Use of hyperspectral imaging as a tool for Fusarium and deoxynivalenol risk management in cereals: A review

Femenias, Antoni; Gatius, Ferran; Ramos, Ana M. González; Sanchis, Vicente; Marı́n, Sonia

doi:10.1016/j.foodcont.2019.106819

Cited by 63 publications

(30 citation statements)

References 79 publications

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“…Many scholars have established APs disease detection models based on the HSI [11,12]. For example, Abid Hussain et al [13] created a partial least squares-discriminant analysis (PLS-DA) model based on effective attenuation coefficients, and used the model to classify tomatoes at various stages of maturity.…”

Section: Introductionmentioning

confidence: 99%

Saccharinity Test on Cherry Tomatoes Based on Hyperspectral Imaging

Liu¹,

Shi²,

Zhang³

et al. 2020

IJDNE

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Based on the hyperspectral imaging (HSI) technique, this paper attempts to test the saccharinity of three varieties of cherry tomatoes in a nondestructive manner. The cherry tomato samples of the three varieties were collected, and kept at room temperature for 12h. Then, the spectral curves of the samples were obtained between the wavelengths of 914.91nm and 1,661.91nm. After that, the feature bands were extracted by three algorithms, namely, competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and SPA-CARS. The samples were divided into a correction dataset and a prediction dataset at the ratio of 2:1. Next, the feature bands extracted by the three algorithms were combined with the partial least squares (PLS) and least squares-support vector machine (LS-SVM) into six saccharinity prediction models. Finally, the prediction results of the six models were compared, revealing that the CARS-LS-SVM achieved the best performance with a prediction accuracy of >92%. The evaluation indices of this model are as follows: the correlation coefficient of correction dataset (R), 0.9696; the correlation coefficient of prediction dataset (R), 0.9220; the root mean square error of correction dataset (RMSEC), 0.2768; the root mean square error of prediction dataset (RMSEP), 0.4390. The research results lay the basis for industrial grading of saccharinity of cherry tomatoes in a nondestructive manner.

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Section: Introductionmentioning

confidence: 99%

Saccharinity Test on Cherry Tomatoes Based on Hyperspectral Imaging

Liu¹,

Shi²,

Zhang³

et al. 2020

IJDNE

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“…On the aboveground parts of plants such as corn, sunflower, and flax, the detection of fusariosis was isolated. On maize and sunflower, F. verticillioides was detected (5 and 3.35%, respectively), which is pathogenic associated with these crops [5], [6]. On the leaves and stems of flax, F. oxysporum (2%) was detected, which is one of the most common pathogens of root and root rot of different crops.…”

Section: Resultsmentioning

confidence: 99%

Analysis of infection with fungi of the genus Fusarium seed and vegetative organs of crops

et al. 2021

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Studying the specific and intraspecific structure of populations of causative agents of Fusarium ear of grain crops. The results of experimental studies of samples of grain crops affected by microscopic fungi p.Fusarium. The analysis of the infection of grains, stems and roots of various types of cereals with mitospores fungi was carried out. The variability of pathogenic properties of Fusarium fungi depending on the part of the ear is shown. The results of the research are important for the targeted diagnosis of fusarium of grain crops and plants of the ear.

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“…A significant health risk may result from intoxication by mycotoxins contributed by these plant pathogens. The potential of NIR-HSI technique in the role of the detection tool for mycotoxins in cereals was recently reviewed by Femenias et al., 2020 . As summarized, the analytical performance of HSI approach is decisively superior to both the conventional methods of wet analytical chemistry and multispectral imaging methods.…”

Section: Applications Of Spectroscopic Imaging In Plant-oriented Studmentioning

confidence: 99%

Principles and Applications of Vibrational Spectroscopic Imaging in Plant Science: A Review

et al. 2020

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Detailed knowledge about plant chemical constituents and their distributions from organ level to sub-cellular level is of critical interest to basic and applied sciences. Spectral imaging techniques offer unparalleled advantages in that regard. The core advantage of these technologies is that they acquire spatially distributed semi-quantitative information of high specificity towards chemical constituents of plants. This forms invaluable asset in the studies on plant biochemical and structural features. In certain applications, non-invasive analysis is possible. The information harvested through spectral imaging can be used for exploration of plant biochemistry, physiology, metabolism, classification, and phenotyping among others, with significant gains for basic and applied research. This article aims to present a general perspective about vibrational spectral imaging/micro-spectroscopy in the context of plant research. Within the scope of this review are infrared (IR), near-infrared (NIR) and Raman imaging techniques. To better expose the potential and limitations of these techniques, fluorescence imaging is briefly overviewed as a method relatively less flexible but particularly powerful for the investigation of photosynthesis. Included is a brief introduction to the physical, instrumental, and data-analytical background essential for the applications of imaging techniques. The applications are discussed on the basis of recent literature.

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Use of hyperspectral imaging as a tool for Fusarium and deoxynivalenol risk management in cereals: A review

Abstract: Està subjecte a una llicència de Reconeixement-NoComercial-SenseObraDerivada 3.0 de Creative Commons

Cited by 63 publications

References 79 publications

Saccharinity Test on Cherry Tomatoes Based on Hyperspectral Imaging

Saccharinity Test on Cherry Tomatoes Based on Hyperspectral Imaging

Analysis of infection with fungi of the genus Fusarium seed and vegetative organs of crops

Principles and Applications of Vibrational Spectroscopic Imaging in Plant Science: A Review

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