Feasibility of detecting Aflatoxin B1 in single maize kernels using hyperspectral imaging

Wang, Wei; Ni, Xinzhi; Lawrence, Kurt C.; Yoon, Seung-Chul; Heitschmidt, Gerald W.; Feldner, Peggy

doi:10.1016/j.jfoodeng.2015.06.009

Cited by 61 publications

(34 citation statements)

References 30 publications

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“…By diluting Aspergillus flavus aflatoxin (Sigma‐Aldrich, 3050 Spruce St., St. Louis, Mo., U.S.A.) with methanol, which was also used to kill any residual mold spores in AFB 1 , and according to the average kernel weight 0.33 g, 4 concentrations of stock solutions, that is 10, 20, 100, and 500 ppb, were prepared. For the detailed procedure please refer to Wang and others (). Once the solutions were prepared, the kernels were divided into 5 groups.…”

Section: Methodsmentioning

confidence: 99%

“…Before imaging, kernels from a given group (30 at a time) were placed on a Teflon® sample holder containing 30 shallow, elliptically shaped wells arranged in 6 rows × 5 columns (Figure A). As a safety measure, the Teflon holder was placed inside of a special transparent sealed box before imaging (Wang and others ).…”

Section: Methodsmentioning

confidence: 99%

“…Indirect lighting was provided by 2 softboxes with 500 W Tungsten–Halogen lamps positioned at approximately 45° angles above and lateral to the samples (SilverDome® nxt: small, Photoflex, Watsonville, Calif., U.S.A.). The hyperspectral imaging system was spectrally calibrated using a series of pencil style calibration lamps and lasers (Wang and others ). A 75% Spectralon® reflectance panel was imaged and used for image calibration (SRT‐75‐050, Labsphere, North Sutton, N.H., U.S.A.).…”

Section: Methodsmentioning

confidence: 99%

“…HyperVisual was used to spectrally subset the imagery to 400 to 1000 nm and to calibrate it to percent reflectance. Noise inherent to these images was then removed using ENVIs (Exelis Visual Information Solutions, Boulder, Co., U.S.A.) minimum noise fraction (MNF)/inverse MNF processing flow (Wang and others ).…”

Section: Methodsmentioning

confidence: 99%

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Feasibility of Detecting Aflatoxin B₁ on Inoculated Maize Kernels Surface using Vis/NIR Hyperspectral Imaging

Wang

Heitschmidt

Windham

et al. 2014

Journal of Food Science

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The feasibility of using a visible/near-infrared hyperspectral imaging system with a wavelength range between 400 and 1000 nm to detect and differentiate different levels of aflatoxin B1 (AFB1 ) artificially titrated on maize kernel surface was examined. To reduce the color effects of maize kernels, image analysis was limited to a subset of original spectra (600 to 1000 nm). Residual staining from the AFB1 on the kernels surface was selected as regions of interest for analysis. Principal components analysis (PCA) was applied to reduce the dimensionality of hyperspectral image data, and then a stepwise factorial discriminant analysis (FDA) was performed on latent PCA variables. The results indicated that discriminant factors F2 can be used to separate control samples from all of the other groups of kernels with AFB1 inoculated, whereas the discriminant factors F1 can be used to identify maize kernels with levels of AFB1 as low as 10 ppb. An overall classification accuracy of 98% was achieved. Finally, the peaks of β coefficients of the discrimination factors F1 and F2 were analyzed and several key wavelengths identified for differentiating maize kernels with and without AFB1 , as well as those with differing levels of AFB1 inoculation. Results indicated that Vis/NIR hyperspectral imaging technology combined with the PCA-FDA was a practical method to detect and differentiate different levels of AFB1 artificially inoculated on the maize kernels surface. However, indicated the potential to detect and differentiate naturally occurring toxins in maize kernel.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Feasibility of Detecting Aflatoxin B₁ on Inoculated Maize Kernels Surface using Vis/NIR Hyperspectral Imaging

Wang

Heitschmidt

Windham

et al. 2014

Journal of Food Science

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“…Near-infrared hyperspectral imaging technique to detect different quality parameters, insect infestation, and fungal infection associated with different stored grains and oil seeds were reported by , and Teena et al (2013). NIR hyperspectral imaging technique in the wavelength range between 1000 and 2500 nm, was utilized to detect aflatoxin B1 in single maize kernels with classification accuracy between 80 and 96.1% (Wang et al, 2015a), Aflatoxin B1 concentration of as low as 10 ppb on maize kernel surfaces was identified using NIR hyperspectral imaging in the wavelength range between 1000 and 2500 nm with a classification accuracy of 88% . Wang et al (2015b) identified wavelengths 1729 and 2344 nm corresponding to the presence of aflatoxin B1 in maize kernels.…”

Section: Applications Of Near-infrared Hyperspectral Imaging Techniqumentioning

confidence: 99%

Detection of fungal infection and Ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

Senthilkumar

Jayas

White

et al. 2016

Journal of Stored Products Research

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Fungal infection and mycotoxin contamination in agricultural products are a serious food safety issue. The detection of fungal infection and mycotoxin contamination in food products should be in a rapid way. A Near-Infrared (NIR) hyperspectral imaging system was used to detect fungal infection in 2013 crop year canola, wheat, and barley at different periods after inoculation and different concentration levels of ochratoxin A in wheat and barley. Artificially fungal infected (Fungi: Aspergillus glaucus, Penicillium spp.) kernels of canola, wheat and barley, were subjected to single kernel imaging after 2, 4, 6, 8, and 10 weeks post inoculation in the NIR region from 1000 to 1600 nm at 61 evenly distributed wavelengths at 10 nm intervals.The acquired image data were in the three-dimensional hypercube forms, and these were transformed into two-dimensional data. The two-dimensional data were subjected to principal component analysis to identify significant wavelengths based on the highest principal component factor loadings. Wavelengths 1100, 1130, 1250, and 1300 nm were identified as significant for detection of fungal infection in canola kernels, wavelengths 1280, 1300, and 1350 nm were identified as significant for detection of fungal infection in wheat kernels, and wavelengths 1260, 1310, and 1360 nm were identified as significant for detection of fungal infection in barley kernels. The linear, quadratic and Mahalanobis statistical discriminant classifiers differentiated healthy canola kernels with > 95% and fungal infected canola kernels with > 90% classification accuracy. All the three classifiers discriminated healthy wheat and barley kernels with > 90% and fungal infected wheat and barley kernels with > 80% classification accuracy.ii The wavelengths 1300, 1350, and 1480 nm were identified as significant for detection of ochratoxin A contaminated wheat kernels, and wavelengths 1310, 1360, 1480 nm were identified as significant for detection of ochratoxin A contaminated barley kernels. All the three statistical classifiers differentiated healthy wheat and barley kernels and ochratoxin A contaminated wheat and barley kernels with a classification accuracy of 100%. The classifiers were able to discriminate between different durations of fungal infections in canola, wheat, and barley kernels with classification accuracy of more than 80% at initial periods (2 weeks) of fungal infection and 100% at the later periods of fungal infection. Different concentration levels of ochratoxin A contamination in wheat and barley kernels were discriminated with a classification accuracy of > 98% at ochratoxin A concentration level of ≤ 72 ppb in wheat kernels and ≤ 140 ppb in barley kernels and with 100% classification accuracy at higher concentration levels.iii

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Hyperspectral Imaging

Femenias¹,

Marı́n²

2021

Electromagnetic Technologies in Food Science

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Feasibility of detecting Aflatoxin B1 in single maize kernels using hyperspectral imaging

Cited by 61 publications

References 30 publications

Feasibility of Detecting Aflatoxin B₁ on Inoculated Maize Kernels Surface using Vis/NIR Hyperspectral Imaging

Feasibility of Detecting Aflatoxin B₁ on Inoculated Maize Kernels Surface using Vis/NIR Hyperspectral Imaging

Detection of fungal infection and Ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

Hyperspectral Imaging

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Feasibility of detecting Aflatoxin B1 in single maize kernels using hyperspectral imaging

Cited by 61 publications

References 30 publications

Feasibility of Detecting Aflatoxin B1 on Inoculated Maize Kernels Surface using Vis/NIR Hyperspectral Imaging

Feasibility of Detecting Aflatoxin B1 on Inoculated Maize Kernels Surface using Vis/NIR Hyperspectral Imaging

Detection of fungal infection and Ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

Hyperspectral Imaging

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Product

Resources

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Feasibility of Detecting Aflatoxin B₁ on Inoculated Maize Kernels Surface using Vis/NIR Hyperspectral Imaging

Feasibility of Detecting Aflatoxin B₁ on Inoculated Maize Kernels Surface using Vis/NIR Hyperspectral Imaging