Rapid screening of ochratoxin A in wheat by infrared spectroscopy

Girolamo, Annalisa De; Holst, Christoph von; Cortese, Marina; Cervellieri, Salvatore; Pascale, Michelangelo; Longobardi, Francesco; Catucci, Lucia; Porricelli, Anna Chiara Raffaella; Lippolis, Vincenzo

doi:10.1016/j.foodchem.2019.01.008

Cited by 29 publications

(20 citation statements)

References 21 publications

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“…However, the prediction uncertainty was significantly decreased based on fusion features, thus making the model more robust. In similar studies, De Girolamo et al, (2019b) applied spectroscopy techniques for the classification of wheat bran samples contaminated with deoxynivalenol (DON) using a cut‐off value of 400 µg kg −1 . The overall discrimination rates obtained by PLS‐DA were from 87% to 91% for FT‐NIR and from 86% to 87% for FT‐MIR.…”

Section: Resultsmentioning

confidence: 99%

Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology

Zhang

Jiang

Shen

et al. 2020

Int J of Food Sci Tech

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Section: Resultsmentioning

confidence: 99%

Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology

Zhang

Jiang

Shen

et al. 2020

Int J of Food Sci Tech

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“…De Girolamo and her co‐workers also reported two models for the rapid screening of OTA in wheat ( n = 255) using NIRS and MIRS. Their method has an impressive false compliant rate during classification of 6% (De Girolamo, von Holst, et al., 2019). Kos et al.…”

Section: Applicability Of Ir Spectroscopy At Guideline Valuesmentioning

confidence: 99%

The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops

Freitag

Sulyok

Logan

et al. 2022

Comp Rev Food Sci Food Safe

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Infrared (IR) spectroscopy is increasingly being used to analyze food crops for quality and safety purposes in a rapid, nondestructive, and eco-friendly manner.The lack of sensitivity and the overlapping absorption characteristics of major sample matrix components, however, often prevent the direct determination of food contaminants at trace levels. By measuring fungal-induced matrix changes with near IR and mid IR spectroscopy as well as hyperspectral imaging, the indirect determination of mycotoxins in food crops has been realized. Recent studies underline that such IR spectroscopic platforms have great potential for the rapid analysis of mycotoxins along the food and feed supply chain. However, there are no published reports on the validation of IR methods according to official regulations, and those publications that demonstrate their applicability in a routine analytical set-up are scarce. Therefore, the purpose of this review is to discuss the current state-of-the-art and the potential of IR spectroscopic methods for the rapid determination of mycotoxins in food crops. The study critically reflects on the applicability and limitations of IR spectroscopy in routine analysis and provides guidance to non-spectroscopists from the food and feed sector considering implementation of IR spectroscopy for rapid mycotoxin screening. Finally, an outlook on trends, possible fields of applications, and different ways of implementation in the food and feed safety area are discussed.

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“…Near-infrared (NIR) spectroscopy is an efficient and fast modern analysis technology, which combines computer technology, spectroscopy technology, chemometrics, and other disciplines, and has been widely used in many fields with its unique advantages [10][11][12]. In terms of food safety detection, Jiang et al used the NIR spectroscopy to quantitatively detect the AFB 1 in mildewed wheat [13]; Gaspardo et al used Fourier-transform near-infrared spectroscopy to rapidly detect FB 1 and FB 2 in maize flour [14]; and De Girolamo et al used infrared spectroscopy to rapidly screen OTA in wheat [15]. Although the technique has been successfully applied in the monitoring of mycotoxins in grains and their products, there are still shortcomings: (1) The acquisition of high-quality spectral data.…”

Section: Introductionmentioning

confidence: 99%

Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize

Wang

Deng

Jiang

2022

Foods

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This work provides a novel approach to monitor the aflatoxin B1 (AFB1) content in maize by near-infrared (NIR) spectra-based deep learning models that integrates Markov transition field (MTF) image coding and a convolutional neural network (CNN) strategy. According to the data structure characteristics of near-infrared spectra, new structures of one-dimensional CNN (1D-CNN) and two-dimensional MTF-CNN (2D-MTF-CNN) were designed to construct a deep learning model for the monitoring of AFB1 in maize. The results obtained showed that compared with the 1D-CNN model, the performance of the 2D-MTF-CNN model had been significantly improved, and its root mean square error of prediction, coefficient of predictive determination, and relative percent deviation were 1.3591 μg·kg−1, 0.9955, and 14.9386, respectively. The results indicate that the MTF is an effective data encoding technique for converting one-dimensional spectra into two-dimensional images. It more intuitively reflects the intrinsic characteristics of the NIR spectra from a new perspective and provides richer spectral information for the construction of deep learning models, which can ensure the detection accuracy and generalization performance of deep learning quantitative detection models. This study provides a new analytical perspective for the chemometrics analysis of the NIR spectroscopy.

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Rapid screening of ochratoxin A in wheat by infrared spectroscopy

Cited by 29 publications

References 21 publications

Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology

Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology

The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops

Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize

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