Evidence map›Paper›PMID 42588000›Full record

ReviewFoods (Basel, Switzerland)2026

Recognition-Element-Driven Rapid Detection of Biogenic Amines in Foods: From Molecular Recognition to On-Site Sensing.

Jing Wang, Ruoxi Zhang, Mengyao Chen, Yixuan Wang, Huilin Liu, Huijuan Yang

Abstract readReview
In one paragraph

Review in Foods (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

6 authors.

Jing WangKey Laboratory of Digital-Intelligence and Dynamic Perception for Food Quality of China Light Industry, Beijing Technology and Business University, Beijing 100048, China.
Ruoxi ZhangKey Laboratory of Digital-Intelligence and Dynamic Perception for Food Quality of China Light Industry, Beijing Technology and Business University, Beijing 100048, China.
Mengyao ChenKey Laboratory of Digital-Intelligence and Dynamic Perception for Food Quality of China Light Industry, Beijing Technology and Business University, Beijing 100048, China.
Yixuan WangKey Laboratory of Digital-Intelligence and Dynamic Perception for Food Quality of China Light Industry, Beijing Technology and Business University, Beijing 100048, China.
Huilin LiuKey Laboratory of Digital-Intelligence and Dynamic Perception for Food Quality of China Light Industry, Beijing Technology and Business University, Beijing 100048, China.ORCID 0000-0002-7912-2611
Huijuan YangKey Laboratory of Digital-Intelligence and Dynamic Perception for Food Quality of China Light Industry, Beijing Technology and Business University, Beijing 100048, China.ORCID 0000-0001-5355-1170

Funding

National Natural Science Foundation of China 32402217
6 · The paper itself

Abstract

Biogenic amines (BAs) are nitrogenous compounds formed by microbial decarboxylation of amino acids in protein-rich foods. Their accumulation indicates spoilage and poses health risks. Traditional methods like high-performance liquid chromatography (HPLC) and gas chromatography (GC) are sensitive but time-consuming, limiting on-site use. Rapid technologies based on specific recognition molecules offer feasible alternatives for real-time monitoring. This review summarizes five categories of recognition elements: antibodies, aptamers, molecularly imprinted polymers (MIPs), enzymes, and peptides for BA detection in foods. These elements convert BA concentrations into optical, electrical, or colorimetric signals, establishing a complete biosensing chain. Integration with portable platforms (lateral flow assays (LFAs), microfluidic chips, smart labels, and smartphone devices) is also discussed. Recognition-element-based sensing enables high-selectivity and rapid monitoring of BAs in foods. Antibody/aptamer systems excel in specific histamine detection, enzyme platforms in rapid total amine assessment, and MIPs in chemical stability and matrix tolerance. Yet practical application is limited by poor selectivity for similar amines, matrix interference, insufficient real-food validation, and device standardization. Our future focus will be on AI-assisted design, multi-target arrays, smartphone quantification, and IoT-enabled freshness monitoring.

Indexed as

biogenic aminebiosensorsfood safetyon-site testingrecognition element

Identifiers

PMID42588000
PMCPMC13465165

What OpenQuestion holds

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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.