Nanomaterial based electrochemical sensors for the safety and quality control of food and beverages
Bibliographic record
Abstract
The issue of foodborne related illnesses due to additives and contaminants poses a significant challenge to food processing industries. The efficient, economical and rapid analysis of food additives and contaminants is therefore necessary in order to minimize the risk of public health issues. Electrochemistry offers facile and robust analytical methods, which are desirable for food safety and quality assessment over conventional analytical techniques. The development of a wide array of nanomaterials has paved the way for their applicability in the design of high-performance electrochemical sensing devices for medical diagnostics and environment and food safety. The design of nanomaterial based electrochemical sensors has garnered enormous attention due to their high sensitivity and selectivity, real-time monitoring and ease of use. This review article focuses predominantly on the synthesis and applications of different nanomaterials for the electrochemical determination of some common additives and contaminants, including hydrazine (N2H4), malachite green (MG), bisphenol A (BPA), ascorbic acid (AA), caffeine, caffeic acid (CA), sulfite (SO32-) and nitrite (NO2-), which are widely found in food and beverages. Important aspects, such as the design, fabrication and characterization of graphene-based materials, gold nanoparticles, mono- and bimetallic nanoparticles and metal nanocomposites, sensitivity and selectivity for electrochemical sensor development are addressed. High-performance nanomaterial based electrochemical sensors have and will continue to have myriad prospects in the research and development of advanced analytical devices for the safety and quality control of food and beverages.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".