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Updated: Jul 15, 2025
Fruit Volatile Analysis Using an Electronic Nose
Published on: March 30, 2012
Analysis of the Response Signals of an Electronic Nose Sensor for Differentiation between Fusarium Species
Piotr Borowik1, Valentyna Dyshko2, Rafał Tarakowski1
1Faculty of Physics, Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warszawa, Poland.
Abstract:
Fusarium is a genus of fungi found throughout the world. It includes many pathogenic species that produce toxins of agricultural importance. These fungi are also found in buildings and the toxins they spread can be harmful to humans. Distinguishing Fusarium species can be important for selecting effective preventive measures against their spread. A low-cost electronic nose applying six commercially available TGS-series gas sensors from Figaro Inc. was used in our research. Different modes of operation of the electronic nose were applied and compared, namely, gas adsorption and desorption, as well as modulation of the sensor's heating voltage. Classification models using the random forest technique were applied to differentiate between measured sample categories of four species: F. avenaceum, F. culmorum, F. greaminarum, and F. oxysporum. In our research, it was found that the mode of operation with modulation of the heating voltage had the advantage of collecting data from which features can be extracted, leading to the training of machine learning classification models with better performance compared to cases where the sensor's response to the change in composition of the measured gas was exploited. The optimization of the data collection time was investigated and led to the conclusion that the response of the sensor at the beginning of the heating voltage modulation provides the most useful information. For sensor operation in the mode of gas desorption/absorption (i.e., modulation of the gas composition), the optimal time of data collection was found to be longer.
Insights
This study demonstrates an electronic nose can differentiate harmful Fusarium fungal species. Heating voltage modulation provided superior data for machine learning models, enabling accurate fungal identification.
Area of Science:
- Mycology
- Sensor Technology
- Machine Learning
Background:
- Fusarium fungi are globally distributed, causing agricultural damage and human health issues due to toxin production.
- Accurate identification of Fusarium species is crucial for effective control and prevention strategies.
- Existing methods for fungal identification can be time-consuming and costly.
Purpose of the Study:
- To develop and evaluate a low-cost electronic nose for differentiating key Fusarium species.
- To compare different electronic nose operational modes for optimal performance.
- To optimize data collection parameters for accurate fungal species classification.
Main Methods:
- Utilized a low-cost electronic nose with six TGS-series gas sensors.
- Investigated gas adsorption/desorption and heating voltage modulation modes.
- Employed random forest machine learning for classification of four Fusarium species (F. avenaceum, F. culmorum, F. greaminarum, F. oxysporum).
- Optimized data collection time for sensor response analysis.
Main Results:
- Electronic nose with heating voltage modulation outperformed gas adsorption/desorption mode.
- Machine learning models trained on heating voltage modulation data showed improved performance.
- Early sensor response during heating voltage modulation yielded the most informative data.
- Optimal data collection time was shorter for heating voltage modulation and longer for gas adsorption/desorption.
Conclusions:
- Electronic nose technology, particularly with heating voltage modulation, offers a promising approach for rapid Fusarium species differentiation.
- Machine learning integration enhances the analytical power of electronic noses for mycotoxin-producing fungi detection.
- Optimized data collection strategies are key to maximizing the efficiency and accuracy of electronic nose-based fungal identification.
