Evidence map›Paper›PMID 35571784›Full record

ReviewACS omega2022

Application of Molecularly Imprinted Polymers for the Detection of Volatile and Off-Odor Compounds in Food Matrices.

Nurten Cengiz, Gamze Guclu, Hasim Kelebek, Esra Capanoglu, Serkan Selli

Open access · goldAbstract readReview
In one paragraph

Review in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.5field-weighted citation impact, top 10% of its field
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

9 citing papers in PubMed, 25 citations in OpenAlex.

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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

5 authors at 3 institutions in 1 country.

Nurten CengizDepartment of Food Engineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, 01250 Adana, Turkey.
Gamze GucluDepartment of Food Engineering, Faculty of Agriculture, Cukurova University, 01130 Adana, Turkey.
Hasim KelebekDepartment of Food Engineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, 01250 Adana, Turkey.
Esra CapanogluDepartment of Food Engineering, Faculty of Chemical and Metallurgical Engineering, Istanbul Technical University, 34469 Maslak, Istanbul, Turkey.ORCID https://orcid.org/0000-0003-0335-9433
Serkan SelliDepartment of Food Engineering, Faculty of Agriculture, Cukurova University, 01130 Adana, Turkey.ORCID https://orcid.org/0000-0003-0450-2668
Adana Science and Technology University · TRCukurova University · TRIstanbul Technical University · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecularly imprinted polymers (MIPs) are synthetic receptors having specific cavities intended for a template molecule with a retention mechanism that depends on molecular recognition of the targeted constituent. They were initially established for the detection of minor molecules including drugs, pesticides, or pollutants. One of the most remarkable areas where MIPs have potential utilization is in food analysis, especially in terms of volatile compounds which are found in very low concentrations in foods but play a crucial role for consumer preference and acceptance. In recent years, these polymers have been used extensively for sensing volatile organic and off-odor compounds in terms of food quality for selective high-extraction purposes. This review first summarizes the basic principles and production processes of MIPs. Second, their recent applications in the separation, identification, and quantification of volatile and off-odor compounds in food samples are elucidated.

Identifiers

PMID35571784
PMCPMC9096822
OpenAlexW4225009995

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.