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Related Concept Videos

Atomic Absorption Spectroscopy: Lab01:21

Atomic Absorption Spectroscopy: Lab

For AAS measurements, samples must be introduced as clear solutions, often requiring extensive preliminary treatment to dissolve materials like soils, animal tissues, and minerals. Common methods for sample preparation include treatment with hot mineral acids, wet ashing, combustion in closed containers, high-temperature ashing, or fusion with reagents.
 Solutions containing organic solvents, such as low-molecular-mass alcohols, esters, or ketones, enhance absorbances by increasing nebulizer...

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Related Experiment Video

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Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays
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High Accuracy Quantification of Aflatoxin B1 via a Compact Smart Gas Sensing System Assisted by Dual-Branch

Changyi Liu1, Yu Guo1, Qi Bao2

  • 1School of Integrated Circuits, Jiangnan University, Wuxi 214122, China.

Foods (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

A new smart gas sensor system detects mycotoxin contamination in grains using volatile organic compounds (VOCs). This rapid, non-destructive method enhances food security by enabling real-time fungal detection and quantification.

Keywords:
AFB1VOCsdual-branch CNNgas sensorgrainsreal-time detection

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Sensor Technology

Background:

  • Mycotoxin contamination poses a significant threat to global food security, with conventional detection methods lacking real-time capabilities.
  • Effective monitoring of fungal growth and mycotoxin presence in stored grains is crucial for preventing spoilage and ensuring food safety.

Purpose of the Study:

  • To develop a compact, smart gas sensing system for the non-destructive, real-time detection and quantification of mycotoxins in grains.
  • To analyze volatile organic compounds (VOCs) emitted by fungi for identifying infected grains and determining Aflatoxin B1 (AFB1) levels.

Main Methods:

  • A smart gas sensing system was designed to analyze VOCs emitted by fungal-infected grains.
  • A dual-branch convolutional neural network (DB-CNN) was employed for in-depth analysis of VOCs signal characteristics.
  • The system was validated for identifying grains infected with *Fusarium graminearum* and *Aspergillus flavus* and quantifying Aflatoxin B1.

Main Results:

  • The system achieved 100% accuracy in identifying infected grains (corn, peanuts, wheat, rice) by analyzing fungal VOCs.
  • The DB-CNN model demonstrated high performance in quantitative analysis with RMSE = 1.0292 μg/kg and R² = 0.9994 for AFB1 detection.
  • The detection process was completed within 4 minutes, with wireless data transmission capabilities for smartphone connectivity.

Conclusions:

  • The developed smart gas sensing system offers a rapid, non-destructive, and accurate method for real-time mycotoxin detection in grains.
  • This technology provides an innovative foundation for dynamic monitoring, early warning systems, and quality control in the food supply chain.
  • The system's portability and smartphone integration facilitate remote monitoring and data management, significantly contributing to food security efforts.