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Current methods for detecting aflatoxin contamination of agricultural and food commodities are generally based on wet chemical analyses, which are time-consuming, destructive to test samples, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening and on-site detection. In this study, we utilized visible-near-infrared (Vis-NIR) spectroscopy over the spectral range of 400-2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B 1 (AFB 1 ). The artificially contaminated samples were prepared by dropping known amounts of aflatoxin standard dissolved in 50:50 (v/v) methanol/water onto peanut kernel surface to achieve different contamination levels. The partial least squares discriminant analysis (PLS-DA) models established using the full spectra over different ranges achieved good prediction results. The best overall accuracy of 88.57% and 92.86% were obtained using the full spectra when taking 20 and 100 parts per billion (ppb), respectively, as the classification threshold. The random frog (RF) algorithm was used to find the optimal characteristic wavelengths for identifying the surface AFB 1 -contamination of peanut kernels. Using the optimal spectral variables determined by the RF algorithm, the simplified RF-PLS-DA classification models were established. The better RF-PLS-DA models attained the overall accuracies of 90.00% and 94.29% with the 20 ppb and 100 ppb thresholds, respectively, which were improved compared to using the full spectral variables. Compared to using the full spectral variables, the employed spectral variables of the simplified RF-PLS-DA models were decreased by at least 94.82%. The present study demonstrated that the Vis-NIR spectroscopic technique combined with appropriate chemometric methods could be useful in identifying AFB 1 contamination of peanut kernels.

Aflatoxin contamination caused by Aspergillus flavus infection of corn is a significant and chronic threat to corn being used as food or feed. Contamination of crops at levels of 20 ng g(-1) or higher (as regulated by the USFDA) by this toxin and potent carcinogen makes the crop unsalable, resulting in a significant economic burden on the producer. This review focuses on elimination of this contamination in corn which is a major US crop and the basis of many products. Corn is also "nature's example" of a crop containing heritable resistance to aflatoxin contamination, thereby serving as a model for achieving resistance to aflatoxin contamination in other crops as well. This crop is the largest production grain crop worldwide, providing food for billions of people and livestock and critical feedstock for production of biofuels. In 2011, the economic value of the US corn crop was US$76 billion, with US growers producing an estimated 12 billion bushels, more than one-third of the world's supply. Thus, the economics and significance of corn as a food crop and the threat to food safety due to aflatoxin contamination of this major food crop have prompted the many research efforts in many parts of the world to identify resistance in corn to aflatoxin contamination. Plant breeding and varietal selection has been used as a tool to develop varieties resistance to disease. This methodology has been employed in defining a few corn lines that show resistance to A. flavus invasion; however, no commercial lines have been marketed. With the new tools of proteomics and genomics, identification of resistance mechanisms, and rapid resistance marker selection methodologies, there is an increasing possibility of finding significant resistance in corn, and in understanding the mechanism of this resistance.

Aflatoxin contamination of maize is a major food safety issue worldwide. The problem is of special significance in African countries because maize is a staple food. This manuscript describes a low-cost, portable, non-invasive device for detecting and sorting aflatoxin-contaminated maize kernels. We developed a prototype employing a modified, normalized difference fluorescence index (NDFI) detection method to identify potentially aflatoxin-contaminated maize kernels. Once identified, these contaminated kernels can be manually removed by the user. The device consists of a fluorescence excitation light source, a tablet for image acquisition, and detection/visualization software. Two experiments using maize kernels artificially infected with toxigenic Aspergillus flavus were implemented to evaluate the performance and efficiency of the device. The first experiment utilized highly contaminated kernels (71.18 ppb), while mildly contaminated kernels (1.22 ppb) were used for the second experiment. Evidently, the combined approach of detection and sorting was effective in reducing aflatoxin levels in maize kernels. With a maize rejection rate of 1.02% and 1.34% in the two experiments, aflatoxin reduction was achieved at 99.3% and 40.7%, respectively. This study demonstrated the potential of using this low-cost and non-invasive fluorescence detection technology, followed by manual sorting, to significantly reduce aflatoxin levels in maize samples. This technology would be beneficial to village farmers and consumers in developing countries by enabling safer foods that are free of potentially lethal levels of aflatoxins. [ABSTRACT FROM AUTHOR]

Near infrared hyperspectral imaging over the spectral range of 900–2500 nm was investigated for its potential to identify maize kernels inoculated with aflatoxigenic fungus (AF13) from healthy kernels and kernels inoculated with non-aflatoxigenic fungus (AF36). A total of 900 kernels were used with 3 treatments, namely, each 300 kernels inoculated with AF13, AF36 and sterile distilled water as control, separately. One hundred kernels from each treatment of 300 kernels were incubated for 3, 5 and 8 days, to obtain diverse samples. Based on the full mean spectra extracted from the same kernel side(s), the best mean overall prediction accuracies achieved were 96.3% for the 3-class (control, non-aflatoxigenic and aflatoxigenic) classification and 97.8% for the 2-class (aflatoxigenic-negative and -positive) classification using the partial least-squares discriminant analysis (PLS-DA) method. The 3-class and 2-class models using the full mean spectra extracted from different kernel sides had the best mean overall prediction accuracies of 91.5% and 95.1%. Using the most important 30, 55 and 100 variables determined by the random frog (RF) algorithm, the simplified type I-RF-PLSDA models achieved the mean overall prediction accuracies of 87.7%, 93.8% and 95.2% for the 2-class discrimination using different kernel sides' information. Among the most important 55 and 100 variables, a total of 25 and 67 variables were consistently selected in the 100 random runs and were therefore used further for establishing the type II-RF-PLSDA models. Using these 25 and 67 variables, the type II-RF-PLSDA models obtained the mean overall prediction accuracies of 82.3% and 94.9% separately. • NIR hyperspectral imaging to detect aflatoxigenic fungal infection on maize kernels. • Effect of maize kernel side on discrimination results was addressed. • Random frog algorithm was employed to optimise the discriminant analysis. • Both 3-class and 2-class models were established using different types of spectra. • Visualisation of predicted kernel class was proposed and demonstrated. [ABSTRACT FROM AUTHOR]

In an effort to control aflatoxin contamination in food and/or feed grains, a segment of research has focused on host resistance to eliminate aflatoxin from susceptible crops, including maize. To this end, screening tools are key to identifying resistant maize genotypes. The traditional field screening techniques, the kernel screening laboratory assay (KSA), and analytical methods (e.g., ELISA) used for evaluating corn lines for resistance to fungal invasion, all ultimately require sample destruction. A technological advancement on the basic BGYF presumptive screening test, fluorescence hyperspectral imaging offers an option for non-destructive and rapid image-based screening. The present study aimed to differentiate fluorescence spectral signatures of representative resistant and susceptible corn hybrids infected by a toxigenic (SRRC-AF13) and an atoxigenic (SRRC-AF36) strain of Aspergillus flavus , at several time points (5, 7, 10, and 14 days), in order to evaluate fluorescence hyperspectral imaging as a viable technique for early, non-invasive aflatoxin screening in resistant and susceptible corn lines. The study utilized the KSA to promote fungal growth and aflatoxin production in corn kernels inoculated under laboratory conditions and to provide actual aflatoxin values to relate with the imaging data. Each time point consisted of 78 kernels divided into four groups (30-susceptible, 30-resistant, 9-susceptible control, and 9-resistant control), per inoculum. On specified days, kernels were removed from the incubator and dried at 60°C to terminate fungal growth. Dry kernels were imaged with a VNIR hyperspectral sensor (image spectral range of 400–1000 nm), under UV excitation centered at 365 nm. Following imaging, kernels were submitted for the chemical AflaTest assay (VICAM). Fluorescence emissions were compared for all samples over 14 days. Analysis of strain differences separating the fluorescence emission peaks of resistant from the susceptible strain indicated that the emission peaks of the resistant strain and the susceptible strains differed significantly (p < 0.01) from each other, and there was a significant difference in fluorescence intensity between the treated and control kernels of both strains. These results indicate a viable role of fluorescence hyperspectral imaging for non-invasive screening of maize lines with divergent resistance to invasion by aflatoxigenic fungi. [ABSTRACT FROM AUTHOR]

In an effort to address the problem of rapid detection of aflatoxin in grain, particularly oilseeds, the current study assessed the spectral differences of aflatoxin production in kernels from a cornfield inoculated with spores from 2 different strains of toxigenic Aspergillus flavus. Aflatoxin production in corn from the same field due to natural infestation was also assessed. A small corn plot in Baton Rouge, La., U.S.A., was used during the 2008-growing season. Two groups of 400 plants were inoculated with 2 different inocula and 1 group of 400 plants was designated as controls. Any contamination detected in the controls was attributed to natural infestation. A subset of each group was imaged with a visible near infra red (VNIR) hyperspectral system under ultra violet (UV) excitation and subsequently analyzed for aflatoxin using affinity column fluorometry. Group differences were statistically analyzed. Results indicate that when all the spectral data across all groups were averaged, any potential differences between groups (treated and untreated) were obscured. However, spectral analysis based on contaminated 'hot' pixel classification showed a distinct spectral shift/separation between contaminated and clean ears with fluorescence peaks at 501 and 478 nm, respectively. All inoculated and naturally infected control ears had fluorescence peaks at 501 nm that differed from uninfected corn ears. Results from this study may be useful in evaluating rapid, noninvasive instrumentation and/or methodology for aflatoxin detection in grain. [ABSTRACT FROM AUTHOR]

Naturally occurring Aspergillus flavus strains can be either toxigenic or atoxigenic, indicating their ability to produce aflatoxin or not. The objective was to assess, with the use of a hyperspectral sensor, the difference in fluorescence emission between maize kernels inoculated with toxigenic and atoxigenic inoculums of A. flavus. Maize ears were inoculated with AF13, a toxigenic strain of A. flavus, and AF38, an atoxigenic strain of A. flavus, at dough stage of development and harvested 8 weeks after inoculation. After harvest, single kernels were divided into three groups prior to imaging: control, adjacent, and glowing. Both sides of the kernel, germ and endosperm, were imaged separately using a fluorescence hyperspectral imaging system. After imaging each single kernel was processed with affinity column fluorimetry to determine aflatoxin level. Results from discriminant analysis of the imaging data found that the classification accuracies of the three visually designated groups were not promising. The separation of maize kernels based on different fungal inoculums yielded better results. The best results were achieved with the germ side of the maize kernels. The kernels were later grouped into ‘contaminated’ and ‘healthy’ with 20 ppb and 100 ppb thresholds. The contaminated kernels all had longer peak wavelength than did the healthy ones. Results from the discriminant analysis classification indicated overall higher classification accuracy for the 100 ppb threshold on the germ side (94.4%). The germ side was also more useful at discriminating healthy from contaminated kernels for the 20 ppb threshold. [ABSTRACT FROM AUTHOR]

The potential of line-scan Raman hyperspectral imaging system equipped with a 785 nm line laser was examined for discrimination of uninfected control, AF36-inoculated and AF13-inoculated corn kernels in this study. The AF13 and AF36 strains were used as representatives of the aflatoxigenic and non-aflatoxigenic A. flavus fungi. A total of 300 kernels were used with 3 treatments, namely, 100 kernels inoculated with the AF13 fungus, 100 kernels inoculated with the AF36 fungus, and 100 kernels inoculated with sterile distilled water as control. The kernels were incubated at 30 °C for 8 days, dried and surface wiped to remove exterior signs of mold. The kernels were imaged on both endosperm and germ sides over the wavenumber range of 103–2831 cm−1. The mean spectrum was extracted from the Raman image of each kernel, and preprocessed with adaptive iteratively reweighted penalized least squares, Savitzky-Golay smoothing and min-max normalization. Based upon the preprocessed group mean spectra, a total of 36 and 51 local Raman peaks were identified from the endosperm side and embryo area of germ side, respectively. With the spectral variables at the identified local peak locations as inputs of discriminant models, the 3-class principal component analysis-linear discriminant analysis models ran 50 random times, achieved mean overall prediction accuracies of 89.47% and 75.55% using the endosperm and embryo data, respectively. The corresponding standard deviations were 3.48% and 4.34%. The results demonstrate usefulness of the line-scan Raman imaging technology in differentiating uninfected control corn kernels and corn kernels infected with aflatoxigenic and non-aflatoxigenic fungi. [Display omitted] • Line-scan Raman imaging detected Aspergillus flavus infection on corn kernels. • Corn kernels inoculated with aflatoxigenic and non-aflatoxigenic fungi included. • Mean spectra were extracted from endosperm side/area and embryo area of germ side. • Three-class discriminant models were established using identified Raman peaks. • Corn endosperm side/area performed better than embryo area. [ABSTRACT FROM AUTHOR]

Abstract: Aspergillus flavus (A. flavus) produces secondary metabolites, aflatoxins, that are harmful to both humans and animals. Because of stringent federal regulation requirements as well as the limitations of available detection methods, there is an urgent need for rapid, non-invasive and effective techniques such as hyperspectral imaging, for the detection of the toxigenic strains of A. flavus. Hyperspectral images of toxigenic and atoxigenic strains of A. flavus were classified. Principal component analysis (PCA) was applied for data decorrelation and dimensionality reduction. A Genetic Algorithm (GA) was implemented for the selection of principal components (PCs) based on Bhattacharya Distance (B-Distance). A Support Vector Machine (SVM) was successfully applied for the classification. Under halogen light sources, in average 83% of the toxigenic fungus pixels and 74% of the atoxigenic fungus pixels were correctly classified; while under UV light sources, 67% of the toxigenic fungus pixels and 85% of the atoxigenic fungus pixels were correctly classified. The pair-wise classification accuracies between toxigenic AF13 and each atoxigenic fungus species (AF38, AF283 and AF2038) were 80%, 91% and 95% under halogen light sources, and 75%, 97% and 99% under UV lights, respectively. [Copyright &y& Elsevier]