Evidence map›Paper›PMID 39769967›Full record

ArticleMolecules (Basel, Switzerland)2024

Differentiation of Insect Flours by Elemental Analysis and Chemometrics: A Study Using Inductively Coupled Plasma Mass Spectrometry (ICP-MS).

Mattia Montanaro, Alessandra Biancolillo, Angelo Antonio D'Archivio, Martina Foschi

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Mattia MontanaroDepartment of Physical and Chemical Sciences, University of L'Aquila, Via Vetoio snc, 67100 L'Aquila, Italy.
Alessandra BiancolilloDepartment of Physical and Chemical Sciences, University of L'Aquila, Via Vetoio snc, 67100 L'Aquila, Italy.ORCID 0000-0002-9461-9597
Angelo Antonio D'ArchivioDepartment of Physical and Chemical Sciences, University of L'Aquila, Via Vetoio snc, 67100 L'Aquila, Italy.ORCID 0000-0002-1198-6747
Martina FoschiDepartment of Physical and Chemical Sciences, University of L'Aquila, Via Vetoio snc, 67100 L'Aquila, Italy.ORCID 0000-0002-7398-5221

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to validate a method for characterizing and quantifying the multi-elemental profiles of different insect flours to enable their distinction, identification, and quality assessment. The focus was on three insect species: cricket (

methodsMealworms were powdered in the laboratory through mechanical processing. Sample analysis involved acid digestion using a microwave digester, followed by profiling with Inductively Coupled Plasma Mass Spectrometry (ICP-MS). This technique enabled rapid, multi-elemental analysis at trace levels. Chemometric methods, including Principal Component Analysis (PCA) for exploratory analysis, Covariance Selection-Linear Discriminant Analysis (CovSel-LDA), alongside forward stepwise LDA classification methods, were applied and compared.

resultsICP-MS accurately detected elements at micro trace levels. Both classification models, based on different variable selection methods and externally validated on a test set comprising 45% of the available samples, proved effective in classifying samples based on slightly different pools of trace elements. CovSel-LDA selected Mg and Se, whereas the stepwise-LDA focused on Mg, K, and Mn.

conclusionsthe validated methods demonstrated high accuracy and generalizability, supporting their potential use in food industry applications. This model could assist in quality control, facilitating the introduction of insect-based flour into European and international markets as novel foods.

Indexed as

FlourMass SpectrometryAnimalsChemometricsDiscriminant AnalysisInsectaPrincipal Component AnalysisTenebrioTrace ElementsTrace Elementscovariance selectioncricket flourdiscriminant classificationexplorative analysisICP-MSinsect flourLDAPCAvariable selection

Identifiers

PMID39769967
PMCPMC11676781

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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.