Multi-spectral kernel sorting to reduce aflatoxins and fumonisins in Kenyan maize

Determination of fumonisin content in maize using near-infrared hyperspectral imaging (NIR-HSI) technology and chemometric methods

Maize (Zea mays L.) is of socioeconomic importance as an essential food for human and animal nutrition. However, cereals are susceptible to attack by mycotoxin-producing fungi, which can damage health. The methods most commonly used to detect and quantify mycotoxins are expensive and time-consuming. Therefore, alternative non-destructive methods are required urgently. The present study aimed to use near-infrared spectroscopy with hyperspectral imaging (NIR-HSI) and multivariate image analysis to develop a rapid and accurate method for quantifying fumonisins in whole grains of six naturally contaminated maize cultivars. Fifty-eight samples, each containing 40 grains, were subjected to NIR-HSI. These were subsequently divided into calibration (38 samples) and prediction sets (20 samples) based on the multispectral data obtained. The averaged spectra were subjected to various pre-processing techniques (standard normal variate (SNV), first derivative, or second derivative). The most effective pre-treatment performed on the spectra was SNV. Partial least squares (PLS) models were developed to quantify the fumonisin content. The final model presented a correlation coefficient (R2) of 0.98 and root mean square error of calibration (RMSEC) of 508 µg.kg-1 for the calibration set, an R2 of 0.95 and root mean square error of prediction (RMSEP) of 508 µg.kg-1 for the test validation set and a ratio of performance to deviation of 4.7. It was concluded that NIR-HSI with partial least square regression is a rapid, effective, and non-destructive method to determine the fumonisin content in whole maize grains.

Exploring the Interplay of Food Security, Safety, and Psychological Wellness in the COVID-19 Era: Managing Strategies for Resilience and Adaptation

The global population surge presents a dual challenge and opportunity in the realms of food consumption, safety, and mental well-being. This necessitates a projected 70% increase in food production to meet growing demands. Amid this backdrop, the ongoing COVID-19 pandemic exacerbates these issues, underscoring the need for a deeper understanding of the intricate interplay between food consumption patterns and mental health dynamics during this crisis. Mitigating the spread of COVID-19 hinges upon rigorous adherence to personal hygiene practices and heightened disease awareness. Furthermore, maintaining stringent food quality and safety standards across both public and private sectors is imperative for safeguarding public health and containing viral transmission. Drawing upon existing research, this study delves into the pandemic's impact on mental health, food consumption habits, and food safety protocols. Through a comprehensive analysis, it aims to elucidate the nuanced relationship among food, food safety, and mental well-being amid the COVID-19 pandemic, highlighting synergistic effects and dynamics that underpin holistic human welfare. Our study offers a novel approach by integrating psychological wellness with food security and safety. In conceiving this review, we aimed to comprehensively explore the intricate interplay among food security, safety, and psychological wellness amid the backdrop of the COVID-19 pandemic. Our review is structured to encompass a thorough examination of existing research, synthesizing insights into the multifaceted relationships among food consumption patterns, mental health dynamics, and food safety protocols during the crisis. Our findings provide valuable insights and practical recommendations for enhancing food security and psychological well-being, thus supporting both academic research and real-world applications in crisis management and policy development.

Advancing Mycotoxin Detection in Food and Feed: Novel Insights from Surface-Enhanced Raman Spectroscopy (SERS)

The implementation of low-cost and rapid technologies for the on-site detection of mycotoxin-contaminated crops is a promising solution to address the growing concerns of the agri-food industry. Recently, there have been significant developments in surface-enhanced Raman spectroscopy (SERS) for the direct detection of mycotoxins in food and feed. This review provides an overview of the most recent advancements in the utilization of SERS through the successful fabrication of novel nanostructured materials. Various bottom-up and top-down approaches have demonstrated their potential in improving sensitivity, while many applications exploit the immobilization of recognition elements and molecular imprinted polymers (MIPs) to enhance specificity and reproducibility in complex matrices. Therefore, the design and fabrication of nanomaterials is of utmost importance and are presented herein. This paper uncovers that limited studies establish detection limits or conduct validation using naturally contaminated samples. One decade on, SERS is still lacking significant progress and there is a disconnect between the technology, the European regulatory limits, and the intended end-user. Ongoing challenges and potential solutions are discussed including nanofabrication, molecular binders, and data analytics. Recommendations to assay design, portability, and substrate stability are made to help improve the potential and feasibility of SERS for future on-site agri-food applications.

Multispectral Sorting Based on Visibly High-Risk Kernels Sourced from Another Country Reduces Fumonisin and Toxigenic Fusarium on Maize Kernels

Fusarium species infect maize crops leading to the production of fumonisin by their toxigenic members. Elimination of microbes is critical in mitigating further postharvest spoilage and toxin accumulation. The current study investigates the efficacy of a previously described multispectral sorting technique to analyze the reduction of fumonisin and toxigenic Fusarium species found contaminating maize kernels in Kenya. Maize samples (n = 99) were collected from six mycotoxin hotspot counties in Kenya (Embu, Meru, Tharaka Nithi, Machakos, Makueni, and Kitui County) and analyzed for aflatoxin and fumonisin using commercial ELISA kits. Aflatoxin levels in majority (91%) of the samples were below the 10 ng/g threshold set by the Kenya Bureau of Standards and therefore not studied further. The 23/99 samples that had >2,000 ng/g of fumonisin were selected for sorting. The sorter was calibrated using kernels sourced from Ghana to reject visibly high-risk kernels for fumonisin contamination using reflectance at nine distinct wavelengths (470-1,550 nm). Accepted and rejected streams were tested for fumonisin using ELISA, and the presence of toxigenic Fusarium using qPCR. After sorting, there was a significant (p < 0.001) reduction of fumonisin, by an average of 1.8 log ng/g (98%) and ranging between 0.14 and 2.7 log ng/g reduction (28-99.8%) with a median mass rejection rate of 1.9% (ranged 0% to 48%). The fumonisin rejection rate ranged between 0 and 99.8% with a median of 77%. There was also a significant reduction (p = 0.005) in the proportion of DNA represented by toxigenic Fusarium, from a mean of 30-1.4%. This study demonstrates the use of multispectral sorting as a potential postharvest intervention tool for the reduction of Fusarium species and preformed fumonisin. The spectral sorting approach of this study suggests that classification algorithms based on high-risk visual features associated with mycotoxin can be applied across different sources of maize to reduce fumonisin.

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.

A Low-Cost, Portable Device for Detecting and Sorting Aflatoxin-Contaminated Maize Kernels

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.

Lactobacillus plantarum and Deoxynivalenol Detoxification: A Concise Review

ABSTRACT

Mycotoxins are toxic secondary fungal metabolites that contaminate feeds, and their levels remain stable during feed processing. The economic impact of mycotoxins on animal production happens mainly due to losses related to direct effects on animal health and trade losses related to grain rejection. Deoxynivalenol (DON) is a trichothecene mycotoxin that has contaminated approximately 60% of the grains worldwide. Ingestion of DON induces many toxic effects on human and animal health. Detoxification strategies to decrease DON levels in food and feeds include physical and chemical methods; however, they are not very effective when incorporated into the industrial production process. A valuable alternative to achieve this aim is the use of lactic acid bacteria. These bacteria can control fungal growth and thus overcome DON production or can detoxify the mycotoxin through adsorption and biotransformation. Some Lactobacillus spp. strains, such as Lactobacillus plantarum, have demonstrated preventive effects against DON toxicity in poultry and swine. This beneficial effect is associated with a binding capacity of lactic acid bacteria cell wall peptidoglycan with mycotoxins. Moreover, several antifungal compounds have been isolated from L. plantarum supernatants, including lactic, acetic, caproic, phenyl lactic, 3-hydroxylated fatty, and cyclic dipeptide acids. Biotransformation of DON by L. plantarum into other products is also hypothesized, but the mechanism remains unknown. In this concise review, we highlight the use of L. plantarum as an alternative approach to reduce DON levels and toxicity. Although the action mechanism of L. plantarum is still not fully understood, these bacteria are a safe, efficient, and low-cost strategy to reduce economic losses from mycotoxin contamination cases.

HIGHLIGHTS

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.

Industrial-Scale Cleaning Solutions for the Reduction of Fusarium Toxins in Maize

Grain cleaning is the most effective non-destructive post-harvest mitigation strategy to reduce high levels of mycotoxins on account of the removal of mold-infected grains and grain fractions with high mycotoxin content. In this study, the reduction in the concentration of some co-occurring Fusarium toxins in maize, namely deoxynivalenol (DON), zearalenone (ZEA) and fumonisins B1 and B2 (FBs), was evaluated at an industrial-scale level by mechanical removal (sieving and density separation) of dust, coarse, small, broken, shriveled and low-density kernels and/or optical sorting of defected kernels. Samples were dynamically collected according to the Commission Regulation No. 401/2006 along the entire process line. Mycotoxin analyses of water-slurry aggregate samples were performed by validated LC methods. Depending on the contamination levels in raw incoming maize, the overall reduction rates ranged from 36 to 67% for DON, from 67 to 87% for ZEA and from 27 to 67% for FBs. High levels of DON, ZEA and FBs were found in all rejected fractions with values, respectively, up to 3030%, 1510% and 2680%, compared to their content in uncleaned maize. Results showed that grain cleaning equipment based on mechanical and or optical sorting technologies can provide a significant reduction in Fusarium toxin contamination in maize.

Application of reflectance spectroscopy to identify maize genotypes and aflatoxin levels in single kernels

Spectroscopy is a rapid, non-destructive, and low-cost analytical technique that has the potential to complement more resource-intensive analytical methods. We explored the use of spectral methods to differentiate maize genotypes and assess aflatoxin (AF) contamination in maize kernels. We compared the performance of two instruments: a research-grade ultraviolet-visible-near infrared (UV-Vis-NIR) spectrometer that measures reflectance from 304 -1,085 nm, and a miniaturised NIR spectrometer that measures reflectance from 740-1,070 nm. Both systems were used to predict AF levels in maize kernels from a single genotype and across 10 genotypes, and to predict genotype for the latter. A partial least square discriminant analysis model was trained on 70% of the kernels and tested on the remaining 30%. The classification accuracy for 10 maize genotypes was 71-72% using the UV-Vis-NIR instrument on 1,170 kernels, and 65-66% using the NIR device on 740 kernels. The classification accuracy for 247 AF-contaminated kernels of a single genotype using the UV-Vis-NIR instrument was 71, 82, and 92% for AF thresholds of 20, 100, and 1000 μg/kg, respectively. Using the same spectrometer on 872 kernels from 10 genotypes, AF classification accuracy was 67, 90, and 95% in validation sets for AF thresholds of 20, 100, and 1000 μg/kg, respectively. The UV-Vis-NIR instrument and the NIR device had similar classification accuracies for AF thresholds of 100 and 1000 μg/kg, whereas the NIR device had higher accuracy for the AF threshold of 20 μg/kg. Reflectance spectroscopy outperformed visual sorting and the bright greenish yellow fluorescence test in identifying AF levels. Applying spectral analysis to estimate mycotoxin levels and to identify maize genotypes could contribute to regional toxin surveillance and action efforts. Further, using AF-associated spectral features for grain sorting can reduce AF exposure.

Identification of Maize with Different Moldy Levels Based on Catalase Activity and Data Fusion of Hyperspectral Images

Maize is susceptible to mold infection during growth and storage due to its large embryo and high moisture content. Therefore, it is essential to distinguish the moldy sample from healthy groups to prevent the spread of mold and avoid huger economic losses. Catalase is a metabolite in the growth of microorganisms; hence, all maize samples were accurately divided into four moldy grades (health, mild, moderate, and severe levels) by determining their catalase activity. The visible and shortwave near-infrared (Vis-SWNIR) and longwave near-infrared (LWNIR) hyperspectral images were investigated to jointly identify the moldy levels of maize. Spectra and texture information of each maize sample were extracted and used to build the classification models of maize with different moldy levels in pixel-level fusion and feature-level fusion. The result showed that the feature-level fusion of spectral and texture within Vis-SWNIR and LWNIR regions achieved the best results, overall prediction accuracy reached 95.00% for each moldy level, all healthy maize was correctly classified, and none of the moldy samples were misclassified as healthy level. This study illustrated that two hyperspectral image systems, with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve the classification accuracy of maize with different moldy levels.

Quantitative detection of Aflatoxin B1 by subpixel CNN regression

Aflatoxin is a highly toxic substance dispersed in peanuts, which seriously harms the health of humans and animals. In this paper, we propose a new method for aflatoxin B1(AFB1) detection inspired by quantitative remote sensing. Firstly, we obtained the relative content of AFB1 at the sub-pixel level by subpixel decomposition (endmember extraction, nonnegative matrix decomposition). Then we modified the transfer learning models (LeNet5, AlexNet, VGG16, and ResNet18) to construct a deep learning regression network for quantitative detection of AFB1. There are 67,178 pixels used for training and 67,164 pixels used for testing. After subpixel decomposition, each aflatoxin pixel was determined to contain content, and each pixel had 400 hyperspectral values (415-799 nm). The experimental results showed that, among the four models, the modified ResNet18 model achieved the best effect, with R² of 0.8898, RMSE of 0.0138, and RPD of 2.8851. Here, we implemented a sub-pixel model for quantitative AFB1 detection and proposed a regression method based on deep learning. Meanwhile, the modified convolution classification model has high predictive ability and robustness. This method provides a new scheme in designing the sorting machine and has practical value.

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.

Validation and Application of a Low-Cost Sorting Device for Fumonisin Reduction in Maize

Fumonisin mycotoxins are a persistent challenge to human and livestock health in tropical and sub-tropical maize cropping systems, and more efficient methods are needed to reduce their presence in food systems. We constructed a novel, low-cost device for sorting grain, the “DropSort”, and tested its effectiveness on both plastic kernel models and fumonisin-contaminated maize. Sorting plastic kernels of known size and shape enabled us to optimize the sorting performance of the DropSort. The device sorted maize into three distinct fractions as measured by bulk density and 100-kernel weight. The level of fumonisin was lower in the heaviest fractions of maize compared to the unsorted samples. Based on correlations among fumonisin and bulk characteristics of each fraction, we found that light fraction 100-kernel weight could be an inexpensive proxy for unsorted fumonisin concentration. Single kernel analysis revealed significant relationships among kernel fumonisin content and physical characteristics that could prove useful for future sorting efforts. The availability of a low-cost device (materials~USD 300) that can be used to reduce fumonisin in maize could improve food safety in resource-limited contexts in which fumonisin contamination remains a pressing challenge.

Multi-mycotoxin profiling in maize reveals prevalence of Fusarium mycotoxins in South and West Ethiopia

Multi-mycotoxin exposure data are missing to guide risk assessment and legislation in Ethiopia. This study therefore aimed to determine mycotoxin contamination levels in maize samples from 176 randomly selected household storages in three agro ecological zones of South (Sidama zone) and West (Jimma zone) Ethiopia, and to examine the post-harvest practices and household processing. Liquid chromatography coupled to tandem mass spectrometry was used to quantify 23 mycotoxins. The majority of the households regularly consumed maize (more than once per day). More (77%) samples in Sidama were contaminated with Fusarium mycotoxins deoxynivalenol than in Jimma (29%) (P<0.001); Similar distributions of fumonisin B1 (19%), fumonisin B2 (19%) and fumonisin B3 (12%, Sidama vs 13%, Jimma) contamination were observed (P>0.05). In Sidama, only one sample was contaminated with the Aspergillus mycotoxin aflatoxin B2 and another sample with aflatoxin B1. From all samples, 40% were contaminated with 3-5 types of Fusarium mycotoxins and only 4% of the samples were contaminated with 6-8 types of mycotoxins. After the harvested maize was dried on the field, the majority of respondents in Jimma reported that they removed the maize within one day, which was less practiced in Sidama. The majority of households in Sidama, and some in Jimma, reported that they dried maize before storage, mainly using the sun. Close to two third of the study participants in the two zones reported that they applied the chemical dichloro-diphenyl-trichloroethane (DDT) during maize storage. All households in both zones reported that they sorted visible mouldy maize grains before preparation of maize flour while most of them kept the mouldy maize for animal feed. Protective strategies of Fusarium mycotoxin contamination, with special focus on deoxynivalenol and zearalenone, should be well promoted in the study areas as they are possible human and animal health threats.

Aflatoxin rapid detection based on hyperspectral with 1D-convolution neural network in the pixel level

Aflatoxin is commonly exists in moldy foods, it is classified as a class one carcinogen by the World Health Organization. In this paper, we used one dimensional convolution neural network (1D-CNN) to classify whether a pixel contains aflatoxin. Firstly we found the best combination of 1D-CNN parameters were epoch = 30, learning rate = 0.00005 and 'relu' for active function, the highest test accuracy reached 96.35% for peanut, 92.11% for maize and 94.64% for mix data. Then we compared 1D-CNN with feature selection and methods in other papers, result shows that neural network has greatly improved the detection efficiency than feature selection. Finally we visualized the classification result of different training 1D-CNN networks. This research provides the core algorithm for the intelligent sorter with aflatoxin detection function, which is of positive significance for grain processing and the prenatal detoxification of foreign trade enterprises.

Mycotoxins contamination of market maize and the potential of density sorting in reducing exposure in unregulated food systems in Kenya

Aflatoxins and fumonisins commonly contaminate key food staples in tropical countries, causing recurring acute and chronic public health problems. The present study was conducted to assess the potential of a simple device designed for density-based sorting of maize for its potential to reduce aflatoxins and fumonisins in diverse samples of naturally contaminated maize. A cross sectional survey was conducted, analysing market maize samples (n=204) from eight counties in Western Kenya. A quarter (25%) of the maize samples were contaminated with aflatoxin B1 above the legal limit of 5 μg/kg and nearly half (48%) were contaminated with fumonisins at levels above the legal limit of 2 μg/g. Analysis of additional samples (n=24) from Meru County showed that contamination of maize with the two toxins was also common in Eastern Kenya. A simple density sorter was used to separate grain samples into heavy and light fractions. With an out-sort rate of 31%, density sorting was effective in separating maize by bulk density and 100-kernel weight (P<0.001). Bulk density was negatively correlated with aflatoxins in unsorted (r=-0.20, P<0.01) and heavy fractions (r=-0.32, P<0.01). Density sorting was effective at reducing fumonisins; for maize samples with >1 μg/g, the heavy (accepted) fraction had 66% lower fumonisins than the unsorted maize. After density sorting, the light and heavy fractions fumonisin levels differed by an average of 8.38 μg/g (P<0.001). However, sorting was not effective at significantly reducing aflatoxin levels in maize that was highly contaminated. A simple density sorting equipment that could be adopted by local small-scale millers has potential to reduce fumonisins in maize. Additional and complementary sorting technologies, such as size screening and spectral sorting might improve the effectiveness of reducing aflatoxins in maize.

Physical and Chemical Methods for Reduction in Aflatoxin Content of Feed and Food

Aflatoxins (AFs) are among the most harmful fungal secondary metabolites imposing serious health risks on both household animals and humans. The more frequent occurrence of aflatoxins in the feed and food chain is clearly foreseeable as a consequence of the extreme weather conditions recorded most recently worldwide. Furthermore, production parameters, such as unadjusted variety use and improper cultural practices, can also increase the incidence of contamination. In current aflatoxin control measures, emphasis is put on prevention including a plethora of pre-harvest methods, introduced to control Aspergillus infestations and to avoid the deleterious effects of aflatoxins on public health. Nevertheless, the continuous evaluation and improvement of post-harvest methods to combat these hazardous secondary metabolites are also required. Already in-use and emerging physical methods, such as pulsed electric fields and other nonthermal treatments as well as interventions with chemical agents such as acids, enzymes, gases, and absorbents in animal husbandry have been demonstrated as effective in reducing mycotoxins in feed and food. Although most of them have no disadvantageous effect either on nutritional properties or food safety, further research is needed to ensure the expected efficacy. Nevertheless, we can envisage the rapid spread of these easy-to-use, cost-effective, and safe post-harvest tools during storage and food processing.

The aflatoxin situation in Africa: Systematic literature review

Contamination of African staple foods is a major issue for human and animal health, nutrition, and trade. This review aimed to collect and synthesize the available evidence on geographical spread, scale of contamination, disease burden, economic impact, and mitigation measures for aflatoxins in Africa by way of a systematic literature review. This knowledge can enhance management strategies for the major challenges to combat aflatoxins. The search was conducted by applying a predefined search strategy, using bibliographic databases and websites, covering the period 2010 to 2018. Results showed that maize, peanuts, and animal feeds were the most studied commodities. For maize, all studies indicated mean AFB₁ to exceed the European Union legal limit. From studies on contamination levels and biomarkers, it is clear that overall exposure is high, leading to a substantial increase in long-term disease burden. In addition, concentrations in food occasionally can reach very high levels, causing acute aflatoxicoses. The trade-related impact of aflatoxin contamination was mainly evaluated from the standpoint of aflatoxin regulation affecting products imported from Africa. There was a limited number of studies on health-related economic impacts, pointing out a gap in peer-reviewed literature. A number of mitigation measures have been developed, but proof of cost-effectiveness or even costs alone of the practices is often lacking. We recommend more emphasis to be put in peer-reviewed studies on evidence-based cost-effective mitigation strategies for aflatoxins, on the scale and spread of the problem and its impacts on public health and economics for use in evidence-based policies.

Perspectives on Global Mycotoxin Issues and Management From the MycoKey Maize Working Group

During the last decade, there have been many advances in research and technology that have greatly contributed to expanded capabilities and knowledge in detection and measurement, characterization, biosynthesis, and management of mycotoxins in maize. MycoKey, an EU-funded Horizon 2020 project, was established to advance knowledge and technology transfer around the globe to address mycotoxin impacts in key food and feed chains. MycoKey included several working groups comprising international experts in different fields of mycotoxicology. The MycoKey Maize Working Group recently convened to gather information and strategize for the development and implementation of solutions to the maize mycotoxin problem in light of current and emerging technologies. This feature summarizes the Maize WG discussion and recommendations for addressing mycotoxin problems in maize. Discussions focused on aflatoxins, deoxynivalenol, fumonisins, and zearalenone, which are the most widespread and persistently important mycotoxins in maize. Although regional differences were recognized, there was consensus about many of the priorities for research and effective management strategies. For preharvest management, genetic resistance and selecting adapted maize genotypes, along with insect management, were among the most fruitful strategies identified across the mycotoxin groups. For postharvest management, the most important practices included timely harvest, rapid grain drying, grain cleaning, and carefully managed storage conditions. Remediation practices such as optical sorting, density separation, milling, and chemical detoxification were also suggested. Future research and communication priorities included advanced breeding technologies, development of risk assessment tools, and the development and dissemination of regionally relevant management guidelines.

Low-cost grain sorting technologies to reduce mycotoxin contamination in maize and groundnut

The widespread contamination of foods by mycotoxins continues to be a public health hazard in sub-Saharan Africa, with maize and groundnut being major sources of contamination. This study was undertaken to assess the hypothesis that grain sorting can be used to reduce mycotoxin contamination in grain lots by removing toxic kernels. We tested a set of sorting principles and methods for reducing mycotoxin levels in maize and groundnut from a variety of genotypes and environments. We found that kernel bulk density (KBD) and 100-kernel weight (HKW) were associated with the levels of aflatoxins (AF) and fumonisins (FUM) in maize grain. A low-cost sorter prototype (the 'DropSort' device) that separated maize grain based on KBD and HKW was more effective in reducing FUM than AF. We then evaluated the effectiveness of DropSorting when combined with either size or visual sorting. Size sorting followed by DropSorting was the fastest method for reducing FUM to under 2 ppm, but was not effective in reducing AF levels in maize grain to under 20 ppb, especially for heavily AF-contaminated grain. Analysis of individual kernels showed that high -AF maize kernels had lower weight, volume, density, length, and width and higher sphericity than those with low AF. Single kernel weight was the most significant predictor of AF concentration. The DropSort excluded kernels with lower single kernel weight, volume, width, depth, and sphericity. We also found that visual sorting and bright greenish-yellow fluorescence sorting of maize single kernels were successful in separating kernels based on AF levels. For groundnut, the DropSort grouped grain based on HKW and did not significantly reduce AF concentrations, whereas size sorting and visual sorting were much more effective.

When to use one-dimensional, two-dimensional, and Shifted Transversal Design pooling in mycotoxin screening

While complex sample pooling strategies have been developed for large-scale experiments with robotic liquid handling, many medium-scale experiments like mycotoxin screening by Enzyme-Linked Immunosorbent Assay (ELISA) are still conducted manually in 48- and 96-well plates. At this scale, the opportunity to save on reagent costs is offset by the increased costs of labor, materials, and risk-of-error caused by increasingly complex pooling strategies. This paper compares one-dimensional (1D), two-dimensional (2D), and Shifted Transversal Design (STD) pooling to study whether pooling affects assay accuracy and experimental cost and to provide guidance for when a human experimentalist might benefit from pooling. We approximated mycotoxin contamination in single corn kernels by fitting statistical distributions to experimental data (432 kernels for aflatoxin and 528 kernels for fumonisin) and used experimentally-validated Monte-Carlo simulation (10,000 iterations) to evaluate assay sensitivity, specificity, reagent cost, and pipetting cost. Based on the validated simulation results, assay sensitivity remains 100% for all four pooling strategies while specificity decreases as prevalence level rises. Reagent cost could be reduced by 70% and 80% in 48- and 96-well plates, with 1D and STD pooling being most reagent-saving respectively. Such a reagent-saving effect is only valid when prevalence level is < 21% for 48-well plates and < 13%-21% for 96-well plates. Pipetting cost will rise by 1.3-3.3 fold for 48-well plates and 1.2-4.3 fold for 96-well plates, with 1D pooling by row requiring the least pipetting. Thus, it is advisable to employ pooling when the expected prevalence level is below 21% and when the likely savings of up to 80% on reagent cost outweighs the increased materials and labor costs of up to 4 fold increases in pipetting.

Mycotoxins in Feed and Food and the Role of Ozone in Their Detoxification and Degradation: An Update

Mycotoxins are secondary metabolites produced by some filamentous fungi, which can cause toxicity in animal species, including humans. Because of their high toxicological impacts, mycotoxins have received significant consideration, leading to the definition of strict legislative thresholds and limits in many areas of the world. Mycotoxins can reduce farm profits not only through reduced crop quality and product refusal, but also through a reduction in animal productivity and health. This paper briefly addresses the impacts of mycotoxin contamination of feed and food on animal and human health, and describes the main pre- and post-harvest systems to control their levels, including genetic, agronomic, biological, chemical, and physical methods. It so highlights (i) the lack of effective and straightforward solutions to control mycotoxin contamination in the field, at pre-harvest, as well as later post-harvest; and (ii) the increasing demand for novel methods to control mycotoxin infections, intoxications, and diseases, without leaving toxic chemical residues in the food and feed chain. Thus, the broad objective of the present study was to review the literature on the use of ozone for mycotoxin decontamination, proposing this gaseous air pollutant as a powerful tool to detoxify mycotoxins from feed and food.

Aflatoxin Reduction in Maize by Industrial-Scale Cleaning Solutions

Different batches of biomass/feed quality maize contaminated by aflatoxins were processed at the industrial scale (a continuous process and separate discontinuous steps) to evaluate the effect of different cleaning solutions on toxin reduction. The investigated cleaning solutions included: (i) mechanical size separation of coarse, small and broken kernels, (ii) removal of dust/fine particles through an aspiration channel, (iii) separation of kernels based on gravity and (iv) optical sorting of spatial and spectral kernel defects. Depending on the sampled fraction, dynamic or static sampling was performed according to the Commission Regulation No. 401/2006 along the entire cleaning process lines. Aflatoxin analyses of the water-slurry aggregate samples were performed according to the AOAC Official Method No. 2005.008 based on high-performance liquid chromatography and immunoaffinity column cleanup of the extracts. A significant reduction in aflatoxin content in the cleaned products, ranging from 65% to 84% with respect to the uncleaned products, was observed when continuous cleaning lines were used. Additionally, an overall aflatoxin reduction from 55% to 94% was obtained by combining results from separate cleaning steps. High levels of aflatoxins (up to 490 µg/kg) were found in the rejected fractions, with the highest levels in dust and in the rejected fractions from the aspirator and optical sorting. This study shows that a cleaning line combining both mechanical and optical sorting technologies provides an efficient solution for reducing aflatoxin contamination in maize.

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

Current detection methods for contamination of aflatoxin and fumonisin used in the corn industry are based on bulk level. However, literature demonstrates that contamination of these mycotoxins is highly skewed and bulk samples do not always represent accurately the overall contamination in a batch of corn. Single kernel analysis can provide an insightful level of analysis of the contamination of aflatoxin and fumonisin, as well as suggest a possible remediation to the skewness present in bulk detection. Current literature describes analytical methods capable of detecting aflatoxin and fumonisin at a single kernel level, such as liquid chromatography, fluorescence imaging, and reflectance imaging. These methods could provide tools to classify mycotoxin contaminated kernels and study potential co-occurrence of aflatoxin and fumonisin. Analysis at a single kernel level could provide a solution to the skewness present in mycotoxin contamination detection and offer improved remediation methods through sorting that could impact food security and management of food waste.

International Plant Pathology: Past and Future Contributions to Global Food Security

The challenge of feeding the current and future world population is widely recognized, and the management of plant diseases has an important role in overcoming this. This paper explores the ways in which international plant pathology has contributed and continues to support efforts to secure adequate, safe and culturally appropriate nourishment and livelihoods for present and future generations. For the purposes of this paper, "international plant pathology" refers to the work that plant pathologists do when they work across international borders, with a focus on enhancing food security in tropical regions. Significant efforts involve public and philanthropic resources from the global North for addressing plant disease concerns in the global South, where food security is a legitimate and pressing concern. International disease management efforts are also aimed at protecting domestic food security, for example when pathogens of major staples migrate across national borders. In addition, some important crops are largely produced in tropical countries and consumed globally, including in industrialized countries; the diseases of these crops are of international interest, and they are largely managed by the private sector. Finally, host-microbe interactions are fascinating biological systems, and basic research on plant diseases of international relevance has often yielded insights and technologies with both scientific and practical implications.

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

Aflatoxins are wide-spread harmful carcinogenic secondary metabolites produced by Aspergillus species, which cause serious feed and food contaminations and affect farm animals deleteriously with acute or chronic manifestations of mycotoxicoses. On farm, both pre-harvest and post-harvest strategies are applied to minimize the risk of aflatoxin contaminations in feeds. The great economic losses attributable to mycotoxin contaminations have initiated a plethora of research projects to develop new, effective technologies to prevent the highly toxic effects of these secondary metabolites on domestic animals and also to block the carry-over of these mycotoxins to humans through the food chain. Among other areas, this review summarizes the latest findings on the effects of silage production technologies and silage microbiota on aflatoxins, and it also discusses the current applications of probiotic organisms and microbial products in feeding technologies. After ingesting contaminated foodstuffs, aflatoxins are metabolized and biotransformed differently in various animals depending on their inherent and acquired physiological properties. These mycotoxins may cause primary aflatoxicoses with versatile, species-specific adverse effects, which are also dependent on the susceptibility of individual animals within a species, and will be a function of the dose and duration of aflatoxin exposures. The transfer of these undesired compounds from contaminated feed into food of animal origin and the aflatoxin residues present in foods become an additional risk to human health, leading to secondary aflatoxicoses. Considering the biological transformation of aflatoxins in livestock, this review summarizes (i) the metabolism of aflatoxins in different animal species, (ii) the deleterious effects of the mycotoxins and their derivatives on the animals, and (iii) the major risks to animal health in terms of the symptoms and consequences of acute or chronic aflatoxicoses, animal welfare and productivity. Furthermore, we traced the transformation and channeling of Aspergillus-derived mycotoxins into food raw materials, particularly in the case of aflatoxin contaminated milk, which represents the major route of human exposure among animal-derived foods. The early and reliable detection of aflatoxins in feed, forage and primary commodities is an increasingly important issue and, therefore, the newly developed, easy-to-use qualitative and quantitative aflatoxin analytical methods are also summarized in the review.

Enhancing Food Safety through Adoption of Long-Term Technical Advisory, Financial, and Storage Support Services in Maize Growing Areas of East Africa

Grain production and storage are major components in food security. In the ancient times, food security was achieved through gathering of fruits, grains, herbs, tubers, and roots from the forests by individual households. Advancements in human civilization led to domestication of crops and a need to save food for not only a household, but the nation. This extended need for food security led to establishment of national reservoirs for major produces and this practice varies greatly in different states. Each of the applied food production, handling, and storage approaches has its benefits and challenges. In sub-Saharan Africa, several countries have a public funded budget to subsidize production costs, to buy grains from farmers, and to store the produce for a specific period and/or until the next harvests. During the times of famine, the stored grains are later sold at subsidized prices or are given for free to the starving citizens. If there is no famine, the grain is sold to retailers and/or processors (e.g., millers) who later sell it to the consumers. This approach works well if the produce (mainly grain) is stored under conditions that do not favor growth of molds, as some of these microbes could contaminate the grain with toxic and carcinogenic metabolites called mycotoxins. Conditions that alleviate contamination of grains are required during production, handling, and storage. Most of the grain is produced by smallholder farmers under sub-optimal conditions, making it vulnerable to colonization and contamination by toxigenic fungi. Further, the grain is stored in silos at large masses, where it is hard to monitor the conditions at different points of these facilities, and hence, it becomes vulnerable to additional contamination. Production and storage of grain under conditions that favor mycotoxins poses major food health and safety risks to humans and livestock who consume it. This concept paper focuses on how establishment of a local grain production and banking system (LGPBS) could enhance food security and safety in East Africa. The concept of LGPBS provides an extension of advisory and finance support within warehouse receipt system to enhance grain production under optimal conditions. The major practices at the LGPBS and how each could contribute to food security and safety are discussed. While the concept paper gives more strength on maize production and safety, similar practices could be applied to enhance safety of other grains in the same LGPBS.

Diverse Components of Resistance to Fusarium verticillioides Infection and Fumonisin Contamination in Four Maize Recombinant Inbred Families

The fungus Fusarium verticillioides can infect maize ears, causing Fusarium ear rot (FER) and contaminating the grain with fumonisins (FUM), which are harmful to humans and animals. Breeding for resistance to FER and FUM and post-harvest sorting of grain are two strategies for reducing FUM in the food system. Kernel and cob tissues have been previously associated with differential FER and FUM. Four recombinant inbred line families from the maize nested associated mapping population were grown and inoculated with F. verticillioides across four environments, and we evaluated the kernels for external and internal infection severity as well as FUM contamination. We also employed publicly available phenotypes on innate ear morphology to explore genetic relationships between ear architecture and resistance to FER and FUM. The four families revealed wide variation in external symptomatology at the phenotypic level. Kernel bulk density under inoculation was an accurate indicator of FUM levels. Genotypes with lower kernel density-under both inoculated and uninoculated conditions-and larger cobs were more susceptible to infection and FUM contamination. Quantitative trait locus (QTL) intervals could be classified as putatively resistance-specific and putatively shared for ear and resistance traits. Both types of QTL mapped in this study had substantial overlap with previously reported loci for resistance to FER and FUM. Ear morphology may be a component of resistance to F. verticillioides infection and FUM accumulation.

Thin Films Sensor Devices for Mycotoxins Detection in Foods: Applications and Challenges

Mycotoxins are a group of secondary metabolites produced by different species of filamentous fungi and pose serious threats to food safety due to their serious human and animal health impacts such as carcinogenic, teratogenic and hepatotoxic effects. Conventional methods for the detection of mycotoxins include gas chromatography and high-performance liquid chromatography coupled with mass spectrometry or other detectors (fluorescence or UV detection), thin layer chromatography and enzyme-linked immunosorbent assay. These techniques are generally straightforward and yield reliable results; however, they are time-consuming, require extensive preparation steps, use large-scale instruments, and consume large amounts of hazardous chemical reagents. Rapid detection of mycotoxins is becoming an increasingly important challenge for the food industry in order to effectively enforce regulations and ensure the safety of food and feed. In this sense, several studies have been done with the aim of developing strategies to detect mycotoxins using sensing devices that have high sensitivity and specificity, fast analysis, low cost and portability. The latter include the use of microarray chips, multiplex lateral flow, Surface Plasmon Resonance, Surface Enhanced Raman Scattering and biosensors using nanoparticles. In this perspective, thin film sensors have recently emerged as a good candidate technique to meet such requirements. This review summarizes the application and challenges of thin film sensor devices for detection of mycotoxins in food matrices.

Dissecting Symptomatology and Fumonisin Contamination Produced by Fusarium verticillioides in Maize Ears

The fungus Fusarium verticillioides can infect maize ears, contaminating the grain with mycotoxins, including fumonisins. This global public health threat can be managed by breeding maize varieties that are resistant to colonization by F. verticillioides and by sorting grain after harvest to reduce fumonisin levels in food systems. Here, we employed two F. verticillioides inoculation techniques representing distinct infection pathways to dissect ear symptomatology and morphological resistance mechanisms in a diverse panel of maize inbred lines. The "point" method involved penetrating the ear with a spore-coated toothpick and the "inundative" method introduced a liquid spore suspension under the husk of the ear. We evaluated quantitative and qualitative indicators of external and internal symptom severity as low-cost proxies for fumonisin contamination, and found that kernel bulk density was predictive of fumonisin levels (78 to 84% sensitivity; 97 to 99% specificity). Inundative inoculation resulted in greater disease severity and fumonisin contamination than point inoculation. We also found that the two inoculation methods implicated different ear tissues in defense, with cob morphology being a more important component of resistance under point inoculation. Across both inoculation methods, traits related to cob size were positively associated with disease severity and fumonisin content. Our work demonstrates that (i) the use of diverse modes of inoculation is necessary for combining complementary mechanisms of genetic resistance, (ii) kernel bulk density can be used effectively as a proxy for fumonisin levels, and (iii) trade-offs may exist between yield potential and resistance to fumonisin contamination.

Prevalence and mitigation of aflatoxins in Kenya (1960-to date)

Aflatoxins are highly toxic metabolites of several Aspergillus species widely distributed throughout the environment. These toxins have adverse effects on humans and livestock at a few micrograms per kilogram (μg/kg) concentrations. Strict regulations on the concentrations of aflatoxins allowed in food and feed exist in many nations in the developing world. Loopholes in implementing regulations result in the consumption of dangerous concentrations of aflatoxins. In Kenya, where 'farm-to-mouth' crops become severely contaminated, solutions to the aflatoxins problem are needed. Across the decades, aflatoxins have repeatedly caused loss of human and animal life. A prerequisite to developing viable solutions for managing aflatoxins is understanding the geographical distribution and severity of food and feed contamination, and the impact on lives. This review discusses the scope of the aflatoxins problem and management efforts by various players in Kenya. Economic drivers likely to influence the choice of aflatoxins management options include historical adverse health effects on humans and animals, cost of intervention for mitigation of aflatoxins, knowledge about aflatoxins and their impact, incentives for aflatoxins safe food and intended scope of use of interventions. It also highlights knowledge gaps that can direct future management efforts. These include: sparse documented information on human exposure; few robust tools to accurately measure economic impact in widely unstructured value chains; lack of long-term impact studies on benefits of aflatoxins mitigation; inadequate sampling mechanisms in smallholder farms and grain holding stores/containers; overlooking social learning networks in technology uptake and lack of in-depth studies on an array of aflatoxins control measures followed in households. The review proposes improved linkages between agriculture, nutrition and health sectors to address aflatoxins contamination better. Sustained public awareness at all levels, capacity building and aflatoxins related policies are necessary to support management initiatives.

Cultural and Genetic Approaches to Manage Aflatoxin Contamination: Recent Insights Provide Opportunities for Improved Control

Aspergillus flavus is a morphologically complex species that can produce the group of polyketide derived carcinogenic and mutagenic secondary metabolites, aflatoxins, as well as other secondary metabolites such as cyclopiazonic acid and aflatrem. Aflatoxin causes aflatoxicosis when aflatoxins are ingested through contaminated food and feed. In addition, aflatoxin contamination is a major problem, from both an economic and health aspect, in developing countries, especially Asia and Africa, where cereals and peanuts are important food crops. Earlier measures for control of A. flavus infection and consequent aflatoxin contamination centered on creating unfavorable environments for the pathogen and destroying contaminated products. While development of atoxigenic (nonaflatoxin producing) strains of A. flavus as viable commercial biocontrol agents has marked a unique advance for control of aflatoxin contamination, particularly in Africa, new insights into the biology and sexuality of A. flavus are now providing opportunities to design improved atoxigenic strains for sustainable biological control of aflatoxin. Further, progress in the use of molecular technologies such as incorporation of antifungal genes in the host and host-induced gene silencing, is providing knowledge that could be harnessed to develop germplasm that is resistant to infection by A. flavus and aflatoxin contamination. This review summarizes the substantial progress that has been made to understand the biology of A. flavus and mitigate aflatoxin contamination with emphasis on maize. Concepts developed to date can provide a basis for future research efforts on the sustainable management of aflatoxin contamination.

Updated Overview of Infrared Spectroscopy Methods for Detecting Mycotoxins on Cereals (Corn, Wheat, and Barley)

Each year, mycotoxins cause economic losses of several billion US dollars worldwide. Consequently, methods must be developed, for producers and cereal manufacturers, to detect these toxins and to comply with regulations. Chromatographic reference methods are time consuming and costly. Thus, alternative methods such as infrared spectroscopy are being increasingly developed to provide simple, rapid, and nondestructive methods to detect mycotoxins. This article reviews research conducted over the last eight years into the use of near-infrared and mid-infrared spectroscopy to monitor mycotoxins in corn, wheat, and barley. More specifically, we focus on the Fusarium species and on the main fusariotoxins of deoxynivalenol, zearalenone, and fumonisin B1 and B2. Quantification models are insufficiently precise to satisfy the legal requirements. Sorting models with cutoff levels are the most promising applications.