The AOAC Analytical Solutions Forum serves as an “idea incubator.” It is a science-based, open meeting where global stakeholders from government, industry and academia can convene to share emerging issues that are impacting food safety, food defense and food security; and, to examine the role (and need) for analytical standards and methods.

Because of the unique circumstance related to the COVID19 global pandemic, the ASF during the 2020 AOAC INTERNATIONAL Annual Meeting will be a completely virtual affair. It will be held over a two-day period with morning and afternoon sessions on September 8th and September 9th following the same general format as that of our 2020 Midyear Meeting.  Four morning plenary presentations from representatives of Latin American and African countries, India and Europe will provide insights on emerging food safety concerns and explore opportunities to support capacity building programs to address these concerns.

Afternoon sessions on specific emerging topics and technologies will feature short presentations and open discussion.  This year’s topics were chosen based on their timely relevance on food safety and their potential impact on global public health and trade. They are designed to provide an opportunity for substantial international engagement and for future international collaboration in these areas. Topics will include per- and polyfluorinated alkyl substances (PFAS); in silico analysis; chemometrics to control risk; and; artificial intelligence and early warning systems to address emerging food safety concerns.


September 8, 2020


  1. Welcome and Introductory Remarks
    Palmer Orlandi, AOAC Chief Science Officer and Deputy Executive Director
  2. Updates on AOAC INTERNATIONAL’s Core Science Programs
    Deborah McKenzie, Sr. Director, Standards
    Scott Coates, Sr. Director, AOAC Research Institute
    Arlene Fox, Sr. Director, AOAC Laboratory Proficiency Testing Program
  3. Plenary Presentations
    • Dr. Nuri Gras Rebolledo, Ph.D.
      Executive Secretary, The Chilean Food Safety and Quality Agency
    • Dr. Kaushik Banerjee, Ph.D.
      Chairman, AOAC India Section


Emerging Topics and Technologies, part 1

PFAS: The “Forever” Chemicals

Dr. Neal Saab
Sr. Science Program Manager, ILSI-NA

Dr. Susan Genauldi
U.S. Food and Drug Administration

PFAS is a class of per- and poly-fluoroalkyl substances recognized as persistent bioaccumulative and toxic contaminants present throughout the environment. Their ubiquity presents a significant concern to global human health. Whereas most of the surveillance and testing thus far for PFAS has focused on soil, sediment and water using validated and uniformly accepted methodologies, their pervasive presence in the environment and their use in food contact paper and packaging has created a need to expand testing capabilities to foods and other food-related matrices as well. Currently, validated analytical methods to compile exposure data on PFAS in foods are limited to a single-laboratory validated method developed by the US Food & Drug Administration in 2019. Though limited in scope, their method has laid the groundwork for future method (matrix) extension. However, the high level of global concern and the disparate regulatory trends worldwide requires that a consensus and harmonization of method performance standards be achieved for future method development and testing needs. This session will highlight the need for the global development and adoption of consensus standards and methods for broader food and food contact surface applicability.

In silico Analysis

John SantaLucia, Jr., Ph.D.
Professor, Wayne State University
CEO, DNA Software, Inc.

Dr. Shanmuga Sozhamannan
Defense Biological Product Assurance Office’s Joint Program Executive Office

Dr. Sharon L. Brunelle
AOAC Technical Consultant

AOAC INTERNATIONAL is breaking new ground in method validation by implementing in silico analysis in its recently launched project to evaluate and certify the performance of test kits that detect the SARS-CoV-2 coronavirus, the causative agent of COVID-19 illness, on environmental surfaces. The term in silico – or “in silicon” — refers to harnessing the power of modern databases and computational power in biological experiments. In the future, AOAC plans to use in silico analysis as an essential component in the validation of all genomic-based methods to detect and identify microorganism, e.g. PCR, microarray, and next-generation sequencing. The basis for in silico analysis is the comparison of the genetic sequence targeted by a molecular test kit against a database of whole genome sequences of target and non-target organisms.  Traditionally, the selectivity of an assay is experimentally determined in a laboratory using a set of target strains (inclusivity), near-neighbor strains (exclusivity), and matrix-relevant organisms (background microbial flora).  Laboratory determination of inclusivity and exclusivity is time-consuming, expensive, and usually limited to at most 150 species/strains. In silico analysis has a significant advantage over wet-lab testing alone in that genetic sequences from tens of thousands of target strains and near neighbors can be analyzed for inclusivity and exclusivity.  This session will discuss the basics of in silico analysis; the pros and cons of in silico analysis; review the AOAC Official Methods of AnalysisSM, Appendix Q: Recommendations for Developing Molecular Assays for Microbial Pathogen Detection Using Modern In Silico Approaches; review the application of in silico analysis to the evaluation of test kits that detect the SARS-CoV-2 coronavirus; and explore the future of in silico analysis in AOAC validations.

September 9, 2020


  1. Perspectives from the Analytical Steering Committee: An Open Discussion
  2. Plenary Presentations
    Dr. Owen Fraser, Ph.D.
    President, AOAC Sub-Saharan Africa Section

    Dr Hermogène Nsengimana
    Secretary General, African Organisation for Standardisation  (ARSO)

    Dr. Frans Verstraete, Ph.D.
    Deputy Director General for Food Safety, Directorate General Health and Food Safety
    European Commission Health And Consumers Directorate-General


Emerging Topics and Technologies, part 2: Chemometric and Artificial Intelligence Applications to Control Risk in Food Safety

Chemometrics is the science of extracting information from chemical systems by data-driven means. Chemometrics methods have become a leading tool among the scientific communities towards faster analysis of results and shorter product development time.  The classical approach analyzes one factor at a time. The model is derived from theory and the data are searched to show the validity of the model. The chemometrics approach considers all variables at the same time.  In this way, the model is fit to the data.  Chemometrics is inherently interdisciplinary, using methods frequently employed in core data-analytic disciplines. Chemometrics can be used to recognize complex patterns.  Recognizing these patterns makes it a useful tool for many analyses including botanical identification.  The authentication of botanical materials is critical to the safe use of these materials. Chemometrics provides prediction.  Prediction means declaration in advance, especially foretelling based on observation, experience, or scientific reason. Prediction concerns not only temporal processes but also, for example, the toxicity of a compound on the basis of similar compounds. Resolving the complexities of risk analysis is an important role for chemometrics.  Chemometrics can be used to model horizon risks in the context of specific opportunities, threats or scenarios of concern. This session will examine the role for chemometrics in recognizing and controlling risk as an important tool for an early warning system.

Deriving Information and Knowledge from Data

James Harnly, Pei Chen, Kyle McKillop, Jianghao Sun, Ping Geng

Methods and Applications Food Composition Lab, Beltsville Human Nutrition Research Center, Agriculture Research Service, US Department of Agriculture, Beltsville, MD

This is the era of big data.  We have data, data, and more data.  Sophisticated analytical methods like UHPLC-HRAM/MSn turn out terabytes of data in a single analysis.  However, all this data is meaningless without context and interpretation.  Conversion of data to information and knowledge requires machine learning (a subset of artificial intelligence).  When applied to botanicals and food systems, machine learning is usually defined as chemometrics.  In general, raw data is the convolution of fundamental composition patterns with systematic variance (metadata factors such as genetics, environment, management, and processing) and random variance (biological variability).  To derive information or patterns, the raw data must be deconvoluted with respect to the metadata factors.  Chemometric methods such as principal component analysis (PCA) and partial least squares-discriminate analysis (PLS-DA) are standard methods for detectin patterns in data.  An approach for deconvolution is multivariate analysis of variance PCA (mANOVA-PCA).  This method also allows the variability of each factor to be determined as well as its statistical significance.  These methods are easily implemented with commercial spreadsheets and chemometric platforms.  This talk will present examples of the deconvolution of data for international dry milk samples, botanical materials (genera, species, and supplements), a human garlic feeding study, and an exploratory project to determine the similarity of the nutrient content of vegetables, fruits, legumes, nuts, and cereal grains.  Chemometrics is a powerful method for deriving information and knowledge from raw data.

Early Warning Signaling in Food Safety

Dr. Peter Embarek
INFOSAN Secretariat, Department of Food Safety and Zoonoses
World Health Organization

Dr. Franz Ulberth
Head of Unit, Standards for Food Bioscience
Joint Research Centre, EC, EU

Global and domestic regulations, rules, and standards for food safety require the use of preventative measures.  Such preventative measures require stringent quality control programs that include traceability, routine monitoring and surveillance along supply chain.  The goal for implementing any kind of food safety program is public health and public safety of consumers.   One of the most notable approaches to food safety involves establishing hazard analysis critical control points (HACCP) system which has the primary goal of prevention and is considered an effective way of assuring food safety from beginning to end of the supply chain through consumption. If a control failure is detected, then steps taken to reestablish control to prevent potentially hazardous product from reaching the consumer.  However, what about receiving intelligence on pending hazards before they become part of the supply chain?  This proactive approach, an early warning alert system or signaling, is a horizon scanning tactic that provides intelligence that can only enhance HACCP and other preventive measures used in food safety.  Regulatory bodies have their approaches to early warning signaling such as INFOSAN (WHO) and CORE (US FDA).  These involve collection of and maintenance of databases of information on target items that may present a hazard.  Additionally, the private sector also produces early warning signaling systems, making this capability accessible to the industry at large.  Collective knowledge, effective use of artificial intelligence, and an open global network presents a capability for providing horizon scanning intelligence that complements HACCP, facilitates public – private partnerships, and moves food safety into the 21st century.   This session will examine the role of early warning signaling, approaches to early warning signaling, and how the information from an early warning signaling system can be used to ensure food safety.