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

Zhang, Bin; Jiang, Xuesong; Shen, Fei; He, Xueming; Fang, Yong; Hu, Qiuhui

doi:10.1111/ijfs.14775

Cited by 9 publications

(4 citation statements)

References 29 publications

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“…Computer vision and NIR were combined to measure freshness of fish storage, and based on BP-ANN model, the discrimination rate achieved 93.33% . After that, computer vision has cooperated with NIR or FTIR (Fourier Transform Infrared) spectra, and with the assistance of PCA, PLS-DA, and SVM, breakthroughs were obtained. − The results have proved that a combination of CV and NIR improved the performance by 13%, which was prominent …”

Section: Sensor Combinations For Solid Waste Sorting Systemsmentioning

confidence: 96%

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

Zhao

2022

Environ. Sci. Technol.

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The increase in global population and improvement of living standards have stirred up a continuous increase in solid waste generation, while simple incineration and landfilling bring about serious environmental and health concerns. In order to improve resource recovery and mitigate pollution, noncontacting and nondestructive sensor-based waste sorting systems are applied to enhance solid waste classification. In recent years, in addition to the rapid development of computer hardware, especially improvements of GPU computing capacity, complicated and efficient classification algorithms have emerged and been widely used in industrial sectors. These advances allow computers to process signals from sensors more quickly and accurately and to classify matters automatically. This article introduces widely applied sensor-based technologies in solid waste sorting and analyzes applicable conditions for each specific method. The latest developed algorithms are critically compared with competitive counterparts. Successful practices are described, and findings are highlighted. Though spectroscopic-based and vision-based waste classifications have achieved high performance in accuracy and detection speed, challenges and future directions can still provide wide development opportunities. Concretely, these opportunities generally comprise classification of indistinct plastics, application of the latest object detection algorithms, appropriate data set formulating, and sensor combination for multiple sorting tasks within a single system.

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Section: Sensor Combinations For Solid Waste Sorting Systemsmentioning

confidence: 96%

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

Zhao

2022

Environ. Sci. Technol.

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“…Wheat Fungal infection, DON, ergosterol DR, NIT (Beyer et al, 2010;De Girolamo et al, 2014;De Girolamo et al, 2009;Delwiche, 2003;Delwiche & Hareland, 2004;Dowell et al, 1999;Dvořáček et al, 2012;Peiris et al, 2017;Pettersson & Åberg, 2003;Rasch et al, 2010;Shen et al, 2019;Siuda et al, 2008;B. Zhang et al, 2021) Corn Ergosterol, DON, FUMs , AFLA B 1 , ZEN DR (Berardo et al, 2005;Darnell et al, 2018;Della Riccia Giacomo, 2013;Falade et al, 2017;Fernández-Ibañez et al, 2009;Shen et al, 2022;Tao, Yao, Zhu, et al, 2019;Tyska et al, 2021) Barley Ergosterol, DON DR, NIT (Börjesson et al, 2007;Caramês et al, 2020;Fernández-Ibañez et al, 2009;Ruan et al, 2002) Rice (Li et al, 2019;S.…”

Section: Target Contaminants Spectral Acquisition Modes Referencementioning

confidence: 99%

“…Accurate quantitative prediction of ZEN and FUMs in corn and DON in barley using NIRS has been demonstrated (Caramês et al., 2020; Tyska et al., 2021). Methods aiming at the classification of high and low contaminated samples with an accuracy bigger than 90% include the prediction of DON in wheat and corn and OTA in wheat using NIRS and MIRS (De Girolamo, von Holst, et al., 2019; Öner et al., 2019; B. Zhang et al., 2021). Category B: Those methods fall into this category, which show good promise for routine analytical applications in the near future.…”

Section: Conclusion Trends and Outlookmentioning

confidence: 99%

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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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TaqMan Probes for Plant Species Identification and Quantification in Food and Feed Traceability

Campos

Cardoso

2023

Methods in Molecular Biology

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

Abstract: This work developed an online experimental system and evaluated the feasibility of Vis/NIR spectroscopy‐computer vision coupling technology for rapid screening of DON contamination in whole wheat meals.

Cited by 9 publications

References 29 publications

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

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

TaqMan Probes for Plant Species Identification and Quantification in Food and Feed Traceability

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