Cited by A black tea quality testing method for scale production using CV and NIRS with TCN for spectral feature extraction

Response to letter to the Editor from Y. Takefuji on “Beyond principal component analysis: Enhancing feature reduction in electronic noses through robust statistical methods” DOI

Zichen Zheng, Kewei Liu,

Yiwen Zhou

et al.

Trends in Food Science & Technology, Journal Year: 2025, Volume and Issue: unknown, P. 104918 - 104918

Published: Feb. 1, 2025

Language: Английский

Citations

A Neural Network with Multiscale Convolution and Feature Attention Based on an Electronic Nose for Rapid Detection of Common Bunt Disease in Wheat Plants DOI

Zhizhou Ren, Kun Liang, Y. H. Liu

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(4), P. 415 - 415

Published: Feb. 16, 2025

Common bunt disease in wheat is a serious threat to crops and food security. Rapid assessments of its severity are essential for effective management. The electronic nose (e-nose) system used capture volatile organic compounds (VOCs), particularly trimethylamine (TMA), which serves as key marker common wheat. In this paper, the GFNN (gas feature neural network) model proposed detecting VOCs from e-nose system, providing lightweight efficient approach assessing severity. Multiscale convolution employed extract both global local features gas data, three attention mechanisms focus on important features. achieves 98.76% accuracy, 98.79% precision, 98.77% recall, an F1-score 98.75%, with only 0.04 million parameters 0.42 floating-point operations per second (FLOPS). Compared traditional current deep learning models, demonstrates superior performance, small-sample-size scenarios. It significantly improves performance extracting This study offers practical, rapid, cost-effective method monitoring managing wheat, enhancing crop protection

Language: Английский

Citations

Exploration of Simulated Human Olfactory System and Its Integration With Machine Learning Algorithms for Food Quality Assessment: A Review DOI

Shilpa Gite,

Moumita Karmakar,

S.D. Mokashi

et al.

Trends in Food Science & Technology, Journal Year: 2025, Volume and Issue: unknown, P. 104977 - 104977

Published: March 1, 2025

Language: Английский

Citations

A black tea quality testing method for scale production using CV and NIRS with TCN for spectral feature extraction DOI

Jianhua Liang, Jiaming Guo,

Hongling Xia

et al.

Food Chemistry, Journal Year: 2024, Volume and Issue: 464, P. 141567 - 141567

Published: Oct. 9, 2024

Language: Английский

Citations