Using Disease-Burden Method to Evaluate the Strategies for Reduction of Aflatoxin Exposure in Peanuts.
Aflatoxin is a fungal secondary metabolite with high toxicity that is capable of contaminating various types of food crops. It has been identified as a Group 1 human carcinogen by the International Agency for Research on Cancer. Chronic aflatoxin exposure has caused worldwide concern as a matter of public food safety. Peanuts and peanut products are the major sources of aflatoxin exposure. Therefore, some reduction interventions have been developed to minimize contamination throughout the peanut production chain. The purpose of this study is to estimate the efficacy of interventions in reducing the health impact of hepatocellular carcinoma caused by aflatoxin contamination in peanuts. The estimated total Disability-Adjusted Life Years (DALYs) were calculated using FDA-iRISK software. Six aflatoxin reduction strategies were evaluated, including good agricultural practice (GAP), biocontrol, Purdue Improved Crop Storage packaging, basic processing, ozonolysis, and ultraviolet irradiation. The results indicated that basic processing could prevent huge public health loss of 4,079.7-21,833 total DALYs per year. In addition, GAP and biocontrol were both found to be effective strategies in the farm field. Meanwhile, the other three interventions had limited effectiveness in reducing total DALYs. In conclusion, this study could help farmers, processing plants, and government policy makers to alleviate aflatoxin contamination issues in the peanut production chain.
Identification of fungi-contaminated peanuts using hyperspectral imaging technology and joint sparse representation model.
Peanuts with fungal contamination may contain aflatoxin, a highly carcinogenic substance. We propose the use of hyperspectral imaging to quickly and noninvasively identify fungi-contaminated peanuts. The spectral data and spatial information of hyperspectral images were exploited to improve identification accuracy. In addition, successive projection was adopted to select the bands sensitive to fungal contamination. Furthermore, the joint sparse representation based classification (JSRC), which considers neighboring pixels as belonging to the same class, was adopted, and the support vector machine (SVM) classifier was used for comparison. Experimental results show that JSRC outperforms SVM regarding robustness against random noise and considering pixels at the edge of the peanut kernel. The classification accuracy of JSRC reached 99.2% and 98.8% at pixel scale, at least 98.4% and 96.8% at kernel scale for two peanut varieties, retrieving more accurate and consistent results than SVM. Moreover, fungi-contaminated peanuts were correctly marked in both learning and test images.
Comprehensive comparison of multiple quantitative near‐infrared spectroscopy models for Aspergillus flavus contamination detection in peanut.
BACKGROUND: Aspergillus flavus is a major pollutant in moldy peanuts, and it has a great influence on the taste of food. The secondary metabolites of Aspergillus flavus, including aflatoxin B1 (AFB1) and aflatoxin B2 (AFB2), are highly toxic and can expose humans to high risk. Total molds count (TMC) is an important index to determine the contamination degree and hygiene quality of peanut. RESULTS: To explore the feasibility of near-infrared spectroscopy (NIRS) technique for rapid detection of the TMC in peanut, quantitative calibration models based on full-band wavelengths and characteristic wavelengths combined with chemometric methods were established. The Successive Projection Algorithm (SPA) and Elimination of Uninformative Variables (UVE) algorithms were used to extract the characteristic wavelengths. In comparison, the model built by original spectrum selected with UVE algorithm displayed best result with correlation coefficient in prediction set (RP ) of 0.9577, root mean squared error for the prediction set (RMSEP) of 0.2336 Log CFU/g, residual predictive deviation (RPD) of 3.5041. CONCLUSIONS: The results showed that NIRS is a rapid practicable analysis method for peanut Aspergillus flavus contamination quantitative detection. NIRS is a promising method for detecting moldy peanut, which provides an insight to guarantee peanut food safety.
The effects of different packaging materials, temperatures and water activities to control aflatoxin B1 production by Aspergillus flavus and A. parasiticus in stored peanuts.
Aflatoxins (AFs) are secondary metabolites produced by aflatoxigenic strains of Aspergillus flavus and A. parasiticus, the most toxic being aflatoxin B1 (AFB1). The purpose of the present work was to investigate the effects of industrial-grade packaging materials (low-density polyethylene, polypropylene, polyethylene-laminated aluminium); temperatures (25 °C, 30 °C); and water activities (0.74 a w, 0.85 a w) on AFB1production by A. flavus and A. parasiticus in stored peanut kernels. Commercially-obtained samples were segregated into packaging materials, separately inoculated with the aflatoxigenic Aspergillus spp., and stored for 1 month under various °C + a w regimes. AFB1 production was quantified by high performance liquid chromatography with fluorescence detector (HPLC-FLD). For A. flavus in PELA, no AFB1 was detected (100% reduction) at 25 °C for both a w tested. For A. parasiticus in PELA, no AFB1 was detected at 25 °C (0.85 a w) and 30 °C (0.74 a w). Highest concentration of AFB1 was detected in LDPE for both A. flavus(46.41 ppb) and A. parasiticus (414.42 ppb), followed by PP (A. flavus 24.29 ppb; A. parasiticus 386.73 ppb). In conclusion, storing peanut kernels in PELA in a dry place at room temperature has been demonstrated as an adequate and inexpensive method in inhibiting growth of Aspergillus spp. and lowering AFB1contamination in peanuts.
Inhibition of Aflatoxin Formation in Aspergillus Species by Peanut (Arachis hypogaea) Seed Stilbenoids in the Course of Peanut-Fungus Interaction.
Common soil fungi, Aspergillus flavus and Aspergillus parasiticus, are opportunistic pathogens that invade preharvest peanut seeds. These fungi often produce carcinogenic aflatoxins that pose a threat to human and animal health through food chains and cause significant economic losses worldwide. Detection of aflatoxins and further processing of crops are mandated to ensure that contaminated agricultural products do not enter food channels. Under favorable conditions, the fungus-challenged peanut seeds produce phytoalexins, structurally related stilbenoids, capable of retarding fungal development. The purpose of the present study was to evaluate the potential influence of peanut phytoalexins on fungal development and aflatoxin formation in the course of peanut-fungus interaction. The present research revealed that during such interaction, aflatoxin formation was completely suppressed in A. flavus and A. parasiticus strains tested, when low concentrations of spores were introduced to wounded preincubated peanuts. In most of the experiments, when fungal spore concentrations were 2 orders of magnitude higher, the spores germinated and produced aflatoxins. Of all experimental seeds that showed fungal growth, 57.7% were aflatoxin-free after 72 h of incubation. The research provided new knowledge on the aflatoxin/phytoalexin formation in the course of peanut-fungus interaction.
Development of a chemiluminescent aptasensor for ultrasensitive and selective detection of aflatoxin B1 in peanut and milk.
More and more attention about food safety leads to a research hotspot to develop new detection methods for food contaminant. To address the problems of serious interference and low sensitivity, a chemiluminescent aptasensor for the detection of aflatoxin B1(AFB1) in food was developed in this paper. It is based on horseradish peroxidase (HRP) catalyze the luminol chemiluminescence reaction. The hybridization chain reaction (HCR) signal amplification strategy has been used to improve the detection sensitivity. Magnetic separation could further reduce background signal obviously at the same time. AFB1 as a model of analyte to test the capability of our developed assay system. Under the optimal experimental conditions, CL intensity showed a good linear correlation with the concentrations of AFB1 ranging from 0.5 to 40 ng mL-1. The limit of detection was estimated 0.2 ng mL-1 based on 3 times of the signal-to-noise ratio which is lower than those of the previously reported sensors. It could be used to detect AFB1 content in real samples, such as peanuts and milk which were purchased in local supermarket. The results proved that the sensing system has good anti-interference and selectivity. In all, it has potential for practical application in food safety field.
Application of Multiplexing Fiber Optic Laser Induced Fluorescence Spectroscopy for Detection of Aflatoxin B1 Contaminated Pistachio Kernels.
To explore the effect of signal acquisition way on screening ability, the multiplexing fiber optic laser induced fluorescence spectroscopy (LIFS) system with one-, two- and three-probe, were employed respectively to detect artificially aflatoxin B1 (AFB1, 5, 10, 20, 30, and 50 ppb) contaminated 300 pistachio kernels in this study. Compared to one- and two-probe modes, highest accuracy (≥97.0%) by support vector machine (SVM) employing 390-660 nm were obtained using three-probe, which also showed the most attractive precision (root mean square error of prediction (RMSEP) < 4.5 ppb) for AFB1 by stepwise multiple linear regression (SMLR) using 174-1100 nm. These suggested that the effective collection of spatial information could improve the performance of model, and the three-probe LIFS had a preliminary feasibility for discriminating pistachios contaminated with low concentration of AFB1. Further study on classifying naturally contaminated samples is needed to validate the applicability of this system.
EXPRESS: Use of Visible–Near-Infrared (Vis–NIR) Spectroscopy to Detect Aflatoxin B1 on Peanut Kernels.
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 B1 (AFB1). 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 AFB1-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 AFB1 contamination of peanut kernels.
A rapid fluorometric method for determination of aflatoxin B 1 in plant-derived food by using a thioflavin T-based aptasensor.
A fluorometric aptamer-based method is described for the determination of aflatoxin B1 (AFB1). The fluorescent dye thioflavin T (ThT) forms a complex with the aptamer against AFB1 (aptamer/ThT), and the fluorescence of the complex is strongly enhanced. On addition of AFB1, it will bind to the aptamer and release ThT. The fluorescence of free ThT is much weaker. The fluorescence of the system, best measured at excitation/emission wavelengths of 440/487 nm, drops gradually in the AFB1 concentration range from 0.2 to 200 ng·mL-1, exhibiting good linearity. The detection limits are 0.2 ng·mL-1 in buffer solution, and 1 ng·mL-1 when applied to plant-derived food. The recovery of AFB1 from spiked foodstuff ranges from 74.7% to 121%. The assay can be performed within 20 min. Graphical abstract Schematic presentation of label-free thioflavin T (ThT)-based fluorescence aptasensor using aflatoxin B1(AFB1) aptamer/ThT G-quadruplex complex and the AFB1 aptamer/AFB1 complex fluorescent signal system for the rapid and sensitive detection of AFB1 in soy sauce, spirits, rice, corn and peanuts.
Effect of pretreatments on mycotoxin profiles and levels in dried figs.
The aim of this explorative study was to investigate how effective drying preservation methods are in reducing mycotoxin content in figs. Dried autochthonous varieties of white and dark figs (Petrovača Bijela and Šaraguja, respectively) were analysed for mycotoxins using an LC-MS/MS "dilute and shoot" method capable of determining 295 fungal and bacterial secondary metabolites. Before drying in a cabinet dryer the figs were preserved with 0.5 % citric acid solution or 0.5 % ascorbic acid solution or 0.3 % L-cysteine solution or 0.2 % chestnut extract solution or 0.15 % Echinacea extract solution by immersion. We found nine metabolites: aflatoxin B1 (AFB1), ochratoxin A, ochratoxin alpha, kojic acid, emodin, altenuene, alternariol methyl ether, brevianamide F, and tryptophol. The most efficient preserver was L-cysteine (15 % reduction), while ascorbic acid favoured mycotoxin production (158 % increase). However, all pretreatment solutions reduced AFB1, which is a major fig contaminant.