Evidence map›Paper›PMID 28911398›Full record

ReviewJournal of food and drug analysis2016

Nanomaterial-based sensors for detection of foodborne bacterial pathogens and toxins as well as pork adulteration in meat products.

B Stephen Inbaraj, B H Chen

Open access · hybridAbstract readReview
In one paragraph

Review in Journal of food and drug analysis, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
12.2field-weighted citation impact, top 1% 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

50 citing papers in PubMed, 243 citations in OpenAlex.

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  9. Active Quantum Biomaterials-Enhanced Microrobots for Food Safety.Small (Weinheim an der Bergstrasse, Germany) · 2024
    Article
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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

2 authors at 1 institution in 1 country.

B Stephen InbarajDepartment of Food Science, Fu Jen Catholic University, Taipei, Taiwan.
B H ChenDepartment of Food Science, Fu Jen Catholic University, Taipei, Taiwan.
Fu Jen Catholic University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Food safety draws considerable attention in the modern pace of the world owing to rapid-changing food recipes and food habits. Foodborne illnesses associated with pathogens, toxins, and other contaminants pose serious threat to human health. Besides, a large amount of money is spent on both analyses and control measures, which causes significant loss to the food industry. Conventional detection methods for bacterial pathogens and toxins are time consuming and laborious, requiring certain sophisticated instruments and trained personnel. In recent years, nanotechnology has emerged as a promising field for solving food safety issues in terms of detecting contaminants, enabling controlled release of preservatives to extend the shelf life of foods, and improving food-packaging strategies. Nanomaterials including metal oxide and metal nanoparticles, carbon nanotubes, and quantum dots are gaining a prominent role in the design of sensors and biosensors for food analysis. In this review, various nanomaterial-based sensors reported in the literature for detection of several foodborne bacterial pathogens and toxins are summarized highlighting their principles, advantages, and limitations in terms of simplicity, sensitivity, and multiplexing capability. In addition, the application through a noncross-linking method without the need for any surface modification is also presented for detection of pork adulteration in meat products.

Indexed as

bacterial pathogensfood analysisfood toxinsnanomaterialspork adulteration

Identifiers

PMID28911398
PMCPMC9345428
OpenAlexW1003083253

What OpenQuestion holds

Texttitle and abstract
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.