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Over the past decade, seafood mislabeling has been increasingly documented, raising public concern over the identity, safety, and sustainability of seafood. Negative outcomes from seafood mislabeling are suspected to be substantial and pervasive as seafood is the world’s most highly traded food commodity. Here we provide empirical systems-level evidence that enabling conditions exist for seafood mislabeling in the United States (US) to lead to negative impacts on marine populations and support consumption of products from poorly managed fisheries. Using trade, production, and mislabeling data, we determine that substituted products are more likely to be imported than the product listed on the label. We also estimate that about 60% of US mislabeled apparent consumption associated with the established pairs involves products that are exclusively wild caught. We use these wild-caught pairs to explore population and management consequences of mislabeling. We find that, compared to the product on the label, substituted products come from fisheries with less healthy stocks and greater impacts of fishing on other species. Additionally, substituted products are from fisheries with less effective management and with management policies less likely to mitigate impacts of fishing on habitats and ecosystems compared with the label product. While we provide systematic evidence of environmental impacts from food fraud, our results also highlight the current challenges with production, trade, and mislabeling data, which increase the uncertainty surrounding seafood mislabeling consequences. More integrated, holistic, and collaborative approaches are needed to understand mislabeling impacts and design interventions to minimize mislabeling.

Shark populations worldwide are declining rapidly, primarily due to overfishing, habitat degradation, and mislabeling of seafood products, which exacerbates their exploitation and conservation challenges. This study investigates the presence of shark meat being sold under false labels as fish species in Ecuadorian markets, spanning both coastal and highland regions. Using primers derived from the nuclear ribosomal ITS2 region for molecular identification, 97 samples sold as fish meat in Ecuadorian markets were analyzed for the presence or absence of shark DNA. The results revealed that 47.42% of the samples corresponded to shark meat. These samples came from cities in the highlands (Ambato, Cuenca, Ibarra and Quito). No shark meat was identified in the samples from coastal cities (Guayaquil and Manta). Four shark species were identified: Alopias pelagicus (Endangered), Carcharhinus falciformis (Vulnerable), Sphyrna zygaena (Vulnerable), and Prionace glauca (Near Threatened). These findings highlight the ongoing sale of threatened shark species under misleading labels in the highlands region of Ecuador, posing significant risks to marine biodiversity and consumer rights. The study underscores the need for robust traceability systems, routine monitoring, and public education to combat seafood fraud and support shark conservation efforts.

The Food suitable for babies during the weaning period is known as weaning foods. It plays a crucial role in nutrition, growth and development of children. The availability of ready-to-eat, commercial baby foods help working parents by providing quick and reliable feeding options However, discrete incidences reported on mislabeling in such products are matter of concern. Mislabeling in weaning foods may lead to serious risks, such as obesity from over consumption of carbohydrate and fat or cognitive issues caused by harmful additives. Current popularity and ever-increasing demand for such products make weaning food market highly vulnerable towards mislabeling and adulteration. Therefore, it is important to assess the occurrence and severity of mislabeling in weaning foods, test the accuracy of their nutritional labels and check the presence of harmful preservatives and colorants. In this project, a total of 110 such products, available on online platforms, were manually screened to detect any forms of labeling violations. Ten products, which showed maximum occurrence of labeling violations, were selected for quantitative estimation of fat, carbohydrate and detection of unpermitted colors and preservatives such as Metanil yellow, Malachite green, Butylated Hydroxy Anisole (BHA) and Butylated Hydroxy Toluene (BHT). 70% of the 110 screened products showed mislabeling in one or multiple forms. Printed and estimated values of carbohydrate and fat did not match in majority of the tested samples. Content variation was detected as high as 114% (for carbohydrate) and 400% (for fat). BHA was present in 40% samples, revealing the lack of chemical food safety in the ready-to-eat weaning foods.

Background and objective: Commercially available cannabidiol (CBD) products are increasingly being used for medicinal purposes, including for the treatment of various neurological conditions, but there are growing concerns around adherence to quality control measures that protect consumers. This study was conducted to assess the purity and label accuracy of commercially available CBD products. Methods: Commercially available CBD products were chosen from the open stream of commerce in the United States based on formulations as a tincture, gummy, vape, or topical product. Cannabinoid concentrations were analyzed to verify label accuracy including “full spectrum,” “broad spectrum,” and “CBD isolate” claims on the product label. Analysis for the presence of contaminants included evaluation for heavy metals, pesticides, and residual solvents. Labeled and actual total amounts of CBD and levels of impurities such as heavy metals, residual solvents, and pesticides were measured. Results: A total of 202 CBD products (100 tinctures, 48 gummies, 34 vape products, and 20 topicals) were chosen to represent a broad sample in the United States. Of the products tested (full spectrum, n = 84; broad spectrum, n = 28; CBD isolate, n = 37), 26% did not meet the definition for product type claimed on the packaging. The majority of products (74%) deviated from their label claim of CBD potency by at least 10%. Heavy metals were detected 52 times across 44 of the 202 products tested, with lead being the most prevalent heavy metal. Residual solvents were detected 446 times across 181 of 202 products, with the highest concentrations reported for hexane, m/p-xylene, methanol, and o-xylene. Of 232 pesticides tested, 26 were found 55 times across 30 products. A total of 3% of heavy metals, 1% of residual solvents, and 1% of pesticides violated >1 regulatory threshold. Discussion: This study demonstrated that the majority of commercially available CBD products tested within the current study are inaccurately labeled. Heavy metals, residual solvents, and pesticides were found in several products, some of which violated regulatory thresholds. Thus, uniform compliance with CBD quality control measures is lacking and raises consumer protection concerns. Improved regulatory oversight of this industry is recommended.

Entropic outlier sparsification (EOS) is proposed as a cheap and robust computational strategy for learning in the presence of data anomalies and outliers. EOS dwells on the derived analytic solution of the (weighted) expected loss minimization problem subject to Shannon entropy regularization. An identified closed-form solution is proven to impose additional costs that depend linearly on statistics size and are independent of data dimension. Obtained analytic results also explain why the mixtures of spherically symmetric Gaussians—used heuristically in many popular data analysis algorithms—represent an optimal and least-biased choice for the nonparametric probability distributions when working with squared Euclidean distances. The performance of EOS is compared to a range of commonly used tools on synthetic problems and on partially mislabeled supervised classification problems from biomedicine. Applying EOS for coinference of data anomalies during learning is shown to allow reaching an accuracy of 97% ± 2% when predicting patient mortality after heart failure, statistically significantly outperforming predictive performance of common learning tools for the same data.

We propose a new active learning method for classification, which handles label noise without relying on multiple oracles (i.e., crowdsourcing). We propose a strategy that selects (for labeling) instances with a high influence on the learned model. An instance x is said to have a high influence on the model h , if training h on x (with label y = h ( x ) ) would result in a model that greatly disagrees with h on labeling other instances. Then, we propose another strategy that selects (for labeling) instances that are highly influenced by changes in the learned model. An instance x is said to be highly influenced, if training h with a set of instances would result in a committee of models that agree on a common label for x but disagree with h ( x ). We compare the two strategies and we show, on different publicly available datasets, that selecting instances according to the first strategy while eliminating noisy labels according to the second strategy, greatly improves the accuracy compared to several benchmarking methods, even when a significant amount of instances are mislabeled.

The United States’ current Seafood Import Monitoring Program (SIMP) and a potential extension are undergoing review, yet quantitative evaluation of the current program is lacking. The SIMP is a traceability program aimed at reducing imports of seafood products that are of illegal, unreported, and unregulated (IUU) origin or associated with seafood fraud. We conducted a quantitative examination of the SIMP’s current scope and design by synthesizing publicly available trade data along with measures of IUU fishing and seafood mislabeling. We found prioritized shipments amounted to 33% of 2016 imported tonnage. The SIMP species groups had higher IUU scores and mislabeling rates relative to non-SIMP groups, but the difference was consistent with random prioritization suggesting potential benefits from program expansion. Furthermore, two-thirds of imported volume lacked a mislabeling rate and 5% lacked species information, underlining the urgent need for improved open-access data on globalized seafood supply chains.

Chicken is known to be the most common meat type involved in food mislabeling and adulteration. Establishing a method to authenticate chicken content precisely and identifying chicken breeds as declared in processed food is crucial for protecting consumers’ rights. Categorizing the authentication method into their respective omics disciplines, such as genomics, transcriptomics, proteomics, lipidomics, metabolomics, and glycomics, and the implementation of bioinformatics or chemometrics in data analysis can assist the researcher in improving the currently available techniques. Designing a vast range of instruments and analytical methods at the molecular level is vital for overcoming the technical drawback in discriminating chicken from other species and even within its breed. This review aims to provide insight and highlight previous and current approaches suitable for countering different circumstances in chicken authentication.

Background Mislabeled, contaminated or poorly performing samples can threaten power in methylation microarray analyses or even result in spurious associations. We describe a set of quality checks for the popular Illumina 450K and EPIC microarrays to identify problematic samples and demonstrate their application in publicly available datasets. Methods Quality checks implemented here include 17 control metrics defined by the manufacturer, a sex check to detect mislabeled sex-discordant samples, and both an identity check for fingerprinting sample donors and a measure of sample contamination based on probes querying high-frequency SNPs. These checks were tested on 80 datasets comprising 8327 samples run on the 450K microarray from the GEO repository. Results Nine hundred forty samples were flagged by at least one control metric and 133 samples from 20 datasets were assigned the wrong sex. In a dataset in which a subset of samples appear contaminated with a single source of DNA, we demonstrate that our measure based on outliers among SNP probes was strongly correlated (> 0.95) with another independent measure of contamination. Conclusions A more complete examination of samples that may be mislabeled, contaminated, or have poor performance due to technical problems will improve downstream analyses and replication of findings. We demonstrate that quality control problems are prevalent in a public repository of DNA methylation data. We advocate for a more thorough quality control workflow in epigenome-wide association studies and provide a software package to perform the checks described in this work. Reproducible code and supplementary material are available at https://doi.org/10.5281/zenodo.1172730 .

Global fish consumption is increasing in tandem with population growth, resulting in the dilemma of overfishing. Overfished high-value fish are often replaced with other fish in markets. Therefore, the accurate identification of fish products in the market is important. In this study, full-DNA and mini-DNA barcoding were used to detect fish product fraud in Guiyang, Guizhou Province, People's Republic of China. The molecular results revealed that 39 (20.42%) of the 191 samples were inconsistent with the labels. The percentages of mislabeling of fresh, frozen, cooked, and canned fish products were 11.70, 20.00, 34.09, and 50.00%, respectively. The average Kimura two-parameter distances of mini-DNA barcoding within species and within genera were 0.56 and 6.42%, respectively, and those of full-DNA barcoding were 0.53 and 7.25%, respectively. Commercial fraud was evident in this study; most high-priced fish were replaced with low-priced fish with similar features. Our findings indicate that DNA barcoding is an effective tool for identifying fish products and could be used to enhance transparency and fair trade in domestic fisheries.