ReviewFoods (Basel, Switzerland)2021
Non-Destructive Spectroscopic and Imaging Techniques for the Detection of Processed Meat Fraud.
Review in Foods (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Food Fraud Implications and Regulatory Challenges in South Africa: A Review.Foods (Basel, Switzerland) · 2026Review
- The contribution of vibrational spectroscopy and data analytics towards the achievement of the global sustainability goals.Journal of the science of food and agriculture · 2026Review
- Rapid Detection of Chicken Residues on Poultry Plant Surfaces Using Color and Fluorescence Spectrometry.Foods (Basel, Switzerland) · 2025Article
- Integration of non-invasive and conventional systems for quality assessment and authentication of meat stuffs: A review with bibliometric analysis.Food chemistry: X · 2025Review
- The Effect of the Level of Goat Liver Addition to Goat Minced Meat on the Near-Infrared Spectra, Colour, and Shelf Life of Samples.Foods (Basel, Switzerland) · 2025Article
- Chemical Toxicants Used for Food Preservation in Africa. Is it a Case of Ignorance or Food Fraud? A Review.Health science reports · 2025Article
- Identification of individual goat animals by means of near infrared spectroscopy and chemometrics analysis of commercial meat cuts.Journal of food science and technology · 2024Article
- Discrimination among Fresh, Frozen-Stored and Frozen-Thawed Beef Cuts by Hyperspectral Imaging.Foods (Basel, Switzerland) · 2024Article
- Evaluating the Use of a Similarity Index (SI) Combined with near Infrared (NIR) Spectroscopy as Method in Meat Species Authenticity.Foods (Basel, Switzerland) · 2023Article
- A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose.Foods (Basel, Switzerland) · 2022Article
- Spectroscopic techniques for authentication of animal origin foods.Frontiers in nutrition · 2022Review
- Rapid Full-Cycle Technique to Control Adulteration of Meat Products: Integration of Accelerated Sample Preparation, Recombinase Polymerase Amplification, and Test-Strip Detection.Molecules (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
4 authors.
Funding
Abstract
In recent years, meat authenticity awareness has increased and, in the fight to combat meat fraud, various analytical methods have been proposed and subsequently evaluated. Although these methods have shown the potential to detect low levels of adulteration with high reliability, they are destructive, time-consuming, labour-intensive, and expensive. Therefore, rendering them inappropriate for rapid analysis and early detection, particularly under the fast-paced production and processing environment of the meat industry. However, modern analytical methods could improve this process as the food industry moves towards methods that are non-destructive, non-invasive, simple, and on-line. This review investigates the feasibility of different non-destructive techniques used for processed meat authentication which could provide the meat industry with reliable and accurate real-time monitoring, in the near future.
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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.