Advancement in non-destructive methodologies for the determination of meat fraud

Authors

  • Prantic Kumar Goswami Department of Animal Science, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
  • A. M. M. Nurul Alam Division of Applied Life Science (BK Four), Gyeongsang National University, Jinju 52828, Korea
  • Jahan Ara Monti Division of Applied Life Science (BK Four), Gyeongsang National University, Jinju 52828, Korea
  • M. A. Hashem Department of Animal Science, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
  • M. A. K. Azad Department of Animal Science, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
Article ID: 333
527 Views

DOI:

https://doi.org/10.18686/fnc333

Keywords:

food fraud; nondestructive methods; NIR spectroscopy; machine learning algorithm; linear regression

Abstract

Food fraud is a new term worldwide and can involve many different stages of the production process. It affects safety, quality, consumer acceptance, and profitability. Food fraud assessment methods need to be very precise and reliable. Most animal-origin foods, including milk, dairy products, meat and meat products, eggs, fish, and fisheries goods, are vulnerable to food fraud. Identifying any adulteration in them is essential to stop unfair competition and protect consumer rights. Due to financial benefits, meat and meat products are vulnerable to various forms of adulteration. The meat business is transitioning from laborious and time-consuming analytical procedures to quick, non-invasive, non-destructive, repeatable, and trustworthy analytical technologies. This reviews precision analytical methods like near-infrared (NIR) spectroscopy and machine learning algorithms, linear regression, principal component analysis, etc., for detecting food fraud in meat and meat products.

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Published

2025-05-06

How to Cite

Goswami, P. K., Alam, A. M. M. N., Monti, J. A., Hashem, M. A., & Azad, M. A. K. (2025). Advancement in non-destructive methodologies for the determination of meat fraud. Food Nutrition Chemistry, 3(2), 333. https://doi.org/10.18686/fnc333

Issue

Vol. 3 No. 2 (2025)

Section

Review

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Copyright (c) 2025 Author(s)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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