ReviewBiosensors2022
Assessing the Food Quality Using Carbon Nanomaterial Based Electrodes by Voltammetric Techniques.
Review in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
6 citing papers in PubMed, 71 citations in OpenAlex.
- Recent advances in detection techniques for vitamin analysis: A comprehensive review.Food chemistry: X · 2025Review
- Electrochemical sensing and quantification of theobromine in cocoa products at polyvaline functionalized graphite paste sensor electrode.ADMET & DMPK · 2025Article
- Unlocking the potential of titanium dioxide nanoparticles: an insight into green synthesis, optimizations, characterizations, and multifunctional applications.Microbial cell factories · 2024Review
- Preparation of Fresh-Keeping Paper Using Clove Essential Oil through Pickering Emulsion and Maintaining the Quality of Postharvest Cherry Tomatoes.Foods (Basel, Switzerland) · 2024Article
- Pencil and Gold Electrode Materials for the Electrochemical Study and Analysis of Dinitrotoluene.ACS omega · 2024Article
- Multivariate Optimization of Electrochemical Biosensors for the Determination of Compounds Related to Food Safety-A Review.Biosensors · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The world is facing a global financial loss and health effects due to food quality adulteration and contamination, which are seriously affecting human health. Synthetic colors, flavors, and preservatives are added to make food more attractive to consumers. Therefore, food safety has become one of the fundamental needs of mankind. Due to the importance of food safety, the world is in great need of developing desirable and accurate methods for determining the quality of food. In recent years, the electrochemical methods have become more popular, due to their simplicity, ease in handling, economics, and specificity in determining food safety. Common food contaminants, such as pesticides, additives, and animal drug residues, cause foods that are most vulnerable to contamination to undergo evaluation frequently. The present review article discusses the electrochemical detection of the above food contaminants using different carbon nanomaterials, such as carbon nanotubes (CNTs), graphene, ordered mesoporous carbon (OMC), carbon dots, boron doped diamond (BDD), and fullerenes. The voltammetric methods, such as cyclic voltammetry (CV) and differential pulse voltammetry (DPV), have been proven to be potential methods for determining food contaminants. The use of carbon-based electrodes has the added advantage of electrochemically sensing the food contaminants due to their excellent sensitivity, specificity, large surface area, high porosity, antifouling, and biocompatibility.
Indexed as
Identifiers
What OpenQuestion holds
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.