Webinars

The list below shows upcoming webinars held by IAFP and IAFP’s Professional Development Groups (PDGs). Please note that all opinions and statements are those of the individual making the presentations and not necessarily the opinion or view of IAFP.

Upcoming Webinars

  • AI in Food Safety Research: A Focus on Algorithms and Their Applications Part 1 Oct 16, 2023 (1:00 PM - 2:00 PM Eastern Time)

    Organized by: IAFP's Data Management and Analytics PDG and Student PDG

    In the fast-moving field of artificial intelligence, new algorithms are reported practically every day. This rapid evolution, combined with the voluminous body of literature, can make understanding how to apply AI for food safety research very challenging. In this webinar series, the participants will be introduced to fundamental machine learning algorithms used in food safety research. By the end of the session, participants will have an overview of each models’ mathematical foundation, evaluation metrics, and limitations. Insight on the models’ applications in food safety will be gained via ‘case studies’ from invited speakers’ research. Participants will have the opportunity to ask questions at the end of the session.

    Part 1: Regression and Classification for Predicting Microbial Responses

    Model(s): Logistic Regression, Support Vector Machine, Random Forest Models

    Topics:

    Talk 1: Evaluating algorithms for monitoring E. coli in surface water: Yakov Pachepsky, USDA-ARS, USA

    Talk 2: Comparing machine learning algorithms for genome-based classification of Salmonellaenterica disease severity: Shraddha Karanth/ Abani K. Pradhan, University of Maryland, USA

    Exploring the predictive capability of advanced machine learning in identifying severe disease phenotype in Salmonella enterica. https://doi.org/10.1016/j.foodres.2021.110817

    In this webinar, participants will learn about machine learning models often used for classifying and quantitatively predicting quantitative microbial responses of foodborne pathogens. Through case-study, we will cover how to select, optimize, evaluate, and apply logistic regression, support vector machine, and random forest models to food safety research.

    Outcomes: By the end of the webinar, participants will gain an understanding of:

    • Each models’ advantages and limitations.
    • What are hyperparameters and how to optimize each model.
    • How to compare the models’ performance using evaluation metrics.
    • How the models are currently being used in food safety research
    Presenters
    • Yakov Pachepsky, Presenter USDA-ARS
    • Matthew Stocker USDA-ARS
    • Shraddha Karanth, Presenter University of Maryland
    • Abani K. Pradhan, Presenter University of Maryland
    • Olivia Haley and Manreet Bhullar, Moderators
  • AI in Food Safety Research: A Focus on Algorithms and Their Applications Part 2 Oct 23, 2023 (9:00 AM - 10:00 AM (Eastern Time))

    Organized by: Data Management and Analytics PDG and Student PDG

    In the fast-moving field of artificial intelligence, new algorithms are reported practically every day. This rapid evolution, combined with the voluminous body of literature, can make understanding how to apply AI for food safety research very challenging. In this webinar series, the participants will be introduced to fundamental machine learning algorithms used in food safety research. By the end of the session, participants will have an overview of each models’ mathematical foundation, evaluation metrics, and limitations. Insight on the models’ applications in food safety will be gained via ‘case studies’ from invited speakers’ research. Participants will have the opportunity to ask questions at the end of the session.

    Part 2: Machine Learning Models for Detecting Adulterants and Food Fraud

    Model(s): tSNE (t-distributed Stochastic Neighbor Embeding), PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis)

    Topics: 

    Talk 1: Leveraging dimensionality reduction for genomic biomarker discovery

    Talk 2: Comparing models for validating the origins of meat floss: Kuwat Triyana, Universitas Gadjah Mada, Indonesia

    In this webinar, participants will learn about unsupervised machine learning models used to study the spoilage and quality of food products. Through case-study, we will review how to select, optimize, evaluate, and apply algorithms such as t-Stochastic Neighbor Embedding (tSNE), Principal Component Analysis (PCA), and linear discriminant analysis (LDA) in food safety research.

    Outcomes: By the end of the webinar, participants will gain an understanding of:

    1. Unsupervised vs Supervised machine learning.

    2. How to select features for unsupervised machine learning algorithms.

    3. The advantages and limitations of each algorithm.

    4. How the models are currently being used in food safety research.

    Presenters
    • Talk 1: TBD
    • Kuwat Triyana, Presenter Universitas Gadjah Mada, Indonesia
    • Harsimran Kapoor, Moderator
  • Pre-harvest Internalization:  Water-mediated Biological Internalization of Pathogens into Produce Oct 23, 2023 (1:30 PM - 2:30 PM (Eastern Time))

    Organized by: IAFP's Fruit and Vegetable Safety and Quality PDG

    What’s worse than pathogens on your produce? Pathogens IN your produce. This concept may be accepted as common sense by the fresh produce industry. Two related concepts are explicitly written into the Food Safety Modernization Act, Produce Safety Rule (21 CFR 112) and addressed in regulatory language:
    1) Infiltration during postharvest washing due, in part, to temperature differential in wash water (§ 112.48, as published in 2015) and
    2) Internalization while growing due, in part, to commodity susceptibility to adhesion and internalization (proposed § 112.43, as re-numbered in the proposal published in 2021)
    In this seminar, subject-matter experts will discuss the state of the science about pathogen internalization while growing as caused by three different mechanisms (incorporation into ovary during blossom stage, plant-pathogen interactions on leaf surfaces, uptake through roots). Following the three 15-minute presentations, the presenters will discuss the risk to produce from pre-harvest pathogen internalization and take questions from viewers.

    Learning objectives:
    1. Gain a conceptual understanding of mechanisms that lead to preharvest pathogen internalization into fresh produce
    2. Understand the research-based data describing how, when, and how much preharvest pathogen internalization might occur in various processes and commodities
    3. Understand how existing data can be applied to assess risk related to preharvest pathogen internalization for various processes and commodities
    4. Identify gaps in knowledge that may lead to further research related to preharvest pathogen internalization
    5. Recognize uncertainties caused by gaps in knowledge, which may hinder attempts at assessing risk related to preharvest pathogens

    Presenters
    • Kellie Burris, Presenter NC State University
    • Shirley Micallef, Presenter University of Maryland
    • Kalmia Kniel, Presenter University of Delaware
    • Enrique Garcia, Moderator FirstFruits Farms
  • Managing Meat Shelf Life and Spoilage to Ensure Food Security Oct 24, 2023 (2:00 PM - 3:00 PM Eastern Time)

    Organized by: Meat and Poultry Safety and Quality PDG

    The estimated economic impact due to the food waste and spoilage amounts to a loss of a third of all the food produced. No matter how this data is sliced, microbial spoilage has been implicated to a large portion of this loss. According to one estimate, a quarter of all food supply is lost to the microbial activity alone. This is partially because food safety is a huge emphasis and limitation of technology for testing. However, technology has evolved to include faster more sophisticated tools for diagnosis and prevention in the arena of food spoilage. With proper research, dedication and focus reducing food waste due to spoilage could be improved.
    This discussion is proposed to stimulate provocative forward thinking on new designs and processes to reduce spoilage and food loss. The key panelist will include a combination of production industry, alliance groups such as NAMI, NCC, academia and retail to discuss the overall impact of food spoilage waste and the changes needed to implement for the future of food security.

    Learning Objectives:
    The key objective is to discuss measures to reduce waste through addressing food spoilage which support growth in food security. This is a huge financial and growing concern in industry and retail members. The key panelist would be a combination of production industry, alliance groups such as NAMI, Academia and retail to discuss the overall impact of food spoilage waste on future supply. The topics would fall under the following categories: Safety and Microbial Quality of Foods (Dairy, Meat and Poultry, Seafood, Produce, Water) and General-applied Food Safety Microbiology (for example, advances in sanitation, laboratory methods, quality assurance, food safety systems), at a minimum.

    Presenters
    • Sherry Trujillo, Presenter JBS
    • Kaitlyn Compart, Presenter Smithfield Foods
    • Jesus Valazco, Presenter HEB
    • Harshavardhan Thippareddi University of Georgia
    • Manpreet Singh, Presenter University of Georgia
    • Heath LaFevers, Moderator bioMerieux
  • Food Safety and Quality under the Auspices of Data Science Oct 24, 2023 (9:00 AM - 10:00 AM Eastern Time)

    Organized by: Modelling and Risk Analysis PDG

    The current controls in the food value chain are commonly inadequately applied, and thus fail to prevent microbial contamination of food products, compromising consumer confidence in food safety and quality. On the other hand, to meet market demands food business operators (producers, retailers, resellers) and regulators need to develop and apply structured quality and safety assurance systems, based on thorough risk analysis and prevention, through monitoring, recording and controlling of critical parameters covering the entire product’s life cycle. However, the production, supply, and processing sectors of the food chain are fragmented and this lack of cohesion results in failure to adopt new and innovative technologies, products and processes. The potential of using information technologies (e.g., data repositories in cloud platforms) in tandem with data science (data mining, pattern recognition, uncertainty modelling, artificial intelligence etc.), throughout the farm-to-fork continuum, will provide stakeholders with novel tools regarding the implementation of more efficient than the currently applied food safety management systems.

    Presenters
    • George Nychas, Presenter Agricultural University of Athens
    • Panagiotis Skandamis, Moderator Agricultural University of Athens
  •  AI in Food Safety Research: A Focus on Algorithms and Their Applications Part 3 Oct 30, 2023 (1:00 PM - 2:00 PM Eastern Time)

    Organized by IAFP's Data Management and Analytics PDG and Student PDG

    In the fast-moving field of artificial intelligence, new algorithms are reported practically every day. This rapid evolution, combined with the voluminous body of literature, can make understanding how to apply AI for food safety research very challenging. In this webinar series, the participants will be introduced to fundamental machine learning algorithms used in food safety research. By the end of the session, participants will have an overview of each models’ mathematical foundation, evaluation metrics, and limitations. Insight on the models’ applications in food safety will be gained via ‘case studies’ from invited speakers’ research. Participants will have the opportunity to ask questions at the end of the session.

    Part 3: Neural Networks for Biosensor Image Analysis

    Model(s): RNN,CNN, MLP

    Topics:

    Talk 1: Developing an AI-enabled biosensing pipeline for pathogen detection: Jiyoon Yi, Michigan State University, USA

    Talk 2: Analyzing optical imaging data to quantify foodborne pathogens: Luyao Ma, University of Florida, USA

    In this webinar, participants will learn about machine learning models used for image analysis in food safety research. Through case-study, we will review how to select, optimize, evaluate, and apply neural networks for the analysis of diagnostic imaging used to detect foodborne pathogens.

    Outcomes: By the end of the webinar, participants will gain an understanding of:

    • Traditional machine learning versus deep learning.
    • How neural networks work.
    • Different types of neural networks.
    • How neural networks are currently being used in food safety research.

     

    Presenters
    • Jiyoon Yi, Presenter Michigan State University
    • Luyao Ma, Presenter Florida State University
    • Zhenjiao Du, Moderator
  • AI in Food Safety Research: A Focus on Algorithms and Their Applications Part 4 Nov 6, 2023 (1:00 PM - 2:00 PM Eastern Time)

    Organized by IAFP's Data Management and Analytics PDG and Student PDG

    In the fast-moving field of artificial intelligence, new algorithms are reported practically every day. This rapid evolution, combined with the voluminous body of literature, can make understanding how to apply AI for food safety research very challenging. In this webinar series, the participants will be introduced to fundamental machine learning algorithms used in food safety research. By the end of the session, participants will have an overview of each models’ mathematical foundation, evaluation metrics, and limitations. Insight on the models’ applications in food safety will be gained via ‘case studies’ from invited speakers’ research. Participants will have the opportunity to ask questions at the end of the session.

    Part 4: Large Language Models for Foodborne Outbreak Tracking/Epidemiology

    Model(s): Large Language Models (LLMs)

    Topics:

    Talk 1: Developing LLMs for food recall prediction and safety enhancement: Georgios Makridis, University of Piraeus, Greece

    Talk 2: Probabilistic models for locating the source of foodborne outbreaks: Abigail Horne, Massachusetts Institute of Technology, USA

    In this webinar, participants will learn about machine learning models used for language and textual analysis in foodborne outbreak epidemiology. Through case-study, we will review the how to select, optimize, evaluate, and apply large language models for tracking and source attribution of foodborne outbreaks.

    Outcomes: By the end of the webinar, participants will gain an understanding of:

    • How large language models work.
    • How to select and fine-tune a LLM for downstream tasks.”
    • How the models are currently being used in food safety.

    Presenters
    • TBD
    • Claire Zoellner, Moderator