Identification of aflatoxin B1 on maize kernel surfaces using hyperspectral imaging

Wang, Wei; Heitschmidt, Gerald W.; Ni, Xinzhi; Windham, William R.; Hawkins, Samantha A.; Chu, Xuan

doi:10.1016/j.foodcont.2014.01.038

Cited by 80 publications

(54 citation statements)

References 30 publications

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“…The different methods can be characterized by several parameters such as accuracy, applicability, reproducibility, limit of detection and so on (Sheppard, 2008;Alshannaq and Yae-Hiuk, 2017;Shanakhat et al, 2018). Trucksess and Zhang (2016) argued that all practically useful analytical methods should meet the basic Fluorescence polarization immunoassay 30 ng/ml Maragos, 2009 Biosensors 0.05 ml 0.005 µg/l Gurban et al, 2017;Man et al, 2017 Indirect methods Spectroscopy 4 µg/kg Wacoo et al, 2014 Emerging technologies Hyperspectral imaging 10 µg/kg Wang et al, 2014 Electronic nose 5 µ/kg Ottoboni et al, 2018 Aptamer-based biosensors ECL 0.1 pg/ml Shim et al, 2014;Castillo et al, 2015;Guo et al, 2016;Jia et al, 2019;Kordasht et al, 2019; guidelines of reproducibility in different laboratory settings. Based on these premises, protocols that are used in different laboratories from sampling to analysis were compiled, and systems relying on certified material samples (CRMs) are also closely related to this.…”

Section: Qualitative and Quantitative Aflatoxin Analytical Methods -Ementioning

confidence: 99%

Adverse Effects, Transformation and Channeling of Aflatoxins Into Food Raw Materials in Livestock

et al. 2019

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Section: Qualitative and Quantitative Aflatoxin Analytical Methods -Ementioning

confidence: 99%

Adverse Effects, Transformation and Channeling of Aflatoxins Into Food Raw Materials in Livestock

et al. 2019

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“…They were 1145, 1408, 1935, 2103, and 2383 nm. The wavelength of 1146 nm ascribed to the 2nd overtone (OT) of aromatic -CH-group, showed the existence of the benzene ring, which can be attributed to some kinds of the few amino acids such as tyrosine [16]. The wavelength of 1405 nm, with an O-H stretch first overtone attributed to an ROH structure, was associated with starch [26,29].…”

Section: Classification Based On Data Of the Mixture Of Maize Kernelsmentioning

confidence: 99%

“…A wavelength of 2100 nm was associated with carbohydrate [29]. In addition, 2382 nm was ascribed to the 2nd overtone of C-H stretching from aliphatic compounds [16,29]. These indicated that variation in different groups was mainly caused by changes of ingredient contents of maize germ due to the fungal growth.…”

Section: Classification Based On Data Of the Mixture Of Maize Kernelsmentioning

confidence: 99%

“…Firrao et al [15] used multispectral imaging (720-940 nm) to predict fumonisin content of milled maize. Wang et al [16][17][18] studied the use of hyperspectral imaging, with the range of 400-1000 nm and 1000-2500 nm, to detect aflatoxin B1 on maize kernels, which were artificially titrated on the kernel surface in the laboratory and inoculated with Aspergillus flavus conidia in the field, respectively. Kandpal et al [19] verified the feasibility of short wave infrared (SWIR) hyperspectroscopy to detect aflatoxin on the surface of three different corn varieties (yellow, white, and purple).…”

Section: Introductionmentioning

confidence: 99%

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Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

et al. 2017

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Fungi infection in maize kernels is a major concern worldwide due to its toxic metabolites such as mycotoxins, thus it is necessary to develop appropriate techniques for early detection of fungi infection in maize kernels. Thirty-six sterilised maize kernels were inoculated each day with Aspergillus parasiticus from one to seven days, and then seven groups (were determined based on the incubated time. Another 36 sterilised kernels without inoculation with fungi were taken as control (DC). Hyperspectral images of all kernels were acquired within spectral range of 921-2529 nm. Background, labels and bad pixels were removed using principal component analysis (PCA) and masking. Separability computation for discrimination of fungal contamination levels indicated that the model based on the data of the germ region of individual kernels performed more effectively than on that of the whole kernels. Moreover, samples with a two-day interval were separable. Thus, four groups, DC, D 1-2 (the group consisted of D 1 and D 2 ), D 3-4 (D 3 and D 4 ), and D 5-7 (D 5 , D 6 , and D 7 ), were defined for subsequent classification. Two separate sample sets were prepared to verify the influence on a classification model caused by germ orientation, that is, germ up and the mixture of germ up and down with 1:1. Two smooth preprocessing methods (Savitzky-Golay smoothing, moving average smoothing) and three scatter-correction methods (normalization, standard normal variate, and multiple scatter correction) were compared, according to the performance of the classification model built by support vector machines (SVM). The best model for kernels with germ up showed the promising results with accuracies of 97.92% and 91.67% for calibration and validation data set, respectively, while accuracies of the best model for samples of the mixed kernels were 95.83% and 84.38%. Moreover, five wavelengths (1145, 1408, 1935, 2103, and 2383 nm) were selected as the key wavelengths in the discrimination of fungal contamination levels. In general, near-infrared hyperspectral imaging can be used for early detection of fungal contamination in maize kernels.

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“…In this work, low signal-to-noise sub-images of the two ends were excluded and only the images in the wavelength ranging from 400 to 1000 nm (285 wavebands) were retained for the following data analysis. For a detailed explanation of the system correction and image acquisition, please refer to Wang et al [22].…”

Section: Hyperspectral Images Acquisition and Calibrationmentioning

confidence: 99%

Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging

2019

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The detection of carrageenan adulteration in chicken meat using a hyperspectral imaging (HSI) technique associated with three spectroscopic transforms was investigated. Minced chicken was adulterated with carrageenan solution (2% w/v) in the volume range of 0–5 mL at an increment of 1 mL. Hyperspectral images of prepared samples were captured in a reflectance mode in a Visible/Near-Infrared (Vis/NIR, 400–1000 nm) region. The reflectance (R) spectra were first extracted from regions of interest (ROIs) by applying a mask that was built using band math combined with thresholding and were then transformed into two other spectral units, absorbance (A) and Kubelka-Munck (KM). Partial least squares regression (PLSR) models based on full raw and preprocessed spectra in the three profiles were established and A spectra were found to perform best with Rp2 = 0.92, root mean square error of prediction set (RMSEP) = 0.48, and residual predictive deviation (RPD) = 6.18. To simplify the models, several wavelengths were selected using regression coefficients (RC) based on all three spectral units, and 10 wavelengths selected from A spectra (409, 425, 444, 521, 582, 621, 763, 840, 893, and 939 nm) still performed best with the Rp2, RMSEP, and RPD of 0.85, 0.93, and 3.20, respectively. Thus, the preferred simplified RC-A-PLSR model was selected and transferred into each pixel to obtain the distribution maps and finally, the general different adulteration levels of different samples were readily discernible. The overall results ascertained that the HSI technique demonstrated to be an effective tool for detecting and visualizing carrageenan adulteration in authentic chicken meat, especially in the absorbance mode.

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Identification of aflatoxin B1 on maize kernel surfaces using hyperspectral imaging

Cited by 80 publications

References 30 publications

Adverse Effects, Transformation and Channeling of Aflatoxins Into Food Raw Materials in Livestock

Adverse Effects, Transformation and Channeling of Aflatoxins Into Food Raw Materials in Livestock

Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging

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