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Sirakov I, Velichkova K, Dinev T, Slavcheva-Sirakova D, Valkova E, Yorgov D, Veleva P, Atanasov V, Atanassova S. Detection of Fungal Diseases in Lettuce by VIR-NIR Spectroscopy in Aquaponics. Microorganisms 2023; 11:2348. [PMID: 37764192 PMCID: PMC10537723 DOI: 10.3390/microorganisms11092348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/14/2023] [Accepted: 09/17/2023] [Indexed: 09/29/2023] Open
Abstract
One of the main challenges facing the development of aquaponics is disease control, due on one hand to the fact that plants cannot be treated with chemicals because they can lead to mortality in cultured fish. The aim of this study was to apply the visible-near-infrared spectroscopy and vegetation index approach to test aquaponically cultivated lettuce (Lactuca sativa L.) infected with different fungal pathogens (Aspergillus niger, Fusarium oxysporum, and Alternaria alternata). The lettuces on the third leaf formation were placed in tanks (with dimensions 1 m/0.50 m/0.35 m) filled up with water from the aquaponics system every second day. In this study, we included reference fungal strains Aspergillus niger NBIMCC 3252, Fusarium oxysporum NBIMCC 125, and Alternaria alternata NBIMCC 109. Diffuse reflectance spectra of the leaves of lettuce were measured directly on the plants using a USB4000 spectrometer in the 450-1100 nm wavelength range. In near-infrared spectral range, the reflectance values of infected leaves are lower than those of the control, which indicates that some changes in cell structures occurred as a result of the fungal infection. All three investigated pathogens had a statistically significant effect on leaf water content and water band index. Vegetative indices such as Chlorophyll Absorption in Reflectance Index (CARI), Modified chlorophyll absorption in reflectance index (MCARI), Plant Senescence Reflectance Index (PSRI), Red Edge Index (REI2), Red Edge Index (REI3), and Water band index (WBI) were found to be effective in distinguishing infected plants from healthy ones, with WBI demonstrating the greatest reliability.
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Affiliation(s)
- Ivaylo Sirakov
- Department of Animal Husbandry-Non-Ruminants and Other Animals, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
| | - Katya Velichkova
- Department of Biological Sciences, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
| | - Toncho Dinev
- Department of Biological Sciences, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
| | - Desislava Slavcheva-Sirakova
- Department of Botany and Agrometeorology, Faculty of Agronomy, Agricultural University, 12 Mendeleev blvd, 4000 Plovdiv, Bulgaria
| | - Elica Valkova
- Department of Biological Sciences, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
| | - Dimitar Yorgov
- Department of Agricultural Engineering, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
| | - Petya Veleva
- Department of Agricultural Engineering, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
| | - Vasil Atanasov
- Department of Biological Sciences, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
| | - Stefka Atanassova
- Department of Agricultural Engineering, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
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Atanassova S, Yorgov D, Veleva P, Stoyanchev T, Zlatev Z. Cheese quality assessment by use of near-infrared spectroscopy. BIO Web Conf 2023. [DOI: 10.1051/bioconf/20235802007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023] Open
Abstract
Dairy products are worldwide spread and have great commercial importance. Rapid and reliable analysis of cheese would be highly desirable both for the manufacturers and consumers. The results of experiments, related to the application of near-infrared spectroscopy for cheese quality estimation will be presented. Several kinds of Bulgarian white brine cheese - natural from cow milk, imitation products with vegetable oil, and cheese with different water content were investigated. Fatty acids composition of samples was determined by using gas chromatography and moisture content by the oven-dry method. Spectra of all tested samples were obtained with a scanning NIRQuest 512 (Ocean Optics, Inc.) instrument in the range of 900-1700 nm using a reflection fiber-optics probe. PLS models were developed for quantitative determination and SIMCA for classification. The misclassification rate of the SIMCA model for discrimination of natural cheese and imitation products with vegetable oil was 2.9%. Quantitative determination of water content based on NIR spectra showed high accuracy, Models for classification of cheese samples into 3 groups according to water content achieved 5.64% misclassification rate for the independent test set. Results showed the potential of near-infrared spectroscopy as a non-destructive and rapid screening tool for assessing cheese quality and detecting adulteration.
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