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The Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence, as outlined in ISO 16756:2024 | IDF 259:2024 for milk and milk products, is an advanced TD-NMR technique that enables direct fat measurement without the use of solvents or calibrations. The adoption of ISO and IDF standards have demonstrated the utility, flexibility, and reliability of the methodology for the dairy industry. However, this technique extends beyond dairy applications, including the analysis of fat in meats, and plant-based proteins. Facilitating rapid and reliable direct fat analysis in under a minute, it has the ability to enhance quality control and efficiency across various industries. Accurate fat analysis will be demonstrated across diverse food matrices within the AOAC food triangle.

2025 AOAC INTERNATIONAL Annual Meeting &amp; Exposition

Revolutionizing Fat Analysis: Fast and Accurate Measurement for Dairy and Food

#### 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.

* **Scientists**: Build professional expertise and network with your community to share information and best practices.

* **Regulators**: Leverage unprecedented opportunity for stakeholder collaboration on complex testing and analysis challenges, helping improve compliance and public safety.

* **Academia**: Engage with colleagues – both students and faculty – at all career and research levels.


#### Keep Pace with Innovation
* Take advantage of emerging methods, best practices, and trending topics like food fraud and cannabis purity and potency. 
* Discover new technologies, from DNA authentication to genomic microbial identification. 
* Get professional insights from industry insiders through the AOAC Spotlight.

#### Experience the AOAC Analytical Solutions Forum

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.

Choline, a vital nutrient essential for cognitive functions, including memory, executive functions, and learning. Humans obtain choline from two sources: dietary intake and hepatic biosynthesis. The biosynthesis of choline is regulated by estrogen which diminished with ageing, impacting post-menopausal women. However, the recent NHANES shows only 2% of post-menopausal women in the U.S. meet the adequate intake (AI) of choline, highlighting the critical need for a novel ingredient to increase choline consumption. We hypothesize Whey Protein Phospholipid Concentrate (WPPC), a byproduct of cheese processing, is a bioavailable and safe source of choline because of its richness of choline precursors phosphatidylcholine (PC) and unique triple-layered phospholipid structure. To test this hypothesis, we will conduct a single-blinded parallel designed randomized control trial in post-menopausal women. The changes in serum choline metabolites after WPPC intervention will be measured by UHPLC-HRMS/MS as the primary outcome. The safety of WPPC consumption will be determined by dissecting the changes in detrimental metabolites of choline, trimethylamine (TMA) and its oxidized product (TMAO), using UHPLC-HRMS/MS, and alteration of oxidative stress and proinflammatory biomarkers, such as 8-hydroxydeoxyguanosine, IL-1, IL-6, TNF-a, and hsCRP by utilizing ELISA and immunoturbidimetry. Lastly, the abundance and diversity of gut microbes will be measured using 16S rRNA sequencing to explore the impacts of WPPC on cognition-related microbe populations. Our findings will demonstrate the safety and bioavailability profile of WPPC and its application in supplementing choline intake in post-postmenopausal women. The valuable clinical data will support the establishment of a Recommended Daily Allowance (RDA) for choline.

Assessing Choline Bioavailability and Metabolism from a New Dairy Ingredient (WPPC) for Post-Menopausal Women: A Randomized Control Trial

Q&A with panel of speakers

Predicting sensory mouthfeel from instrumental measurements remains a key challenge in food quality assessment. This study presents an AI and machine learning (ML) tool designed to predict sensory mouthfeel in yogurt formulations using analytical chemistry, rheology, tribology, and texture profile analysis. Standardized instrumental and sensory testing protocols were developed to ensure consistent data collection and improve model accuracy. The AI/ML model was trained on instrumental data alongside sensory panel evaluations, establishing correlations between physical properties and perceived mouthfeel attributes.  By leveraging this approach, the tool enhances lab efficiency by reducing reliance on costly and time-intensive sensory panels while maintaining accuracy in product evaluation. Additionally, the implementation of standardized methods improves reproducibility and reliability in food texture analysis. This presentation will outline analytical instrument method development, data collection strategies, model development, and validation techniques, offering insights into how AI can optimize analytical workflows in food quality testing. The findings highlight the potential for AI-driven methodologies to advance instrumental analysis, providing a scalable and efficient alternative for sensory prediction in dairy and other food systems. 

Analytical Method Development and Instrumental Testing for AI-Driven Prediction of Sensory Mouthfeel in Yogurt

Ensuring food safety requires accurate, high-throughput analytical workflows to detect contaminants, verify authenticity, and monitor lipid quality. A novel Generative AI and Limited Sample Model (LSM) technology, automates untargeted high-resolution mass spectrometry (HRMS) analysis, significantly reducing data processing time and provides actionable insights in real-time.
In a case study, LSM reduced lipid identification and result validation from two weeks to 20 minutes using a Vanquish Horizon HPLC system and a quadrupole-Orbitrap mass spectrometer. Covering a 200–2000 Da mass range at 120,000 FWHM resolution, it leveraged Diffusion AI to perform real-time analysis without predefined compound libraries. This enabled the discovery of unknown lipids with 99.8% accuracy in complex sample matrices. The system’s adaptive learning further identified novel compounds, marking a breakthrough in untargeted HRMS.
This AI-driven workflow enhances food safety applications, including contaminant detection (oxidized lipids, industrial pollutants, pesticide residues), authenticity verification (vegetable oils, dairy, meat), nutritional labeling and quality control (fatty acid profiling, omega-3/6 balance), shelf-life and spoilage monitoring (lipid oxidation, rancidity prevention), food fraud detection (adulteration of high-value oils), and microbial/toxin analysis (lipid-based bacterial and fungal biomarkers).
The demonstrated advanced AI-driven workflow ensures regulatory compliance, enhanced decision-making, and reduced manual processing. Generative AI and LSM empower laboratories to validate results efficiently, safeguarding food products while optimizing resource allocation and supply chain integrity.

Automated Nontargeted Screening and Result Validation for Food Safety Using Generative AI in HRMS Lipid Analysis

To develop and validate a comprehensive analytical framework for strawberry maturity authentication by integrating artificial intelligence with spectroscopic and chemical analyses, addressing the critical need for objective and automated quality assessment in the food industry's increasingly complex supply chains.
We analyzed 90 strawberry samples across three maturity stages using a multi-modal approach. Near-infrared spectroscopy (400-1700 nm) data was processed through 1,220 preprocessing pipelines to optimize spectral analysis. Chemical analysis included titratable acidity and total dissolved solids measurements. For visual assessment, 270 high-resolution images were collected and analyzed using three state-of-the-art deep learning architectures: RegNet_Y_800MF, RegNet_Y_8GF, and Swin Transformer V2, with comprehensive data augmentation techniques.
The optimal NIR preprocessing pipeline achieved a Silhouette score of 0.5744 using localized standard normal variate with seven windows. The Swin Transformer V2 model demonstrated perfect classification accuracy (100%), outperforming traditional RegNet architectures while maintaining computational efficiency. Chemical analysis revealed the Brix-to-acidity ratio as the most reliable maturity indicator (4.92±0.80 immature, 6.18±0.98 semi-mature, 8.12±2.40 mature). Principal component analysis of preprocessed NIR spectra showed strong correlations with both visual and chemical properties, validating the multi-modal approach.
This research advances food authenticity testing by providing a rapid, non-destructive method for objective maturity assessment. The validated multi-modal approach offers superior accuracy compared to traditional single-method analyses, supporting both producers and regulators in ensuring product quality and preventing fraud. The developed framework demonstrates broad applicability across the fresh produce industry, contributing to improved food safety and quality control while reducing food waste through precise maturity determination.

Advanced Deep Learning and Multi-Modal Analysis for Automated Strawberry Maturity Classification: Integration of Visual, Chemical, and Spectral Properties

The National Institute of Standards and Technology (NIST) Dietary Supplement Laboratory Quality Assurance Program (DSQAP) was established in 2007 as part of a collaboration with the National Institutes of Health (NIH) Office of Dietary Supplements (ODS). The DSQAP was created to facilitate improved measurement capabilities of the dietary supplement (DS) and natural product communities. Participating laboratories are able to assess their in-house analytical methods through a variety of studies including toxic and nutritional elements, fat- and water-soluble vitamins, fatty acids, organic contaminants, and marker compounds in samples that represent DS ingredients or finished products. These studies are designed to address analytical challenges in the community, e.g. emerging ingredients and products, material content, and lowering limits of detection. Insights into community measurement performance and analytical needs derived from DSQAP studies also informs NIST and NIH ODS in the production and maintenance of references materials for DSs. Results from DSQAP studies can be utilized to identify problematic matrices or analytes within the DS measurement communities. These results can also determine the need for new reference materials and/or which analytes should be targeted to aid in the standardization of measurements. This presentation will highlight results from elemental studies from previous and current DSQAP exercises that will benefit the DS analytical communities through the continued improvement of nutritional and toxic element characterization in DS ingredients and the development and maintenance of reference materials at NIST.

NIST Dietary Supplement Laboratory Quality Assurance Program (DSQAP): Overview of Elemental Studies and Development of Reference Materials

Whether for ethical, dietary, health, or social reasons, there are increasing numbers of people globally who follow a vegan or vegetarian diet.  The food industry is aware of the rise in popularity of meat-free food and is actively investigating processes and products that can help meet the demand.
To ensure that non-meat based protein or cultured protein products are safe for human consumption, manufacturers must comply with Good Manufacturing Practice (GMP).  The FDA currently implements comprehensive methods for the elemental analysis in food digests.  This presentation will discuss the sample preparation using microwave assisted decomposition and the associated elemental analysis by ICP-MS.  The validation of the method will also be discussed to ensure data accuracy and instrument performance.

Elemental Analysis of Plant-Based Alternative Meat Sample and Cell Culture Solutions by ICP-MS

Heavy metals contaminate our food and drinking water by soil and air pollution via pesticides, industrial waste or accidental release. Plants uptake metals from the environment, depositing them throughout the plant including flowers, pollen and fruit. In some cases, these are beneficial minerals necessary for bone structure and cognitive health, but when grown in contaminated soil heavy metals can contaminate our food sources as well. Some forms of these heavy metals have more toxic forms, like methylmercury or hexavalent chromium when compared to other mercury or chromium forms. ICP-MS alone cannot identify concentrations of these more harmful species, as a total value is collected for the element.
We have paired separation by HPLC followed by ICP-MS to better understand the concentrations of the more harmful forms contaminating our food and water. Demonstrated will be examples including the separation of inorganic mercury and methyl mercury in 5 minutes, followed by detection using ICP-MS. All tested elements were tested for sensitivity at 10% and 20% of the Food Safety and Standards Authority of India and reported excellent repeatability and RSD below 3%.

Improving Risk Assessment and Mediation of Heavy Metals in Foods Using Speciation by HPLC Coupled ICP-MS

The trend towards a healthier diet and lifestyle has expanded the functional mushroom market and continues to grow exponentially. Finished products now come in a variety of forms and flavours. However, the boom of the medicinal mushroom industry has led to an urgent need for the development of fit-for-purpose methods to support authenticity claims and improve consumer trust.   
The key active component found in mushroom products is 1,3/1,6-β-glucan. While the measurement of 1,3:1,4-β-glucan found in cereals and the 1,3/1,6-β-glucan found in yeast can be achieved using a selective enzymatic hydrolysis approach, an equivalent method is not available for the measurement of 1,3/1,6-β-glucan in mushrooms, due to the complex structure presented. Instead, an indirect method involving subtraction of enzymatically determined a-glucan content from an acid hydrolysis/chemo-enzymatic derived “total glucan” measurement, can be employed as described by McCleary et al. in 2016. Although this method readily allows for the measurement of β-glucan in mushroom products which are contaminated with α-glucans comprising starch, maltodextrins and/or sucrose, other components may present challenges.
As such, a number of assay parameters have been thoroughly investigated, and the results of this study are described herein. 

Measurement of β-glucan in Mushrooms and Derived Products: Critical Evaluation and Improved Methodology

Co-author:Arun Krishnamurthy & Amber Thelen, Purity-IQ Inc
Functional mushroom products are available in various forms, including whole vouchers, extracts, blends, and mycelia fermented grains, each adding complexity to product analysis. While targeted methods provide valuable species-level identification, they may not fully capture the broader chemical profile that reflects product quality, which can be influenced by manufacturing practices, processing, and geographical origin. To complement these targeted techniques, robust, non-targeted analytical tools are essential for a comprehensive evaluation of both species-authenticity and product quality — key components of consumer trust.
Nuclear Magnetic Resonance (NMR) spectroscopy offers both targeted and non-targeted chemical profiling capabilities, providing deep insight into the species identity, purity, and quality of functional mushroom products. When combined with multivariate statistical analysis, NMR enhances fraud detection and supports quality control by identifying chemical variations that might otherwise go unnoticed. A validated NMR spectral library, developed using authentic materials obtained from credible sources and cross-verified with other orthogonal methods, enables robust species discrimination, extract characterization, and quality assessment.
NMR’s versatility strengthens existing analytical frameworks by offering enhanced detection of adulteration, consistency verification, and chemical characterization. This study demonstrates the practical application of NMR metabolomics and statistical analysis in fungal product evaluation, showcasing its value as a powerful, routine testing method for authenticity, purity, and overall quality in the mushroom supplement market.

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