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Over 10 years have passed since the Fukushima Daiichi Nuclear Power Plant accident. This study verifies the efficacy of longitudinal regulation on internal exposure doses and analyzes food group contributions to radiation doses using accumulated monitoring test results. The committed effective doses in 10,000 virtual persons from fiscal year (FY) 2012 to 2021, with and without regulation, were estimated as products of radioactivity concentrations randomly sampled from the test results, food intake, and dose coefficient. The distributed values of food intake rather than a mean value in dose estimation were assumed to reflect food intake variations and avoid underestimation of internal exposure doses for high-intake consumers. Furthermore, the ingestion of radioactive cesium from the calculation was analyzed per food group. The 95th percentile of the internal exposure dose (the dose of a “representative person”) was less than 1 mSv/year in both FYs. The regulation effect was substantial in FY 2012, and no noticeable difference in radiation doses was found between the regulation and no regulation conditions after FY 2016. Internal exposure doses decreased until approximately FY 2016 and then remained constant. It was also shown that not only radioactivity concentration but also food intake is a major factor affecting cesium intake. In summary, it was confirmed that Japan had ensured food safety regarding radioactive materials.

Background Naturally existing and human-produced heavy metals are released into the environment and cannot be completely decomposed by microorganisms, but they continue to accumulate in water and sediments, causing organisms to be exposed to heavy metals. Results This study designs and proposes heavy metal hazard decision trees for aquatic products, which are divided into seven categories including pelagic fishes, inshore fishes, other fishes, crustaceans, shellfish, cephalopods, and algae. Based on these classifications, representative fresh and processed seafood products are at the root of the heavy metal hazard decision trees. This study uses 2,107 cases of eating 556 cooked fresh or processed seafood product samples. The constructions of the proposed decision trees consist of 12 heavy metals, which include inorganic arsenic (iAs), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), strontium (Sr), thallium (Tl), and zinc (Zn). The heavy metal concentrations in cooked fresh and processed seafood product samples are subjected to a food safety risk assessment. Conclusions The results indicate the relationships among the seven categories of aquatic products, the relationships among 12 heavy metals in aquatic products, and the relationships among potential human health risks. Finally, the proposed heavy metal hazard decision trees for aquatic products can be used as a reference model for researchers and engineers.

The fresh-cut industry supplies the food market with healthy fresh fruit and vegetables and, in that way, may contribute to improve the nutritional status of the general population. On the other hand, over the last few years increasing concerns have been raised regarding the environmental impact of the fresh-cut industry, human health risks from exposure to disinfection by-products found in fresh-cut products and chlorine-based disinfection treatments during produce processing. This review provides a comprehensive view of the main interlinked aspects related to food safety and environmental impact of processing of fresh-cut vegetables. Advantages and downsides of the mainstream disinfection strategy, based on the use of chlorine-related disinfecting agents, along with some alternative treatments close to a wide commercial application, are discussed. Limitation in the application of these strategies to processing of organic fresh-cut produce are also highlighted, examining the specific environmental and food safety problems in the organic sector. Areas where lack of available information hinders at present a clear understanding of priorities of research and action are pointed out. Innovative conceptual tools are proposed to address these multiple and interlinking issues and to overcome limitations of currently available technologies. A comprehensive and multidisciplinary approach is suggested to move toward a more safe and environmentally sustainable production of fresh-cut products.

Grain processing products constitute an essential component of the human diet and are among the main sources of heavy metal intake. Therefore, a systematic assessment of risk factors and early-warning systems are vital to control heavy metal hazards in grain processing products. In this study, we established a risk assessment model to systematically analyze heavy metal hazards and combined the model with the K-means++ algorithm to perform risk level classification. We then employed deep learning models to conduct a multi-step prediction of risk levels, providing an early warning of food safety risks. By introducing a voting-ensemble technique, the accuracy of the prediction model was improved. The results indicated that the proposed model was superior to other models, exhibiting the overall accuracy of 90.47% in the 7-day prediction and thus satisfying the basic requirement of the food supervision department. This study provides a novel early-warning model for the systematic assessment of the risk level and further allows the development of targeted regulatory strategies to improve supervision efficiency.

Veterinary drug residues in animal-derived foods, including antimicrobials, anthelmintics, non-steroidal anti-inflammatory drugs, and hormonal agents, pose significant risks to food safety, human health, and antimicrobial resistance mitigation. Persistent contamination arises from interspecies pharmacokinetic variability, non-compliance with withdrawal periods, and inadequate analytical infrastructure, particularly in low- and middle-income countries. Established detection platforms such as liquid chromatography coupled with tandem mass spectrometry and enzyme-linked immunosorbent assays remain the foundation of regulatory monitoring, while emerging tools including CRISPR-Cas biosensors, nanomaterial-based electrochemical assays, and artificial intelligence assisted spectroscopic systems show promise but remain largely at the proof-of-concept or research stage and are not yet routinely applicable in regulatory enforcement. Thermal treatments, fermentation, and storage can modify drug stability, generate degradation products, and alter toxicological profiles, yet these transformations are rarely incorporated into surveillance strategies or Maximum Residue Limit determinations. This review synthesizes evidence across pharmacokinetic determinants, processing-induced residue transformations, analytical innovation, and international regulatory frameworks within a One Health and food safety risk assessment perspective. It argues that integrating processing-adjusted exposure data, harmonizing Maximum Residue Limit policies across jurisdictions, and strengthening analytical capacity in resource-limited settings are essential for equitable food safety governance and sustainable animal production systems.

Focusing on the importance of wild vegetables for local residents, this study aims to validate the effects of food regulations under the current criteria (e.g., 100 Bq/kg for general foods) established approximately a year after the Fukushima Dai-ichi Nuclear Power Plant accident. Over 2,500,000 monitoring tests were performed under the criteria until fiscal year (FY) 2020. We estimated changes in internal exposure dose using test results. The effective dose was estimated using the radioactive concentration randomly sampled from the results, food intake, and dose conversion factor. As a new attempt, dose estimation reflecting the intake of wild vegetables that may have irreplaceable value for local residents was conducted. The median, 95th, and 99th percentile of the estimated dose without reflecting the wild vegetables’ intake were 0.0485, 0.183, and 10.6 mSv/year, respectively, in the estimation with all test results (no regulation) and 0.0431, 0.0786, and 0.236 mSv/year, respectively, in the estimation with results within the standard limits (regulated) in FY2012. These doses decreased with time. Although estimated doses with or without the reflection of wild vegetables’ intake were similar, estimation that is more plausible is possible, particularly for a high percentile, by reflecting the wild vegetables’ intake. Radiation doses (regulated) were significantly less than 1 mSv/year in different FYs. In Japan, food regulation measures benefit food safety.

In Taiwan’s vegetarian community, which encompasses around 2.5 million individuals, the consumption of food preservatives and pesticides poses potential health risks that have yet to be thoroughly investigated. This study employs a duplicate diet methodology to evaluate these risks, analyzing all foods ingested over a 3-day period. The detection results of food preservatives show that the detection rate of benzoic acid is higher than that of sorbic acid, while dehydroacetic acid, p-hydroxybenzoic acid, and salicylic acid are not detected. At the same time, the food categories where benzoic acid and sorbic acid were detected include dried bean products (0.16 g/kg and 0.06 g/kg), other soy protein products (0.09 g/kg and 0.03 g/kg), and pickled vegetables (0.2 g/kg and 0.06 g/kg). In the detection results of pesticide residues, we found that the concentration of fipronil (0.003 ppm) detected in pickled vegetables is higher than the allowable standard for pesticide residues in Taiwan (0.001 ppm). The results of the food safety risk assessment show that the risks of vegetarians ingesting benzoic acid, sorbic acid, and fipronil are 13.2%ADI, 2.4%ADI, and 24.32%ADI, respectively, all of which are within acceptable ranges. However, the intake levels of benzoic acid and fipronil exceed the 10%ADI suggested by the Codex Alimentarius Commission as a cautionary benchmark. Given these findings, it is recommended that future research should focus on food categories with significant contributions to overall exposure, particularly other soy protein products and pickled vegetables, and maintain vigilance regarding fipronil exposure among vegetarians. Graphical Abstract

Campylobacter has consistently posed a food safety issue in broiler meat. This study aimed to create a quantitative microbial risk assessment model from retail to consumption, designed to evaluate the seasonal risk of campylobacteriosis associated with broiler meat consumption in the United States. To achieve this, data was gathered to build distributions that would enable us to predict the growth of Campylobacter during various stages such as retail storage, transit, and home storage. The model also included potential fluctuations in concentration during food preparation and potential cross-contamination scenarios. A Monte Carlo simulation with 100,000 iterations was used to estimate the risk of infection per serving and the number of infections in the United States by season. In the summer, chicken meat was estimated to have a median risk of infection per serving of 9.22 × 10−7 and cause an average of about 27,058,680 infections. During the winter months, the median risk of infection per serving was estimated to be 4.06 × 10−7 and cause an average of about 12,085,638 infections. The risk assessment model provides information about the risk of broiler meat to public health by season. These results will help understand the most important steps to reduce the food safety risks from contaminated chicken products.

In this study, the metal accumulation (Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) and toxicity status in the brain, eye, skeleton, and skin tissues of Carassius gibelio species of fish fed with wastewater treated with secondary treatment were investigated, and the usability of wastewater and fish for human food against climate change and food crisis was investigated. Treated wastewater (TWW) complied with Turkish aquaculture standards, but was not found to comply with drinking and irrigation water standards. The national and international food standards for metal concentrations varied. Cd and Pb were found to be high in all tissues according to all standards, but Cu, Cr, Mn, Ni, Fe, and Zn were found to vary according to tissues. It was determined that Fe and Zn concentrations were generally higher than those of the other metals in all tissues. The orders of the metals according to their annual mean concentrations were: Zn > Fe > Mn > Pb > Ni > Cr > Cu > Cd in skeletons; Zn > Fe > Pb > Ni > Cr > Cu > Mn > Cd in skins; Zn > Fe > Cu > Pb > Mn > Ni > Cr > Cd in eyes; and Fe > Zn > Pb > Cu > Cr > Ni > Mn > Cd in brains. Concentrations in tissues were higher in the summer months, but seasonal changes were statistically insignificant (p ≥ 0.05). According to Principal Component Analysis (PCA), ANOVA, and Pearson correlation analysis, it was statistically determined that Cd, Cr, Cu, Mn, Ni, and Pb, and the other two elements (Fe and Zn), showed similar accumulation characteristics among themselves. According to transfer factor (TF) calculations, it was determined that there was bioaccumulation (TF > 1) in all tissues for all metals throughout the year, but according to hazard coefficient (HQ) values, only Pb was determined to be >1 and carcinogenic. As a result, after the wastewater is treated with different advanced treatment methods and brought to potable water standards, the accumulation of metals and other micropollutants in the tissues of different species of fish should be monitored for many years.

Contaminants are the critical targets of food safety supervision and risk assessment. In existing research, food safety knowledge graphs are used to improve the efficiency of supervision since they supply the relationship between contaminants and foods. Entity relationship extraction is one of the crucial technologies of knowledge graph construction. However, this technology still faces the issue of single entity overlap. This means that a head entity in a text description may have multiple corresponding tail entities with different relationships. To address this issue, this work proposes a pipeline model with neural networks for multiple relations enhanced entity pairs extraction. The proposed model can predict the correct entity pairs in terms of specific relations by introducing the semantic interaction between relation identification and entity extraction. We conducted various experiments on our own dataset FC and on the open public available data set DuIE2.0. The results of experiments show our model reaches the state-of-the-art, and the case study indicates our model can correctly extract entity-relationship triplets to release the problem of single entity overlap.