A Review of the Methodology of Analyzing Aflatoxin and Fumonisin in Single Corn Kernels and the Potential Impacts of These Methods on Food Security

Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques

Aflatoxins and fumonisins, commonly found in maize and maize-derived products, frequently co-occur and can cause dangerous illness in humans and animals if ingested in large amounts. Efforts are being made to develop suitable analytical methods for screening that can rapidly detect mycotoxins in order to prevent illness through early detection. A method for classifying contaminated maize by applying hyperspectral imaging techniques including reflectance in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, and fluorescence was investigated. Machine learning classification models in combination with different preprocessing methods were applied to screen ground maize samples for naturally occurring aflatoxin and fumonisin as single contaminants and as co-contaminants. Partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) with the radial basis function (RBF) kernel were employed as classification models using cut-off values of each mycotoxin. The classification performance of the SVM was better than that of PLS-DA, and the highest classification accuracies for fluorescence, VNIR, and SWIR were 89.1%, 71.7%, and 95.7%, respectively. SWIR imaging with the SVM model resulted in higher classification accuracies compared to the fluorescence and VNIR models, suggesting that as an alternative to conventional wet chemical methods, the hyperspectral SWIR imaging detection model may be the more effective and efficient analytical tool for mycotoxin analysis compared to fluorescence or VNIR imaging models. These methods represent a food safety screening tool capable of rapidly detecting mycotoxins in maize or other food ingredients consumed by animals or humans.

Understanding Current Methods for Sampling of Aflatoxins in Corn and to Generate a Best Practice Framework

Aflatoxin contamination in corn is a significant issue, posing substantial health threats to humans and animals. Aflatoxin testing protects consumer health, ensures the safe global trade of corn, and verifies compliance with legislation; however, effective sampling procedures are essential to ensure reliable results. While many sampling procedures exist, there is no evidence to indicate which is the best approach to ensure accurate detection. Using scientific and gray literature sources, this review analyzed sampling procedures to determine an optimum approach to guide the development of standard practices. Results revealed that sampling is the major source of error in the accurate assessment of aflatoxin levels in food and crucial for obtaining reliable results. To guarantee low variability and sample bias-increased sample size and sampling frequency, the use of automatic dynamic sampling techniques, adequate storage, and homogenization of aggregate samples for analysis are advised to ensure a representative sample. However, there is a lack of evidence to support this or indicate the current utilization of the reviewed procedures. Inadequate data prevented the recommendation of sample sizes or frequency for optimum practice, and thus, further research is required. There is an urgent need to make sampling procedures fit-for-purpose to obtain accurate and reliable aflatoxin measurements.

The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops

Infrared (IR) spectroscopy is increasingly being used to analyze food crops for quality and safety purposes in a rapid, nondestructive, and eco-friendly manner. The lack of sensitivity and the overlapping absorption characteristics of major sample matrix components, however, often prevent the direct determination of food contaminants at trace levels. By measuring fungal-induced matrix changes with near IR and mid IR spectroscopy as well as hyperspectral imaging, the indirect determination of mycotoxins in food crops has been realized. Recent studies underline that such IR spectroscopic platforms have great potential for the rapid analysis of mycotoxins along the food and feed supply chain. However, there are no published reports on the validation of IR methods according to official regulations, and those publications that demonstrate their applicability in a routine analytical set-up are scarce. Therefore, the purpose of this review is to discuss the current state-of-the-art and the potential of IR spectroscopic methods for the rapid determination of mycotoxins in food crops. The study critically reflects on the applicability and limitations of IR spectroscopy in routine analysis and provides guidance to non-spectroscopists from the food and feed sector considering implementation of IR spectroscopy for rapid mycotoxin screening. Finally, an outlook on trends, possible fields of applications, and different ways of implementation in the food and feed safety area are discussed.

Aflatoxin Contamination, Its Impact and Management Strategies: An Updated Review

Aflatoxin, a type of mycotoxin, is mostly produced by Aspergillus flavus and Aspergillus parasiticus. It is responsible for the loss of billions of dollars to the world economy, by contaminating different crops such as cotton, groundnut, maize, and chilies, and causing immense effects on the health of humans and animals. More than eighteen different types of aflatoxins have been reported to date, and among them, aflatoxins B1, B2, G1, and G2 are the most prevalent and lethal. Early detection of fungal infection plays a key role in the control of aflatoxin contamination. Therefore, different methods, including culture, chromatographic techniques, and molecular assays, are used to determine aflatoxin contamination in crops and food products. Many countries have set a maximum limit of aflatoxin contamination (2-20 ppb) in their food and agriculture commodities for human or animal consumption, and the use of different methods to combat this menace is essential. Fungal infection mostly takes place during the pre- and post-harvest stage of crops, and most of the methods to control aflatoxin are employed for the latter phase. Studies have shown that if correct measures are adopted during the crop development phase, aflatoxin contamination can be reduced by a significant level. Currently, the use of bio-pesticides is the intervention employed in many countries, whereby atoxigenic strains competitively reduce the burden of toxigenic strains in the field, thereby helping to mitigate this problem. This updated review on aflatoxins sheds light on the sources of contamination, and the on occurrence, impact, detection techniques, and management strategies, with a special emphasis on bio-pesticides to control aflatoxins.

Hollow-Structured Microporous Organic Networks Adsorbents Enabled Specific and Sensitive Identification and Determination of Aflatoxins

Aflatoxin (AFT) contamination, commonly in foods and grains with extremely low content while high toxicity, has caused serious economic and health problems worldwide. Now researchers are making an effort to develop nanomaterials with remarkable adsorption capacity for the identification, determination and regulation of AFT. Herein, we constructed a novel hollow-structured microporous organic networks (HMONs) material. On the basis of Fe3O4@MOF@MON, hydrofluoric acid (HF) was introduced to remove the transferable metal organic framework (MOF) to give hollow MON structures. Compared to the original Fe3O4@MOF@MON, HMON showed improved surface area and typical hollow cavities, thus increasing the adsorption capacity. More importantly, AFT is a hydrophobic substance, and our constructed HMON had a higher water contact angle, greatly enhancing the adsorption affinity. From that, the solid phase extraction (SPE-HPLC) method developed based on HMONs was applied to analyze four kinds of actual samples, with satisfied recoveries of 85-98%. This work provided a specific and sensitive method for the identification and determination of AFT in the food matrix and demonstrated the great potential of HMONs in the field of the identification and control of mycotoxins.

Incidence of aflatoxins and fumonisins in grain, masa and corn tortillas in four municipalities in the department of Lempira, Honduras

In Honduras, corn is the most important staple food for the majority of the population. This high-demand crop is susceptible to biological contamination with mycotoxins, which could represent a latent hazard for consumers. To assess the incidence of aflatoxins and fumonisins in grain, masa and tortilla, and the dietary exposure to these substances among consumers, a study was conducted in four municipalities in the department of Lempira. Total aflatoxin and fumonisin content were quantified by fluorometry in 144 samples from 48 farmers. Sixty five percent of the samples were contaminated with aflatoxins with levels of 1.28-32.05, 1.15 to 12.61, and 1.01-5.98 μg/kg in grain, masa and tortilla, respectively. Fumonisins were detected in 100% of the samples at levels between 0.82 and 28.04, 0.66 and 14.36, and 0.63 and 12.04 mg/kg in grain, masa and tortilla, respectively. The reduction in aflatoxin and fumonisin contamination after processing grains into tortillas was of 83% and 52%, respectively. The difference in aflatoxin and fumonisin concentration in the three products was significant (p < 0.05). With a per capita tortilla consumption of 490 g/day, dietary exposure was estimated between 0.003 and 0.073 μg/kg bw/day for aflatoxins and 6.16 and 151.98 μg/kg bw/day for fumonisins. Therefore, the risk of exposure to mycotoxins in the evaluated communities was considered high. Mixed effect models showed that postharvest grain management and the nixtamalization process affect the incidence of mycotoxins in corn-based products.

Evaluation of the Impact of Skewness, Clustering, and Probe Sampling Plan on Aflatoxin Detection in Corn

Probe sampling plans for aflatoxin in corn attempt to reliably estimate concentrations in bulk corn given complications like skewed contamination distribution and hotspots. To evaluate and improve sampling plans, three sampling strategies (simple random sampling, stratified random sampling, systematic sampling with U.S. GIPSA sampling schemes), three numbers of probes (5, 10, 100, the last a proxy for autosampling), four clustering levels (1, 10, 100, 1,000 kernels/cluster source), and six aflatoxin concentrations (5, 10, 20, 40, 80, 100 ppb) were assessed by Monte-Carlo simulation. Aflatoxin distribution was approximated by PERT and Gamma distributions of experimental aflatoxin data for uncontaminated and naturally contaminated single kernels. The model was validated against published data repeatedly sampling 18 grain lots contaminated with 5.8-680 ppb aflatoxin. All empirical acceptance probabilities fell within the range of simulated acceptance probabilities. Sensitivity analysis with partial rank correlation coefficients found acceptance probability more sensitive to aflatoxin concentration (-0.87) and clustering level (0.28) than number of probes (-0.09) and sampling strategy (0.04). Comparison of operating characteristic curves indicate all sampling strategies have similar average performance at the 20 ppb threshold (0.8-3.5% absolute marginal change), but systematic sampling has larger variability at clustering levels above 100. Taking extra probes improves detection (1.8% increase in absolute marginal change) when aflatoxin is spatially clustered at 1,000 kernels/cluster, but not when contaminated grains are homogenously distributed. Therefore, taking many small samples, for example, autosampling, may increase sampling plan reliability. The simulation is provided as an R Shiny web app for stakeholder use evaluating grain sampling plans.

Integrated Mycotoxin Management System in the Feed Supply Chain: Innovative Approaches

Exposure to mycotoxins is a worldwide concern as their occurrence is unavoidable and varies among geographical regions. Mycotoxins can affect the performance and quality of livestock production and act as carriers putting human health at risk. Feed can be contaminated by various fungal species, and mycotoxins co-occurrence, and modified and emerging mycotoxins are at the centre of modern mycotoxin research. Preventing mould and mycotoxin contamination is almost impossible; it is necessary for producers to implement a comprehensive mycotoxin management program to moderate these risks along the animal feed supply chain in an HACCP perspective. The objective of this paper is to suggest an innovative integrated system for handling mycotoxins in the feed chain, with an emphasis on novel strategies for mycotoxin control. Specific and selected technologies, such as nanotechnologies, and management protocols are reported as promising and sustainable options for implementing mycotoxins control, prevention, and management. Further research should be concentrated on methods to determine multi-contaminated samples, and emerging and modified mycotoxins.

Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts

Cereal grains and nuts are represented as the economic backbone of many developed and developing countries. Kernels of cereal grains and nuts are prone to mold infection under high relative humidity and suitable temperature conditions in the field as well as storage conditions. Health risks caused by molds and their molecular metabolite mycotoxins are, therefore, important topics to investigate. Strict regulations have been developed by international trade regulatory bodies for the detection of mold growth and mycotoxin contamination across the food chain starting from the harvest to storage and consumption. Molds and aflatoxins are not evenly distributed over the bulk of grains, thus appropriate sampling for detection and quantification is crucial. Existing reference methods for mold and mycotoxin detection are destructive in nature as well as involve skilled labor and hazardous chemicals. Also, these methods cannot be used for inline sorting of the infected kernels. Thus, analytical methods have been extensively researched to develop the one that is more practical to be used in commercial detection and sorting processes. Among various analytical techniques, optical imaging and spectroscopic techniques are attracting growers' attention for their potential of nondestructive and rapid inline identification and quantification of molds and mycotoxins in various food products. This review summarizes the recent application of rapid and nondestructive optical imaging and spectroscopic techniques, including digital color imaging, X-ray imaging, near-infrared spectroscopy, fluorescent, multispectral, and hyperspectral imaging. Advance chemometric techniques to identify very low-level mold growth and mycotoxin contamination are also discussed. Benefits, limitations, and challenges of deploying these techniques in practice are also presented in this paper.

Aflatoxins: History, Significant Milestones, Recent Data on Their Toxicity and Ways to Mitigation

In the early 1960s the discovery of aflatoxins began when a total of 100,000 turkey poults died by hitherto unknown turkey "X" disease in England. The disease was associated with Brazilian groundnut meal affected by Aspergillus flavus. The toxin was named Aspergillus flavus toxin-aflatoxin. From the point of view of agriculture, aflatoxins show the utmost importance. Until now, a total of 20 aflatoxins have been described, with B1, B2, G1, and G2 aflatoxins being the most significant. Contamination by aflatoxins is a global health problem. Aflatoxins pose acutely toxic, teratogenic, immunosuppressive, carcinogenic, and teratogenic effects. Besides food insecurity and human health, aflatoxins affect humanity at different levels, such as social, economical, and political. Great emphasis is placed on aflatoxin mitigation using biocontrol methods. Thus, this review is focused on aflatoxins in terms of historical development, the principal milestones of aflatoxin research, and recent data on their toxicity and different ways of mitigation.

An overview of nanomaterial based biosensors for detection of Aflatoxin B1 toxicity in foods

Aflatoxin B1 (AFB1) is one of the most potent mycotoxin contaminating several foods and feeds. It suppresses immunity and consequently increases mutagenicity, carcinogenicity, teratogenicity, hepatotoxicity, embryonic toxicity and increasing morbidity and mortality. Continuous exposure of AFB1 causes liver damage and thus increases the prevalence of cirrhosis and hepatic cancer. This article was planned to provide understanding of AFB1 toxicity and provides future directions for fabrication of cost effective and user-friendly nanomaterials based analytical devices. In the present article various conventional (chromatographic & spectroscopic), modern (PCR & immunoassays) and nanomaterials based biosensing techniques (electrochemical, optical, piezoelectrical and microfluidic) are discussed alongwith their merits and demerits. Nanomaterials based amperometric biosensors are found to be more stable, selective and cost-effective analytical devices in comparison to other biosensors. But many unresolved issues about their stability, toxicity and metabolic fate needs further studies. In-depth studies are needed for development of advanced nanomaterials integrated biosensors for specific, sensitive and fast monitoring of AFB1 toxicity in foods. Integration of biosensing system with micro array technology for simultaneous and automated detection of multiple AFs in real samples is also needed. Concerted efforts are also required to reduce their possible hazardous consequences of nanomaterials based biosensors.

A Fumonisin Prevention Tool for Targeting and Ranking Agroclimatic Conditions Favoring Exposure in French Maize-Growing Areas

The levels of fumonisins (FUMO)-mycotoxins produced by Fusarium verticillioides-in maize for food and feed are subject to European Union regulations. Compliance with the regulations requires the targeting of, among others, the agroclimatic factors influencing fungal contamination and FUMO production. Arvalis-Institut du végétal has created a national, multiyear database for maize, based on field survey data collected since 2003. This database contains information about agricultural practices, climatic conditions and FUMO concentrations at harvest for 738 maize fields distributed throughout French maize-growing regions. A linear mixed model approach highlights the presence of borers and the use of a late variety, high temperatures in July and October, and a water deficit during the maize cycle as creating conditions favoring maize contamination with Fusarium verticillioides. It is thus possible to target a combination of risk factors, consisting of this climatic sequence associated with agricultural practices of interest. The effects of the various possible agroclimatic combinations can be compared, grouped and classified as promoting very low to high FUMO concentrations, possibly exceeding the regulatory threshold. These findings should facilitate the creation of a national, informative and easy-to-use prevention tool for producers and agricultural cooperatives to manage the sanitary quality of their harvest.

Sphinganine-Analog Mycotoxins (SAMs): Chemical Structures, Bioactivities, and Genetic Controls

Sphinganine-analog mycotoxins (SAMs) including fumonisins and A. alternata f. sp. Lycopersici (AAL) toxins are a group of related mycotoxins produced by plant pathogenic fungi in the Fusarium genus and in Alternaria alternata f. sp. Lycopersici, respectively. SAMs have shown diverse cytotoxicity and phytotoxicity, causing adverse impacts on plants, animals, and humans, and are a destructive force to crop production worldwide. This review summarizes the structural diversity of SAMs and encapsulates the relationships between their structures and biological activities. The toxicity of SAMs on plants and animals is mainly attributed to their inhibitory activity against the ceramide biosynthesis enzyme, influencing the sphingolipid metabolism and causing programmed cell death. We also reviewed the detoxification methods against SAMs and how plants develop resistance to SAMs. Genetic and evolutionary analyses revealed that the FUM (fumonisins biosynthetic) gene cluster was responsible for fumonisin biosynthesis in Fusarium spp. Sequence comparisons among species within the genus Fusarium suggested that mutations and multiple horizontal gene transfers involving the FUM gene cluster were responsible for the interspecific difference in fumonisin synthesis. We finish by describing methods for monitoring and quantifying SAMs in food and agricultural products.