Evidence map›Paper›PMID 41600353›Full record

ReviewSensors (Basel, Switzerland)2026

New Developments and Future Challenges of Non-Destructive Near-Infrared Spectroscopy Sensors in the Cheese Industry.

Maria Tarapoulouzi, Wenyang Jia, Anastasios Koidis

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Maria TarapoulouziDepartment of Chemistry, Faculty of Pure and Applied Science, University of Cyprus, Nicosia 1678, Cyprus.ORCID 0000-0003-0206-4860
Wenyang JiaInstitute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, UK.ORCID 0000-0001-6800-6706
Anastasios KoidisInstitute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, UK.ORCID 0000-0002-9199-8704

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Near-infrared (NIR) spectroscopy has emerged as a pivotal non-destructive analytical technique within the cheese industry, offering rapid and precise insights into the chemical composition and quality attributes of various cheese types. This review explores the evolution of NIR spectral sensors, highlighting key technological advancements and their integration into cheese production processes as well as final products already in markets. In addition, the review discusses challenges such as calibration complexities, the influence of sample heterogeneity and the need for robust data and interpretation models through spectroscopy coupled with AI methods. The future potential of NIR spectral sensors, including real-time in-line monitoring and the development of portable devices for on-site analysis, is also examined. This review aims to provide a critical assessment of current NIR spectral sensors and their impact on the cheese industry, offering insights for researchers and industry professionals aiming to enhance quality control and innovation in cheese production, as well as authenticity and fraud studies. The review concludes that the integration of advanced NIR spectroscopy with AI represents a transformative approach for the cheese industry, enabling more accurate, efficient and sustainable quality assessment practices that can strengthen both production consistency and consumer trust.

Indexed as

cheese qualityNIR sensing technologiesnon-invasive testingspectroscopic quality assessment

Identifiers

PMID41600353
PMCPMC12845609

What OpenQuestion holds

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LicenceCC BY
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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.