Updated Overview of Infrared Spectroscopy Methods for Detecting Mycotoxins on Cereals (Corn, Wheat, and Barley)

Levasseur-Garcia, Cécile

doi:10.3390/toxins10010038

Cited by 66 publications

(44 citation statements)

References 66 publications

(101 reference statements)

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“…All were selected by the UVE algorithm and were distributed throughout the spectrum. The growth of the microorganism is accompanied by the consumption of various nutrients in the peanuts, such as starch, proteins, fats, sugars, vitamins, and water, so the absorption wavelengths of the reactive nutrients may also play a role in TMC prediction . It therefore makes sense that more wavelengths selected by the UVE algorithm are related to the prediction of the TMC directly or indirectly.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…It is commonly used to detect mycotoxins in maize, wheat, and barley. Qualitative prediction is more feasible than quantitative prediction . Fernandez‐Ibanez et al .…”

Section: Introductionmentioning

confidence: 97%

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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“…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 …”

Section: Resultsmentioning

confidence: 99%

“…Indeed, in two recent overviews on IR methods for the analysis of mycotoxins in cereals it was clearly evident that the majority of referenced papers focused on DON followed by fumonisins, aflatoxins, zearalenone, ochratoxin A, and nivalenol, while the most investigated matrices were maize and wheat. Furthermore, most applications of IR for mycotoxin analysis in these commodities were based on the NIR spectral range …”

Section: Introductionmentioning

confidence: 99%

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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“…Data exploration allows finding sample groups, the relation between variables and management with outliers samples by means of a PCA or a parallel factor analysis (PARAFAC) (Bro, ; Rodrigues, Condino, Pinheiro, & Nunes, ). Data preprocessing can be handled with preprocessing algorithms, such as smoothing methods (Savitzky–Golay, Gaussian filter, median filter, moving average), normalization and scaling, detrending (Levasseur‐garcia, ), 1 st Derivate, 2 nd Derivate–Savitzky Golay (Savitzky & Golay, ), Standard Normal Variation (Teye, Uhomoibhi, & Wang, ), Orthogonal Signal Correction (Wold, Antti, Lindgren, & Öhman, ), and Multiple Scatter Correction to build and enhance calibration models (Su & Sun, ). The selected preprocessing method can be related to data features to, for example, rid up multiplicative and additive effects in spectra.…”

Section: Fast Nondestructive Technologies Applied In the Cocoa Industrymentioning

confidence: 99%

Roadmap of cocoa quality and authenticity control in the industry: A review of conventional and alternative methods

Quelal-Vásconez

Lerma‐García

Pérez‐Esteve

et al. 2020

Comp Rev Food Sci Food Safe

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Cocoa (Theobroma cacao L.) and its derivatives are appreciated for their aroma, color, and healthy properties, and are commodities of high economic value worldwide. Wide ranges of conventional methods have been used for years to guarantee cocoa quality. Recently, however, demand for global cocoa and the requirements of sensory, functional, and safety cocoa attributes have changed. On the one hand, society and health authorities are increasingly demanding new more accurate quality control tests, including not only the analysis of physicochemical and sensory parameters, but also determinations of functional compounds and contaminants (some of which come in trace quantities). On the other hand, increased production forces industries to seek quality control techniques based on fast, nondestructive online methods. Finally, an increase in global cocoa demand and a consequent rise in prices can lead to future cases of fraud. For this reason, new analytes, technologies, and ways to analyze data are being researched, developed, and implemented into research or quality laboratories to control cocoa quality and authenticity. The main advances made in destructive techniques focus on developing new and more sensitive methods such as chromatographic analysis to detect metabolites and contaminants in trace quantities. These methods are used to assess cocoa quality; study new functional properties; control cocoa authenticity; or detect frequent emerging frauds. Regarding nondestructive methods, spectroscopy is the most explored technique, which is conducted within the near infrared range, and also within the medium infrared range to a lesser extent. It is applied mainly in the postharvest stage of cocoa beans to analyze different biochemical parameters or to assess the authenticity of cocoa and its derivatives.

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Updated Overview of Infrared Spectroscopy Methods for Detecting Mycotoxins on Cereals (Corn, Wheat, and Barley)

Cited by 66 publications

References 66 publications

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

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

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

Roadmap of cocoa quality and authenticity control in the industry: A review of conventional and alternative methods

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