Evidence map›Paper›PMID 39847583›Full record

ArticlePloS one2025

Vis-NIRS as an auxiliary tool in the classification of bovine carcasses.

Gabriela Zardo Pereira, Gabriel de Morais Pereira, Rodrigo da Costa Gomes, Gelson Luís Dias Feijó, Lucy Mery Antonia Surita, Marília Williani Filgueira Pereira, Gilberto Romeiro de Oliveira Menezes, Jaqueline Rodrigues Ferreira Cara, Luis Carlos Vinhas Ítavo, Saulo da Luz E Silva and 2 more

Abstract read
In one paragraph

Article in PloS one, 2025. 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
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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

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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

12 authors.

Gabriela Zardo PereiraCollege of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.
Gabriel de Morais PereiraCollege of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.ORCID 0000-0001-5200-9283
Rodrigo da Costa GomesEmbrapa Beef Cattle, Brazilian Agricultural Research Company, Campo Grande, Mato Grosso do Sul, Brazil.
Gelson Luís Dias FeijóEmbrapa Beef Cattle, Brazilian Agricultural Research Company, Campo Grande, Mato Grosso do Sul, Brazil.
Lucy Mery Antonia SuritaCollege of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.ORCID 0000-0003-0501-6063
Marília Williani Filgueira PereiraDepartment of Animal Science, Federal Rural University of Semi-Arid Region, Mossoro, Rio Grande do Norte, Brazil.
Gilberto Romeiro de Oliveira MenezesEmbrapa Beef Cattle, Brazilian Agricultural Research Company, Campo Grande, Mato Grosso do Sul, Brazil.
Jaqueline Rodrigues Ferreira CaraCollege of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.ORCID 0000-0002-4938-1970
Luis Carlos Vinhas ÍtavoCollege of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.
Saulo da Luz E SilvaCollege of Animal Science and Food Engineering, University of São Paulo, Pirassununga, São Paulo, Brazil.
Melissa AminCollege of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.ORCID 0000-0002-1898-7056
Marina de Nadai Bonin GomesCollege of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work aimed to evaluate the use of Visible and Near-infrared Spectroscopy (Vis-NIRS) as a tool in the classification of bovine carcasses. A total of 133 animals (77 females, 29 males surgically castrated and 27 males immunologically castrated) were used. Vis-NIRS spectra were collected in a chilling room 24 h postmortem directly on the hanging carcasses over the longissimus thoracis between the surface of the 5th and 6th ribs. The data were evaluated by principal component analysis (PCA) and the partial least squares regression (PLSR) method. For the prediction of sex, the best model was the Standard Normal Variate (SNV) because it presented a relatively high coefficient of determination for prediction, presenting a percentage of correctness of 75.51% and an error of 24.49%. Regarding age, none of the models were able to differentiate the samples through Vis-NIRS. The findings confirm that Vis-NIRS prediction models are a valuable tool for differentiating carcasses based on sex. To further enhance the precision of these predictions, we recommend using Vis-NIRS equipment with the full infrared wavelength range to collect and predict sex and age in intact beef samples.

Indexed as

Red MeatAnimalsCattleFemaleLeast-Squares AnalysisMalePrincipal Component AnalysisSpectroscopy, Near-Infrared

Identifiers

PMID39847583
PMCPMC11756776

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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.