Evidence map›Paper›PMID 42794093›Full record

ReviewFoods (Basel, Switzerland)2026

Integration of Laccase-like Nanozymes and Sensing Strategies for Food Quality and Safety Monitoring.

Man Zhou, Tian Jiang, Chenglin Li, Chunhua Dai

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

4 authors.

Man ZhouSchool of Food Science and Engineering, Jiangsu University, Zhenjiang 212013, China.ORCID 0000-0002-6786-5055
Tian JiangSchool of Food Science and Engineering, Jiangsu University, Zhenjiang 212013, China.
Chenglin LiSchool of Food Science and Engineering, Jiangsu University, Zhenjiang 212013, China.
Chunhua DaiSchool of Food Science and Engineering, Jiangsu University, Zhenjiang 212013, China.

Funding

National Natural Science Foundation of China 22308130
6 · The paper itself

Abstract

Laccase-like nanozymes (LLNs) are a class of functional nanomaterials inspired by natural laccases. Due to their excellent catalytic performance, structural tunability, and superior stability, LLNs have emerged as promising tools for food quality and safety monitoring. This review establishes a critical structure-activity-application framework for LLNs, correlating bioinspired and structural engineering strategies, with the regulation of active sites, electronic structures, and catalytic microenvironments, and, ultimately, with their analytical performance in food matrices. Key insights are provided into how LLNs are integrated with colorimetric, fluorometric, electrochemical, and multimodal sensing platforms. Furthermore, particular emphasis is placed on their applications in food quality monitoring, including the detection of bioactive markers, freshness monitoring of meat products, authentication and adulteration analysis of tea, and assessment of other quality-relevant parameters. For food safety, the sensitive detection of mycotoxins, pesticide and antibiotic residues, and food additives is specifically highlighted. Despite these advances, challenges remain in elucidating catalytic mechanisms and active-site structures, establishing standardized evaluation criteria, and improving selectivity and reliability in complex food matrices. Future research should therefore focus on rational active-site engineering, in situ mechanistic characterization, standardized performance evaluation, and the integration of LLNs with multi-mode, portable, and intelligent sensing technologies to facilitate practical food analysis.

Indexed as

food qualityfood safetylaccase-like nanozymessensing strategies

Identifiers

PMID42794093
PMCPMC13606079

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.