Discrimination of internal crack for rice seeds using near infrared spectroscopy

It is an important thing to identify internal crack in seeds from normal seeds for evaluating the quality of rice seeds (Oryza sativa L.). In this study, non-destructive discrimination of internal crack in rice seeds using near infrared spectroscopy and chemometrics is proposed. Principal component analysis (PCA) was used to analyze the rice seeds spectra. Four supervised classification techniques(partial least squares discriminate analysis (PLS-DA), support vector machines (SVM), k-nearest neighbors (KNN) and random forest (RF)) with four different pre-processing techniques (standard normal variate (SNV), multiplicative scatter correction (MSC), first and second derivative with Savitzky-Golay (SG) smoothing) were applied. The best results (Sn = 0.8824, Sp = 0.9429, Acc = 0.913) were achieved by PLS-DA with the raw spectral data. The performance of the best SVM model was inferior to that of PLS-DA, but superior to that of RF and KNN. Except for PLS-DA, four different preprocessing techniques were improved the performance of the developed models. The important variables for discriminating internal cracks in rice seeds were related to the amylose. Overall, the all results demonstrated the feasibility of non-destructive discrimination of internal crack for rice seeds (Oryza sativa L.) using near infrared spectroscopy and chemometrics.

NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contamination

BACKGROUND

Maize is frequently contaminated with deoxynivalenol (DON) and fumonisins B1 (FB1) and B2 (FB2). In the European Union, these mycotoxins are regulated in maize and maize-derived products. To comply with these regulations, industries require a fast, economic, safe, non-destructive and environmentally friendly analysis method.

RESULTS

In the present study, near-infrared hyperspectral imaging (NIR-HSI) was used to develop regression and classification models for DON, FB1 and FB2 in maize kernels. The best regression models presented the following root mean square error of cross validation and ratio of performance to deviation values: 0.848 mg kg-1 and 2.344 (DON), 3.714 mg kg-1 and 2.018 (FB1) and 2.104 mg kg-1 and 2.301 (FB2). Regarding classification, European Union legal limits for DON and FB1 + FB2 were selected as thresholds to classify maize kernels as acceptable or not. The sensitivity and specificity were 0.778 and 1 for the best DON classification model and 0.607 and 0.938 for the best FB1 + FB2 classification model.

CONCLUSION

NIR-HSI can help reduce DON and fumonisins contamination in the maize food and feed chain. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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.

Aflatoxins in Cereals and Cereal-Based Products: Occurrence, Toxicity, Impact on Human Health, and Their Detoxification and Management Strategies

Cereals and cereal-based products are primary sources of nutrition across the world. However, contamination of these foods with aflatoxins (AFs), secondary metabolites produced by several fungal species, has raised serious concerns. AF generation in innate substrates is influenced by several parameters, including the substrate type, fungus species, moisture content, minerals, humidity, temperature, and physical injury to the kernels. Consumption of AF-contaminated cereals and cereal-based products can lead to both acute and chronic health issues related to physical and mental maturity, reproduction, and the nervous system. Therefore, the precise detection methods, detoxification, and management strategies of AFs in cereal and cereal-based products are crucial for food safety as well as consumer health. Hence, this review provides a brief overview of the occurrence, chemical characteristics, biosynthetic processes, health hazards, and detection techniques of AFs, along with a focus on detoxification and management strategies that could be implemented for food safety and security.

Detecting Aflatoxin B1 in Peanuts by Fourier Transform Near-Infrared Transmission and Diffuse Reflection Spectroscopy

Aflatioxin B₁ (AFB₁) has been recognized by the International Agency of Research on Cancer as a group 1 carcinogen in animals and humans. A fast, batch, and real-time control and no chemical pollution method was developed for the discrimination and quantification prediction of AFB₁-infected peanuts by applying Fourier transform near-infrared (FT-NIR) coupled with chemometrics. Initially, the near-infrared transmission (NIRT) and diffuse reflection (NIRR) modules were applied to collect spectra of the samples. The principal component analysis (PCA) method was employed to extract the characteristic wavelength, followed by different preprocessing methods (seven methods) to build an effective linear discriminant analysis (LDA) classification and partial least squares (PLS) quantification models. The results showed that, for both the NIRT or NIRR modules, the LDA classification models satisfactorily distinguished peanuts infected with AFB₁ or from those not infected, with external validation showing a 100% correct identification rate and a 0% misjudgment rate. In addition, combined with the concentration of AFB₁ in peanuts determined by enzyme-linked immunoassay assay, the best partial least squares (PLS) models were established, with a combination of the first derivative and the Norris derivative filter smoothing pretreatment (R_c² = 0.937 and 0.984, RMSECV = 3.92% and 2.22%, RPD = 3.98 and 7.91 for NIRR and NIRT, respectively). The correlation coefficient between the predicted value and the reference value in the external verification was 0.998 and 0.917, respectively. This study highlights that both spectral acquisition modules meet the requirements of online, rapid, and accurate identification of peanut AFB₁ infection in the early stages.

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.

Analysis and classification of peanuts with fungal diseases based on real-time spectral processing

The study presents an approach to the analysis and classification of peanuts performed in order to detect kernels with fungi diseases, i.e. kernels prone to contamination with mycotoxigenic Aspergillus flavus (Aspergillus parasiticus). The aim of this study was to evaluate the effectiveness of luminescent spectroscopy with a violet laser (405 nm wavelength) as the excitation source of the fluorescence when applied for real-time detection of mould in peanuts performed by means of multispectral processing based on machine learning methods. We suggest a laboratory unit used to form, register, and process the luminescence spectra of peanuts in visible and near-infrared wavelength ranges in the real-time mode. The study demonstrated that contaminated peanuts have increased luminous intensity and show a redshift in the fluorescence peaks of the contaminated samples as compared to the pure ones. The difference in the fluorescence spectra of pure and contaminated kernels is compatible with the results obtained when traditional UV-light sources are used (365 nm). To classify peanuts by their spectral characteristics, neural network algorithms were used combined with dimensionality reduction methods. The paper presents the probabilities of incorrect recognition of the peanuts' type depending on the number of relevant secondary features determined when reducing the dimensionality of the initial data. When 10 spectral components were used, the error ratios were 0.7% or 0.3% depending on the method of reducing the dimensionality of the initial data.

Assessment of multi-mycotoxin contamination throughout the supply chain of maize-based poultry feed from selected regions of Malaysia by LC-MS/MS

The objective of this study was to examine the occurrence of multi-mycotoxin contamination throughout the supply chain of maize-based poultry feed. Different sampling points throughout the feed supply chain were selected from two companies that manufactured the poultry feed. A total of 51 samples, consisting of grain maize and maize-based poultry feeds, were collected. The samples were analysed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) to determine the occurrence of multi-mycotoxin. The results revealed that 100% of samples throughout the maize-based poultry feed supply chain were spoiled with more than one mycotoxin. Fumonisin B1 (8.02-1,220 µg/kg) and fumonisin B2 (11.1-1,109 µg/kg) were the main mycotoxins detected at all sampling points throughout the feed supply chain. Zearalenone (ZEA) (6.63-7.50 µg/kg) was also detected in 11.7% (n = 6) (out of a total of 51) samples. As the supply chain progresses, a reduction in mycotoxin contamination was observed. Aflatoxins, ochratoxin A (OTA), deoxynivalenol (DON), HT-2, and T-2 toxin were not detected. The levels of mycotoxins detected throughout the supply chain were below the international regulatory limits, thus indicating the low risk of exposure to mycotoxins in maize-based poultry feed in Malaysia. Nevertheless, due to the presence of multiple ingredients in most food and feed, efforts to understand and address challenges associated with mycotoxins throughout the entire supply chain need to be more holistic to protect public health.

Effect of cleaning corn on mycotoxin concentration and nursery pig growth performance

Mycotoxins are naturally produced hazards that result from molds grown on cereal grains and other commodities. These molds may produce carcinogenic mycotoxins, which can be harmful to humans and animals. Removing broken kernels has been demonstrated to reduce mycotoxin concentration, but with high variability. Therefore, two experiments were conducted to quantify the magnitude of natural mycotoxin concentration that may be reduced by cleaning corn. Two loads of corn that were naturally contaminated with mycotoxins were procured. Corn for Experiment 1 was contaminated with aflatoxin (1,074 parts per billion; ppb), fumonisin (8.3 parts per million; ppm), and ochratoxin A (206 ppb), while corn for Experiment 2 was contaminated with only fumonisin (5.5 ppm). Corn was cleaned by mechanical sieving. For each experiment, corn was divided into twenty 150 kg runs. Runs were randomly assigned to 1 of 4 experimental treatments: 1) no screen 2) 12.7 mm screen, 3) 4.8 mm screen, and 4) 12.7 + 4.8-mm screen. The corn cleaner was sanitized between runs. Three 5 kg corn samples were collected from each run, and analyzed for mycotoxin concentration. In Experiment 1, cleaning reduced (P < 0.05) aflatoxin and fumonisin concentration by an average of 26% and 45%, respectively, compared to the original uncleaned corn level, but did not impact (P > 0.10) ochratoxin A. The resultant screenings had nearly four times the aflatoxin (4,224 ppb) and 7.5 times the fumonisin concentration (60.4 ppm) as the uncleaned corn. In Experiment 2, cleaning reduced (P < 0.05) fumonisin concentration by 32%. The resultant screenings had 19.6 times the fumonisin concentration (65.4 ppm) as the uncleaned corn. To determine the effect that cleaning corn may have on nursery pig growth performance, 360 nursery pigs were used in Experiment 3 to evaluate the impact of cleaning or pelleting on growth performance. Treatments were arranged in a 2 × 3 factorial with corn type (uncleaned vs. cleaned) and feed form (mash vs. pelleted from either mill A or B). Neither cleaning corn nor pellet mill type affected (P > 0.19) nursery pig growth performance. Pelleting improved (P < 0.0001) gain to feed ratio (G:F) by 7.6% compared to mash diets. These data suggest that cleaning is an effective method to legally reduce aflatoxin and fumonisin concentration, but does not impact animal growth performance. Screenings should be used cautiously when feeding to animals.

Potential of Near Infrared Spectroscopy as a Rapid Method to Discriminate OTA and Non-OTA-Producing Mould Species in a Dry-Cured Ham Model System

The ripening process of dry-cured meat products is characterised by the development of fungi on the product's surface. This population plays a beneficial role, but, uncontrolled moulds represent a health risk, since some of them may produce mycotoxins, such as ochratoxin A (OTA). The aim of the present work is to assess the potential of near-infrared spectroscopy (NIRS) for the detection of OTA-producing mould species on dry-cured ham-based agar. The collected spectra were used to develop Support Vector Machines-Discriminant Analysis (SVM-DA) models by a hierarchical approach. Firstly, an SVM-DA model was tested to discriminate OTA and non-OTA producers; then, two models were tested to discriminate species among the OTA producers and the non-OTA producers. OTA and non-OTA-producing moulds were discriminated with 85% sensitivity and 86% specificity in the prediction. Furthermore, the SVM-DA model could differentiate non-OTA-producing species with a 95% sensitivity and specificity. Promising results were obtained for the prediction of the four OTA-producing species tested, with a 69% and 90% sensitivity and specificity, respectively. The preliminary approach demonstrated the high potential of NIR spectroscopy, coupled with Chemometrics, to be used as a real-time automated routine monitorization of dry-cured ham surfaces.

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.

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.

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.

Reduction of Aflatoxin B1 in Corn by Water-Assisted Microwaves Treatment and Its Effects on Corn Quality

Aflatoxin B₁ (AFB₁) is one of the most commonly found mycotoxin in corn, which is highly toxic, carcinogenic, teratogenic, and mutagenic for the health of humans and animals. In order to reduce the AFB₁ in corn, corn kernels were processed with Water-assisted Microwaves Treatment (WMT) and the feasibility of WMT processing on AFB₁ reduction and its effects on corn quality were analyzed. Increasing the treatment time and microwave power could increase the reduction of AFB₁, and the maximum reduction rate could reach 58.6% and 56.8%, respectively. There was no significant correlation between the initial concentration of AFB₁ and the reduction rate of AFB₁. During WMT, the main toxigenic molds were sterilized completely, and the moisture content of corn climbed up and then declined to the initial level. WMT could obviously increase the fatty acid value and pasting temperature of corn and reduce the all paste viscosity of corn. However, it had little effect on the color of corn. The results indicated that WMT could reduce AFB₁ effectively and avoid the vast appearance of heat-damaged kernels simultaneously. Undoubtedly, water played an important role in WMT. This result provides a new idea for the reduction of AFB₁ by microwave.

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.

Detection of mycotoxins and toxigenic fungi in cereal grains using vibrational spectroscopic techniques: a review

Nutrition-rich cereal grains and oil seeds are the major sources of food and feed for human and livestock, respectively. Infected by fungi and contaminated with mycotoxins are serious problems worldwide for cereals and oil seeds before and after harvest. The growth and development activities of fungi consume seed nutrients and destroy seed structures, leading to dramatic declines of crop yield and quality. In addition, the toxic secondary metabolites produced by these fungi pose a well-known threat to both human and animals. The existence of fungi and mycotoxins has been a redoubtable problem worldwide for decades but tends to be a severe food safety issue in developing countries and regions, such as China and Africa. Detection of fungal infection at an early stage and of mycotoxin contaminants, even at a small amount, is of great significance to prevent harmful toxins from entering the food supply chains worldwide. This review focuses on the recent advancements in utilising infrared spectroscopy, Raman spectroscopy, and hyperspectral imaging to detect fungal infections and mycotoxin contaminants in cereals and oil seeds worldwide, with an emphasis on recent progress in China. Brief introduction of principles, and corresponding shortcomings, as well as latest advances of each technique, are also being presented herein.

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.

Decontamination of Mycotoxin-Contaminated Feedstuffs and Compound Feed

Mycotoxins are known worldwide as fungus-produced toxins that adulterate a wide heterogeneity of raw feed ingredients and final products. Consumption of mycotoxins-contaminated feed causes a plethora of harmful responses from acute toxicity to many persistent health disorders with lethal outcomes; such as mycotoxicosis when ingested by animals. Therefore, the main task for feed producers is to minimize the concentration of mycotoxin by applying different strategies aimed at minimizing the risk of mycotoxin effects on animals and human health. Once mycotoxins enter the production chain it is hard to eliminate or inactivate them. This paper examines the most recent findings on different processes and strategies for the reduction of toxicity of mycotoxins in animals. The review gives detailed information about the decontamination approaches to mitigate mycotoxin contamination of feedstuffs and compound feed, which could be implemented in practice.

Exposure assessment and risk characterization of aflatoxins intake through consumption of maize products in the adult populations of Serbia, Croatia and Greece

The main objective of this research was to perform an exposure assessment of aflatoxins intake through consumption of maize products in Serbia, Croatia, and Greece. Food consumption survey of maize-based products has been performed during 2017 in the three countries with at least 1,000 interviewees per country covering their dietary habits and body weight. Values for the concentration of aflatoxins were extracted from available research published in the last ten years. Finally, a Monte Carlo analysis of 100,000 iterations was performed to estimate the intake of aflatoxins from consumption of maize-based products. Results revealed that the estimated average exposure of adults to aflatoxins, from maize consumption, in each of the three countries was between 0.44 ng kg^-1 bw day^-1 and 5.59 ng kg^-1 bw day^-1. Margin of exposure values for the mean exposure levels, in all three countries, were between 30 and 389. Estimations for hepatocellular carcinoma cases/year/10⁵ individuals, depending on the HBsAg+ prevalence, were 0.075-0.098, 0.006-0.008 and 0.020-0.026 for Serbia, Croatia and Greece, respectively.

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.

Post-harvest interventions decrease aflatoxin and fumonisin contamination in maize and subsequent dietary exposure in Tanzanian infants: a cluster randomised-controlled trial

A cluster randomised controlled trial was performed in three agro-ecological zones of Tanzania to evaluate the effectiveness of locally available post-harvest mitigation strategies in preventing and reducing aflatoxin and fumonisin contamination in maize. A total of 300 children, each from one household, were randomly selected from 30 villages (intervention: n=15). The mitigation strategies focused on hand sorting (prior to storage and use), drying maize on mat/raised platforms, proper sun drying, application of storage insecticides and de-hulling before milling. Maize sample was collected from each household at harvest (baseline) and six months after harvest. Maize intake by each child, estimated using the 24 h dietary recall technique and its body weight measured using standard procedures were taken at six months after harvest. Aflatoxins and fumonisins in the maize samples were determined using HPLC. Follow-up (six month after harvest) data were available for 261 of the 300 households (intervention: n=136). Mean concentration of aflatoxins, or fumonisins was significantly (P<0.05) lower in the intervention than in the control group: intervention effects: μg/kg (95% confidence interval (CI)) -4.9 (-7.3,-2.5), and -405, (-647,-162), respectively. The difference corresponds to 83 and 70% for aflatoxins, and fumonisins, respectively. At the end of the intervention, aflatoxin and fumonisin estimated mean intakes were lower in the intervention than in the control group by 78 and 65%, respectively. Six months after harvest, prevalence of underweight in the intervention group was 6.7% lower (P=0.014) than in the control group. Mean weight-for-age Z-score difference between the groups was 0.57 (95% CI; 0.16,-0.98; P=0.007). Post-harvest practices are effective in preventing and reducing aflatoxin and fumonisin contamination in maize and subsequent dietary exposure to infants. The interventions may be applied in these and other communities with similar environmental conditions or agricultural practices that favour production of aflatoxin and fumonisins in food crops. The trial was registered at ClinicalTrials.gov identifier: NCT02438774.

Non-destructive techniques for the detection of fungal infection in cereal grains

Infection of cereal grains by fungi is a serious problem worldwide. Depending on the environmental conditions, cereal grains may be colonised by different species of fungi. These fungi cause reduction in yield, quality and nutritional value of the grain; and of major concern is their production of mycotoxins which are harmful to both humans and animals. Early detection of fungal contamination is an essential control measure for ensuring storage longevity and food safety. Conventional methods for detection of fungal infection, such as culture and colony techniques or immunological methods are either slow, labour intensive or difficult to automate. In recent years, there has been an increasing need to develop simple, rapid, non-destructive methods for early detection of fungal infection and mycotoxins contamination in cereal grains. Methods such as near infrared (NIR) spectroscopy, NIR hyperspectral imaging, and electronic nose were evaluated for these purposes. This paper reviews the different non-destructive techniques that have been considered thus far for detection of fungal infection and mycotoxins in cereal grains, including their principles, application and limitations.

Tools for Defusing a Major Global Food and Feed Safety Risk: Nonbiological Postharvest Procedures To Decontaminate Mycotoxins in Foods and Feeds

Mycotoxin contamination of foods and animal feeds is a worldwide problem for human and animal health. Controlling mycotoxin contamination has drawn the attention of scientists and other food and feed stakeholders all over the world. Despite best efforts targeting field and storage preventive measures, environmental conditions can still lead to mycotoxin contamination. This raises a need for developing decontamination methods to inactivate or remove the toxins from contaminated products. At present, decontamination methods applied include an array of both biological and nonbiological methods. The targeted use of nonbiological methods spans from the latter half of last century, when ammoniation and ozonation were first used to inactivate mycotoxins in animal feeds, to the novel techniques being developed today such as photosensitization. Effectiveness and drawbacks of different nonbiological methods have been reported in the literature, and this review examines the utility of these methods in addressing food safety. Particular consideration is given to the application of such methods in the developing world, where mycotoxin contamination is a serious food safety issue in staple crops such as maize and rice.

Impact of food processing and detoxification treatments on mycotoxin contamination

Mycotoxins are fungal metabolites commonly occurring in food, which pose a health risk to the consumer. Maximum levels for major mycotoxins allowed in food have been established worldwide. Good agricultural practices, plant disease management, and adequate storage conditions limit mycotoxin levels in the food chain yet do not eliminate mycotoxins completely. Food processing can further reduce mycotoxin levels by physical removal and decontamination by chemical or enzymatic transformation of mycotoxins into less toxic products. Physical removal of mycotoxins is very efficient: manual sorting of grains, nuts, and fruits by farmers as well as automatic sorting by the industry significantly lowers the mean mycotoxin content. Further processing such as milling, steeping, and extrusion can also reduce mycotoxin content. Mycotoxins can be detoxified chemically by reacting with food components and technical aids; these reactions are facilitated by high temperature and alkaline or acidic conditions. Detoxification of mycotoxins can also be achieved enzymatically. Some enzymes able to transform mycotoxins naturally occur in food commodities or are produced during fermentation but more efficient detoxification can be achieved by deliberate introduction of purified enzymes. We recommend integrating evaluation of processing technologies for their impact on mycotoxins into risk management. Processing steps proven to mitigate mycotoxin contamination should be used whenever necessary. Development of detoxification technologies for high-risk commodities should be a priority for research. While physical techniques currently offer the most efficient post-harvest reduction of mycotoxin content in food, biotechnology possesses the largest potential for future developments.

Antifungal Attributes of Lactobacillus plantarum MYS6 against Fumonisin Producing Fusarium proliferatum Associated with Poultry Feeds

Fumonisins, being common in occurrence in maize-based feeds, pose a great threat to animal and human health. The present study is aimed at determining the antifungal activity of Lactobacillus plantarum MYS6 against a fumonisin producing fungus, Fusarium proliferatum MYS9. The isolate was subjected to standard tests for determining its probiotic attributes and antifungal properties. L. plantarum MYS6 thrived well at pH 3.0 and 6.0, and exhibited strong resistance up to 3% bile. The isolate showed a high degree of cell surface hydrophobicity corresponding to its strong adhesion to chicken crop epithelial cells. Co-inoculation with the fungus on modified de Man Rogosa Sharpe medium revealed the inhibitory effect of L. plantarum MYS6 on fungal growth and biomass. Observation using scanning electron microscopy showed distortion of hyphal structures, swollen tips and disrupted conidia. Conidia germination inhibition assay restrained germination and showed deformed hyphae. The bioprotective feature of the isolate was evident by the inhibition of fungal development in maize-kernel treated with the cell free supernatant of L. plantarum MYS6. Both the isolate and its extracellular metabolites lowered fumonisin content in feed model up to 0.505 mg/Kg of feed and 0.3125 mg/Kg of feed respectively when compared to the level of 0.870 mg/Kg of feed in control. The major antifungal compounds produced by the isolate were 10-Octadecenoic acid, methyl ester; palmitic acid, methyl ester; heptadecanoic acid, 16-methyl ester; stearic acid and lauric acid. L. plantarum MYS6 reduced 61.7% of fumonisin possibly by a binding mechanism. These findings suggest the application of L. plantarum MYS6 as an efficient probiotic additive and biocontrol agent in feed used in poultry industry. Additionally, the antifungal metabolites pose a conspicuous inhibition of Fusarium growth and fumonisin production.

Assessment of Aflatoxin and Fumonisin Contamination of Maize in Western Kenya

We conducted a survey of aflatoxin and fumonisin in maize in western Kenya. In a regional survey of aflatoxin conducted in 2009 across three agroecological zones within three administrative regions, milled maize samples were collected from 985 patrons of 26 hammer mills. Aflatoxin contamination was detected in 49% of samples and was above the regulatory (10 ppb) in 15% of the samples overall; 65% of samples from a drought-prone area were over the limit. In a detailed survey in Bungoma County, we investigated aflatoxin and fumonisin contamination in four popular maize varieties at harvest and after 2 and 4 months of storage. We collected whole-grain samples from farmers' storage sheds and milled samples from patrons of local mills. Mean aflatoxin contamination was identical for storage sheds and mills at 2.3 ppb. In all, 41% of the samples from mills had detectable aflatoxin, with 4% over the regulatory limit, whereas 87% had detectable fumonisin, with 50% over the regulatory limit (1 ppm). Mean contamination levels did not change during storage. Maize varieties differed in fumonisin contamination, with the most popular varieties vulnerable to both mycotoxins and weevils, which are potential factors in exacerbating mycotoxin contamination. Mycotoxin surveillance is important not just in areas known previously for aflatoxin contamination and acute poisoning but also is needed in all maize-producing regions.

Aflatoxins in cereals: worldwide occurrence and dietary risk assessment

The worldwide occurrence of aflatoxins (AFB1, AFB2, AFG1, AFG2), genotoxic mycotoxins, in raw maize, rice, sorghum and wheat samples collected since the year 2000 was evaluated using published data and occurrence data from the GEMS/Food database (https://extranet.who.int/gemsfood). Dietary risk assessments were conducted using GEMS/Food total aflatoxin occurrence and food consumption data obtained from the 17 Cluster Diets. Risk characterisation arising from aflatoxin exposure was conducted using both cancer risk and margin of exposure (MOE) approaches. A total of 89 publications were retrieved from the literature, reporting data related to 18,097 samples, of which 37.6% were positive for at least one aflatoxin. The total upper bound (UB) mean for all samples analysed was 13.6 μg/kg, and was higher for rice (24.6 μg/kg) and sorghum (25.9 μg/kg). Of data related to the analysis of 4,536 samples reported to GEMS/Food database, 12.7% were positive for at least one aflatoxin. The total UB mean was 1.9 μg/kg, and was higher for rice (2.4 μg/kg) and maize (1.6 μg/kg). Total intakes ranged from 3.0 ng/kg bw/day (Cluster C11) to 17.1 ng/kg bw/day (Cluster C09). On average, the consumption of rice contributed to 41.6% of the total aflatoxin intake in all clusters, followed by wheat (35.4%), maize (21.2%) and sorghum (1.8%). The lowest cancer risk was found in cluster C11 (0.057 cancers/year/105 individuals), and the highest in cluster C09 (0.467 cancers/year/105 individuals). MOE ranged from 56 (C11) to 10 (C09), indicating a potential risk to consumers. These results highlight the need for continuous action by health authorities to decrease aflatoxin contamination in cereals, as they are staple foods in diets worldwide. These actions include the enforcement of code of practices at the national level and the establishment of maximum contamination levels by the Codex System.

A recent review of non-biological remediation of aflatoxin-contaminated crops

Aflatoxins are highly toxic, mutagenic, teratogenic and carcinogenic compounds produced predominantly as secondary metabolites by certain species of fungi belonging to the Aspergillus genus. Owing to the significant health risks and economic impacts associated with the presence of aflatoxins in agricultural commodities, a considerable amount of research has been directed at finding methods to prevent toxicity. This review compiles the recent literature of methods for the detoxification and management of aflatoxin in post-harvest agricultural crops using non-biological remediation.

Applications of single kernel conventional and hyperspectral imaging near infrared spectroscopy in cereals

Single kernel (SK) near infrared (NIR) reflectance and transmittance technologies have been developed during the last two decades for a range of cereal grain physical quality and chemical traits as well as detecting and predicting levels of toxins produced by fungi. Challenges during the development of single kernel near infrared (SK-NIR) spectroscopy applications are modifications of existing NIR technology to present single kernels for scanning as well as modifying reference methods for the trait of interest. Numerous applications have been developed, and cover almost all cereals although most have been for key traits including moisture, protein, starch and oil in the globally important food grains, i.e. maize, wheat, rice and barley. An additional benefit in developing SK-NIR applications has been to demonstrate the value in sorting grain infected with a fungus or mycotoxins such as deoxynivalenol, fumonisins and aflatoxins. However, there is still a need to develop cost-effective technologies for high-speed sorting which can be used for small grain samples such as those from breeding programmes or commercial sorting; capable of sorting tonnes per hour. Development of SK-NIR technologies also includes standardisation of SK reference methods to analyse single kernels. For protein content, the use of the Dumas method would require minimal standardisation; for starch or oil content, considerable development would be required. SK-NIR, including the use of hyperspectral imaging, will improve our understanding of grain quality and the inherent variation in the range of a trait. In the area of food safety, this technology will benefit farmers, industry and consumers if it enables contaminated grain to be removed from the human food chain.

Limitations and current applications of Near Infrared Spectroscopy for single seed analysis

Near Infrared Spectroscopy (NIRS) analysis at the single seed level is a useful tool for breeders, farmers, feeding facilities, and food companies according to current researches. As a non-destructive technique, NIRS allows for the selection and classification of seeds according to specific traits and attributes without alteration of their properties. Critical aspects in using NIRS for single seed analysis such as reference method, sample morphology, and spectrometer suitability are discussed in this review. A summary of current applications of NIRS technologies at single seed level is also presented.

Near- and mid-infrared spectroscopy as efficient tools for detection of fungal and mycotoxin contamination in agricultural commodities

In the last decade, near-infrared (NIR) and mid-infrared (MIR) spectroscopy have proven to be most promising tools for the detection of fungal contamination and estimation of mycotoxins in agricultural commodities, particularly of cereals. Owing to significant economic losses incurred from fungal contamination of foodstuffs, producers and processors are looking for fast, reliable, and less-expensive methods for the detection of fungal damage. In this context, NIR and MIR spectroscopy offer a fast, less-expensive, non-destructive, and relatively simple analytical method. Results from published studies indicate that NIR and MIR spectroscopy can be successfully applied to identifying fungal contamination and estimating specific mycotoxins. This review will focus on the applications of NIR and MIR spectroscopy to the classification of fungal contamination and the determination of specific mycotoxin contamination levels, and to compare this technology with traditional analytical methods.