X-Ray Applications in Food and Agriculture: A Review

Mathanker, Sunil K.; Weckler, Paul; Bowser, Timothy J.

doi:10.13031/trans.56.9785

Cited by 22 publications

(11 citation statements)

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“…Such restraints include the limitation of detection to density-changing tissues and not chemical composition or mechanical damage forms, the high cost of X-ray inspection systems, and low operational speed (Chen et al, 2002;Butz et al, 2005;Mathanker et al, 2013).…”

Section: Imaging-based Methodsmentioning

confidence: 99%

Rapid and/or nondestructive quality evaluation methods for potatoes: A review

Rady

Guyer

2015

Computers and Electronics in Agriculture

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Section: Imaging-based Methodsmentioning

confidence: 99%

Rapid and/or nondestructive quality evaluation methods for potatoes: A review

Rady

Guyer

2015

Computers and Electronics in Agriculture

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“…X-ray tests are well known Fig. 8 Overview of ventral (a) and dorsal (b) surface of three seedlots (Lots 1, 2 and 3) of Jatropha curcas in detecting internal seed defects, which contributes to predict problems in the future such as physiological abnormalities during imbibition, germination and seedling development [8,9,39,40]. It has been successfully used to identify insect damages in Triticum aestivum [39,41] and Glycine max seeds [42], desiccation sensitivity of Quercus rubra seeds [43], and mechanical damage in Archontophoenix cunninghamii seeds [44].…”

Section: Discussionmentioning

confidence: 99%

Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

et al. 2021

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Background The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time. Results We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serves as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (> 0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds. Conclusions Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.

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“…Different from MSI that generates reflectance images, X-ray image is a transmittance image as a result of short wavelengths (range of 0.01 to 10 nm) that can penetrate the seed tissues [30,31]. X-ray tests are well known in detecting internal defects of seeds, which can help to predict possible problems in the future, such as physiological abnormalities during imbibition, germination or seedling growth [14,[31][32][33]. It has been successfully used to identify seeds damaged by insects in Triticum aestivum [32,34] and Glycine max [35], as well as the desiccation sensitivity of Quercus rubra seeds [36] and mechanical damage in Archontophoenix cunninghamii seeds [37].…”

Section: Discussionmentioning

confidence: 99%

Multispectral and X-ray Images for Characterization of Jatropha Curcas L. Seed Quality

Bianchini

Mascarin

Silva

et al. 2020

Preprint

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Background: Jatropha curcas is an oilseed plant with great potential for biodiesel production. In agricultural industry, the seed quality is still estimated by manual inspection, using destructive, time-consuming and subjective tests that depend on the seed analyst experience. Recent advances in machine vision combined with artificial intelligence algorithms can provide spatial and spectral information for characterization of biological images, reducing subjectivity and optimizing the analysis process.Results: We present a new method for automatic characterization of jatropha seed quality, based on multispectral imaging (MSI) combined with X-ray imaging. We propose an approach along with X-ray images in order to investigate internal problems such as damages in the embryonic axis and endosperm, considering the fact that seed surface profiles can be negatively affected, but without reaching important internal regions of the seeds. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to classify spatial and spectral patters according to the classes of seed quality. Spectral reflectance signatures in a range of 780 to 970 nm and the X-ray images can efficiently predict quality traits such as normal seedlings, abnormal seedlings and dead seeds.Conclusions: MSI and X-ray images have a strong relationship with physiological performance of Jatropha curcas L. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of jatropha seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.

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X-Ray Applications in Food and Agriculture: A Review

Cited by 22 publications

References 0 publications

Rapid and/or nondestructive quality evaluation methods for potatoes: A review

Rapid and/or nondestructive quality evaluation methods for potatoes: A review

Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

Multispectral and X-ray Images for Characterization of Jatropha Curcas L. Seed Quality

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