Iranian Food Science and Technology Research Journal
with the collaboration of Iranian Food Science and Technology Association (IFSTA)

Detection of Adulteration of Ground Meat by Spectral-based Techniques and Artificial Intelligence (2020-2024)

Document Type : Review Article-en

Authors

Amir Kazemi 1
Asghar Mahmoudi 1
Mostafa Khojasteh Najand 2

1 Department of Biosystems Engineering, University of Tabriz, Tabriz, Iran

2 Department of Mechanical Engineering, University of Bonab, Bonab, Iran

https://doi.org/10.22067/ifstrj.2024.88158.1335
Abstract
Meat is a significant source of important nutrients and has a vital role in the human diet. Lack of monitoring of the quality and safety of meat can result in posing health threats. Determining safety through chemical methods is costly and time-consuming, without the ability to monitor in real-time. Therefore, nowadays assessing the quality of meat by applying spectral techniques such as spectroscopic and spectral imaging, considered as promising tools and these strategies have recently undergone swift advancements and garnered heightened public attention. Therefore, the purpose of the present review paper is to give an overview of the latest advancements in spectral methods for assessing ground meat safety. The basic working principles, fundamental settings, analysis process, and applications of these techniques are described. By investigating the practical utilization possibilities of spectral detection technologies in the evaluation of meat safety, researchers discussed the present challenges and upcoming research prospects. Furthermore, the newest advances in the application of artificial intelligence accompanied by the mentioned techniques were also discussed.

Keywords

Adulteration
Machine learning
Minced meat
NIR spectroscopy
Spectral imaging
Subjects

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

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Iranian Food Science and Technology Research Journal
Volume 20, Issue 6 - Serial Number 90
January and February 2025
Pages 201-224

  • Receive Date 22 May 2024
  • Revise Date 09 September 2024
  • Accept Date 09 September 2024
  • First Publish Date 09 September 2024

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