A mycotoxin transporter (4D) from a library of deoxynivalenol-tolerant microorganisms

Jímenez-Sánchez, Celía; Wilson, Nina; McMaster, Nicole; Gantulga, Dash; Freedman, Benjamin G.; Senger, Ryan S.; Schmale, David G.

doi:10.1016/j.toxcx.2020.100023

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…Briefly, the method compares the peak area of DON with a standard and follows several steps such as extraction, cleanup, ion monitoring, and computing DON concentration using the calibration curve. Detailed procedures of DON quantification have been described elsewhere [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels

Dhakal

Sivaramakrishnan

Zhang

et al. 2023

Sensors

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Fusarium head blight (FHB) is a disease of small grains caused by the fungus Fusarium graminearum. In this study, we explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels. We evaluated the use of HSI for disease classification and correlated the damage with the mycotoxin deoxynivalenol (DON) content. Computational analyses were carried out to determine which machine learning methods had the best accuracy to classify different levels of damage in wheat kernel samples. The classes of samples were based on the DON content obtained from Gas Chromatography–Mass Spectrometry (GC-MS). We found that G-Boost, an ensemble method, showed the best performance with 97% accuracy in classifying wheat kernels into different severity levels. Mask R-CNN, an instance segmentation method, was used to segment the wheat kernels from HSI data. The regions of interest (ROIs) obtained from Mask R-CNN achieved a high mAP of 0.97. The results from Mask R-CNN, when combined with the classification method, were able to correlate HSI data with the DON concentration in small grains with an R2 of 0.75. Our results show the potential of HSI to quantify DON in wheat kernels in commercial settings such as elevators or mills.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels

Dhakal

Sivaramakrishnan

Zhang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Specific features of antagonism of <i>Bacillus</i> bacteria against toxinogenic <i>Fusarium</i> fungi in protecting plants against disease and contamination with mycotoxins (review)

Сидорова

Асатурова

Allakhverdyan

2022

Ûg Ross.: èkol. razvit.

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Федеральное государственное бюджетное научное учреждение «Федеральный научный центр биологической защиты растений», Краснодар, Россия Контактное лицо Татьяна М. Сидорова, кандидат биологических наук, старший научный сотрудник ФНЦ биологической защиты растений; 350039 Россия, Краснодарский край, г. Краснодар, п/о 39. Тел.

show abstract

A mycotoxin transporter (4D) from a library of deoxynivalenol-tolerant microorganisms

Cited by 2 publications

References 30 publications

Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels

Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels

Specific features of antagonism of <i>Bacillus</i> bacteria against toxinogenic <i>Fusarium</i> fungi in protecting plants against disease and contamination with mycotoxins (review)

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