Cited by Electrochemical Detection of Aflatoxins Using a Zno Nanowire-Modified Biosensor with a Droplet-Based Approach

The Impact of Aflatoxin B1 on Animal Health: Metabolic Processes, Detection Methods, and Preventive Measures DOI

Tianyang Wang, Rutao Cui,

Hai‐Fan Yu

et al.

Toxicon, Journal Year: 2025, Volume and Issue: unknown, P. 108262 - 108262

Published: Jan. 1, 2025

Language: Английский

Citations

Coordinative interaction-enhanced aggregation-induced electrochemiluminescence signal enables ultrasensitive aflatoxin B1 sensing in corn DOI

Ya Gao, Haibo Liu, Shaowei Li

et al.

Food Chemistry, Journal Year: 2025, Volume and Issue: 475, P. 143246 - 143246

Published: Feb. 6, 2025

Language: Английский

Citations

Deep Learning-Based Detection of Aflatoxin B1 Contamination in Almonds Using Hyperspectral Imaging: A Focus on Optimized 3D Inception–ResNet Model DOI

Md. Ahasan Kabir, Ivan Lee, Sang‐Heon Lee

et al.

Toxins, Journal Year: 2025, Volume and Issue: 17(4), P. 156 - 156

Published: March 22, 2025

Aflatoxin B1, a toxic carcinogen frequently contaminating almonds, nuts, and food products, poses significant health risks. Therefore, rapid non-destructive detection method is crucial to detect aflatoxin B1-contaminated almonds ensure safety. This study introduces novel deep learning approach utilizing 3D Inception–ResNet architecture with fine-tuning classify using hyperspectral images. The proposed model achieved higher classification accuracy than traditional methods, such as support vector machine (SVM), random forest (RF), quadratic discriminant analysis (QDA), decision tree (DT), for classifying B1 contaminated almonds. A feature selection algorithm was employed enhance processing efficiency reduce spectral dimensionality while maintaining high accuracy. Experimental results demonstrate that the (Lightweight) achieves superior performance 90.81% validation accuracy, an F1-score of 0.899, area under curve value 0.964, outperforming approaches. Lightweight model, 381 layers, offers computationally efficient alternative suitable real-time industrial applications. These research findings highlight potential imaging combined in supports development automated screening systems safety, reducing contamination-related risks

Language: Английский

Citations

Electrochemical Detection of Aflatoxins Using a Zno Nanowire-Modified Biosensor with a Droplet-Based Approach DOI

James Salveo Olarve, Gil Nonato C. Santos, Sang Sub Kim

et al.

Published: Jan. 1, 2025

Language: Английский

Citations