Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (758 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
The presence in food of harmful, unpalatable, or otherwise objectionable foreign substances, e.g. chemicals, microorganisms or diluents, before, during, or after processing or storage.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Contamination, Food; Contaminations, Food; Food Contaminations; Food Adulteration; Adulteration, Food; Adulterations, Food; Food Adulterations.
Where it fits in the MeSH classification
- Broader topics
- Food Safety; Environmental Pollution
- Narrower topics
- Food Contamination, Radioactive; Food Microbiology; Food Parasitology
Research landscape
PubMed indexes 92,931 records tagged with the MeSH heading “Food Contamination” (counts retrieved 2026-10-31). 21,485 of them (23.1%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 29,331 records.
Reviews account for 9,465 records (10.2%) and meta-analyses for 267. Randomized controlled trials number 175 (0.2%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 21,026 |
| 2000-2009 | 21,089 |
| 2010-2019 | 29,331 |
| 2020 onward | 21,485 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 9,465 | 10.2% |
| Meta-analyses | 267 | 0.3% |
| Randomized controlled trials | 175 | 0.2% |
Method: PubMed query "Food Contamination"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-31.
Reading the evidence in public health and nutrition
Population and nutrition studies mostly observe what people eat, breathe or are exposed to and then look for links with health, which shows association and not proof of cause. Self-reported diet is imprecise, people who eat or live differently also differ in other ways, and effects measured across a population may not apply to one person. Check the study design, how exposure was measured, what else was accounted for and the size of the effect, not only whether it was statistically significant.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Chloramphenicol Residue Analysis in Food and Environmental Matrices: Regulatory Framework and Advances in Trace-Level Determination | 2026 | Review article Design not recorded in structured metadata | 3 | cc by PMID 42123807 · DOI · PMC13165133 |
| Optimized SPE-UPLC-MS/MS Method for Sensitive Determination of Cereulide in Complex InfantFormula Matrices | 2026 | Research article Design not recorded in structured metadata | 2 | cc by PMID 42188624 · DOI · PMC13211625 |
| A two-stage hierarchical support vector machine framework detects roasted coffee adulterants through principal component analysis of hyperspectral imaging data | 2026 | Research article Design not recorded in structured metadata | 1 | cc by PMID 42426150 · DOI · PMC13350853 |
Chloramphenicol Residue Analysis in Food and Environmental Matrices: Regulatory Framework and Advances in Trace-Level Determination [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Chloramphenicol is a broad-spectrum antimicrobial agent whose use in food-producing animals is prohibited in many countries due to its association with severe adverse effects, including idiosyncratic aplastic anemia and genotoxicity.
- Closing excerpt
- Finally, current regulatory frameworks and analytical challenges related to zero-tolerance policies are discussed, emphasizing the importance of robust and validated analytical methods for effective monitoring and food safety assurance.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (214 words)
Chloramphenicol is a broad-spectrum antimicrobial agent whose use in food-producing animals is prohibited in many countries due to its association with severe adverse effects, including idiosyncratic aplastic anemia and genotoxicity. Despite these restrictions, chloramphenicol residues continue to be detected in food products, environmental compartments, and biological matrices, highlighting the need for reliable and sensitive analytical monitoring. This review provides a comprehensive overview of current analytical strategies for the detection of drugs in food and environmental samples, covering screening and confirmatory techniques, sample preparation approaches, and regulatory aspects. Rapid screening methods, such as enzyme-linked immunosorbent assays (ELISAs), lateral flow immunoassays (LFIAs), and biosensors based on antibodies, aptamers, and molecularly imprinted polymers, enable fast and cost-effective preliminary detection. Recent advances in nanomaterials and signal amplification strategies, including fluorescent reporters and surface-enhanced Raman scattering (SERS), have significantly improved sensitivity and assay performance. However, confirmatory methods based on liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) remain the reference standard due to their superior selectivity, sensitivity, and quantitative reliability. Attention is given to sample preparation workflows, including QuEChERS-based protocols and microextraction techniques, which enable efficient analysis of complex matrices. Finally, current regulatory frameworks and analytical challenges related to zero-tolerance policies are discussed, emphasizing the importance of robust and validated analytical methods for effective monitoring and food safety assurance.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42123807 · DOI · PMC13165133
Optimized SPE-UPLC-MS/MS Method for Sensitive Determination of Cereulide in Complex InfantFormula Matrices [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Cereulide is a heat-stable cyclic depsipeptide toxin produced by Bacillus cereus and is responsible for foodborne emetic syndrome.
- Closing excerpt
- This method provides a reliable analytical tool for the monitoring of cereulide in infant formula and contributes to improved food safety surveillance and exposure risk assessment.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (288 words)
Cereulide is a heat-stable cyclic depsipeptide toxin produced by Bacillus cereus and is responsible for foodborne emetic syndrome. Recent reports of Bacillus cereus contamination and cereulide occurrence in infant formula have raised increasing food safety concerns. Due to the immature immune and metabolic systems of infants, exposure to cereulide through contaminated formula may lead to potential health risks. However, direct application of existing cereulide analytical methods to infant formula remains challenging because of the unique processing technologies, encapsulated nutrients, and variable matrix composition of this product category, which may hinder toxin release and cause significant matrix interference. In practical analysis, inter-laboratory comparisons revealed that existing methods exhibited relatively large deviations and insufficient sensitivity, making them not specifically optimized for infant formula matrices. The present study was motivated by the need for a matrix-specific, sensitive, and reliable analytical method for cereulide determination in infant formula. In this study, a method based on solid-phase extraction coupled with ultra-performance liquid chromatography-tandem mass spectrometry (SPE-UPLC-MS/MS) was developed and validated. To improve the applicability of cereulide analysis to infant formula, this method incorporates a hydration-assisted extraction step tailored to infant formula, which increased the detected cereulide response by approximately fourfold, together with optimized SPE clean-up and improved chromatographic conditions to reduce matrix effects and enhance quantitative reproducibility. The method showed good linearity (0.1-10 μg·L-1, R2 > 0.999), low values for limit of detection (LOD) (0.03 μg·kg-1) and limit of quantification (LOQ) (0.1 μg·kg-1), and acceptable recoveries (94.4-110.3%) with RSDs below 3.7%. The developed method was successfully applied to commercial infant formula samples, and cereulide-positive samples were identified. This method provides a reliable analytical tool for the monitoring of cereulide in infant formula and contributes to improved food safety surveillance and exposure risk assessment.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42188624 · DOI · PMC13211625
A two-stage hierarchical support vector machine framework detects roasted coffee adulterants through principal component analysis of hyperspectral imaging data [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Economically motivated adulteration (EMA) of roasted coffee presents a critical challenge to global market integrity and consumer safety, specifically regarding the surreptitious inclusion of high-risk allergens like barley and soybeans.
- Closing excerpt
- By providing pixel-wise, automated risk assessments, this work establishes a data-driven proof-of-concept for 'Smart Food Safety' systems, highlighting the potential for forensic authentication in future industrial quality control environments.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (256 words)
Economically motivated adulteration (EMA) of roasted coffee presents a critical challenge to global market integrity and consumer safety, specifically regarding the surreptitious inclusion of high-risk allergens like barley and soybeans. The detection of such contaminants is historically hindered by the Maillard reaction, a thermal convergence during roasting that renders adulterants visually and spectrally indistinguishable from the coffee matrix. To address this forensic gap, this study presents a targeted hyperspectral imaging (HSI) framework (400-1000 nm) integrated with a two-stage hierarchical Support Vector Machine (SVM) designed to decouple detection from specific biological diagnosis. A core contribution of this methodology is the implementation of Principal Component Analysis (PCA) to resolve spectral redundancy across the 128-band hypercube. By distilling the data into two primary components capturing over 92% of the cumulative variance, the framework establishes a "spectrochemical bridge" that isolates hidden chromatic and biochemical variances invisible to traditional RGB sensors. This high-significance feature space allows the SVM to overcome the "Euclidean trap" inherent in unsupervised clustering, which frequently suffers from "class collapse" in roasted materials. Experimental results demonstrate that the hierarchical pipeline achieves an optimal overall accuracy of 88.6% and a Kappa coefficient of 0.378. The system attained high reliability during the Stage-1 binary screening, achieving an F1-score of 0.922 to protect the primary coffee matrix, while the Stage-2 multi-class model successfully mapped the spatial distribution of the highly camouflaged allergens. By providing pixel-wise, automated risk assessments, this work establishes a data-driven proof-of-concept for 'Smart Food Safety' systems, highlighting the potential for forensic authentication in future industrial quality control environments.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42426150 · DOI · PMC13350853
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Worldwide Epidemiology of Salmonella Serovars in Animal-Based Foods: a Meta-analysis [4]
- Research article Large-scale genome-wide analysis links lactic acid bacteria from food with the gut microbiome [5]
- Research article Population attributable risk of aflatoxin-related liver cancer: systematic review and meta-analysis [6]
- Research article Microplastic Contamination of Seafood Intended for Human Consumption: A Systematic Review and Meta-Analysis [7]
- Research article A Meta-Analysis of Major Foodborne Pathogens in Chinese Food Commodities Between 2006 and 2016 [8]
- Research article Investigating the Determinants of Toxoplasma gondii Prevalence in Meat: A Systematic Review and Meta-Regression [9]
References
- Aresta AM, De Vietro N, Mancini G, Zambonin C. Chloramphenicol Residue Analysis in Food and Environmental Matrices: Regulatory Framework and Advances in Trace-Level Determination. . 2026. PMID 42123807 · DOI · PMC13165133
- Zhou Z, Wang Z, Mu C, Qi Y, Zhang J, Cui X, et al.. Optimized SPE-UPLC-MS/MS Method for Sensitive Determination of Cereulide in Complex InfantFormula Matrices. . 2026. PMID 42188624 · DOI · PMC13211625
- Al-Agouz A, Ebrahem M, Hosni MI, Abdallah A, Mahmoud A. A two-stage hierarchical support vector machine framework detects roasted coffee adulterants through principal component analysis of hyperspectral imaging data. . 2026. PMID 42426150 · DOI · PMC13350853
- Ferrari RG, Rosario DKA, Cunha-Neto A, Mano SB, Figueiredo EES, Conte-Junior CA. Worldwide Epidemiology of Salmonella Serovars in Animal-Based Foods: a Meta-analysis. Appl Environ Microbiol. 2019. PMID 31053586 · DOI · PMC6606869
- Pasolli E, De Filippis F, Mauriello IE, Cumbo F, Walsh AM, Leech J, et al.. Large-scale genome-wide analysis links lactic acid bacteria from food with the gut microbiome. Nat Commun. 2020. PMID 32451391 · DOI · PMC7248083
- Liu Y, Chang CC, Marsh GM, Wu F. Population attributable risk of aflatoxin-related liver cancer: systematic review and meta-analysis. Eur J Cancer. 2012. PMID 22405700 · DOI · PMC3374897
- Danopoulos E, Jenner LC, Twiddy M, Rotchell JM. Microplastic Contamination of Seafood Intended for Human Consumption: A Systematic Review and Meta-Analysis. Environ Health Perspect. 2020. PMID 33355482 · DOI · PMC7757379
- Paudyal N, Pan H, Liao X, Zhang X, Li X, Fang W, et al.. A Meta-Analysis of Major Foodborne Pathogens in Chinese Food Commodities Between 2006 and 2016. Foodborne Pathog Dis. 2018. PMID 29652195 · DOI
- Belluco S, Mancin M, Conficoni D, Simonato G, Pietrobelli M, Ricci A. Investigating the Determinants of Toxoplasma gondii Prevalence in Meat: A Systematic Review and Meta-Regression. PLoS One. 2016. PMID 27082633 · DOI · PMC4833317
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-11-04; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-11-04.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.