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

и другие.

Food Chemistry, Год журнала: 2024, Номер 464, С. 141567 - 141567

Опубликована: Окт. 9, 2024

Язык: Английский

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

и другие.

Trends in Food Science & Technology, Год журнала: 2025, Номер unknown, С. 104918 - 104918

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

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 Creative Commons
Zhizhou Ren, Kun Liang, Y. H. Liu

и другие.

Agriculture, Год журнала: 2025, Номер 15(4), С. 415 - 415

Опубликована: Фев. 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

Язык: Английский

Процитировано

0

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

и другие.

Trends in Food Science & Technology, Год журнала: 2025, Номер unknown, С. 104977 - 104977

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

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

и другие.

Food Chemistry, Год журнала: 2024, Номер 464, С. 141567 - 141567

Опубликована: Окт. 9, 2024

Язык: Английский

Процитировано

1