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Borowik P, Tkaczyk M, Pluta P, Okorski A, Stocki M, Tarakowski R, Oszako T. Distinguishing between Wheat Grains Infested by Four Fusarium Species by Measuring with a Low-Cost Electronic Nose. SENSORS (BASEL, SWITZERLAND) 2024; 24:4312. [PMID: 39001090 PMCID: PMC11244303 DOI: 10.3390/s24134312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 06/26/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024]
Abstract
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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Affiliation(s)
- Piotr Borowik
- Faculty of Physics, Warsaw University of Technology, Ul. Koszykowa 75, 00-662 Warszawa, Poland;
| | - Miłosz Tkaczyk
- Forest Protection Department, Forest Research Institute, Ul. Braci Leśnej 3, 05-090 Sękocin Stary, Poland; (M.T.); (T.O.)
| | - Przemysław Pluta
- Forestry Students’ Scientific Association, Forest Department, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warszawa, Poland;
| | - Adam Okorski
- Department of Entomology, Phytopathology and Molecular Diagnostics, Faculty of Agriculture and Forestry, University of Warmia and Mazury in Olsztyn, Pl. Łódzki 5, 10-727 Olsztyn, Poland;
| | - Marcin Stocki
- Institute of Forest Sciences, Faculty of Civil Engineering and Environmental Sciences, Białystok University of Technology, Ul. Wiejska 45E, 15-351 Białystok, Poland;
| | - Rafał Tarakowski
- Faculty of Physics, Warsaw University of Technology, Ul. Koszykowa 75, 00-662 Warszawa, Poland;
| | - Tomasz Oszako
- Forest Protection Department, Forest Research Institute, Ul. Braci Leśnej 3, 05-090 Sękocin Stary, Poland; (M.T.); (T.O.)
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Borowik P, Dyshko V, Tkaczyk M, Okorski A, Polak-Śliwińska M, Tarakowski R, Stocki M, Stocka N, Oszako T. Analysis of Wheat Grain Infection by Fusarium Mycotoxin-Producing Fungi Using an Electronic Nose, GC-MS, and qPCR. SENSORS (BASEL, SWITZERLAND) 2024; 24:326. [PMID: 38257418 PMCID: PMC10820217 DOI: 10.3390/s24020326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 12/22/2023] [Accepted: 01/03/2024] [Indexed: 01/24/2024]
Abstract
Fusarium graminearum and F. culmorum are considered some of the most dangerous pathogens of plant diseases. They are also considerably dangerous to humans as they contaminate stored grain, causing a reduction in yield and deterioration in grain quality by producing mycotoxins. Detecting Fusarium fungi is possible using various diagnostic methods. In the manuscript, qPCR tests were used to determine the level of wheat grain spoilage by estimating the amount of DNA present. High-performance liquid chromatography was performed to determine the concentration of DON and ZEA mycotoxins produced by the fungi. GC-MS analysis was used to identify volatile organic components produced by two studied species of Fusarium. A custom-made, low-cost, electronic nose was used for measurements of three categories of samples, and Random Forests machine learning models were trained for classification between healthy and infected samples. A detection performance with recall in the range of 88-94%, precision in the range of 90-96%, and accuracy in the range of 85-93% was achieved for various models. Two methods of data collection during electronic nose measurements were tested and compared: sensor response to immersion in the odor and response to sensor temperature modulation. An improvement in the detection performance was achieved when the temperature modulation profile with short rectangular steps of heater voltage change was applied.
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Affiliation(s)
- Piotr Borowik
- Faculty of Physics, Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warszawa, Poland;
| | - Valentyna Dyshko
- Ukrainian Research Institute of Forestry and Forest Melioration Named after G. M. Vysotsky, 61024 Kharkiv, Ukraine;
| | - Miłosz Tkaczyk
- Forest Protection Department, Forest Research Institute, ul. Braci Leśnej 3, 05-090 Sękocin Stary, Poland; (M.T.); (T.O.)
| | - Adam Okorski
- Department of Entomology, Phytopathology and Molecular Diagnostics, Faculty of Agriculture and Forestry, University of Warmia and Mazury in Olsztyn, Pl. Łódzki 5, 10-727 Olsztyn, Poland;
| | - Magdalena Polak-Śliwińska
- Department of Commodity Science and Food Analysis, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Heweliusza 6, 10-719 Olsztyn, Poland
| | - Rafał Tarakowski
- Faculty of Physics, Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warszawa, Poland;
| | - Marcin Stocki
- Institute of Forest Sciences, Faculty of Civil Engineering and Environmental Sciences, Białystok University of Technology, ul. Wiejska 45E, 15-351 Białystok, Poland; (M.S.); (N.S.)
| | - Natalia Stocka
- Institute of Forest Sciences, Faculty of Civil Engineering and Environmental Sciences, Białystok University of Technology, ul. Wiejska 45E, 15-351 Białystok, Poland; (M.S.); (N.S.)
| | - Tomasz Oszako
- Forest Protection Department, Forest Research Institute, ul. Braci Leśnej 3, 05-090 Sękocin Stary, Poland; (M.T.); (T.O.)
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