High Accuracy Quantification of Aflatoxin B1 via a Compact Smart Gas Sensing System Assisted by Dual-Branch Convolutional Neural Network
Changyi Liu, Yu Guo, Qi Bao, Junqiao Li, Peipei Huang, Xiulan Sun
TL;DR
A compact smart gas sensing system with a dual-branch neural network accurately detects and quantifies aflatoxin B1 in contaminated grains, enabling real-time monitoring.
Contribution
A novel compact gas sensing system with a dual-branch CNN for real-time, non-destructive aflatoxin B1 quantification in grains.
Findings
The system achieves 100% accuracy in identifying grains infected with Fusarium graminearum and Aspergillus flavus.
The DB-CNN model shows high quantitative performance (RMSE = 1.0292 μg/kg, R2 = 0.9994) for Aflatoxin B1 detection.
The detection system supports wireless transmission and completes the process within 4 minutes.
Abstract
Mycotoxin contamination of grains during storage and transportation represents a significant threat to global food security. Conventional detection methods exhibit limitations in terms of real-time monitoring. This study presents a compact smart gas sensing system for mycotoxins, facilitating non-destructive testing of corn infected with fungi by analyzing the volatile organic compounds (VOCs) emitted during fungal growth. It also facilitates the precise quantitative detection of Aflatoxin B1 (AFB1). Additionally, a dual-branch convolutional neural network (DB-CNN) model has been developed to conduct an in-depth analysis of the temporal and spatial characteristics of VOCs signals. The system achieves 100% accuracy in identifying grains (corn, peanuts, wheat, and rice) infected with Fusarium graminearum and Aspergillus flavus by extracting the characteristic fingerprint spectra of fungal…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Chemical Sensor Technologies · Gas Sensing Nanomaterials and Sensors · Air Quality Monitoring and Forecasting