Deoxynivalenol in wheat and wheat products from a harvest affected by fusarium head blight

Machado, Lidiane Viera; Mallmann, Carlos Augusto; Mallmann, Adriano Olnei; Coelho, Raquel Durand; Copetti, Marina Venturini

doi:10.1590/1678-457x.05915

Cited by 18 publications

(5 citation statements)

References 15 publications

(17 reference statements)

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“…The main mycotoxins such as are aflatoxins, deoxynivalenol, trichothecenes, fumonisins, zearalenone, and ochratoxin A are produced by Aspergillus, Penicillium, Alternaria, Claviceps and Fusarium species (Udovicki et al, 2018). Among them, deoxynivalenol (DON), also called vomitoxin (type B trichothecene), is one of the most common mycotoxins associated with cereals such as corn, wheat, and barley (Machado et al, 2017;Medina et al, 2019). DON is mainly produced by Fusarium species such as F. graminearum and F. culmorum.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the deoxynivalenol and ochratoxin A levels by high-performance liquid chromatography of cereals sold in the markets in Türkiye

et al. 2023

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The current research was conducted to determine the deoxynivalenol (DON) and ochratoxin A (OTA) levels of 24 pieces of wheat flour, 24 pieces of rice, 24 pieces of corn flour, and 24 pieces of whole wheat flour obtained from the markets in Istanbul by HPLC. DON was detected in 4 of the 96 grain cereal samples at levels between 0.94 and 1.16 µg/kg (LOQ, 0.92 µg/kg). Besides, from the analyzed samples, DON was detected in 3 pieces of ashura wheat, 2 pieces of rice, and 5 pieces of corn flour between LOD and LOQ. The highest DON level was found in the corn flour sample (1.16 µg/kg), which was sold unpacked. OTA was detected in 11 of the samples (0.87-6.97 µg/kg) consisting of 3 corn flours (1.51-2.23 µg/kg) and 8 whole wheat flours (0.87-6.97 µg/kg). The highest OTA level was found in the packaged whole wheat flour sample (6.97 µg/kg) bought from the market. Since DON and OTA exposure can be observed frequently in cereals and may increase to possible risky levels, further work should be done to determine the precautions necessary to minimize the risks of contamination.

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Section: Introductionmentioning

confidence: 99%

Investigation of the deoxynivalenol and ochratoxin A levels by high-performance liquid chromatography of cereals sold in the markets in Türkiye

et al. 2023

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“…Deoxynivalenol (DON), one of the most frequently detected mycotoxins mainly produced by Fusarium species, has attracted extensive attention due to its harmful influence on humans and animals [ 1 ]. DON can not only inhibit the synthesis of proteins and DNA and induce immuno-suppression, but can also result in gastroenteritis, vomiting, diarrhea, and even death [ 1 , 2 , 3 ]. Moreover, the heat resistance and water solubility of DON allow it to enter food chains and cause acute or chronic poisoning [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Rapid, Sensitive On-Site Detection of Deoxynivalenol in Cereals Using Portable and Reusable Evanescent Wave Optofluidic Immunosensor

Liu

Chen

et al. 2022

IJERPH

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This paper develops an improved portable and reusable evanescent wave optofluidic immunosensor (OIP-v2) for rapid and sensitive on-site determination of deoxynivalenol (DON), one of the most frequently detected mycotoxins mainly produced by Fusarium species. Using the bifunctional reagent N,N′-Disuccinimidyl carbonate, deoxynivalenol-bovine-serum-albumin (DON-BSA) were covalently modified onto a bio-probe surface as biorecognition elements, whose robustness allowed it to perform multiple detections without significant activity loss. An indirect competitive immunoassay strategy was applied for DON detection. Under optimal conditions, the limit of detection of 0.11 μg/L and the linear dynamic detection range of 0.43 to 36.61 μg/L was obtained when the concentration of the Cy5.5-anti-DON antibody was 0.25 μg/mL. The OIP-v2 was also applied to detect DON in various cereals, and the recoveries ranged from 81% to 127%. The correlation between OIP-v2 and enzyme-linked immunosorbent assay (ELISA) through the simultaneous detection of maize-positive samples was in good agreement (R2 = 0.9891).

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“…Therefore, healthy and stable wheat growth is important in regard to food security. Fusarium head blight (FHB) is one of the main wheat diseases, which can cause the deterioration of quality and a serious loss of yield [2,3]. Moreover, the pathogen produces toxic deoxynivalenol (DON), which is detrimental to animal and human health [4].…”

Section: Introductionmentioning

confidence: 99%

Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM

Huang

et al. 2021

Agriculture

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Fusarium head blight, caused by a fungus, can cause quality deterioration and severe yield loss in wheat. It produces highly toxic deoxynivalenol, which is harmful to human and animal health. In order to quickly and accurately detect the severity of fusarium head blight, a method of detecting the disease using continuous wavelet analysis and particle swarm optimization support vector machines (PSO-SVM) is proposed in this paper. First, seven wavelet features for fusarium head blight detection were extracted using continuous wavelet analysis based on the hyperspectral reflectance of wheat ears. In addition, 16 traditional spectral features were selected using correlation analysis, including two continuous removal transformed spectral features, six differential spectral features, and eight vegetation indices. Finally, wavelet features and traditional spectral features were used as input features to construct fusarium head blight detection models in combination with the PSO-SVM algorithm, and the results were compared with those obtained using random forest (RF) and a back propagation neural network (BPNN). The results show that, under the same feature variables, the PSO-SVM detection method gave an overall higher accuracy than the BPNN detection method, while the overall accuracy of the RF detection model was the lowest. The overall accuracy of the RF, BPNN and PSO-SVM detection models with wavelet features was higher by 3.7%, 2.9% and 8.3% compared to the corresponding methodological models with traditional spectral features. The detection model with wavelet features combining the PSO-SVM algorithm gave the highest overall accuracies (93.5%) and kappa coefficients (0.903) in the six monitoring models. These results suggest that the PSO-SVM algorithm combined with continuous wavelet analysis can significantly improve the accuracy of fusarium head blight detection on the wheat ears scale.

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Deoxynivalenol in wheat and wheat products from a harvest affected by fusarium head blight

Cited by 18 publications

References 15 publications

Investigation of the deoxynivalenol and ochratoxin A levels by high-performance liquid chromatography of cereals sold in the markets in Türkiye

Investigation of the deoxynivalenol and ochratoxin A levels by high-performance liquid chromatography of cereals sold in the markets in Türkiye

Rapid, Sensitive On-Site Detection of Deoxynivalenol in Cereals Using Portable and Reusable Evanescent Wave Optofluidic Immunosensor

Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM

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