Evidence map›Paper›PMID 32825151›Full record

ArticleMolecules (Basel, Switzerland)2020

Detection of Biomarkers Relating to Quality and Differentiation of Some Commercially Significant Whole Fish Using Spatially Off-Set Raman Spectroscopy.

Jeremy D Landry, Peter J Torley, Ewan W Blanch

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

3 authors.

Jeremy D LandryApplied Chemistry and Environmental Science, School of Science, RMIT University, 124a La Trobe Street, Melbourne, VIC 3000, Australia.ORCID 0000-0003-1180-340X
Peter J TorleyBiosciences and Food Technology, School of Science, Building 223, RMIT University, P.O. Box 71, Bundoora, VIC 3083, Australia.ORCID 0000-0001-6508-0185
Ewan W BlanchApplied Chemistry and Environmental Science, School of Science, RMIT University, 124a La Trobe Street, Melbourne, VIC 3000, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aquaculture represents a major part of the world's food supply. This area of food production is developing rapidly, and as such the tools and analytical techniques used to monitor and assess the quality of fish need to also develop and improve. The use of spatially off-set Raman spectroscopy (SORS) is particularly well-suited for these applications, given the ability of this technique to take subsurface measurements as well as being rapid, non-destructive and label-free compared to classical chemical analysis techniques. To explore this technique for analysing fish, SORS measurements were taken on commercially significant whole fish through the skin in different locations. The resulting spectra were of high quality with subsurface components such as lipids, carotenoids, proteins and guanine from iridophore cells clearly visible in the spectra. These spectral features were characterised and major bands identified. Chemometric analysis additionally showed that clear differences are present in spectra not only from different sections of a fish but also between different species. These results highlight the potential application for SORS analysis for rapid quality assessment and species identification in the aquaculture industry by taking through-skin measurements.

Indexed as

AnimalsBiomarkersCarotenoidsFishesFood ContaminationGuanineLipidsMelanophoresProteinsSkinSpectrum Analysis, RamanBiomarkersCarotenoidsGuanineLipidsProteinschemometricsfish muscleRaman spectroscopyspatially offset Raman

Identifiers

PMID32825151
PMCPMC7503569

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Registered trials

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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.