Cited by Optimizing Food Taste Sensory Evaluation Through Neural Network-Based Taste Electroencephalogram Channel Selection

ColorNet: An AI-based framework for pork freshness detection using a colorimetric sensor array DOI

Guangzhi Wang,

Yuchen Guo, Yang Yu

et al.

Food Chemistry, Journal Year: 2025, Volume and Issue: 471, P. 142794 - 142794

Published: Jan. 7, 2025

Language: Английский

Citations

The insights into sour flavor and organic acids in alcoholic beverages DOI

Yan Yan,

Mingxin Zou,

Cui Tang

et al.

Food Chemistry, Journal Year: 2024, Volume and Issue: 460, P. 140676 - 140676

Published: Aug. 2, 2024

Language: Английский

Citations

Unveiling brain response mechanisms of citrus flavor perception: An EEG-based study on sensory and cognitive responses DOI

Qian Zhao, Peilin Yang, Xiaolei Wang

et al.

Food Research International, Journal Year: 2025, Volume and Issue: 206, P. 116096 - 116096

Published: Feb. 25, 2025

Language: Английский

Citations

Advancing research on odor-induced sweetness enhancement: A EEG local-global fusion transformer network for sweetness quantification combined with EEG technology DOI

Xiuxin Xia,

Yatao Cheng,

Z.G. Zhang

et al.

Food Chemistry, Journal Year: 2024, Volume and Issue: 463, P. 141533 - 141533

Published: Oct. 5, 2024

Language: Английский

Citations

Olfactory EEG induced by odor: Used for food identification and pleasure analysis DOI

Yuchen Guo, Xiuxin Xia, Yan Shi

et al.

Food Chemistry, Journal Year: 2024, Volume and Issue: 455, P. 139816 - 139816

Published: May 25, 2024

Language: Английский

Citations

Origin identification of Angelica dahurica using a bidirectional mixing network combined with an electronic nose system DOI

Yanwei Wang, He Wang, Xiaoming Wen

et al.

Sensors and Actuators B Chemical, Journal Year: 2025, Volume and Issue: 429, P. 137356 - 137356

Published: Jan. 28, 2025

Language: Английский

Citations

Artificial Intelligence in Gas Sensing: A Review DOI

M. Arshad Zahangir Chowdhury, Matthew A. Oehlschlaeger

ACS Sensors, Journal Year: 2025, Volume and Issue: unknown

Published: March 11, 2025

The role of artificial intelligence (AI), machine learning (ML), and deep (DL) in enhancing automating gas sensing methods the implications these technologies for emergent sensor systems is reviewed. Applications AI-based intelligent sensors include environmental monitoring, industrial safety, remote sensing, medical diagnostics. AI, ML, DL can process interpret complex data, allowing improved accuracy, sensitivity, selectivity, enabling rapid detection quantitative concentration measurements based on sophisticated multiband, multispecies systems. These discern subtle patterns signals, to readily distinguish between gases with similar signatures, adaptable, cross-sensitive multigas under various conditions. Integrating AI technology represents a paradigm shift, achieve unprecedented performance, adaptability. This review describes while highlighting approaches AI–sensor integration.

Language: Английский

Citations

Mechanistic study of saltiness enhancement induced by three characteristic volatiles identified in Jinhua dry-cured ham using electroencephalography (EEG) DOI

Qun Wang,

Yongjing Bie,

Xiuxin Xia

et al.

Food Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 144180 - 144180

Published: April 1, 2025

Language: Английский

Citations

TVNet: A times vision network for electrical load forecasting by temporal 2d-variation DOI

Baichun Wang, Ao Sun,

Zhikai Xu

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127619 - 127619

Published: April 1, 2025

Language: Английский

Citations

TasteNet: A novel deep learning approach for EEG-based basic taste perception recognition using CEEMDAN domain entropy features DOI

Sagnik De, Prithwijit Mukherjee, Anisha Halder Roy

et al.

Journal of Neuroscience Methods, Journal Year: 2025, Volume and Issue: 419, P. 110463 - 110463

Published: May 1, 2025

Language: Английский

Citations