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Inglis A, Parnell AC, Subramani N, Doohan FM. Machine Learning Applied to the Detection of Mycotoxin in Food: A Systematic Review. Toxins (Basel) 2024; 16:268. [PMID: 38922162 PMCID: PMC11209146 DOI: 10.3390/toxins16060268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 05/31/2024] [Accepted: 06/06/2024] [Indexed: 06/27/2024] Open
Abstract
Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds can contaminate a variety of crops, leading to economic losses and health risks to both humans and animals. Traditional lab analysis methods for mycotoxin detection can be time-consuming and may not always be suitable for large-scale screenings. However, in recent years, machine learning (ML) methods have gained popularity for use in the detection of mycotoxins and in the food safety industry in general due to their accurate and timely predictions. We provide a systematic review on some of the recent ML applications for detecting/predicting the presence of mycotoxin on a variety of food ingredients, highlighting their advantages, challenges, and potential for future advancements. We address the need for reproducibility and transparency in ML research through open access to data and code. An observation from our findings is the frequent lack of detailed reporting on hyperparameters in many studies and a lack of open source code, which raises concerns about the reproducibility and optimisation of the ML models used. The findings reveal that while the majority of studies predominantly utilised neural networks for mycotoxin detection, there was a notable diversity in the types of neural network architectures employed, with convolutional neural networks being the most popular.
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Affiliation(s)
- Alan Inglis
- Hamilton Institute, Eolas Building, Maynooth University, W23 F2H6 Maynooth, Kildare, Ireland;
| | - Andrew C. Parnell
- Hamilton Institute, Eolas Building, Maynooth University, W23 F2H6 Maynooth, Kildare, Ireland;
| | - Natarajan Subramani
- School of Biology and Environmental Science, University College Dublin, D04 C1P1 Dublin, Ireland; (N.S.); (F.M.D.)
| | - Fiona M. Doohan
- School of Biology and Environmental Science, University College Dublin, D04 C1P1 Dublin, Ireland; (N.S.); (F.M.D.)
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Casu A, Camardo Leggieri M, Toscano P, Battilani P. Changing climate, shifting mycotoxins: A comprehensive review of climate change impact on mycotoxin contamination. Compr Rev Food Sci Food Saf 2024; 23:e13323. [PMID: 38477222 DOI: 10.1111/1541-4337.13323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/20/2024] [Accepted: 02/28/2024] [Indexed: 03/14/2024]
Abstract
Climate change (CC) is a complex phenomenon that has the potential to significantly alter marine, terrestrial, and freshwater ecosystems worldwide. Global warming of 2°C is expected to be exceeded during the 21st century, and the frequency of extreme weather events, including floods, storms, droughts, extreme temperatures, and wildfires, has intensified globally over recent decades, differently affecting areas of the world. How CC may impact multiple food safety hazards is increasingly evident, with mycotoxin contamination in particular gaining in prominence. Research focusing on CC effects on mycotoxin contamination in edible crops has developed considerably throughout the years. Therefore, we conducted a comprehensive literature search to collect available studies in the scientific literature published between 2000 and 2023. The selected papers highlighted how warmer temperatures are enabling the migration, introduction, and mounting abundance of thermophilic and thermotolerant fungal species, including those producing mycotoxins. Certain mycotoxigenic fungal species, such as Aspergillus flavus and Fusarium graminearum, are expected to readily acclimatize to new conditions and could become more aggressive pathogens. Furthermore, abiotic stress factors resulting from CC are expected to weaken the resistance of host crops, rendering them more vulnerable to fungal disease outbreaks. Changed interactions of mycotoxigenic fungi are likewise expected, with the effect of influencing the prevalence and co-occurrence of mycotoxins in the future. Looking ahead, future research should focus on improving predictive modeling, expanding research into different pathosystems, and facilitating the application of effective strategies to mitigate the impact of CC.
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Affiliation(s)
- Alessia Casu
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Marco Camardo Leggieri
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Piero Toscano
- IBE-CNR, Institute of BioEconomy-National Research Council, Firenze, Italia
| | - Paola Battilani
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, Italy
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Stutt ROJH, Castle MD, Markwell P, Baker R, Gilligan CA. An integrated model for pre- and post-harvest aflatoxin contamination in maize. NPJ Sci Food 2023; 7:60. [PMID: 37980424 PMCID: PMC10657429 DOI: 10.1038/s41538-023-00238-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 11/02/2023] [Indexed: 11/20/2023] Open
Abstract
Aflatoxin contamination caused by colonization of maize by Aspergillus flavus continues to pose a major human and livestock health hazard in the food chain. Increasing attention has been focused on the development of models to predict risk and to identify effective intervention strategies. Most risk prediction models have focused on elucidating weather and site variables on the pre-harvest dynamics of A. flavus growth and aflatoxin production. However fungal growth and toxin accumulation continue to occur after harvest, especially in countries where storage conditions are limited by logistical and cost constraints. In this paper, building on previous work, we introduce and test an integrated meteorology-driven epidemiological model that covers the entire supply chain from planting to delivery. We parameterise the model using approximate Bayesian computation with monthly time-series data over six years for contamination levels of aflatoxin in daily shipments received from up to three sourcing regions at a high-volume maize processing plant in South Central India. The time series for aflatoxin levels from the parameterised model successfully replicated the overall profile, scale and variance of the historical aflatoxin datasets used for fitting and validation. We use the model to illustrate the dynamics of A. flavus growth and aflatoxin production during the pre- and post-harvest phases in different sourcing regions, in short-term predictions to inform decision making about sourcing supplies and to compare intervention strategies to reduce the risks of aflatoxin contamination.
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Affiliation(s)
- Richard O J H Stutt
- Epidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK.
| | - Matthew D Castle
- Epidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK
- Cambridge Centre for Data-Driven Discovery, Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK
| | - Peter Markwell
- Mars Global Food Safety Center, Mars Inc., Yanqi Economic Development Zone, Huairou, Beijing, China
| | - Robert Baker
- Mars Global Food Safety Center, Mars Inc., Yanqi Economic Development Zone, Huairou, Beijing, China
| | - Christopher A Gilligan
- Epidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK
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Castano-Duque L, Winzeler E, Blackstock JM, Liu C, Vergopolan N, Focker M, Barnett K, Owens PR, van der Fels-Klerx HJ, Vaughan MM, Rajasekaran K. Dynamic geospatial modeling of mycotoxin contamination of corn in Illinois: unveiling critical factors and predictive insights with machine learning. Front Microbiol 2023; 14:1283127. [PMID: 38029202 PMCID: PMC10646420 DOI: 10.3389/fmicb.2023.1283127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 09/26/2023] [Indexed: 12/01/2023] Open
Abstract
Mycotoxin contamination of corn is a pervasive problem that negatively impacts human and animal health and causes economic losses to the agricultural industry worldwide. Historical aflatoxin (AFL) and fumonisin (FUM) mycotoxin contamination data of corn, daily weather data, satellite data, dynamic geospatial soil properties, and land usage parameters were modeled to identify factors significantly contributing to the outbreaks of mycotoxin contamination of corn grown in Illinois (IL), AFL >20 ppb, and FUM >5 ppm. Two methods were used: a gradient boosting machine (GBM) and a neural network (NN). Both the GBM and NN models were dynamic at a state-county geospatial level because they used GPS coordinates of the counties linked to soil properties. GBM identified temperature and precipitation prior to sowing as significant influential factors contributing to high AFL and FUM contamination. AFL-GBM showed that a higher aflatoxin risk index (ARI) in January, March, July, and November led to higher AFL contamination in the southern regions of IL. Higher values of corn-specific normalized difference vegetation index (NDVI) in July led to lower AFL contamination in Central and Southern IL, while higher wheat-specific NDVI values in February led to higher AFL. FUM-GBM showed that temperature in July and October, precipitation in February, and NDVI values in March are positively correlated with high contamination throughout IL. Furthermore, the dynamic geospatial models showed that soil characteristics were correlated with AFL and FUM contamination. Greater calcium carbonate content in soil was negatively correlated with AFL contamination, which was noticeable in Southern IL. Greater soil moisture and available water-holding capacity throughout Southern IL were positively correlated with high FUM contamination. The higher clay percentage in the northeastern areas of IL negatively correlated with FUM contamination. NN models showed high class-specific performance for 1-year predictive validation for AFL (73%) and FUM (85%), highlighting their accuracy for annual mycotoxin prediction. Our models revealed that soil, NDVI, year-specific weekly average precipitation, and temperature were the most important factors that correlated with mycotoxin contamination. These findings serve as reliable guidelines for future modeling efforts to identify novel data inputs for the prediction of AFL and FUM outbreaks and potential farm-level management practices.
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Affiliation(s)
- Lina Castano-Duque
- Food and Feed Safety Research Unit, Southern Regional Research Center, Agriculture Research Service, United States Department of Agriculture, New Orleans, LA, United States
| | - Edwin Winzeler
- Dale Bumpers Small Farms Research Center, Agriculture Research Service, United States Department of Agriculture, Booneville, AR, United States
| | - Joshua M. Blackstock
- Dale Bumpers Small Farms Research Center, Agriculture Research Service, United States Department of Agriculture, Booneville, AR, United States
| | - Cheng Liu
- Microbiology and Agrochains Wageningen Food Safety Research, Wageningen, Netherlands
| | - Noemi Vergopolan
- Atmospheric and Ocean Science Program, Princeton University, Princeton, NJ, United States
| | - Marlous Focker
- Microbiology and Agrochains Wageningen Food Safety Research, Wageningen, Netherlands
| | - Kristin Barnett
- Agricultural Products Inspection, Illinois Department of Agriculture, Springfield, IL, United States
| | - Phillip Ray Owens
- Dale Bumpers Small Farms Research Center, Agriculture Research Service, United States Department of Agriculture, Booneville, AR, United States
| | | | - Martha M. Vaughan
- Mycotoxin Prevention and Applied Microbiology Research Unit, United States Department of Agriculture, Agricultural Research Service, National Center for Agricultural Utilization Research, Peoria, IL, United States
| | - Kanniah Rajasekaran
- Food and Feed Safety Research Unit, Southern Regional Research Center, Agriculture Research Service, United States Department of Agriculture, New Orleans, LA, United States
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Branstad-Spates EH, Castano-Duque L, Mosher GA, Hurburgh CR, Owens P, Winzeler E, Rajasekaran K, Bowers EL. Gradient boosting machine learning model to predict aflatoxins in Iowa corn. Front Microbiol 2023; 14:1248772. [PMID: 37720139 PMCID: PMC10502509 DOI: 10.3389/fmicb.2023.1248772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/14/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction Aflatoxin (AFL), a secondary metabolite produced from filamentous fungi, contaminates corn, posing significant health and safety hazards for humans and livestock through toxigenic and carcinogenic effects. Corn is widely used as an essential commodity for food, feed, fuel, and export markets; therefore, AFL mitigation is necessary to ensure food and feed safety within the United States (US) and elsewhere in the world. In this case study, an Iowa-centric model was developed to predict AFL contamination using historical corn contamination, meteorological, satellite, and soil property data in the largest corn-producing state in the US. Methods We evaluated the performance of AFL prediction with gradient boosting machine (GBM) learning and feature engineering in Iowa corn for two AFL risk thresholds for high contamination events: 20-ppb and 5-ppb. A 90%-10% training-to-testing ratio was utilized in 2010, 2011, 2012, and 2021 (n = 630), with independent validation using the year 2020 (n = 376). Results The GBM model had an overall accuracy of 96.77% for AFL with a balanced accuracy of 50.00% for a 20-ppb risk threshold, whereas GBM had an overall accuracy of 90.32% with a balanced accuracy of 64.88% for a 5-ppb threshold. The GBM model had a low power to detect high AFL contamination events, resulting in a low sensitivity rate. Analyses for AFL showed satellite-acquired vegetative index during August significantly improved the prediction of corn contamination at the end of the growing season for both risk thresholds. Prediction of high AFL contamination levels was linked to aflatoxin risk indices (ARI) in May. However, ARI in July was an influential factor for the 5-ppb threshold but not for the 20-ppb threshold. Similarly, latitude was an influential factor for the 20-ppb threshold but not the 5-ppb threshold. Furthermore, soil-saturated hydraulic conductivity (Ksat) influenced both risk thresholds. Discussion Developing these AFL prediction models is practical and implementable in commodity grain handling environments to achieve the goal of preventative rather than reactive mitigations. Finding predictors that influence AFL risk annually is an important cost-effective risk tool and, therefore, is a high priority to ensure hazard management and optimal grain utilization to maximize the utility of the nation's corn crop.
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Affiliation(s)
- Emily H. Branstad-Spates
- Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA, United States
| | - Lina Castano-Duque
- USDA, Agriculture Research Service, Southern Regional Research Center, New Orleans, LA, United States
| | - Gretchen A. Mosher
- Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA, United States
| | - Charles R. Hurburgh
- Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA, United States
| | - Phillip Owens
- USDA, Agriculture Research Service, Dale Bumpers Small Farms Research Center, Booneville, AR, United States
| | - Edwin Winzeler
- USDA, Agriculture Research Service, Dale Bumpers Small Farms Research Center, Booneville, AR, United States
| | - Kanniah Rajasekaran
- USDA, Agriculture Research Service, Southern Regional Research Center, New Orleans, LA, United States
| | - Erin L. Bowers
- Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA, United States
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Kyei-Baffour VO, Ketemepi HK, Brew-Sam NN, Asiamah E, Baffour Gyasi LC, Amoa-Awua WK. Assessing aflatoxin safety awareness among grain and cereal sellers in greater Accra region of Ghana: A machine learning approach. Heliyon 2023; 9:e18320. [PMID: 37519649 PMCID: PMC10372392 DOI: 10.1016/j.heliyon.2023.e18320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/09/2023] [Accepted: 07/13/2023] [Indexed: 08/01/2023] Open
Abstract
Studies have established high prevalence of aflatoxin contamination in grains and cereals produced in Ghana. Mitigation strategies have focused mainly on capacity building for farmers, agricultural extension officers, bulk distributors and processors to the detriment of the market women who act as the final link between consumers and producers. This study used supervised machine learning algorithms by means of Classification and Regression Trees (CART) to investigate aflatoxin knowledge and awareness of market women in Greater Accra Region of Ghana. A cross-sectional survey and probability sampling methods were employed for data collection. Ninety-two (92%) of participants had never heard about aflatoxins and yet, 62% reported that they usually observe mould growth in their cereals/grains. Unsurprisingly, 97% of participants indicated that they had no knowledge of the aflatoxin bill passed by the government of Ghana parliament. Despite participants not being aware of aflatoxin menace, the percent correctness of their aflatoxin safety measure score was 40%. A regression tree algorithm showed that, participant's ethnic group was the most significant parameter to consider regarding their aflatoxin safety knowledge. Their educational background and age were 95.5% and 72.5% as significant as their ethnic group. A classification tree algorithm showed that, educational level was the most significant parameter to consider when it comes to sorting of grains/cereals. Their ethnic group and marital status were 92.4% and 89.3% as important as educational level. It is therefore imperative for the Ghana government to extend sensitization and awareness programs to these market women, targeting the uneducated and specific age and ethnic groups.
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Affiliation(s)
- Vincent Owusu Kyei-Baffour
- Council for Scientific and Industrial Research – Food Research Institute (CSIR-FRI), P.O Box M20, Accra, Ghana
- Department of Agro-Processing Technology and Food Bio-Sciences, CSIR College of Science and Technology, Josip Broz Tito Avenue, Accra, Ghana
| | - Hilary Kwesi Ketemepi
- Council for Scientific and Industrial Research – Food Research Institute (CSIR-FRI), P.O Box M20, Accra, Ghana
| | - Nancy Nelly Brew-Sam
- Council for Scientific and Industrial Research – Food Research Institute (CSIR-FRI), P.O Box M20, Accra, Ghana
| | - Ebenezer Asiamah
- Council for Scientific and Industrial Research – Food Research Institute (CSIR-FRI), P.O Box M20, Accra, Ghana
| | | | - Wisdom Kofi Amoa-Awua
- Department of Agro-Processing Technology and Food Bio-Sciences, CSIR College of Science and Technology, Josip Broz Tito Avenue, Accra, Ghana
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Camardo Leggieri M, Arciuolo R, Chiusa G, Castello G, Spigolon N, Battilani P. DEFHAZ: A Mechanistic Weather-Driven Predictive Model for Diaporthe eres Infection and Defective Hazelnut Outbreaks. PLANTS (BASEL, SWITZERLAND) 2022; 11:3553. [PMID: 36559665 PMCID: PMC9784339 DOI: 10.3390/plants11243553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
The browning of the internal tissues of hazelnut kernels, which are visible when the nuts are cut in half, as well as the discolouration and brown spots on the kernel surface, are important defects that are mainly attributed to Diaporthe eres. The knowledge regarding the Diaporthe eres infection cycle and its interaction with hazelnut crops is incomplete. Nevertheless, we developed a mechanistic model called DEFHAZ. We considered georeferenced data on the occurrence of hazelnut defects from 2013 to 2020 from orchards in the Caucasus region and Turkey, supported by meteorological data, to run and validate the model. The predictive model inputs are the hourly meteorological data (air temperature, relative humidity, and rainfall), and the model output is the cumulative index (Dh-I), which we computed daily during the growing season till ripening/harvest time. We established the probability function, with a threshold of 1% of defective hazelnuts, to define the defect occurrence risk. We compared the predictions at early and full ripening with the observed data at the corresponding crop growth stages. In addition, we compared the predictions at early ripening with the defects observed at full ripening. Overall, the correct predictions were >80%, with <16% false negatives, which confirmed the model accuracy in predicting hazelnut defects, even in advance of the harvest. The DEFHAZ model could become a valuable support for hazelnut stakeholders.
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Affiliation(s)
- Marco Camardo Leggieri
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, 29121 Piacenza, PC, Italy
| | - Roberta Arciuolo
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, 29121 Piacenza, PC, Italy
| | - Giorgio Chiusa
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, 29121 Piacenza, PC, Italy
| | - Giuseppe Castello
- Soremartec Italia S.r.l., Piazzale Pietro Ferrero 1, 12051 Alba, CN, Italy
| | - Nicola Spigolon
- Soremartec Italia S.r.l., Piazzale Pietro Ferrero 1, 12051 Alba, CN, Italy
| | - Paola Battilani
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, 29121 Piacenza, PC, Italy
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Castano-Duque L, Vaughan M, Lindsay J, Barnett K, Rajasekaran K. Gradient boosting and bayesian network machine learning models predict aflatoxin and fumonisin contamination of maize in Illinois - First USA case study. Front Microbiol 2022; 13:1039947. [PMID: 36439814 PMCID: PMC9684211 DOI: 10.3389/fmicb.2022.1039947] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 10/13/2022] [Indexed: 09/19/2023] Open
Abstract
Mycotoxin contamination of corn results in significant agroeconomic losses and poses serious health issues worldwide. This paper presents the first report utilizing machine learning and historical aflatoxin and fumonisin contamination levels in-order-to develop models that can confidently predict mycotoxin contamination of corn in Illinois, a major corn producing state in the USA. Historical monthly meteorological data from a 14-year period combined with corresponding aflatoxin and fumonisin contamination data from the State of Illinois were used to engineer input features that link weather, fungal growth, and aflatoxin production in combination with gradient boosting (GBM) and bayesian network (BN) modeling. The GBM and BN models developed can predict mycotoxin contamination with overall 94% accuracy. Analyses for aflatoxin and fumonisin with GBM showed that meteorological and satellite-acquired vegetative index data during March significantly influenced grain contamination at the end of the corn growing season. Prediction of high aflatoxin contamination levels was linked to high aflatoxin risk index in March/June/July, high vegetative index in March and low vegetative index in July. Correspondingly, high levels of fumonisin contamination were linked to high precipitation levels in February/March/September and high vegetative index in March. During corn flowering time in June, higher temperatures range increased prediction of high levels of fumonisin contamination, while high aflatoxin contamination levels were linked to high aflatoxin risk index. Meteorological events prior to corn planting in the field have high influence on predicting aflatoxin and fumonisin contamination levels at the end of the year. These early-year events detected by the models can directly assist farmers and stakeholders to make informed decisions to prevent mycotoxin contamination of Illinois grown corn.
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Affiliation(s)
- Lina Castano-Duque
- USDA, Agriculture Research Service, Southern Regional Research Center, New Orleans, LA, United States
| | - Martha Vaughan
- USDA, Agricultural Research Service, National Center for Agricultural Utilization Research, Mycotoxin Prevention and Applied Microbiology Research Unit University, Peoria, IL, United States
| | - James Lindsay
- Office of National Programs, Agriculture Research Service, USDA, Beltsville, MD, United States
| | - Kristin Barnett
- Illinois Department of Agriculture, Agricultural Products Inspection, Springfield, IL, United States
| | - Kanniah Rajasekaran
- USDA, Agriculture Research Service, Southern Regional Research Center, New Orleans, LA, United States
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Mycotoxins Contamination in Rice: Analytical Methods, Occurrence and Detoxification Strategies. Toxins (Basel) 2022; 14:toxins14090647. [PMID: 36136585 PMCID: PMC9504649 DOI: 10.3390/toxins14090647] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 11/26/2022] Open
Abstract
The prevalence of mycotoxins in the environment is associated with potential crop contamination, which results in an unavoidable increase in human exposure. Rice, being the second most consumed cereal worldwide, constitutes an important source of potential contamination by mycotoxins. Due to the increasing number of notifications reported, and the occurrence of mycotoxins at levels above the legislated limits, this work intends to compile the most relevant studies and review the main methods used in the detection and quantification of these compounds in rice. The aflatoxins and ochratoxin A are the predominant mycotoxins detected in rice grain and these data reveal the importance of adopting safety storage practices that prevent the growth of producing fungi from the Aspergillus genus along all the rice chain. Immunoaffinity columns (IAC) and QuECHERS are the preferred methods for extraction and purification and HPLC-MS/MS is preferred for quantification purposes. Further investigation is still required to establish the real exposition of these contaminants, as well as the consequences and possible synergistic effects due to the co-occurrence of mycotoxins and also for emergent and masked mycotoxins.
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Wang X, Liu C, van der Fels-Klerx H. Regional prediction of multi-mycotoxin contamination of wheat in Europe using machine learning. Food Res Int 2022; 159:111588. [DOI: 10.1016/j.foodres.2022.111588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 11/04/2022]
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Wang X, Bouzembrak Y, Oude Lansink AGJM, van der Fels-Klerx HJ. Designing a monitoring program for aflatoxin B1 in feed products using machine learning. NPJ Sci Food 2022; 6:40. [PMID: 36050333 PMCID: PMC9436978 DOI: 10.1038/s41538-022-00154-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 07/26/2022] [Indexed: 11/29/2022] Open
Abstract
Agricultural commodities used for feed and food production are frequently contaminated with mycotoxins, such as Aflatoxin B1 (AFB1). In Europe, both the government and companies have monitoring programs in place for the presence of AFB1. With limited resources and following risk-based monitoring as prescribed in EU Regulation 2017/625, these monitoring programs focus on batches with the highest probability of being contaminated. This study explored the use of machine learning algorithms (ML) to design risk-based monitoring programs for AFB1 in feed products, considering both monitoring cost and model performance. Historical monitoring data for the presence of AFB1 in feed products (2005–2018; 5605 records in total) were used. Four different ML algorithms, including Decision tree, Logistic regression, Support vector machine and Extreme gradient boosting (XGB), were applied and compared to predict the high-risk feed batches to be considered for further AFB1 sampling and analysis. The monitoring cost included the cost of: sampling and analysis, disease burden, storage, and of recalling and destroying contaminated feed batches. The ML algorithms were able to predict the high-risk batches, with an AUC, recall, and accuracy higher than 0.8, 0.6, and 0.9, respectively. The XGB algorithm outperformed the other three investigated ML. Its incorporation would result into up to 96% reduction in monitoring cost in 2016–2018, as compared to the official monitoring program. The proposed approach for designing risk based monitoring programs can support authorities and industries to reduce the monitoring cost for other food safety hazards as well.
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Affiliation(s)
- X Wang
- Business Economics, Wageningen University, Wageningen, The Netherlands
| | - Y Bouzembrak
- Wageningen Food Safety Research, Wageningen, The Netherlands
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Xu R, Kiarie EG, Yiannikouris A, Sun L, Karrow NA. Nutritional impact of mycotoxins in food animal production and strategies for mitigation. J Anim Sci Biotechnol 2022; 13:69. [PMID: 35672806 PMCID: PMC9175326 DOI: 10.1186/s40104-022-00714-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 04/05/2022] [Indexed: 01/25/2023] Open
Abstract
Mycotoxins are toxic secondary metabolites produced by filamentous fungi that are commonly detected as natural contaminants in agricultural commodities worldwide. Mycotoxin exposure can lead to mycotoxicosis in both animals and humans when found in animal feeds and food products, and at lower concentrations can affect animal performance by disrupting nutrient digestion, absorption, metabolism, and animal physiology. Thus, mycotoxin contamination of animal feeds represents a significant issue to the livestock industry and is a health threat to food animals. Since prevention of mycotoxin formation is difficult to undertake to avoid contamination, mitigation strategies are needed. This review explores how the mycotoxins aflatoxins, deoxynivalenol, zearalenone, fumonisins and ochratoxin A impose nutritional and metabolic effects on food animals and summarizes mitigation strategies to reduce the risk of mycotoxicity.
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Keller B, Russo T, Rembold F, Chauhan Y, Battilani P, Wenndt A, Connett M. The potential for aflatoxin predictive risk modelling in sub-Saharan Africa: a review. WORLD MYCOTOXIN J 2021. [DOI: 10.3920/wmj2021.2683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
This review presents the current state of aflatoxin risk prediction models and their potential for value actors throughout the food chain in sub-Saharan Africa, with a specific focus on improving smallholder farmer management practices. Several empirical and mechanistic models have been developed either in academic research or by private sector aggregators and processors in high-income countries including Australia, the USA, and Southern Europe, but these models have been only minimally applied in sub-Saharan Africa, where there is significant potential and increasing need due to climate variability. Predictions can be made based on historic occurrence data using either a mechanistic microbiological framework for aflatoxin accumulation or an empirical model based on statistical correlations with climate conditions and local agronomic factors. Model results can then be distributed to smallholders through private, public, or mobile extension services, used by policymakers for strategy or policy, or utilised by private sector institutions for management decisions. Specific agricultural advice can be given during the three most critical points in the phenological cycle: preseason insight including sowing timing and crop varieties, preharvest advice about management and harvest timing, and postharvest optimal practices including storage, drying, and market information. Model development for sub-Saharan Africa is limited by a dearth of georeferenced aflatoxin occurrence data and real-time high resolution climate data; the wide diversity of farm typologies each with significant information and technology gaps; a prevalence of informal market structures and lack of economic incentives systems; and general lack of awareness around aflatoxins and best management practices to mitigate risk. Given advancements towards solving these challenges, predictive aflatoxin models can be integrated into decision support platforms to focus on optimisation of value for smallholders by minimising yield and nutritional losses, which can propagate value throughout the production and postharvest phases.
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Affiliation(s)
- B. Keller
- Global Good, 3150 139th Ave SE, Bellevue, WA 98005, USA
| | - T. Russo
- Global Good, 3150 139th Ave SE, Bellevue, WA 98005, USA
| | - F. Rembold
- European Commission, Joint Research Centre, Via E. Fermi 2749, 21027 Ispra, Italy
| | - Y. Chauhan
- Department of Agriculture and Fisheries, 214 Kingaroy Cooyar Road, Kingaroy, QLD 4610, Australia
| | - P. Battilani
- Department of Sustainable Crop Production (DI.PRO.VE.S.): Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
| | - A. Wenndt
- Plant Pathology and Plant-Microbe Biology, School of Integrative Plant Sciences, Cornell University, 334 Plant Science Building, Ithaca, NY 14853-4203, USA
| | - M. Connett
- Global Good, 3150 139th Ave SE, Bellevue, WA 98005, USA
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Comparative Analysis of Machine Learning Methods to Predict Growth of F. sporotrichioides and Production of T-2 and HT-2 Toxins in Treatments with Ethylene-Vinyl Alcohol Films Containing Pure Components of Essential Oils. Toxins (Basel) 2021; 13:toxins13080545. [PMID: 34437416 PMCID: PMC8402422 DOI: 10.3390/toxins13080545] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 07/31/2021] [Indexed: 11/30/2022] Open
Abstract
The efficacy of ethylene-vinyl alcohol copolymer films (EVOH) incorporating the essential oil components cinnamaldehyde (CINHO), citral (CIT), isoeugenol (IEG), or linalool (LIN) to control growth rate (GR) and production of T-2 and HT-2 toxins by Fusarium sporotrichioides cultured on oat grains under different temperature (28, 20, and 15 °C) and water activity (aw) (0.99 and 0.96) regimes was assayed. GR in controls/treatments usually increased with increasing temperature, regardless of aw, but no significant differences concerning aw were found. Toxin production decreased with increasing temperature. The effectiveness of films to control fungal GR and toxin production was as follows: EVOH-CIT > EVOH-CINHO > EVOH-IEG > EVOH-LIN. With few exceptions, effective doses of EVOH-CIT, EVOH-CINHO, and EVOH-IEG films to reduce/inhibit GR by 50%, 90%, and 100% (ED50, ED90, and ED100) ranged from 515 to 3330 µg/culture in Petri dish (25 g oat grains) depending on film type, aw, and temperature. ED90 and ED100 of EVOH-LIN were >3330 µg/fungal culture. The potential of several machine learning (ML) methods to predict F. sporotrichioides GR and T-2 and HT-2 toxin production under the assayed conditions was comparatively analyzed. XGBoost and random forest attained the best performance, support vector machine and neural network ranked third or fourth depending on the output, while multiple linear regression proved to be the worst.
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