Evidence map›Paper›PMID 40852570›Full record

ReviewFood chemistry: X2025

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

Asima Saleem, Aysha Imtiaz, Sanabil Yaqoob, Muhammad Awais, Kanza Aziz Awan, Hiba Naveed, Ibrahim Khalifa, Sezai Ercisli, Robert Mugabi, Saqer S Alotaibi and 3 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Food chemistry: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Asima SaleemSchool of Food Science and Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, China.
Aysha ImtiazSchool of Food Science and Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, China.
Sanabil YaqoobPanvascular Diseases Research Center, The Quzhou Affiliaed Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000,China.
Muhammad AwaisNational Institute of Food Science and Technology, Faculty of Food, Nutrition and Home Sciences, University of Agriculture Faisalabad, Pakistan.
Kanza Aziz AwanDepartment of Food and Nutritional Sciences, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan.
Hiba NaveedDepartment of Food and Nutritional Sciences, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan.
Ibrahim KhalifaFood Technology Department, Faculty of Agriculture, Benha University, 13736 Moshtohor, Qaluobia, Egypt.
Sezai ErcisliDepartment of Horticulture, Faculty of Agriculture, Ataturk University, 25240 Erzurum, Turkey.
Robert MugabiDepartment of Food Technology and Nutrition, Makerere University, Kampala, Uganda.
Saqer S AlotaibiDepartment of Biotechnology, College of Science, Taif University, P.O.Box 11099, Taif 21944, Saudi Arabia.
Gulzar Ahmad NayikMarwadi University Research Centre, Department of Microbiology, Marwadi University, Rajkot, Gujarat 360003, India.
Jian-Ya QianSchool of Food Science and Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, China.
Qing ShenPanvascular Diseases Research Center, The Quzhou Affiliaed Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000,China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Adulteration detectionChemometric analysisMeat authenticationMeat quality assessmentNon-destructive techniquesSpectral databasesSustainable development goalsSustainable meat production

Identifiers

PMID40852570
PMCPMC12369435

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.