Detecting meat fraud in food supply chain
Bibliographic record
Abstract
In recent years, numerous reports have repeatedly highlighted a series of food safety scandals involving contaminated and forged meat and fish products, grains and fruit products, juices, cooking oils, and spices and herbs, distilled beverages and pet treats. Foods that have been found to be stained with chemicals, illicit drug residues, additives and dyes, pathogenic microorganisms and other pests. Some foods enter the market have expired well or are unhygienic. Economic gain is the goal of food fraud. Food fraud and the prevention of such fraud are very important processes in the food industry. Such frauds are economically motivated, rated as criminal behaviour, and the moment we understand criminal behaviour and decision making we will be able to calculate and exclude the risk of food fraud. By analysing financially motivated fraud that combine opportunities, motivations, and inadequate control measures, we can assess the likelihood of fraud in any food product or component. The modified ingredients are specially designed to avoid quality assurance and quality control systems for customers. Only people who manipulate the ingredients know what substances and how to manipulate them. In addition, fraudulent ingredients are often unconventional substances that do not meet the requirements of food safety management systems, and become known only after they are incorporated into the supply chain. International standards for food and regulations address the risk of fraud food adulteration. European Union (EU) Directives, Global Food Safety Initiative (GFSI) Codex Alimentarius has continued work on a food fraud, or how, food fraud fits into their benchmarking. The problem of detection and typing of meat in meat products in the world and lack of research on them in the Republic of North Macedonia was the main goal for this paper. Our task was identification of meat type by ELISA method and proof of counterfeiting of meat products. Analyses are made in the laboratories of the Institute of Food at the Faculty of veterinary medicine in Skopje A total of 350 samples of various heat-treated meat products subgroups of meat sausages in pieces were examined for detection, typifying and quantifying the type of meat used for production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".