United States

2025 AOAC INTERNATIONAL Annual Meeting &amp; Exposition

Q&amp;A with panel of speakers

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

Q&A with panel of speakers

#### 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.

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.

Beyond Species Identification: Leveraging NMR for Comprehensive Quality Assurance in Mushroom Supplements

Fennel fruit consists of the dried ripe fruit of Foeniculum vulgare Mill. (Fam. Apiaceae); it has been used in traditional medicines for a wide range of ailments related to digestive, endocrine, reproductive, and respiratory systems. The marketed fennel fruit is sometimes adulterated with fruits of Carum carvi L., Cuminum cyminum L. and Anethum graveolens L. or is much lower quality due to unsuitable processing and storage.
USP is developing Fennel Fruit quality standards to help protect customers from using incorrect and low-quality fennel fruit. The USP Fennel Fruit monographs provide analytical quality control procedures and acceptance criteria with suitable reference standards.
The monograph contains identification methods utilizing GC, HPTLC and botanical characteristics, and a GC assay method. Both GC and HPTLC identification methods in the monograph can detect anethole, a major component of fennel fruit. The three potential confounders do not contain anethole, which can efficiently differentiate the fennel fruit from the potential confounders. The GC assay can quantify content of anethole, limonene, fenchone, and estragole concurrently. Test results indicated the content ratio of anethole to fenchone in fennel fruit is much different from bitter fennel (Foeniculum vulgare Mill. var. vulgare) fruit a variety of fennel used in Europe. The USP Laboratory tested some commercial fennel products using the monograph methods and found that some of the products did not contain any related components in both GC and HPTLC chromatograms, implying that the fennel fruit maybe an exhausted product which may not be acceptable for use as a dietary ingredient.

USP Monograph Methods Differentiate Fennel Fruit from the Potential Confounders and Closely Related Species

The objective of this work was to develop methods for the analysis of antibiotics and veterinary drugs in milk listed by Food Safety and Standards Authority of India (FSSAI) using a PerkinElmer QSight 420 LC-MS/MS system with a StayClean™ ion source. To improve spiked recoveries and better method performance, two separate methods were developed for the analysis of over 30 analytes in milk due to their different polarities. For polar analytes, a mixed mode LC method was used for analyte separation and a trichloroacetic acid/acetonitrile mixed solution was used for protein precipitation. For other less polar analytes, a reversed phase UHPLC method was used for analyte separation and an acidified acetonitrile extraction was applied for sample preparation. For both methods, EDTA was used to reduce analyte-metal (i.e., calcium) ion complex formation and d-SPE was performed to remove fatty matrix using end-capped C18 sorbent. To reduce matrix effects, matrix-matched calibration with isotopic mass dilution method was performed. Both methods were validated by spiking different concentrations of analytes in a milk sample matrix. The recoveries of most analytes are between 70 to 120%. The results demonstrated that all the analytes could be determined with LOQ below the tolerance limits set by FSSAI. In addition, StayClean ion source allows continuing injections of large amounts “dirty” samples with virtually no instrument cleaning and maintenance.  An instrument robust test result dataset with continuing injections of 25,500 samples will be presented. This methodology can be extended to analysis of similar analytes in milk and milk products regulated by other governmental agencies.

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