ReviewFoods (Basel, Switzerland)2024
Challenges in the Use of AI-Driven Non-Destructive Spectroscopic Tools for Rapid Food Analysis.
Review in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
- Artificial intelligence in poultry processing: applications, validation gaps, and pathways toward intelligent and autonomous processing systems.Poultry science · 2026Review
- Reliable Quantification of Powdered Ginger Adulteration by Vis-NIR Spectroscopy and Chemometrics.Molecules (Basel, Switzerland) · 2026Article
- New Developments and Future Challenges of Non-Destructive Near-Infrared Spectroscopy Sensors in the Cheese Industry.Sensors (Basel, Switzerland) · 2026Review
- Special Issue "Modern Analytical Strategies for Foodomics: From Nutritional Value to Food Security".International journal of molecular sciences · 2026Article
- A homogeneity evaluation method of food soft matter based on moisture content test by casting-near-infrared spectroscopy.Food chemistry: X · 2025Article
- Aflatoxin Contamination in Agri-Food Systems: A Comprehensive Review of Toxicity, Food Security, Economic Impacts, and Sustainable Mitigation Across the Value Chain.Food science & nutrition · 2025Review
- Artificial Intelligence as a Scientific Copilot in Analytical Chemistry: Transforming How We Write, Review, and Publish.Analytical chemistry · 2025Review
- Deep Learning-Enhanced Spectroscopic Technologies for Food Quality Assessment: Convergence and Emerging Frontiers.Foods (Basel, Switzerland) · 2025Review
- Rapid Determination of Crude Protein Content in Alfalfa Based on Fourier Transform Infrared Spectroscopy.Foods (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Routine, remote, and process analysis for foodstuffs is gaining attention and can provide more confidence for the food supply chain. A new generation of rapid methods is emerging both in the literature and in industry based on spectroscopy coupled with AI-driven modelling methods. Current published studies using these advanced methods are plagued by weaknesses, including sample size, abuse of advanced modelling techniques, and the process of validation for both the acquisition method and modelling. This paper aims to give a comprehensive overview of the analytical challenges faced in research and industrial settings where screening analysis is performed while providing practical solutions in the form of guidelines for a range of scenarios. After extended literature analysis, we conclude that there is no easy way to enhance the accuracy of the methods by using state-of-the-art modelling methods and the key remains that capturing good quality raw data from authentic samples in sufficient volume is very important along with robust validation. A comprehensive methodology involving suitable analytical techniques and interpretive modelling methods needs to be considered under a tailored experimental design whenever conducting rapid food analysis.
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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.