United States

Co-author:Arun Krishnamurthy &amp; 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.

2025 AOAC INTERNATIONAL Annual Meeting &amp; Exposition

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

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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2025 AOAC INTERNATIONAL Annual Meeting & Exposition

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

Mushroom dietary supplements are frequently marketed in concentrated forms, emphasizing the enrichment of bioactive compounds and the removal of inert or less beneficial components. This presentation demonstrates how complementary analytical techniques can be applied to assess the composition, integrity, and quality of concentrated mushroom extracts, compared to their source materials and across commercial counterparts.
By integrating high-performance thin-layer chromatography (HPTLC), high-performance liquid chromatography (HPLC), and enzymatic glucan assays (e.g., Megazyme K-YBGL), we systematically examined concentrated extracts and blended products derived from commonly marketed functional fungi, such as Ganoderma lingzhi (reishi mushroom), Inonotus obliquus (chaga canker), Hericium erinaceus (lion’s mane mushroom and mycelium), Cordyceps militaris (cordyceps mushroom), and blends. Analytical outcomes, including detailed chemical profiles, β-glucan content, and levels of primary active compounds (e.g., ganoderic acids, inotodiol, cordycepin), were correlated with processing methodologies (e.g., water and alcohol extractions), stated concentration ratios (e.g., 8:1, 16:1), and the compositions of the unprocessed starting materials.
The findings reveal significant variability among commercial concentrated extracts, with notable discrepancies between claimed concentration ratios and analytical results. Factors such as solvent choice, extraction conditions, and raw material significantly influence extract quality. Robust sampling strategies and authentic reference materials (traceable to source farms, produced under GMP conditions, and verified against source materials to confirm species identity) are emphasized as essential to scientifically rigorous sampling frames. By integrating complementary analytical techniques, this work offers a practical framework for evaluating the true composition and quality of concentrated mushroom supplements, supporting more informed regulatory decisions and consumer trust.

Evaluating Concentrated Mushroom Extracts: Complementary Analytical Methods for Accurate Quality Assessment

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.

Stay Clean LC-MS/MS for Determination of Antibiotics and Veterinary Drugs in Milk

A collaborative study was conducted to validate the suitability of an LC-MS/MS method for quantifying 128 residual veterinary drugs in six food matrices: honey, milk, egg, beef, pork liver, and lard. Twelve laboratories participated, including seven governmental, four commercial, and one incorporated foundation. Standard compounds, extraction powders, and matrices were provided to minimize material-related variations. Laboratories spiked samples with 5 ppb, 25 ppb, and 50 ppb levels, and reported recoveries along with chromatograms. At the 5 ppb spiking level, a significant number of compounds showed recoveries below 60% or above 125%, with the highest occurrence in milk (82 compounds), followed by honey (70), lard (68), pork liver (59), beef (54), and egg (49). However, at the 25 ppb level, the number of compounds with recoveries outside the range of 70%–120% significantly decreased, with pork liver showing the highest number (8  compounds), followed by honey (4), and only 2 compounds in other matrices. At the 50 ppb level, the numbers outside the range further decreased, with pork liver (5  compounds), beef (4), egg (4), lard (3), milk (1), and honey (1). These results indicate that the LC-MS/MS method performs optimally at a limit of quantification (LOQ) of 25 ppb, as the number of compounds with unacceptable recoveries significantly decreases at this level. By setting the LOQ at 25 ppb and filtering out poorly performing compounds, this method can be effectively optimized to serve as a reliable and robust approach for veterinary drug residue analysis in complex food matrices.

Collaborative Test of an LC-MS/MS Method for the Determination of 128 Veterinary Drugs in Six Food Matrices

Biofilm formation on food contact surfaces poses significant challenges to food safety due to increased resistance to disinfectants and risk of cross-contamination. This study aims to develop a machine learning–enhanced nanosensor array for the classification and prediction of bacterial biofilms on food processing surfaces, particularly stainless steel. A fluorescence-based sensor array composed of 2D nanoparticles and ssDNA probes was designed to capture compositional and developmental features of biofilms formed by four pre-screened species: Escherichia coli, Staphylococcus aureus, Salmonella enterica, and Pseudomonas fluorescens. Biofilms were cultivated in 96-well plates and on stainless steel coupons across three timepoints (days 1, 3, and 5). Sensor responses were recorded and analyzed using three machine learning algorithms—random forest (RFC), support vector classification (SVC), and multilayer perceptron (MLP)—to classify species and biofilm maturity (12 total classes). Data preprocessing included normalization, imputation, and stratified train-test splitting. Model performance was evaluated using accuracy, F1 score, and ROC-AUC with a one-vs-rest strategy. Preliminary results indicate species-specific fluorescence patterns and distinct clustering in PCA analysis. The sensor array is expected to achieve over 90% classification accuracy, with slightly lower performance for early-stage biofilms. This approach enables rapid, label-free biofilm identification and maturity assessment, offering a scalable solution for real-time monitoring of microbial contamination in food environments. The findings support the integration of advanced sensing and machine learning techniques for biofilm control strategies in food safety applications.

Machine Learning–Integrated Nanosensor Array for Predictive Biofilm Classification on Food Processing Surfaces

Introduction 
Food allergens are proteins that can trigger an immune response in allergic individuals such as nausea, vomiting, asthma, or in extreme cases anaphylactic shock.  One of the most common food allergies in children is a milk allergy.  Typically caused by cow’s milk, this allergy can also come from sheep, goats, buffalo, and other mammals.  Symptoms include wheezing, vomiting, hives, digestive problems, and even anaphylaxis.  Fortunately, studies have shown that most children will outgrow a milk allergy.
 
Objective 
To evaluate the performance of two rapid lateral flow milk assays on various market basket products. 
 
Materials and Methods 
10 milk-containing (n=6) and non-milk-containing (n=10) products were analyzed.  Milk containing samples were analyzed neat and diluted to extinction.  Non milk containing samples were spiked using a qualified milk spike stock increasing in 1 ppm increments until a positive result was achieved. In addition, milk spikes were swabbed off a stainless-steel surface (n=3 per level) to verify the detection level of a swab for each test kit.  
 
Results 
Both rapid milk test kits performed exceptionally well.  For non-milk containing samples, spiking data showed recovery down to 5 ppm total food (1.8 ppm protein) on all samples.  With market basket samples containing milk, the test kits detected milk below kit detection levels to ensure kit sensitivity within various matrices, down to 3 ppm total food (1.1 ppm protein).  In addition, the surface swabbing analysis indicated that the test kits can detect low levels of milk on food contact surfaces.   
 
Significance 
To ensure the safety of consumers with a milk allergy, it is of high importance to have a quick and reliable test for food manufacturers to use at their facilities.  The data shows that Neogen has two such rapid test kits allowing food manufacturers to have cost effective options when analyzing for milk allergens. 

An Evaluation of Rapid Lateral Flow Assays for Total Milk Detection

Per- and polyfluoroalkyl substances (PFAS) are known potential contaminants in eggs which are commonly consumed by humans. Recently, the European Union has set regulatory limits for four PFAS compounds (PFOA, PFOS, PFNA, PFHxS) in eggs, and interest in testing food for PFAS is growing in the United States. Individuals who consume eggs from backyard chickens may assume they are safer or healthier yet remain unaware of potential PFAS contamination due to unknown exposures in the chickens feed, bedding, water, and environment – potential routes through which PFAS can enter the eggs they produce.
This study compares PFAS concentrations in cage-free store-bought eggs and eggs from backyard chickens, which may be exposed to a broader range of environmental factors and dietary variations. A workflow for analysis of this challenging matrix will be presented which utilizes an automated sample preparation method that reduces analyst involvement, limits variability, and enhances throughput. The automated sample extraction process takes less than 15 minutes per sample, and the automated solid-phase extraction (SPE) system can process up to 8 samples simultaneously in under 70 minutes. This method uses dual-phase Graphitized Carbon Black and Weak Anion Exchange SPE cartridges to clean up challenging samples to ensure precise and repeatable results across samples. The automation of both the sample preparation and SPE limits variability, reduces method time, and increases throughput. Ultimately, this study aims to provide a clearer understanding of PFAS contamination in both commercially and locally sourced eggs, contributing valuable insights for public health and food safety policies.

Cracking the Challenge: Automated Extraction of PFAS in Backyard and Store-Bought Cage-Free Eggs using LC/MS-MS and dual-phase WAX/GCB Cartridges

The D3 Array Combined assay (PTM 012201) is a qualitative test for the detection of Aspergillus flavus, Aspergillus fumigatus, Aspergillus niger, Aspergillus terreus, Salmonella species, and a broad range of Shiga toxin-producing Escherichia coli (STEC) serogroups in cannabis matrixes. The test involves extraction of nucleic acids from samples followed by preamplification, Loci polymerase chain reaction (PCR), and subsequent Labeling PCR. The PCR amplification product is then hybridized to a DNA microarray. Detection of bacterial and fungal pathogens is determined by fluorescence. 
The objective of this study was to validate the D3 Array Combined method with addition of an optional enrichment step in dried cannabis flower, with the use of the Live/Dead Enrichment reagent. The method modification was validated with optional enrichment time points at 0, 2, and 24 hours.
The evaluation followed study designs from the AOAC Microbiology guidelines and AOAC Standard Method Performance Requirements (SMPRs®) 2019.001, 2020.02, and 2020.012. The modified method was compared to SMPR confirmation procedures and an unpaired Aspergillus consensus method. The results were evaluated utilizing a probability of detection (POD) statistical model.
Results showed no statistically significant difference, at the 95% and 90% confidence intervals, between the candidate method presumptive D3 Array Combined positive results and the confirmed cultural positive results at all contamination levels and enrichment time points.
This study provides data that demonstrate the PathogenDx D3 Array Combined is a reliable method for detection of Aspergillus flavus, Aspergillus fumigatus, Aspergillus niger, Aspergillus terreus, Salmonella species, and a broad range of STEC serogroups in dried cannabis flower from 0-24 hours enrichment, for all these organisms. This highly sensitive method provides results within hours of sample acquisition, compared to qPCR or cultural methods where results are obtained in 2-7 days. This method also provides the option of enrichment, providing compliance options for all cannabis testing state regulations.

Unenriched vs Enriched AOAC Performance Test MethodSM 012201 for the Detection of Aspergillus, Salmonella, and Shiga Toxin-Producing Escherichia coli in Dried Cannabis Flower

Per- and polyfluoroalkyl substances (PFAS) are synthetic fluorinated chemicals widely used in industrial and consumer products. Many are environmentally persistent and toxic. Food contact materials (FCM) containing PFAS pose human health risks via ingestion, inhalation, and environmental exposure. In response, twelve U.S. states have implemented regulations banning food packaging with total organic fluorine (TOF) levels above 100 ppm.
Conventional targeted PFAS analysis by liquid chromatography-mass spectrometry (LC-MS) may underestimate total fluorine content due to limited analyte coverage. To address this, we developed a method using a new combustion-ion chromatography (C-IC) system for comprehensive TOF determination in FCM. The method involves sample combustion under oxygen and argon, collection of combustion gases in water, followed by ion separation using anion exchange chromatography with conductivity detection. Total fluorine (TF) is measured by combusting 10–15 mg of FCM, while total inorganic fluorine (TIF) is quantified via water extraction and direct injection. TOF is calculated as TF minus TIF.
This method is implemented on the Thermo Scientific™ Cindion™ C-IC System, which minimizes PFAS contamination sources and includes a compact Z-fold combustion tube alongside full system control via Thermo Scientific™ Chromeleon™ Chromatography Data System (CDS) software. A notable advantage of this new system is the 2-in-1 system configuration for seamless switching between C-IC and standalone IC modes. This provides flexibility, allowing for measurement of TF and TIF easily. Importantly, this method supports compliance with evolving PFAS regulatory limits in FCM, providing a means for easy sample screening and determination of TF, TIF, and TOF in many sample matrices.

Determination of total PFAS in Food Contact Materials using a new combustion- ion chromatography system for total organic fluorine (TOF) analysis

Listeria species are significant foodborne pathogens with a major impact on public health. Detecting Listeria species and L. monocytogenes in foods before it can cause infection is crucial. Current detection methods use a 25 g food sample, but testing larger sample sizes has benefits such as increased sensitivity, reduced cost per test by pooling smaller samples, and meeting regulatory requirements.
This study aimed to validate the performance of the Thermo Scientific™ SureTect™ Listeria monocytogenes PCR Assay and Thermo Scientific™ SureTect™ Listeria species PCR Assay using a 125 g sample size for dairy and multi-component foods. The studies followed ISO 16140-2:2016 and AOAC Appendix J guidelines. In the unpaired study, 125 g samples were tested using the SureTect methods, and 25 g samples were tested using the reference methods. For dairy products, the SureTect method was compared to ISO 11290-1:2017 and FDA BAM Chapter 10 reference methods. For multi-component foods, the SureTect methods were compared to ISO 11290-1:2017 and USDA FSIS MLG 8.15 reference methods.
The sensitivity of the SureTect Listeria monocytogenes method was 91.2%, compared to 90.0% for the reference method. For SureTect Listeria species, it was 91.0%, versus 88.7% for the reference method. The RLOD for dairy and multi-component foods was 0.843 and 1.935 respectively for L. monocytogenes, and 0.843 and 0.780 respectively for Listeria species.
The SureTect Listeria PCR Assays are accurate and reliable for detecting Listeria species and L. monocytogenes from large sample sizes of dairy and multi-component foods. 

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