Analysis of Wheat Grain Infection by <i>Fusarium</i> Mycotoxin-Producing Fungi Using an Electronic Nose, GC-MS, and qPCR
<i>Fusarium graminearum</i> and <i>F. culmorum</i> 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 produc...
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/2/326 |
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| author | Piotr Borowik Valentyna Dyshko Miłosz Tkaczyk Adam Okorski Magdalena Polak-Śliwińska Rafał Tarakowski Marcin Stocki Natalia Stocka Tomasz Oszako |
| author_facet | Piotr Borowik Valentyna Dyshko Miłosz Tkaczyk Adam Okorski Magdalena Polak-Śliwińska Rafał Tarakowski Marcin Stocki Natalia Stocka Tomasz Oszako |
| author_sort | Piotr Borowik |
| collection | DOAJ |
| description | <i>Fusarium graminearum</i> and <i>F. culmorum</i> 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 <i>Fusarium</i> 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 <i>Fusarium</i>. 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. |
| first_indexed | 2024-03-08T09:47:48Z |
| format | Article |
| id | doaj.art-71e6f697e6d94517919816c89f3f0734 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T09:47:48Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-71e6f697e6d94517919816c89f3f07342024-01-29T14:12:56ZengMDPI AGSensors1424-82202024-01-0124232610.3390/s24020326Analysis of Wheat Grain Infection by <i>Fusarium</i> Mycotoxin-Producing Fungi Using an Electronic Nose, GC-MS, and qPCRPiotr Borowik0Valentyna Dyshko1Miłosz Tkaczyk2Adam Okorski3Magdalena Polak-Śliwińska4Rafał Tarakowski5Marcin Stocki6Natalia Stocka7Tomasz Oszako8Faculty of Physics, Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warszawa, PolandUkrainian Research Institute of Forestry and Forest Melioration Named after G. M. Vysotsky, 61024 Kharkiv, UkraineForest Protection Department, Forest Research Institute, ul. Braci Leśnej 3, 05-090 Sękocin Stary, PolandDepartment of Entomology, Phytopathology and Molecular Diagnostics, Faculty of Agriculture and Forestry, University of Warmia and Mazury in Olsztyn, Pl. Łódzki 5, 10-727 Olsztyn, PolandDepartment of Commodity Science and Food Analysis, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Heweliusza 6, 10-719 Olsztyn, PolandFaculty of Physics, Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warszawa, PolandInstitute of Forest Sciences, Faculty of Civil Engineering and Environmental Sciences, Białystok University of Technology, ul. Wiejska 45E, 15-351 Białystok, PolandInstitute of Forest Sciences, Faculty of Civil Engineering and Environmental Sciences, Białystok University of Technology, ul. Wiejska 45E, 15-351 Białystok, PolandForest Protection Department, Forest Research Institute, ul. Braci Leśnej 3, 05-090 Sękocin Stary, Poland<i>Fusarium graminearum</i> and <i>F. culmorum</i> 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 <i>Fusarium</i> 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 <i>Fusarium</i>. 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.https://www.mdpi.com/1424-8220/24/2/326gas sensorapplication of e-nose<i>Fusarium</i>pathogen detectionodor differentiationDON |
| spellingShingle | Piotr Borowik Valentyna Dyshko Miłosz Tkaczyk Adam Okorski Magdalena Polak-Śliwińska Rafał Tarakowski Marcin Stocki Natalia Stocka Tomasz Oszako Analysis of Wheat Grain Infection by <i>Fusarium</i> Mycotoxin-Producing Fungi Using an Electronic Nose, GC-MS, and qPCR Sensors gas sensor application of e-nose <i>Fusarium</i> pathogen detection odor differentiation DON |
| title | Analysis of Wheat Grain Infection by <i>Fusarium</i> Mycotoxin-Producing Fungi Using an Electronic Nose, GC-MS, and qPCR |
| title_full | Analysis of Wheat Grain Infection by <i>Fusarium</i> Mycotoxin-Producing Fungi Using an Electronic Nose, GC-MS, and qPCR |
| title_fullStr | Analysis of Wheat Grain Infection by <i>Fusarium</i> Mycotoxin-Producing Fungi Using an Electronic Nose, GC-MS, and qPCR |
| title_full_unstemmed | Analysis of Wheat Grain Infection by <i>Fusarium</i> Mycotoxin-Producing Fungi Using an Electronic Nose, GC-MS, and qPCR |
| title_short | Analysis of Wheat Grain Infection by <i>Fusarium</i> Mycotoxin-Producing Fungi Using an Electronic Nose, GC-MS, and qPCR |
| title_sort | analysis of wheat grain infection by i fusarium i mycotoxin producing fungi using an electronic nose gc ms and qpcr |
| topic | gas sensor application of e-nose <i>Fusarium</i> pathogen detection odor differentiation DON |
| url | https://www.mdpi.com/1424-8220/24/2/326 |
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