Detection and identification of fungal growth on freeze-dried Agaricus bisporus using spectra and olfactory sensors
© 2020 Society of Chemical Industry..
BACKGROUND: Fungal contamination in food products leads to mustiness, biochemical changes, and undesirable odors, which result in lower food quality and lower market value. To develop a rapid method for detecting fungi, hyperspectral imaging (HSI) was applied to identify five fungi inoculated on plates (Aspergillus niger, Aspergillus flavus, Penicillium chrysogenum, Aspergillus fumigatus, and Aspergillus ochraceus). Near-infrared (NIR) spectroscopy, mid-infrared (MIR) spectroscopy, and an electronic nose (E-nose) were applied to detect and identify freeze-dried Agaricus bisporus infected with the five fungi.
RESULTS: Partial least squares regression (PLSR) models were used to distinguish the HSI spectra of the five fungi on the plates. The A. ochraceus group had the highest calibration performance: coefficient of calibration (Rc 2 ) = 0.786, root mean-square error of calibration (RMSEC) = 0.125 log CFU g-1 . The A. flavus group had the highest prediction performance: coefficient of prediction (Rp 2 ) = 0.821, root mean-square error of prediction (RMSEP) = 0.083 log CFU g-1 . The ratio of performance deviation (RPD) values of all of the models was higher than 2.0 for the NIR, MIR, and E-nose results for freeze-dried A. bisporus infected with different fungi. The fungal species and degree of infection can be distinguished by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) using NIR, MIR, and E-nose, as the discrimination accuracy was more than 90%. The NIR methods had a higher recognition rate than the MIR and E-nose methods.
CONCLUSION: Principal component analysis (PCA) and PLSR models based on full spectra of HSI can achieve good discrimination results for these five fungi on plates. Moreover, NIR, MIR, and the E-nose were proven to be effective in monitoring fungal contamination on freeze-dried A. bisporus. However, NIR could be a more accurate method. © 2020 Society of Chemical Industry.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:100 |
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| Enthalten in: |
Journal of the science of food and agriculture - 100(2020), 7 vom: 24. Mai, Seite 3136-3146 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Liuqing [VerfasserIn] |
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| Links: |
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| Themen: |
Electronic nose |
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| Anmerkungen: |
Date Completed 22.12.2020 Date Revised 22.12.2020 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/jsfa.10348 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a © 2020 Society of Chemical Industry. | ||
| 520 | |a BACKGROUND: Fungal contamination in food products leads to mustiness, biochemical changes, and undesirable odors, which result in lower food quality and lower market value. To develop a rapid method for detecting fungi, hyperspectral imaging (HSI) was applied to identify five fungi inoculated on plates (Aspergillus niger, Aspergillus flavus, Penicillium chrysogenum, Aspergillus fumigatus, and Aspergillus ochraceus). Near-infrared (NIR) spectroscopy, mid-infrared (MIR) spectroscopy, and an electronic nose (E-nose) were applied to detect and identify freeze-dried Agaricus bisporus infected with the five fungi | ||
| 520 | |a RESULTS: Partial least squares regression (PLSR) models were used to distinguish the HSI spectra of the five fungi on the plates. The A. ochraceus group had the highest calibration performance: coefficient of calibration (Rc 2 ) = 0.786, root mean-square error of calibration (RMSEC) = 0.125 log CFU g-1 . The A. flavus group had the highest prediction performance: coefficient of prediction (Rp 2 ) = 0.821, root mean-square error of prediction (RMSEP) = 0.083 log CFU g-1 . The ratio of performance deviation (RPD) values of all of the models was higher than 2.0 for the NIR, MIR, and E-nose results for freeze-dried A. bisporus infected with different fungi. The fungal species and degree of infection can be distinguished by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) using NIR, MIR, and E-nose, as the discrimination accuracy was more than 90%. The NIR methods had a higher recognition rate than the MIR and E-nose methods | ||
| 520 | |a CONCLUSION: Principal component analysis (PCA) and PLSR models based on full spectra of HSI can achieve good discrimination results for these five fungi on plates. Moreover, NIR, MIR, and the E-nose were proven to be effective in monitoring fungal contamination on freeze-dried A. bisporus. However, NIR could be a more accurate method. © 2020 Society of Chemical Industry | ||
| 650 | 4 | |a Evaluation Study | |
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a mid-infrared spectroscopy | |
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| 700 | 1 | |a Hu, Qiuhui |e verfasserin |4 aut | |
| 700 | 1 | |a Pei, Fei |e verfasserin |4 aut | |
| 700 | 1 | |a Mugambi, Mariga Alfred |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Wenjian |e verfasserin |4 aut | |
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