ReviewAnalytical and bioanalytical chemistry2023
Metrological traceability in process analytical technologies and point-of-need technologies for food safety and quality control: not a straightforward issue.
Review in Analytical and bioanalytical chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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Who cites it
9 citing papers in PubMed.
- Colorimetric Sensor Arrays Technology in Food Quality and Safety Analysis.Micromachines · 2026Review
- Data-driven full-chain quality control for Xiaoer Chiqiao Qingre granules: from process risk management to intelligent product testing.Chinese medicine · 2026Article
- From optical architectures to actual deployment a review of online process spectroscopy in industrial environments.Frontiers in chemistry · 2026Review
- Trends on vibrational spectroscopy tools in the agri-food sector.Analytical and bioanalytical chemistry · 2026Review
- Recent Advances in Nucleic Acid-Based Electrochemical Sensors for the Detection of Food Allergens.Sensors (Basel, Switzerland) · 2026Review
- On-Line Monitoring of Vitamin C in Fruit Juice in Processing Plants by Electrochemical Sensor Based on PEDOT-Modified Electrodes: A Feasibility Study.Sensors (Basel, Switzerland) · 2025Article
- Recent Progress in Nanomaterial-Based Surface-Enhanced Raman Spectroscopy for Food Safety Detection.Nanomaterials (Basel, Switzerland) · 2024Review
- Application of Electrospun Nanofiber-Based Electrochemical Sensors in Food Safety.Molecules (Basel, Switzerland) · 2024Review
- Food safety analysis 2.0.Analytical and bioanalytical chemistry · 2024Article
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.
Funding
Abstract
Traditional techniques for food analysis are based on off-line laboratory methods that are expensive and time-consuming and often require qualified personnel. Despite the high standards of accuracy and metrological traceability, these well-established methods do not facilitate real-time process monitoring and timely on-site decision-making as required for food safety and quality control. The future of food testing includes rapid, cost-effective, portable, and simple methods for both qualitative screening and quantification of food contaminants, as well as continuous, real-time measurement in production lines. Process automatization through process analytical technologies (PAT) is an increasing trend in the food industry as a way to achieve improved product quality, safety, and consistency, reduced production cycle times, minimal product waste or reworks, and the possibility for real-time product release. Novel methods of analysis for point-of-need (PON) screening could greatly improve food testing by allowing non-experts, such as consumers, to test in situ food products using portable instruments, smartphones, or even visual naked-eye inspections, or farmers and small producers to monitor products in the field. This requires the attention of the research community and devices manufacturers to ensure reliability of measurement results from PAT strategy and PON tests through the demonstration and critical evaluation of performance characteristics. The fitness for purpose of methods in real-life conditions is a priority that should not be overlooked in order to maintain an effective and harmonized food safety policy.
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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.