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Ultrashort-chain perfluoroalkyl acids (PFAAs), such as trifluoroacetic acid (TFA), perfluoropropanoic acid (PFPrA), perfluoromethane sulfonic acid (PFMS), perfluoroethane sulfonic acid (PFEtS) and perfluoropropane sulfonic acid (PFPrS), represent a subset of PFAS that are characterized by a chain length of 1–3 perfluorinated carbons. Compared to their longer-chain counterparts, these chemicals have historically been overlooked due to their presumed lower toxicity and bioaccumulation potential. However, the high polarity, water solubility and persistence of these ultrashort-chain PFAAs can lead to accumulation in aquatic and plant environments, resulting in increased exposure for aquatic organisms and humans. In particular, TFA has been globally reported in aqueous, solid and biological matrices, often at orders of magnitude higher concentrations than those of the longer-chain PFAAs. There is limited environmental data on ultrashort-chain PFAAs due to analytical challenges. Previous evaluations of HILIC and reverse-phased chromatography have reported issues such as poor retention and separation, matrix effects and limited analyte range (≤7 CF). In contrast, mixed-mode chromatography that combined reverse-phase and ion-exchange functionalities has demonstrated promising results for ultrashort-chain PFAAs; however, its application to longer-chain analytes remains limited. Here, a direct injection LC-MS/MS method based on mixed-mode chromatography enabled the simultaneous quantitation of ultrashort- (≤3 CF­­­­), short- (3–6 CF) and long-chain (≥6 CF) PFAAs at ng/L to µg/L concentrations in drinking water and beverages.

2025 AOAC INTERNATIONAL Annual Meeting &amp; Exposition

Simultaneous Quantitation of Ultrashort-, Short- and Long-Chain PFAS in Drinking Water and Beverages by a Single Direct Injection LC-MS/MS Method

#### The AOAC INTERNATIONAL Annual Meeting: A Unique Analytical Science Opportunity

The AOAC Annual Meeting & Exposition provides unparalleled professional development, networking, and collaboration in methods-based science. 

* **Businesses**: Meet scientific and regulatory experts and engage with new trends and standards.

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This multi-faceted “idea incubator” focuses on regulatory changes and emerging food safety issues. A plenary and two breakout sessions feature thought-provoking ideas to identify and meet analytical needs.

The 2025 AOAC INTERNATIONAL Annual Meeting & Exposition was held in person in San Diego, USA.

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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.

Freeze-dried fruit products are advertised as a "healthy" snack for children and adults. Strawberries, due to their water and sugar content, are prone to mold, and that is why various agrochemicals, such as pesticides, are used. During the freeze-drying process, substances with positive biological activity and possible harmful substances (e.g., pesticide residues) are concentrated and can pose a certain health risk.
A total of 58 freeze-dried fruit products available on the Czech market were analyzed, including 46 samples of strawberries and 12 samples of raspberries, with some products being examined across different batches. QuEChERS extraction procedure was used for multi-residue determination of pesticide residues, followed by LC-MS/MS and GC-MS/MS analysis (and LC-MS/MS analysis of mycotoxins). QuPPe methodology was used for the extraction of polar pesticide residues followed by LC-MS/MS analysis. A total of 111 different pesticide residues (or their metabolites) and 3 mycotoxins were detected. In one of the analysed samples, more than 70 different pesticide residues were detected, of which 12 exceeded the maximum residue limit. Overall, more than 30 different residues were detected in 11 samples. Prohibited substances such as carbofuran, omethoate, and haloxyfop residues were also detected, which can pose a serious health risk.
The study highlights the importance of monitoring pesticide residues in freeze-dried fruits to ensure consumer safety, especially since these products are often consumed by small children. Given the higher levels of contamination found in strawberries, it is crucial to consider the potential health risks, particularly for vulnerable groups such as young children who are more sensitive to chemical exposures.

Is Freeze-Dried Fruit Really a Healthy Snack?

Food fraud threatens safety, industry integrity, and compliance. AI-driven GC-FID & LC-MS enhance detection, automating workflows for accurate, scalable, and reproducible fraud prevention in global supply chains. Traditional methods rely on pattern recognition and expert-driven analysis but struggle with co-elution, matrix interferences, and sample variability. AI overcomes these limitations by: 

Learning authentic spectral patterns from minimal datasets, reducing reliance on extensive training data.
Detects anomalies & fraud via AI-driven peak auditing and molecular profiling.Providing Explainable AI (XAI) insights, ensuring transparency, interpretability, and regulatory defensibility.
AI-powered screening automates fraud detection, strengthens compliance, and standardizes authentication workflows across labs.

AI-Driven GC-FID Authentication: Ensuring Integrity in Natural Flavoring Oils
Natural oils like lemon, lime, and vanilla face adulteration with synthetic esters and petrochemicals. GC-FID is effective but limited by co-elution and subjective interpretation. AI with Expert-Guided Learning improves detection by:

Identifying molecular adulterants such as synthetic esters, petrochemical-derived components, and non-compliant additives.
Automating peak auditing, improving reproducibility.
Learning from expert annotations, refining accuracy over time.

AI-Powered LC-MS Authentication: Detecting Counterfeit Wine
The wine industry faces rising fraud risks, including mislabeling, dilution with lower-quality wines, and contamination. LC-MS provides molecular profiling for authentication but demands expert interpretation and manual data processing. AI-driven LC-MS revolutionizes wine fraud detection by:

Identifying unique chemical markers linked to grape varietals, fermentation conditions, and terroirs with small datasets
Detecting fraudulent labeling, and distinguishing authentic vs. blended wines.
Enhancing forensic analysis by detecting pesticide residues, smoke taint, and cork contamination (TCA).

Ensuring AOAC & Codex compliance, streamlining validation.

Natural Oil and Wine Authentication Using Deep Generative AI: Automated GC-FID and LC-MS/MS for AI-Driven Fraud Detection

Food fraud presents complex challenges due to the diverse and intricate nature of food matrices and the numerous methods by which adulteration can occur. Historically, such complexity suggested the necessity of equally sophisticated analytical approaches. However, recent advances in the standardization and automation of Nuclear Magnetic Resonance (NMR) spectroscopy have transformed this comprehensive technique into an accessible tool, even for non-experts.
Leveraging its quantitative capabilities, NMR combined with database-driven statistical analyses effectively verifies food authenticity, including critical factors such as geographical origin and botanical variety, thereby directly impacting product valuation and quality assurance.
This presentation highlights specific instances illustrating the efficacy of NMR in combating food fraud. Notably, we discuss the detection of adulterated honey containing foreign sugars and the identification of mislabeled wines. These case studies underscore NMR's critical role in safeguarding food integrity and maintaining consumer trust.

Tackling the Complexity of Food Fraud with NMR Spectroscopy

Food authentication is receiving increasing attention, as factors like supply chain disruptions and shifts in consumer purchasing habits continue to tempt bad actors to adulterate products for economic gain.  Food fraud not only harms consumers, who may pay more for inferior quality products, but also producers, who face unfair competition from cheaper, adulterated products and risk reputational damage if their supply chains are compromised.
Isotope ratio mass spectrometry (IRMS) is a technique that precisely measures small differences in the natural isotopic composition of light elements in a sample, providing valuable insights into a product's source and history.  For example, plants have evolved different photosynthetic pathways that lead to variations in the carbon isotope ratios of their tissues; these differences can be used to detect adulteration of products like honey or coconut water with corn syrup or sugarcane.  Similarly, the nitrogen isotopic composition of natural and synthetic fertilizers differs, allowing for the discrimination between conventionally- and organically-grown crops.  Examples will be presented highlighting how IRMS may help verify the authenticity of raw ingredients and finished products, thereby protecting the integrity of the food supply chain.

Detecting Food Fraud using Stable Isotope Ratio Analyses

Seafood substitution poses a significant threat to the global seafood market, driven by increasing consumer demand, and fraudulent practices like species mislabeling. Among seafoods, crustaceans are valuable commodities and are particularly vulnerable to substitutionary activities due to their morphological similarities. This study focuses on developing rapid and accurate identification methods to combat seafood fraud. To address this, we developed species specific C-LAMP and mPCR-LFD assays targeting the COI gene of three mussel species (Perna canaliculus, Mytilus galloprovincialis, and Perna viridis) and three crab species (Portunus pelagicus, Portunus sanguinolentus and Charybdis feriata). The C-LAMP and mPCR - LFD assays conditions were optimized by varying the temperature and time. The C-LAMP assay demonstrated high accuracy at 63 °C for 30 mins. The C-LAMP exhibited high sensitivity for P. viridis and P. canaliculus, with limits of detection of 0.003 and 0.01 ng/ reaction, respectively. Similarly, the mPCR-LFD assay achieved high sensitivity, detecting DNA concentrations as low as 0.1 ng, 0.006 ng, and 0.01 ng for blue swimming crab, three spotted crab and crucifix crab, respectively. Further, in-house validation using various processing methods (boiling, steaming, frying, and canning) using the developed assay demonstrated high sensitivity and robustness. Out-house validation of crab species revealed 28 % of mislabeling rate.  Therefore, the C-LAMP and mPCR-LFD assay developed in this study is simple, rapid, and cost-effective for authenticating seafood products. Hence, these novel molecular techniques will offer a promising solution for routine fishery examinations to stakeholders, industry people, and regulatory authorities, which contributes to seafood safety and authenticity.

C-LAMP and mPCR – LFD Assay: A Novel Approach for Combatting Seafood Fraud in Crustaceans

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.

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

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.

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