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The increasing complexity of botanical product formulations present significant challenges in accurate species identification and quantification. Traditional analytical methods sometimes struggle to precisely determine the composition of processed botanical mixtures, particularly in cases involving mushrooms and other botanically diverse preparations. This research introduces a refined quantitative PCR (qPCR) approach for accurately estimating the composition of mushroom blends.
Quantitative PCR has previously demonstrated utility in both absolute quantification across various biological systems and is used often when testing for microbial pathogens, cell types, and more. Our study expands this methodology to the complex domain of mushroom blend analysis, developing a molecular approach that utilizes species-specific genetic markers that can precisely identify and quantify multiple species within a blend. 
The proposed technique offers significant advantages over traditional morphological and chemical identification methods, providing high-resolution quantification with improved sensitivity and specificity. Our results demonstrate the potential of qPCR as a powerful tool for comprehensive botanical blend analysis, addressing critical needs in quality control, authenticity verification, and regulatory compliance across food, nutraceutical, and pharmaceutical industries.

2025 AOAC INTERNATIONAL Annual Meeting &amp; Exposition

Quantitative Molecular Identification of Botanical Species in Complex Blends Using qPCR

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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.

Botanical genomes contain abundant information, from which species-specific DNA regions can be pinpointed for use as markers. Molecular methods that can reliably access sufficient genomic data can serve as repeatable and definitive identification techniques. Through multiple case studies, we demonstrate how next generation sequencing (NGS) is a powerful tool allowing massive data generation and individual amplicon sequencing that can overcome conventional sequencing challenges: 1) Botanical blend products contain a variety of plant-based ingredients that require authentication. We developed primer sets for ~30 ingredients that can achieve species level identifications using one or more small genomic regions (≤150bp). Individual amplicon sequencing using NGS allowed for precise identification of target material, and detection of non-targets in some cases. NGS was exceptionally cost and resource efficient in high throughput runs (10+ samples) and was designed to be robust in identifying individual species within mixtures. 2) We further developed primer panels to identify individual mushrooms within multi-fungi ingredient matrices like Lion’s Mane, Chaga, Maitake, Shiitake, and Reishi. 3) Differentiating hybrid species from their pure parental counterparts can be difficult due to the extreme similarity of chemical and molecular markers. We developed a method that reliably delineates spearmint and peppermint specimens via targeted NGS of the multi-copy nuclear barcode region, ITS2. Observing a sequence composition that reveals a mix of two parent species effectively identifies a hybrid. In conclusion, the individual-amplicon sequencing ability of NGS allows for development of useful targeted tests that can be employed in a quality control environment.

Implementing Targeted Next Generation Sequencing Methods in Botanical Authentication Programs

Next generation sequencing platforms have been commercially available for approximately the last twenty years.  In that timeframe their use in the food safety sector has steadily increased.  While their initial use was centered around sequencing genomes of individual organisms, advances in the technology began to enable the sequencing of entire communities.  Since then metagenomics, the study of the structure and function of entire nucleotide sequences isolated from all the organisms in a sample, has created the opportunity to greatly increase detection of adulterants that compromise the safety and quality of foods and supplements.  
Here we present three case studies where amplicon sequencing or shotgun metagenomics were employed to successfully detect microbial adulterants and quality defects in food and supplement samples.  In each case, culture-based and/or existing molecular methods were initially employed and proved to be unable to resolve the issues encountered by each product.  We will see how community sequencing approaches were able to provide novel insights where other technologies (e.g., traditional culture-based methods, PCR, qPCR) could not.  We will also cover best practices for employing these approaches in diverse food matrices, as well as areas where these approaches will benefit from further innovations.

Community Sequencing Approaches that Improved Product Quality and Safety

Contamination in the food production chain poses risks to human health, financial losses, and environmental harm due to wasted resources. Conventional methods for identifying and tracing contaminants, whether from pathogenic or spoilage microbes, often have limitations. High-throughput sequencing, combined with bioinformatics, machine learning, and AI, offers faster, more accurate identification and tracing of contamination, along with risk prediction. This novel approach enables preventive actions resulting in reduced losses, and enhanced consumer safety. This presentation will cover the technological foundations, challenges, and opportunities, as well as practical applications in contamination detection, risk prevention, and quality monitoring through real-world case studies.

Practical Examples of the Use of Microbiome Based-Technologies as a Tool for Improving Food Safety and Shelf-Life

A new, PCR-based alternative typing method for L. monocytogenes (LM) strain differentiation addresses the concerns food producers have with whole-genome sequence (WGS)-based approaches (e.g., cgMLST). Specifically, this method is (i) not WGS-based, (ii) fast (results available within 24-72 h), (iii) cost-effective, and (iv) simple (easy PCR workflow followed by automated bioinformatics). While it is not a WGS-based method, comparison to historic WGS data (e.g., GenomeTrakr) can be performed via an in silico PCR analysis (i.e., the WGS data is analyzed to predict the PCR result). Conversely, data from the alternative method cannot be converted to a WGS file. Results from a comparison study between this and an rRNA fingerpinting-based typing method support this new method has greater discriminatory power. Additionally, end users of this alternative typing method are able to access their customer-specific database at any time and are notified of new results as soon as they are uploaded, thus allowing for easy and fast trend analysis. To date, this typing method has provided key insights into LM-positive samples. Notably, a persistent strain issue was identified by comparing newly isolated LM to historic data typed using WGS. Another dataset showed LM isolates from the same environment were clustering with one of two groups, which supported the presence of two unique strains. By adding the laboratory control strain to the customer’s database, each new dataset is automatically evaluated for potential laboratory contamination. Overall, this new alternative typing method has been a valuable tool for investigating and controlling LM.

Strain Typing without Whole-Genome Sequencing: Real-World Experiences with an Alternative Typing Method

AI and ML are transforming food safety and quality control by enabling faster, more accurate testing and compliance. Traditional AI models, including large language models (LLMs), excel at text processing but struggle with complex analytical data from instruments such as GC-MS, LC-MS, HPLC, FTIR, and NMR, posing challenges for accurate contaminant detection and regulatory compliance.
Limited Sample Models (LSM) address these challenges by delivering robust, real-time analytics with minimal, expert-annotated data. Unlike conventional deep learning, LSM technology is optimized for high-accuracy detection and analysis in laboratory environments, enhancing chromatographic deconvolution, impurity profiling, multi-instrument harmonization, and real-time monitoring to meet critical food safety standards.
This session will cover the following key advancements

Chromatographic Deconvolution and Peak Integration: LSM-powered deconvolution techniques minimize manual bias and enable automated contaminant detection in complex food matrices.
Impurity Profiling with Adaptive Models: AI-driven impurity profiling supports precise MOSH/MOAH, pesticide, and mycotoxin analysis, ensuring data reliability and consistency across laboratories.
Multi-Instrument Data Harmonization: Adaptive LSM models address inter-laboratory variability, facilitating harmonized data interpretation from diverse analytical platforms.
Real-Time Monitoring and Predictive Analytics: AI-enhanced pathogen detection and risk assessment improve food safety protocols by delivering real-time insights into contamination events.
Regulatory Alignment: Comprehensive strategies for AOAC-compliant AI integration ensure that automated workflows align with stringent food safety regulations.

Case studies will illustrate how LSM technology reduces manual errors, harmonizes multi-instrument data interpretation, and enhances pathogen detection while ensuring AOAC and FDA compliance. Attendees will gain practical insights into leveraging AI-driven analytical intelligence for improved quality control and regulatory alignment.

Leveraging Limited Sample Models (LSM) for AI-Driven Analytical Automation in Food Safety and Quality Control

Ultrashort-chain (USC) per- and polyfluoroalkyl substances (PFAS) are highly polar compounds with carbon chains shorter than C4. Their widespread occurrence in aquatic environments has raised growing concerns about potential contamination in food products, particularly ready-to-feed liquid milk. To fully assess PFAS contamination, it is essential to include USC compounds in PFAS analysis of milk samples. This study introduces a simple and reliable workflow for the simultaneous analysis of C1 to C14 perfluoroalkyl carboxylic and sulfonic acids, along with other PFAS classes, in various liquid milk matrices. The chromatographic analysis was performed using an inert-coated polar-embedded reversed-phase LC column. Method verification was conducted using three different milk types—dairy milk, almond milk, and infant formula—to demonstrate the workflow’s applicability for measuring 41 PFAS in diverse milk samples. This streamlined procedure was evaluated by accuracy and precision analysis at five fortification levels, ranging from 0.01 to 0.25 µg/kg, equivalent to 10 to 250 ng/L in the final sample solution for LC-MS/MS analysis. Eighteen isotopes, serving as quantitative internal standards, were added to the sample before extraction to ensure accurate quantification by correcting variations in sample preparation and matrix effect. All analytes exhibited recovery values within 30% of the nominal concentration across all fortification levels. Satisfactory method precision was demonstrated with %RSD values below 15%. The established workflow was then applied to the analysis of additional milk samples collected from various grocery stores, providing a comprehensive profile of PFAS contamination across a diverse range of milk matrices.

Integrating Ultrashort-Chain Compounds into Comprehensive PFAS Analysis in Ready-to-Feed Liquid Milk by LC-MS/MS

Per- and polyfluoroalkyl substances (PFAS) are a group of synthetic fluorinated compounds recognized for their exceptional thermal and chemical stability. Since the 1950s, PFAS have been used in a wide range of consumer and industrial products, including non-stick cookware, water-repellent textiles, food packaging, and firefighting foams. Their persistence in the environment has led to widespread contamination of air, water, and soil—resulting in potential human exposure through ingestion, inhalation, or dermal contact. Growing research suggests links between PFAS exposure and adverse health outcomes, such as elevated cholesterol, liver damage, hormone disruption, and potential associations with certain cancers. In response, regulatory agencies like the FDA have taken steps to reduce human exposure—for example, by phasing out PFAS-based grease-proofing agents in food packaging. Additionally, AOAC has established Standard Method Performance Requirements (SMPRs) to guide PFAS testing in laboratories. This method describes the validated use of liquid chromatography-tandem mass spectrometry (LC-MS/MS) for detecting PFAS in food and packaging materials. The procedure includes sample homogenization, fortification with isotopically labeled internal standards, and extraction using acidified acetonitrile, followed by clean-up steps such as QuEChERS and solid-phase extraction. Detection is performed using multiple reaction monitoring (MRM) and retention time confirmation. Quantification is based on calibration curve comparisons and adjusted for dilution factors and sample mass. This robust approach delivers accurate, reliable PFAS data—supporting regulatory compliance and protecting consumer health.

Determination of PFAS in Food and Packaging Using LC-MS/MS

Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals widely used in industrial and consumer products. As dietary exposure to PFAS through foods become a growing concern, the AOAC SMPR 2023.003 provides specific guideline for developing analytical method for PFAS analysis in various food matrices. Although alcohlic drinks are not included in the list, the PFAS residue in alcoholic drinks becomes a concern and thus requires a reliable analytical method for PFAS determination in alcoholic drinks. 
In this study, we developed and validated a workflow for 43 PFAS compounds analysis in alcoholic drinks using QuEChERS extraction followed with EMR mixed-mode passthrough cleanup, and then direct injection of sample eluent for LC-MS/MS detection. The inject program utilized a novel injection mode called feed injection, where sample is fed into LC gradient with excellent mixing with initial mobile phase condition. The feed injection enables direct injection of large volume sample in high organic, delivering excellent chromatography without compromise on early eluted targets' peak shape. The improved instrumetn method further simplified the sample preparation by removing the drying step, delivering the acceptable method LOQ levels but saving up to 50% of preparing time. 
The method achieved LOQs less than 0.01 µg/kg for the core four PFAS targets and ≤ 0.1 µg/kg for other PFAS targets, except PFBA and PFPeA, which have &lt; 1 µg/kg LOQ being demonstrated. Method recovery and reproducibility, selectivity and suitability were evaluated based AOAC SMPR guideline and demonstrated to meet the acceptance criteria. 

A Quick, Easy and Effective Method for Determination of 43 PFAS in Alcoholic Drinks by EMR LC-MS/MS Method

The analysis of pesticides and PFAS in food is crucial for ensuring consumer safety and regulatory compliance. High-resolution mass spectrometry (HRMS) in full scan mode offers accurate and comprehensive detection.  Many labs have adopted the technique for qualitative screening, confirming results from standard SRM triple quadrupole MS analysis, and for non-targeted workflows.  However, its use for routine targeted quantitative analysis has often been neglected due to a) requirement of highly skilled operators, b) cost of HRMS systems c) complex method development involving choice of acquisition mode combinations such as data-dependent and other MS2 precursor isolation techniques and d) overall sensitivity.   
We describe an alternative approach in which the HRMS is coupled to a dual channel liquid chromatography (LC) system and run with dedicated conditions using a single full scan HRMS acquisition method with in-source fragmentation that requires little, or no method development by the user.  It facilitates robust and reliable residue analysis compliant with regulatory guidelines.  To satisfy requirements, 2 ions were monitored per compound. This was obtained through in-source fragmentation at 3 increasing energy values. This approach allows monitoring specific ions and fragments at high resolution / low ppm mass accuracy, along with ion ratio calculations for each analyte. Specifically designed view settings within Chromeleon CDS facilitate data review in line with the SANTE guidelines through a stepwise approach of checking retention times, linearity, ion ratios and finally highlighting positively identified samples compared to regulatory values.  Other advantages related to the dual channel LC system will also be discussed.

Novel High Resolution Mass Spectrometry (HRMS) Quantitative Workflows for Pesticides and PFAS in Food Designed for Routine Testing Laboratories

Illegal pesticides have recently been found at licensed and unlicensed cannabis grow sites in California. These pesticides which are dispersed as chemical fumigants contain prohibited and uncharacterized chemicals posing risks to consumers of marijuana products. Seizures conducted at illicit marijuana grow sites throughout the state of California have revealed a pervasive use of illegal pesticides with a common reoccurrence of “Blue Bag Pesticides.” These products violate multiple laws and statutes regarding their labelling, chemical contents, and directions for use. This work discusses the current landscape of illegal pesticides in cannabis cultivation and the unmitigated exposure risks they pose to consumers of cannabis products.
The contents of unused “Blue Bag Pesticides” have been analyzed by gas chromatography- mass spectrometry (GC-MS), pyrolysis-GC-MS (Py-GC-MS), two-dimensional-GC-MS (GCxGC-MS), and liquid chromatography- tandem mass spectrometry (LC-MS/MS) to characterize their chemical compositions. Multiple techniques were undertaken to perform an in-depth chemical fingerprinting of these products. This work serves as a first step toward mitigating public exposure to these chemicals through cannabis materials and off-target exposures. This work also identifies vulnerabilities in the current regulatory requirements for residual pesticides testing, as set forth by the California Department of Cannabis Control. Subsequent analyses will investigate the direct relationship between seized pesticide composition, their use as fumigants, and the detectable pesticides present on marijuana plants.
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. This work was supported by LDRD project tracking code 25-ERD-025, tracking number LLNL-ABS-2004054.

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