Estimating deoxynivalenol contents of wheat samples containing different levels of Fusarium-damaged kernels by diffuse reflectance spectrometry and partial least square regression

Beyer, Marco; Pogoda, Friederike; Ronellenfitsch, Franz; Hoffmann, Lucien; Udelhoven, Thomas

doi:10.1016/j.ijfoodmicro.2010.07.016

Cited by 34 publications

(28 citation statements)

References 20 publications

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“…Good OD results in the NIR range using LDA classification models were also reported in our previous work for DON screening in wheat, by Dvoracek et al . for DON estimation in intact wheat grain, by Beyer et al . for the screening by NIR of Fusarium ‐damaged wheat kernel containing different DON levels, and by Levasseur‐Garcia and Kleiber for DON estimation in maize samples naturally contaminated by DON.…”

Section: Resultssupporting

confidence: 80%

Section: Resultsmentioning

confidence: 99%

“…Several papers have addressed the use of FTNIR spectroscopy as an indirect analytical tool for the quantitative analysis of DON in cereals; however, most of them showed that FTNIR spectroscopy is only of limited applicability in the quantitative determination of DON in cereals. 24,28,32,[34][35][36] The feasibility of using FTNIR spectroscopy for the screening of DON-contaminated wheat bran samples using the PLS-DA and PC-LDA classification models and a cutoff of 400 μg kg −1 DON was investigated. The different pretreatments investigated gave OD rates that were always higher than 80%, FC rates between 0-17% and FNC rates between 9%-27%, depending on the classification model.…”

Section: Ftnir Spectroscopy Analysismentioning

confidence: 99%

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Fourier transform near‐infrared and mid‐infrared spectroscopy as efficient tools for rapid screening of deoxynivalenol contamination in wheat bran

et al. 2018

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BACKGROUND Deoxynivalenol (DON) is the most common Fusarium mycotoxin occurring in wheat and wheat‐derived products, with several adverse and toxic effects in animals and humans. Although bran fractions produced by milling wheat have numerous health benefits, cereal bran is the part of the grain with the highest concentration of DON, thus representing a risk for consumers. Increased efforts have been made to develop analytical methods suitable for rapid DON screening. RESULTS The applicability of Fourier transform near‐infrared (FTNIR), or mid‐infrared (FTMIR) spectroscopy, and their combination for rapid analysis of DON in wheat bran, was investigated for the classification of samples into compliant and non‐compliant groups regarding the EU legal limit of 750 µg kg−1. Partial least squares‐discriminant analysis (PLS‐DA) and principal component‐linear discriminant analysis (PC‐LDA) were employed as classification techniques using a cutoff value of 400 µg kg−1 DON to distinguish the two classes. Depending on the classification model, overall discrimination rates were from 87% to 91% for FTNIR and from 86% to 87% for the FTMIR spectral range. The FTNIR spectroscopy gave the highest overall classification rate of wheat bran samples, with no false compliant samples and 18% false noncompliant samples when the PC‐LDA classification model was applied. The combination of the two spectral ranges did not provide a substantial improvement in classification results in comparison with FTNIR. CONCLUSIONS Fourier transform near‐infrared spectroscopy in combination with classification models was an efficient tool to screen many DON‐contaminated wheat bran samples and assess their compliance with EU regulations. © 2018 Society of Chemical Industry

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Section: Resultssupporting

confidence: 80%

Section: Resultsmentioning

confidence: 99%

Section: Ftnir Spectroscopy Analysismentioning

confidence: 99%

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Fourier transform near‐infrared and mid‐infrared spectroscopy as efficient tools for rapid screening of deoxynivalenol contamination in wheat bran

et al. 2018

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“…established regression models for determining AFB1 content in corn and barley, and judged the samples to be positive or negative with a boundary of 20 ppb. Beyer et al ., Pettersson et al ., and Dvoracek et al . predicted the deoxynivalenol content and the proportion of wheat seeds affected by Fusarium.…”

Section: Introductionmentioning

confidence: 99%

“…16 Fernandez-Ibanez et al 17 established regression models for determining AFB1 content in corn and barley, and judged the samples to be positive or negative with a boundary of 20 ppb. Beyer et al, 18 Pettersson et al, 19 and Dvoracek et al 20 predicted the deoxynivalenol content and the proportion of wheat seeds affected by Fusarium. Sirisomboon et al 21 built an optical model to predict the proportion of Aspergillus fungi contamination in rice.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive comparison of multiple quantitative near‐infrared spectroscopy models for Aspergillus flavus contamination detection in peanut

Tang

Shen

et al. 2019

J Sci Food Agric

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BACKGROUND Aspergillus flavus is a major pollutant in moldy peanuts, and it has a large influence on the taste of food. The secondary metabolites of Aspergillus flavus, including aflatoxin B1 (AFB1) and aflatoxin B2 (AFB2), are highly toxic and can expose humans to high risk. The total mold count (TMC) is an important index to determine the contamination degree and hygiene quality of peanut. RESULTS Quantitative calibration models were established based on full‐band wavelengths and characteristic wavelengths, combined with chemometric methods, to explore the feasibility of the use of near‐infrared spectroscopy (NIRS) for rapid detection of the TMC in peanuts. The successive projection algorithm (SPA) and elimination of uninformative variables (UVE) algorithms were used to extract the characteristic wavelengths. In comparison, the model built by original spectrum, selected with the UVE algorithm, gave the best result, with a correlation coefficient in a prediction set (RP) of 0.9577, a root mean square error for the prediction set (RMSEP) of 0.2336 Log CFU/g, and a residual predictive deviation (RPD) of 3.5041. CONCLUSIONS The results showed that NIRS is a rapid, practicable method for the quantitative detection of peanut Aspergillus flavus contamination. It is a promising method for detecting moldy peanuts and increasing peanut safety. © 2019 Society of Chemical Industry

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Safety Inspection of Plant Products

Yao

Hruska

Brown

et al. 2015

Food Engineering Series

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Estimating deoxynivalenol contents of wheat samples containing different levels of Fusarium-damaged kernels by diffuse reflectance spectrometry and partial least square regression

Cited by 34 publications

References 20 publications

Fourier transform near‐infrared and mid‐infrared spectroscopy as efficient tools for rapid screening of deoxynivalenol contamination in wheat bran

Fourier transform near‐infrared and mid‐infrared spectroscopy as efficient tools for rapid screening of deoxynivalenol contamination in wheat bran

Comprehensive comparison of multiple quantitative near‐infrared spectroscopy models for Aspergillus flavus contamination detection in peanut

Safety Inspection of Plant Products

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