Your browser doesn't support javascript.
loading
Prediction and visualization of moisture content in Tencha drying processes by computer vision and deep learning.
You, Jie; Li, Dengshan; Wang, Zhen; Chen, Quansheng; Ouyang, Qin.
Afiliação
  • You J; School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P.R. China.
  • Li D; School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P.R. China.
  • Wang Z; National Research and Development Center for Matcha Processing Technology, Jiangsu Xinpin Tea Co., Ltd, Changzhou, P.R. China.
  • Chen Q; School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P.R. China.
  • Ouyang Q; College of Ocean Food and Biological Engineering, Jimei University, Xiamen, P.R. China.
J Sci Food Agric ; 104(9): 5486-5494, 2024 Jul.
Article em En | MEDLINE | ID: mdl-38349009
ABSTRACT

BACKGROUND:

It is important to monitor and control the moisture content throughout the Tencha drying processing procedure so that its quality is ensured. Workers often rely on their senses to perceive the moisture content, leading to relative subjectivity and low reproducibility. Traditional drying methods, which are used for measuring moisture content, are destructive to samples. This research was conducted using computer vision combined with deep learning to detect moisture content during the Tencha drying process. Different color space components of Tencha drying sample images were first extracted by computer vision. The color components were preprocessed using MinMax and Z score. Subsequently, one-dimensional convolutional neural networks (1D-CNN), partial least squares, and backpropagation artificial neural networks models were built and compared.

RESULTS:

The 1D-CNN model and Z score preprocessing achieved superior predictive accuracy, with correlation coefficient of prediction (Rp) = 0.9548 for moisture content. The migration of moisture content during the Tencha drying process was eventually visualized by mapping its spatial and temporal distributions.

CONCLUSION:

The results indicated that computer vision combined with 1D-CNN was feasible for moisture prediction during the Tencha drying process. This study provides technical support for the industrial and intelligent production of Tencha. © 2024 Society of Chemical Industry.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Água / Dessecação / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Água / Dessecação / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2024 Tipo de documento: Article