Evidence map›Paper›PMID 41809495›Full record

ReviewFood science & nutrition2026

Advanced Spectroscopic, Imaging, and Nanotechnology Tools for Diagnosing Fungal Diseases in Fruits.

Vanshika Adiani, Archana Mishra

Abstract readReview
In one paragraph

Review in Food science & nutrition, 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

2 authors.

Vanshika AdianiFood Technology Division Bhabha Atomic Research Centre Mumbai Maharashtra India.
Archana MishraNuclear Agriculture and Biotechnology Division Bhabha Atomic Research Centre Mumbai Maharashtra India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fruits are a critical component of the human diet, as they provide essential dietary nutrients that play an important role in the functioning of the human body and maintaining health. It is well-known that consuming fruits has various benefits, including the prevention of chronic diseases, cancer, and cardiovascular disorders. Thus, wider availability and maintaining the quality of fruits are highly required. Around 25% of global crop losses reported annually are attributed to disease and pest infestations, as per the Food and Agriculture Organization. Fungal pathogens are a major cause of post-harvest diseases, which significantly affect production and lead to economic losses. To address this, disease diagnosis at an early stage is crucial to enable timely monitoring, implementation of prevention techniques, and minimizing storage-related losses. Various methods are available for early pathogen detection; spectroscopic and imaging techniques have been widely applied as they offer cost-effectiveness, potential for real-time analysis, and a non-destructive nature of analysis. When integrated with advanced decision-support tools, these instrumental techniques can enable rapid and accurate detection of fungal diseases in fruits. In recent years, nanotechnology has emerged as a promising approach, with a wide range of nanoparticles being utilized to develop nanobiosensors for various applications. This review also highlights recent advancements in the use of nanomaterials and nanoparticle-based sensing systems for the detection of pathogens, providing an overview of their potential role in improving post-harvest disease diagnostics.

Indexed as

biosensorsfruitsfungal diseasesimaging techniquesnano‐diagnosticspectroscopic techniques

Identifiers

PMID41809495
PMCPMC12968061

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.