:	Artikel
Sprache:	eng
Schlagworte:	artificial neural network convolutional neural network Food quality Kimchi sauce Neural networks nondestructive prediction Physicochemical properties Predictions Quality control Sauces Water content
Online-Zugang:	Volltext
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