Integration of non-invasive and conventional systems for quality assessment and authentication of meat stuffs: A review with bibliometric analysis

The growing global demand and price fluctuations in meat have raised concerns over safety, adulteration, and traceability. Conventional methods are time-consuming, labor-intensive, and reagent-dependent, limiting their use for rapid or on-site screening. This review provides a comprehensive overview...

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Main Authors: Asima Saleem, Aysha Imtiaz, Sanabil Yaqoob, Muhammad Awais, Kanza Aziz Awan, Hiba Naveed, Ibrahim Khalifa, Sezai Ercisli, Robert Mugabi, Saqer S. Alotaibi, Gulzar Ahmad Nayik, Jian-Ya Qian, Qing Shen
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:Food Chemistry: X
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Online Access:http://www.sciencedirect.com/science/article/pii/S259015752500625X
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author Asima Saleem
Aysha Imtiaz
Sanabil Yaqoob
Muhammad Awais
Kanza Aziz Awan
Hiba Naveed
Ibrahim Khalifa
Sezai Ercisli
Robert Mugabi
Saqer S. Alotaibi
Gulzar Ahmad Nayik
Jian-Ya Qian
Qing Shen
author_facet Asima Saleem
Aysha Imtiaz
Sanabil Yaqoob
Muhammad Awais
Kanza Aziz Awan
Hiba Naveed
Ibrahim Khalifa
Sezai Ercisli
Robert Mugabi
Saqer S. Alotaibi
Gulzar Ahmad Nayik
Jian-Ya Qian
Qing Shen
author_sort Asima Saleem
collection DOAJ
description The growing global demand and price fluctuations in meat have raised concerns over safety, adulteration, and traceability. Conventional methods are time-consuming, labor-intensive, and reagent-dependent, limiting their use for rapid or on-site screening. This review provides a comprehensive overview of emerging non-invasive techniques—such as fluorescence, near-infrared, mid-infrared, and Raman spectroscopy—for assessing meat quality and detecting adulteration. The key novelty of this review is its integration of bibliometric analysis with a critical evaluation of advanced technologies aligned with the UN Sustainable Development Goals. The review also highlights the potential of hybrid systems that integrate spectroscopy with chemometrics and machine learning to provide accurate, real-time, and sustainable meat authentication solutions. It also highlights research gaps such as the need for multi-adulterant detection models, standardized validation protocols, and open-access spectral databases. By aligning innovation with regulatory and sustainability frameworks, this review advocates for robust, scalable solutions to build future-ready meat supply chains.
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institution Kabale University
issn 2590-1575
language English
publishDate 2025-07-01
publisher Elsevier
record_format Article
series Food Chemistry: X
spelling doaj-art-2bddbcbaff4b46f29b798d28ff6cfc682025-08-20T03:50:22ZengElsevierFood Chemistry: X2590-15752025-07-012910277810.1016/j.fochx.2025.102778Integration of non-invasive and conventional systems for quality assessment and authentication of meat stuffs: A review with bibliometric analysisAsima Saleem0Aysha Imtiaz1Sanabil Yaqoob2Muhammad Awais3Kanza Aziz Awan4Hiba Naveed5Ibrahim Khalifa6Sezai Ercisli7Robert Mugabi8Saqer S. Alotaibi9Gulzar Ahmad Nayik10Jian-Ya Qian11Qing Shen12School of Food Science and Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, ChinaSchool of Food Science and Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, ChinaPanvascular Diseases Research Center, The Quzhou Affiliaed Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000,China; Laboratory of Food Nutrition and Clinical research, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China; Department of Food and Nutritional Sciences, Faculty of Science and Technology, University of Central Punjab, Lahore, PakistanNational Institute of Food Science and Technology, Faculty of Food, Nutrition and Home Sciences, University of Agriculture Faisalabad, PakistanDepartment of Food and Nutritional Sciences, Faculty of Science and Technology, University of Central Punjab, Lahore, PakistanDepartment of Food and Nutritional Sciences, Faculty of Science and Technology, University of Central Punjab, Lahore, PakistanFood Technology Department, Faculty of Agriculture, Benha University, 13736 Moshtohor, Qaluobia, EgyptDepartment of Horticulture, Faculty of Agriculture, Ataturk University, 25240 Erzurum, TurkeyDepartment of Food Technology and Nutrition, Makerere University, Kampala, Uganda; Corresponding authors.Department of Biotechnology, College of Science, Taif University, P.O.Box 11099, Taif 21944, Saudi ArabiaMarwadi University Research Centre, Department of Microbiology, Marwadi University, Rajkot, Gujarat 360003, IndiaSchool of Food Science and Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, China; Corresponding authors.Panvascular Diseases Research Center, The Quzhou Affiliaed Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000,China; Laboratory of Food Nutrition and Clinical research, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China; Corresponding author at: Panvascular Diseases Research Center, The Quzhou Affiliaed Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000,China.The growing global demand and price fluctuations in meat have raised concerns over safety, adulteration, and traceability. Conventional methods are time-consuming, labor-intensive, and reagent-dependent, limiting their use for rapid or on-site screening. This review provides a comprehensive overview of emerging non-invasive techniques—such as fluorescence, near-infrared, mid-infrared, and Raman spectroscopy—for assessing meat quality and detecting adulteration. The key novelty of this review is its integration of bibliometric analysis with a critical evaluation of advanced technologies aligned with the UN Sustainable Development Goals. The review also highlights the potential of hybrid systems that integrate spectroscopy with chemometrics and machine learning to provide accurate, real-time, and sustainable meat authentication solutions. It also highlights research gaps such as the need for multi-adulterant detection models, standardized validation protocols, and open-access spectral databases. By aligning innovation with regulatory and sustainability frameworks, this review advocates for robust, scalable solutions to build future-ready meat supply chains.http://www.sciencedirect.com/science/article/pii/S259015752500625XSustainable meat productionSustainable development goalsAdulteration detectionMeat quality assessmentChemometric analysisSpectral databases
spellingShingle Asima Saleem
Aysha Imtiaz
Sanabil Yaqoob
Muhammad Awais
Kanza Aziz Awan
Hiba Naveed
Ibrahim Khalifa
Sezai Ercisli
Robert Mugabi
Saqer S. Alotaibi
Gulzar Ahmad Nayik
Jian-Ya Qian
Qing Shen
Integration of non-invasive and conventional systems for quality assessment and authentication of meat stuffs: A review with bibliometric analysis
Food Chemistry: X
Sustainable meat production
Sustainable development goals
Adulteration detection
Meat quality assessment
Chemometric analysis
Spectral databases
title Integration of non-invasive and conventional systems for quality assessment and authentication of meat stuffs: A review with bibliometric analysis
title_full Integration of non-invasive and conventional systems for quality assessment and authentication of meat stuffs: A review with bibliometric analysis
title_fullStr Integration of non-invasive and conventional systems for quality assessment and authentication of meat stuffs: A review with bibliometric analysis
title_full_unstemmed Integration of non-invasive and conventional systems for quality assessment and authentication of meat stuffs: A review with bibliometric analysis
title_short Integration of non-invasive and conventional systems for quality assessment and authentication of meat stuffs: A review with bibliometric analysis
title_sort integration of non invasive and conventional systems for quality assessment and authentication of meat stuffs a review with bibliometric analysis
topic Sustainable meat production
Sustainable development goals
Adulteration detection
Meat quality assessment
Chemometric analysis
Spectral databases
url http://www.sciencedirect.com/science/article/pii/S259015752500625X
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