ReviewJournal of food and drug analysis2020
Gold nanoparticle-based colorimetric and electrochemical sensors for the detection of illegal food additives.
Review in Journal of food and drug analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- A Prospective and Cost-Effective Voltammetric-Chemometric Approach for the Discrimination and Quantification of Melamine and Urea in Milk.ACS omega · 2026Article
- Smart Nucleic Acid Hydrogel-Based Biosensors: From Molecular Recognition and Responsive Mechanisms to Applications.Biosensors · 2025Review
- Golden eyes on pollutants: colorimetric detection of emerging contaminants with AuNPs.RSC advances · 2025Review
- A systematic review of sensors to combat crime and routes to further sensor development.Frontiers in chemistry · 2025Review
- Biosensor in smart food traceability system for food safety and security.Bioengineered · 2024Review
- Highly Sensitive Immunochromatographic Detection of Porcine Myoglobin as Biomarker for Meat Authentication Using Prussian Blue Nanozyme.Foods (Basel, Switzerland) · 2023Article
- Gold nanoparticle-based optical nanosensors for food and health safety monitoring: recent advances and future perspectives.RSC advances · 2022Review
- Growth-Promoting Gold Nanoparticles Decrease Stress Responses in Arabidopsis Seedlings.Nanomaterials (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lately, scandals associated with the illegal addition of poisonous chemicals to food for commercial interests have been gradually disclosed to the public. Problems related to food safety do not only harm public health but also affect the stability of economic and social development. Food safety has become a common issue in society, and strengthening the related regulations have become increasingly important. Although conventional techniques are accurate and sensitive in the detection of the vast majority of illegal food additives, they rely on time-consuming, labor-intensive procedures that depend on expensive instruments. Thus, efficient and rapid identification of poisonous, illegal additives in food is a crucial task in analytical chemistry. Recently, in this context, gold nanoparticles (GNPs) have attracted considerable attention because of their optical, electronic, catalytic, and chemical properties. Their excellent properties have facilitated the widespread use of GNPs in different sensors. This review covers the two most common GNP-based sensors with colorimetric and electrochemical responses, which have proven to be effective in the detection of illegal additives. The GNP-based sensors comply with the requirement of modern analysis, such as high selectivity, sensitivity, simplicity, rapidity, and portability. Thus, they have great potential as powerful sensing tools for food safety screening. This review elucidates the utility and advances of GNP-based colorimetric and electrochemical sensors for the detection of illegal additives in the food industry and in the supervision of food quality and safety. Additionally, an outlook of the trends and future development of research on these sensors is provided.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.