United States

Allergen management and Quantitative Risk Assessment (QRA) play a critical role in informing Precautionary Allergen Labelling (PAL). These processes depend heavily on analytical data to provide evidence and validate assumptions. A noteworthy framework is the VITAL® program (Voluntary Incidental Trace Allergen Labelling), a standardized allergen risk assessment process widely used in the food industry. By evaluating allergens intentionally included and those introduced through cross contact, VITAL determines whether precautionary statements should appear on product labels.
To support allergen management and validate QRA outcomes, the food industry employs a range of analytical methods. These methods vary in complexity, from simple protein swabs used for monitoring to highly specific ELISA-based techniques applied for compliance purposes. The data generated at different stages of the manufacturing process is integral to confirming risk assessment outcomes and underlying assumptions. Crucially, analytical methods must always be fit for their intended purpose, as inappropriate methods could undermine the reliability of results.
Challenges persist in this domain, particularly with ensuring that analytical methods are sensitive enough for their applications and that reporting units are harmonized. Harmonization allows for easy comparison of results with calculated risk assessments. Collaboration between laboratories and Food Business Operators is essential to address these issues effectively, beginning well before analysis is undertaken. Recent findings by the FAO/WHO Ad Hoc Expert Committee for Food Allergens emphasize the need to overcome these barriers to ensure that allergen analysis meaningfully supports robust quantitative risk assessments. Current strategies addressing these challenges will be explored in the presentation.

2025 AOAC INTERNATIONAL Annual Meeting &amp; Exposition

Using Analytical Data for Precautionary Allergen Labeling Decisions in the VITAL® Framework

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2025 AOAC INTERNATIONAL Annual Meeting & Exposition

The AOAC Annual Meeting & Exposition provides unparalleled professional development, networking, and collaboration in methods-based science.

Risk-based approaches for decisions around the use of precautionary allergen labeling (PAL) statements and incident management have been mentioned in recent industry and regulatory guidance documents. Many of these guidance documents reference the recommendations of the ad hoc Food and Agriculture Organization (FAO) and the World Health Organization (WHO) expert consultation on the risk assessment of food allergens that was convened between 2020 and 2023 to evaluate the feasibility of using data from clinical threshold studies to derive reference doses for the priority allergenic food sources.  These reference doses are proposed to be utilized in a risk assessment framework which can be used by the food industry to make decisions around the use of PAL statements but could also be utilized as component of incident management. The framework includes elements of quantitative risk assessment where the consideration of finished product consumption quantities and data from sample analysis using allergen-specific methods for quantification of allergen residues are used to calculate potential allergen residue exposure doses. The sensitivity and accuracy of the analytical methods used to support this quantitative risk assessment approach is critical.  This presentation will provide an overview of the application of analytical methods to support a risk-based approach to incident management. Considerations on the variability of the testing method(s), target protein(s), reporting units and potential conversion factors will be discussed using case studies as an illustration.

Using Analytical Data for Food Allergen Incident Management

Food allergen analysis and risk assessment has historically focused on allergen-specific methods, including submissions for exemption from mandatory allergen source labeling. However, in many cases, ingredients derived from a major food allergen or a food allergen-derived preparation under review for exemption are highly processed with a number of considerations which could render results from different methods, including allergen-specific methods, to be not fit-for-purpose in the overall characterization of the derivative and subsequent required exposure assessment. 
This presentation provides an overview of a risk-based framework proposed by the recent FAO/WHO Expert Consultation on Risk Assessment of Food Allergens for evaluation of potential exemptions from mandatory allergen source labeling. The framework could provide safe, broader food choices for allergic individuals, with protein characterization, total protein quantification, and exposure estimation critical for any derivative assessment. Case studies will highlight when allergen-specific methods are likely insufficient to characterize derivates that have been highly processed (e.g. hydrolyzed), with low levels of protein (e.g. highly refined oils), or are comprised of difficult matrices with extraction challenges (e.g. lecithins). In these instances, methods based on general protein or amino acid analysis may be better suited. Additional considerations include the recommendation to utilize more than one fit-for-purpose method for total protein quantification, each based on different principles, as well as assessing the impact of different fit-for-purpose extraction methods, sampling, and recovery on the overall analysis.

Using Analytical Data in the Context of Allergen Source Labeling Exemptions

Due to the worldwide prevalence of food allergies, many countries have mandated allergen labeling. The presence of undeclared allergens can occur from cross-contamination during food processing, which is often mitigated by including “may contain” product labels. Although "vegan" or "plant-based" products claims are not regulated, traces of egg and milk have been reported in these foods, leading to unexpected allergic reactions. ELISA is commonly used for food allergen quantitation, but this technique suffers from cross-reactivity, variability between supplier kits and is typically limited to only single-allergen detection. In contrast, LC-MS/MS is capable of multi-allergen detection with high sensitivity and selectivity required for food matrices. Here, a sample preparation and LC-MS/MS method achieved limits of quantitation (LOQs) in the range of 0.1–10 mg allergen/kg food for milk and egg allergen peptides. These LOQs enabled detection at levels below the recommended sensitivity required for complying with the VITAL program and AOAC SMPR. When quantified by matrix-matched calibration, matrix spikes at the LOQ and at 10 and 50 mg allergen/kg food demonstrated apparent recoveries of 75–128%. Absolute recoveries ranged from 25–86% in cookie and cheese, while &lt;20% recoveries were obtained in cake mix. These lower recoveries are consistent with the literature and provide realistic insights into how incurred allergens are lost during sample preparation. Method application to commercial foods accurately reflected the presence of milk and egg allergens expected from most products, but several samples exhibited positive detection despite the undeclared allergens in the ingredients list. 

Targeted Quantitation of Milk and Egg Allergens in Vegan Foods

Species identification of botanical products is a crucial aspect of research and regulatory compliance; however, botanical classification can be difficult, especially for morphologically similar species with overlapping genetic and metabolomic markers, like those in the genus Ocimum. Untargeted LC–MS metabolomics coupled with multivariate predictive modeling provides a potential avenue for improving herbal identity investigations, but the current dearth of reference materials for many botanicals limits the applicability of these approaches. This study investigated the potential of using greenhouse-grown, authentic Ocimum to build predictive models for classifying commercially available Ocimum products. We found that three species, O. tenuiflorum, O. gratissimum, and O. basilicum, were chemically distinct based on their untargeted UPLC-MS/ MS profiles when grown in controlled settings. Combining non-targeted fingerprints with high-performance thin-layer chromatography (HPTLC) approach reveled two distinct O. tenuiflorum chemotypes. Three predictive models (partial least squares, LASSO regression, and random forest) were employed to extrapolate these findings to commercially available products. Discrepancies between controlled and commercial samples led to inconclusive classifications. This suggests that environmental and processing variance exceed each model’s tolerance, and an expanded reference library is necessary for successful predictions. Overall, this study highlights that variance in metabolomic profiles is driven by genetic differences (species and cultivar level), environmental factors (greenhouse vs commercial growth), and processing conditions (post-harvest treatment). Future studies may establish variance thresholds for these factors to be included in sample selection and study designs for robust and reliable authentication workflows.

Is Genetic Variation Enough? Addressing Limitations of Predictive Modeling Using Non-Targeted Metabolomics for Herbal Product Authentication

Public genetic databases like the National Center for Biotechnology Information (NCBI) are widely used for species identification, but their reliability hinges on trustworthy source material. Unfortunately, researchers often unknowingly submit mislabeled material. Additionally, excessive sequence submissions can skew results. This talk explores the issue of overreliance on public databases for species identification and discusses the necessary attributes of an accurate test. We will examine the challenges of creating accurate genetic tests in spite of database inconsistencies, including misclassified sequences and taxonomic confusion. We provide case studies with Mexican oregano (Lippia graveolens) and Reishi (Ganoderma lucidum, G. lingzhi). Trust in the available accessions can result in incorrect identifications, regulatory missteps, and compromised product integrity. We propose sourcing authenticated botanical reference material (BRM) with sample numbers that represent the complexity of a given species must be the foundation of genetic testing methods.

Beyond NCBI: The Case for Authenticated Botanical Reference Materials

The variability of chemovars (or chemotypes) in plants can pose significant challenges in our modern supply chain when variability is not evaluated in identity testing methods.  Plants reflect their environment; factors like soil composition, temperature, water availability, and light influence their chemical expression.  The transition from wild collection to cultivation introduces additional variability, as agricultural practices such as irrigation, fertilization, and pest management can all influence secondary metabolite content. As they are exposed to different climatic conditions, these factors can lead to shifts in these chemical markers, which are used for identity.  Furthermore, the processing of the botanical material can introduce additional variation in the chemical expression of the product. This presentation will examine how the variability naturally presented in plants influences botanical identity. Variability presents in test samples, reference materials and methods. These differences can affect the consistency and reliability of High-Performance Thin Layer Chromatography (HPTLC) results and, consequently, the identity and quality control of plant-based products. Understanding the influence of environmental and cultivation factors in reference material, while leveraging method selection and accounting for sound statistical sampling, we can better characterize the results of botanical identity in the face of changing growing conditions and supply chain diversity. Understanding these factors is critical for ensuring the quality and identity of plant-derived products in a changing world.

Reliably Testing for Identity with Variability in Mind

Molecular markers are critical genetic components used to address different biological questions but are beholden to the genome at hand, a dynamic ecosystem with complex and non-uniform compositions and variable evolutionary scales at play. Different marker types—such as RFLP, SNPs, SSRs, and allelic variants—can provide insights at different levels of inter-relatedness, from broad taxonomic classifications to individual-level identification.
 The choice of marker depends on the specific research question and desired level of resolution. For instance, large-scale phylogenetic studies might use slowly evolving markers that have fixed states within entire clades, while population genetics often employ rapidly evolving markers to understand intra-species dynamics.
The selection of appropriate molecular markers requires a fundamental understanding of the evolutionary scale, inheritance patterns, and population dynamics of the genetic markers in question. 
In this talk, I will discuss the utility of different genetic markers and their applications with lion’s mane (Hericium erinaceus) as an example.  I will cover different examples on how markers have been used properly and how they have been used improperly for botanical identification for commercial products.  Choosing the right molecular marker is crucial for accurately addressing the specific evolutionary or genetic question being investigated.

The Genome as Swiss Army Knife: Multiple Tools for Different Jobs in DNA Testing

Edible insects represent an appealing source of future food with the potential to cover the increasing global need for nutrients (mainly proteins). In the EU, 4 species have been authorized as novel food (Regulation (EU) 2015/2283). However, there are still many gaps in the availability of comprehensive laboratory strategies enabling unbiased authenticity assessment. 
In the presented study, attention was focused on metabolomics as a complementary strategy to genomics and proteomics for the authentication of edible insect species. A total of 10 insect species (including both authorized and unauthorized) from different farmers collected over two years differing in rearing conditions and ways of killing were analyzed. Non-targeted screening based on U-HPLC-HRMS/MS followed by multivariate analysis of generated data was employed to characterize both polar and non-polar extracts. Safe distinguishing of individual species was achieved, with polar extracts resulting in tighter clustering. Potential markers of species were selected, and their selectivity was tested using real multi-ingredient foods from the market that included an insect ingredient. In the case of nonpolar extracts, lipidomic screening against MS-Dial lipid library illustrated that the lipid composition is dependent on the used feed. Overall, the great advantage of the metabolomic strategy over genomics and proteomics is the possibility of detecting compounds associated with different killing methods and different feed components, which represents great potential for evaluating the general quality and safety (detection of hazardous compounds) of edible insects and products thereof available on the market.

Metabolomics as a Strategy for Authenticating Edible Insect Species with the Possibility of Quality and Safety Assessment

Among many adulteration practices, doping/spiking spices with dyes to mimic their vibrant colours is quite common. Colorants have no nutritional value, their addition often masks defects in spices, and/or are added to enhance their appearance.  Many synthetic dyes such as metanil yellow, sudan red, and lead chromate are mutagenic, carcinogenic, induce chromosomal abnormalities and may cause severe health issues of great concern to consumers and the food industry. These dyes are making their way into food products due to lack of strict regulatory guidelines and analytical testing capabilities, warranting highly sensitive and accurate analytical techniques. Nuclear Magnetic Resonance (NMR) spectroscopy has gained prominence in recent years as an analytical tool for botanical metabolomics. This technique is highly reproducible, accurate, and sensitive, with easy operation and simple sample preparation, making it an ideal choice for identifying synthetic dyes in botanical products. The metabolites constituting botanical products are species-specific and yield distinctive NMR spectroscopic profiles, making NMR-based metabolomics an attractive and traceable approach for botanical identification. A conjoint spectral data analysis captures the presence of dyes in the product, thus allowing for simultaneous species identification and quality assessment. NMR spectroscopy, combined with multivariate statistical analysis, enables the creation of spectral libraries containing both authentic materials and those with added dyes, facilitating rapid and reliable product authentication and quality verification. This approach is demonstrated through various cases of spice adulteration with different dyes, showcasing how NMR-based methods effectively detect adulteration and verify label claims with precision.

Unveiling Colors: Using NMR Spectroscopy to Detect Natural and Synthetic Dyes in Spices

Efficient, reliable, and sustainable analytical methods for characterizing diverse food products and ingredients are essential for today’s food industry. According to the United States Food & Drug Administration, food authenticity has been an area of concern that has created an estimated annual loss of $40 billion in the industry. Nuclear Magnetic Resonance (NMR) spectroscopy provides a comprehensive solution to this challenge due to its exceptional accuracy, reproducibility, and non-destructive nature.  Fourier-Transform NMR (FT-NMR) is highly effective in identifying and quantifying compounds within complex food matrices, enhancing product formulation, optimizing manufacturing processes, detecting adulteration, and enforcing stringent quality control.  Time-Domain NMR (TD-NMR) is a rapid analytical technique that excels due to its operational simplicity, speed, and precision. As a green analytical chemistry method, TD-NMR requires minimal sample preparation with no need for additional solvents while providing high reproducibility compared to traditional wet chemistry methods. TD-NMR is widely used to understand the Solid Fat Content (SFC) of various fats, the droplet-size of food materials like butters and margarines, and the oil and moisture content of a variety of food matrices like seeds and nuts.  
This presentation will highlight the potential of NMR spectroscopy for food analysis and its validation of functional foods, ultimately enhancing nutritional profiles, and contributing positively to consumer health.

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