About

Dr. Abani K. Pradhan is a Professor in the Department of Nutrition and Food Science and the Center for Food Safety and Security Systems at the University of Maryland in College Park. Since 2017, he has served as the Director of the Graduate Program in Nutrition and Food Science at UMD. Prior to joining UMD in 2011, he was a Research Associate at Cornell University. His research interests broadly encompass food safety and risk assessment, focusing on foodborne pathogens such as Listeria monocytogenes, Salmonella, pathogenic Escherichia coli, and Toxoplasma gondii, which pose serious public health concerns. His work aims to improve food safety by integrating experimental and field data with mathematical modeling, developing predictive and risk models to inform stakeholders including policymakers, government agencies, and the food industry. Dr. Pradhan employs interdisciplinary approaches, utilizing food microbiology, engineering, risk assessment modeling, and advanced data analytics such as artificial intelligence and machine learning to address critical issues related to foodborne pathogens across various food categories. He also works on integrating microbial genomics with risk assessment to evaluate public health risks. His teaching includes courses on food science, quality control, and food safety risk assessment, and he has received numerous awards for his creative work, teaching, and research excellence, including the Dean Gordon Cairns Award and Fellow of the Society for Risk Analysis.

Research topics

• Artificial Intelligence
• Sociology
• Food science
• Social Science
• Computer Science
• Biology
• Waste management
• Political Science
• Engineering
• Business
• Machine Learning
• Genetics
• Marketing
• Environmental science
• Public relations
• Geography
• Biotechnology

Selected publications

• Population structure analysis of Salmonella serovar Muenchen to redefine geno-serotyping using genome indexing approaches

  Frontiers in Microbiology · 2026-02-10

  articleOpen accessSenior author

  Accurate identification of Salmonella serovars for source attribution in foodborne illness outbreaks. Traditional serotyping, which relies on antigenic properties, continues to serve as gold standard; however, advances in whole-genome sequencing (WGS) have enabled to the development of in-silico serotyping tools such as SeqSero2 and Salmonella In Silico Typing Resource (SISTR). Genome-indexing methods, such as bettercallsal, integrate DNA sketching and genome proximity analysis, have emerged as a promising tool for improving serovar resolution. This study examines the performance of DNA sketching-based serotyping in conjunction with established in-silico methods, focusing especially on Salmonella Muenchen, a polyphyletic serovar that ranks among the top 20 serovars linked with human infections in the United States. In this study, SeqSero2 was employed for antigen-based serotyping, SISTR for core genome Multi-locus Sequence Typing (cgMLST)-based phylogenetic clustering, pangenome analysis using PIRATE for microevolutionary insights, and bettercallsal for genome-indexing-based serovar calls. The results demonstrate that bettercallsal, leveraging the National Centre for Biotechnology Information (NCBI) Pathogen Detection database, enhances serovar resolution by incorporating genome proximity calls. The integration of SeqSero2 with bettercallsal yields complementary insights, maintaining historical serotyping nomenclature while enhancing serovar classification. This dual-tool strategy improves the discrimination of genomically distinct but antigenically similar serovars, therefore addressing limitations of traditional and molecular serotyping. Overall, integrating genome indexing through DNA sketching with validated in-silico serotyping tools establishes a robust framework for pathogen characterization. In this study, the tool is specifically applied for Salmonella serovar characterization. This methodology enhances the source attribution accuracy in outbreak investigations and establishes a framework for updating serovar classification in the era of genomic epidemiology.

  PublisherOA PDFDOI

• Benchmarking Metagenomic Pipelines for the Detection of Foodborne Pathogens in Simulated Microbial Communities

  Journal of Food Protection · 2025-07-17 · 5 citations

  articleOpen access

  Foodborne pathogens pose a significant public health threat worldwide, despite modern advances in food safety. While molecular detection of pathogens in complex food matrices has gained attention to support tracking and preventing outbreaks, thorough benchmarking is needed to optimize workflows for specific scenarios. This study evaluated the performance of four metagenomic classification tools: Kraken2, Kraken2/Bracken, MetaPhlAn4, and Centrifuge, for estimating pathogen presence and abundance in simulated microbial communities representing three food products. Specifically, we evaluated workflow performance in predicting varying levels of Campylobacter jejuni, Cronobacter sakazakii, and Listeria monocytogenes in metagenomes of chicken meat, dried food, and milk products. Metagenomes were simulated to include the respective pathogen at defined relative abundance levels (0%-control, 0.01%, 0.1%, 1%, and 30%) within the respective food microbiome. Performance evaluations demonstrated that Kraken2/Bracken achieved the highest classification accuracy, with consistently higher F1-scores across all food metagenomes, whereas Centrifuge exhibited the weakest performance. MetaPhlAn4 also performed well, particularly in predicting C. sakazakii in dried food metagenomes, but was limited in detecting pathogens at the lowest abundance level (0.01%). Overall, Kraken2/Bracken and Kraken2 exhibited the broadest detection range, correctly identifying pathogen sequence reads down to the 0.01% level, whereas MetaPhlAn4 and Centrifuge had higher limits of detection. Our results highlight Kraken2/Bracken as an effective tool for pathogen detection, with MetaPhlAn4 serving as a valuable alternative depending on pathogen prevalence. These findings provide crucial insights for selecting metagenomic tools for applications in food safety and pathogen surveillance applications.

  PublisherDOI

• Transfer of Salmonella enterica, Escherichia coli O157:H7 and Listeria monocytogenes to microgreens and soil from contaminated seeds

  Journal of Agriculture and Food Research · 2025-02-23 · 1 citations

  articleOpen access

  In recent years, the consumption of novel salad greens such as microgreens has increased tremendously around the globe because of their health and nutritional benefits. These benefits that include antioxidant activity among others have been attributed to the presence of helpful bioactive compounds. However, due to their production methods and conditions, they have a risk profile that bears a few similarities to that of sprouts. The persistence trends of Salmonella enterica, Escherichia coli O157:H7 and Listeria monocytogenes were studied over the growth period of 14 days. Seeds of daikon, mustard, broccoli, and red cabbage microgreens were contaminated with the three pathogens at high (∼5 Log CFU/g) and low (∼3 Log CFU/g) levels and the microgreens and soil were sampled on days 7 and 14. Microbiological analysis of soil and microgreen samples was carried out using spiral-plating on pathogen specific selective agars. There was a distinct increase on day 7 in the populations of all three pathogens in both the edible leafy green shoot portions and the soil of the microgreens. However, the prevalence levels of all three pathogens decreased by day 14. On day 7, there was no significant difference in the persistence of E. coli and Salmonella in all four microgreens. However, the L. monocytogenes populations were significantly higher in red cabbage than in broccoli, mustard and daikon. On day 14, the levels of all three pathogens did not significantly vary in the microgreens or the soil in which the microgreens were cultivated. In general, lower persistence of Salmonella and . L. monocytogenes was found in red cabbage and mustard microgreens on day 14, respectively. Results suggest that foodborne pathogens can be transferred from contaminated seeds to microgreens and persist in edible portions of microgreens at the point of harvest. Stringent quality assurance measures are required in maintaining microbial quality of seeds to prevent contamination and potential food safety risks. • Pathogens transfer from contaminated seeds to microgreens. • Pathogen prevalence is higher on day 7 and decreases by day 14. • Microgreen variety does not affect the prevalence of the three pathogens. • Contaminated seeds pose a considerable risk to the safety of microgreens.

  PublisherDOI

• Bioinformatics combined with machine learning unravels differences among environmental, seafood, and clinical isolates of Vibrio parahaemolyticus

  Frontiers in Microbiology · 2025-03-19 · 3 citations

  articleOpen accessSenior authorCorresponding

  Vibrio parahaemolyticus is the leading cause of illnesses and outbreaks linked to seafood consumption across the globe. Understanding how this pathogen may be adapted to persist along the farm-to-table supply chain has applications for addressing food safety. This study utilized machine learning to develop robust models classifying genomic diversity of V. parahaemolyticus that was isolated from environmental ( n = 176), seafood ( n = 975), and clinical ( n = 865) sample origins. We constructed a pangenome of the respective genome assemblies and employed random forest algorithm to develop predictive models to identify gene clusters encoding metabolism, virulence, and antibiotic resistance that were associated with isolate source type. Comparison of genomes of all seafood-clinical isolates showed high balanced accuracy (≥0.80) and Area Under the Receiver Operating Characteristics curve (≥0.87) for all of these functional features. Major virulence factors including tdh , trh , type III secretion system-related genes, and four alpha-hemolysin genes ( hlyA , hlyB , hlyC , and hlyD ) were identified as important differentiating factors in our seafood-clinical virulence model, underscoring the need for further investigation. Significant patterns for AMR genes differing among seafood and clinical samples were revealed from our model and genes conferring to tetracycline, elfamycin, and multidrug (phenicol antibiotic, diaminopyrimidine antibiotic, and fluoroquinolone antibiotic) resistance were identified as the top three key variables. These findings provide crucial insights into the development of effective surveillance and management strategies to address the public health threats associated with V. parahaemolyticus .

  PublisherOA PDFDOI

• Bacterial Contamination and Control in Food Products

  Elsevier eBooks · 2025-01-01 · 1 citations

  book-chapterSenior author
  PublisherDOI

• Persistence of Foodborne Bacterial Pathogens on Microgreens and Soil Irrigated With Contaminated Water

  Journal of Food Protection · 2025-08-06 · 3 citations

  articleOpen access

  Microgreens, like leafy greens, are susceptible to contamination at the preharvest stage, posing food safety concerns, particularly as their consumption rises due to their recognized bioactive benefits. Recalls associated with microgreens have further underscored these concerns. This study aimed to investigate the potential transfer of enteric pathogens to microgreens irrigated with contaminated water. Municipal water (MW) and rainwater (RW) inoculated with low and high concentrations of Salmonella enterica, Escherichia coli O157:H7, or Listeria monocytogenes were used to irrigate daikon, red cabbage, broccoli, and mustard microgreens cultivated on soil beds. Microgreen and soil samples were collected on days 7 and 14 and analyzed using most probable number (MPN) enumeration or spiral plating on selective media. Significant variations in pathogen recovery were observed across days 7 and 14, irrespective of microgreen variety or water source. When irrigated with water at 5 Log CFU/mL contamination level, all pathogens were significantly reduced by ∼2.5-4.7 Log CFU/g on 14th day, irrespective of microgreens or source of irrigation water. A similar trend was observed with pathogens at low inoculation (3 Log CFU/mL); however, the reduction on day 14 was not significant (1.9-3 Log MPN/g) except for broccoli and daikon microgreens inoculated with Salmonella. At low and high levels of inoculums, L. monocytogenes persisted in lower numbers (2-2.5 Log MPN/g and 3.9-4.1 Log CFU/g) on microgreens compared to Salmonella (3.2-3.8 Log MPN/g and 4.2-4.5 Log CFU/g) and E. coli O157:H7 (2.8-3.4 Log MPN/g and 4.5-4.7 Log CFU/g), respectively, throughout the sampling period. The source of irrigation water affected the persistence of pathogens; Salmonella and E. coli O157:H7 persisted in lower numbers (2.2-2.7 and 2.1-2.5 Log MPN/g, respectively) for low inoculum on microgreens irrigated with RW. Recovery of L. monocytogenes from microgreens irrigated with MW at low inoculum was significantly lower compared to that of E. coli O157:H7 at 7 days. These findings highlight the potential for transfer of enteric pathogens from contaminated irrigation water to the edible portions of microgreens, emphasizing the importance of rigorous microbial quality control of irrigation water in controlled environmental agriculture (CEA) to mitigate contamination risks.

  PublisherDOI

• Genomic diversity of Cronobacter sakazakii across the food system to consumers at the global scale

  International Journal of Food Microbiology · 2025-07-02 · 5 citations

  articleOpen access

  Deciphering how foodborne pathogens adapt to changing environments is essential for improving food safety monitoring and control. Cronobacter sakazakii , a persistent opportunistic pathogen associated with powdered infant formula outbreaks, poses critical health risks to neonates and other vulnerable populations. This study tested the hypothesis that genetic variation in C. sakazakii correlates with specific isolation sources and geographic origins across the global food system. We conducted a pangenomics meta-analysis of C. sakazakii derived from food, environmental, and clinical sources spanning North America, Asia, and Europe. A robust fine-tuned Generative Pre-trained Transformer (GPT) model was developed to standardize the categorization of isolate metadata descriptors. C. sakazakii genome assemblies (n = 748) were used to build and annotate the pangenome, and genome size and accessory gene profiles were found to be significantly associated with source type and continent of origin. Isolates from powdered foods, compared to those sourced from alternative foods, had larger genomes and were enriched in functions annotated to Clusters of Orthologous Genes (COG) category L for DNA replication, recombination and repair (e.g., transposase, integrase), among other features. Random forest models using both accessory genes and the subset of virulence factor homologs accurately predicted source attributions, identifying type VI secretion system and heavy metal response genes as key indicators of isolate origins. Several antimicrobial resistance genes associated with efflux (i.e., arlR , facT , oprZ ) also exhibited patterns for biogeography. Overall, this study uncovered the distribution of key accessory genetic elements of C. sakazakii throughout the food system, revealing putative adaptations for its persistence and transmission. Our reproducible and automated workflow has potential applications in molecular surveillance for emerging food safety concerns. • Characterized the pangenome of C. sakazakii from diverse sample types at global scale • Developed an AI fine tune model for automated categorization of isolation sources • Machine learning accurately predicted biogeography of accessory genes. • Genomes of isolates from powdered foods were distinct from other sources. • Key genes of isolate origins included DNA repair, T6SS, and heavy metal response.

  PublisherDOI

• Research gaps and priorities for quantitative microbial risk assessment (QMRA)

  UNC Libraries · 2024-11-20 · 2 citations

  articleOpen access

  The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.

  PublisherDOI

• List of contributors

  Elsevier eBooks · 2024-01-01

  book-chapterOpen access
  PublisherDOI

• Machine learning to predict the relationship between Vibrio spp. concentrations in seawater and oysters and prevalent environmental conditions

  Food Research International · 2024-05-10 · 14 citations

  articleSenior authorCorresponding
  PublisherDOI

Frequent coauthors

• Y.H. Schukken

  De Gezondheidsdienst voor Dieren

  52 shared

• J. P. Dubey

  United States Department of Agriculture

  36 shared

• Yrjo T. Gröhn

  31 shared

• Robert L. Buchanan

  Center for Food Safety and Applied Nutrition

  30 shared

• Dolores E. Hill

  Agricultural Research Service

  23 shared

• Abhinav Mishra

  University of Georgia

  22 shared

• Rebecca L. Smith

  21 shared

• Yanbin Li

  Sichuan Agricultural University

  19 shared

Labs

• Center for Food Safety and Security SystemsPI

Awards & honors

• Dean Gordon Cairns Award for Distinguished Creative Work and…
• Graduate Faculty Mentor of the Year Award, the Graduate Scho…
• Fellow, Society for Risk Analysis (SRA), 2024
• Appointed to the National Food Safety Advisory Committee (NA…
• Harry Haverland Citation Award, International Association fo…