Distinguishing between Wheat Grains Infested by Four Fusarium Species by Measuring with a Low-Cost Electronic Nose.

Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE

Original Publication: Basel, Switzerland : MDPI, c2000-

Fusarium*/isolation & purification ; Fusarium*/classification ; Electronic Nose* ; Triticum*/microbiology ; Triticum*/chemistry; Edible Grain/microbiology ; Edible Grain/chemistry ; Volatile Organic Compounds/analysis ; Plant Diseases/microbiology

An electronic device based on the detection of volatile substances was developed in response to the need to distinguish between fungal infestations in food and was applied to wheat grains. The most common pathogens belong to the fungi of the genus Fusarium : F. avenaceum , F. langsethiae , F. poae , and F. sporotrichioides . The electronic nose prototype is a low-cost device based on commercially available TGS series sensors from Figaro Corp. Two types of gas sensors that respond to the perturbation are used to collect signals useful for discriminating between the samples under study. First, an electronic nose detects the transient response of the sensors to a change in operating conditions from clean air to the presence of the gas being measured. A simple gas chamber was used to create a sudden change in gas composition near the sensors. An inexpensive pneumatic system consisting of a pump and a carbon filter was used to supply the system with clean air. It was also used to clean the sensors between measurement cycles. The second function of the electronic nose is to detect the response of the sensor to temperature disturbances of the sensor heater in the presence of the gas to be measured. It has been shown that features extracted from the transient response of the sensor to perturbations by modulating the temperature of the sensor heater resulted in better classification performance than when the machine learning model was built from features extracted from the response of the sensor in the gas adsorption phase. By combining features from both phases of the sensor response, a further improvement in classification performance was achieved. The E-nose enabled the differentiation of F. poae from the other fungal species tested with excellent performance. The overall classification rate using the Support Vector Machine model reached 70 per cent between the four fungal categories tested.

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BIOSTRATEG3/347105/9/NCBR/2017 National Centre for Research and Development; 30.610.009.110 University of Warmia and Mazury in Olsztyn

Keywords: Fusarium avenaceum; Fusarium langsethiae; Fusarium poae; Fusarium sporotrichioides; application of e-nose; gas sensor

0 (Volatile Organic Compounds)

Date Created: 20240713 Date Completed: 20240713 Latest Revision: 20240715

20250114

PMC11244303

10.3390/s24134312

39001090