Evidence map›Paper›PMID 37512584›Full record

ArticleMetabolites2023

Investigation of Seasonal Variation in Fatty Acid and Mineral Concentrations of Pecorino Romano PDO Cheese: Imputation of Missing Values for Enhanced Classification and Metabolic Profile Reconstruction.

Leonardo Sibono, Massimiliano Grosso, Stefania Tronci, Massimiliano Errico, Margherita Addis, Monica Vacca, Cristina Manis, Pierluigi Caboni

Abstract read
In one paragraph

Article in Metabolites, 2023. 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. Review
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

8 authors.

Leonardo SibonoDepartment of Mechanical, Chemical and Materials Engineering, University of Cagliari, Via Marengo 2, 09123 Cagliari, Italy.
Massimiliano GrossoDepartment of Mechanical, Chemical and Materials Engineering, University of Cagliari, Via Marengo 2, 09123 Cagliari, Italy.ORCID 0000-0002-7793-3920
Stefania TronciDepartment of Mechanical, Chemical and Materials Engineering, University of Cagliari, Via Marengo 2, 09123 Cagliari, Italy.ORCID 0000-0002-6554-3255
Massimiliano ErricoDepartment of Green Technology, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark.ORCID 0000-0002-2172-2921
Margherita AddisAgris Sardegna, Servizio Ricerca Prodotti di Origine Animale, Agris Sardegna, Loc., Bonassai, 07040 Sassari, Italy.ORCID 0000-0002-1536-7433
Monica VaccaServizio Ricerca Studi Ambientali, Difesa delle Colture e Qualità delle Produzioni, Viale Trieste, 09123 Cagliari, Italy.
Cristina ManisDipartimento di Scienze della vita e Ambiente, Cittadella Universitaria di Monserrato Blocco A, 09012 Monserrato, Italy.
Pierluigi CaboniDipartimento di Scienze della vita e Ambiente, Cittadella Universitaria di Monserrato Blocco A, 09012 Monserrato, Italy.ORCID 0000-0003-2448-3767

Funding

Regione Autonoma della Sardegna (Italy) POR FESR 2014-2020 Azione 1.2.2. Programma di Ricerca e Sviluppo Agroindustria CUP F26C18000480002
6 · The paper itself

Abstract

Seasonal variation in fatty acids and minerals concentrations was investigated through the analysis of Pecorino Romano cheese samples collected in January, April, and June. A fraction of samples contained missing values in their fatty acid profiles. Probabilistic principal component analysis, coupled with Linear Discriminant Analysis, was employed to classify cheese samples on a production season basis while accounting for missing data and quantifying the missing fatty acid concentrations for the samples in which they were absent. The levels of rumenic acid, vaccenic acid, and omega-3 compounds were positively correlated with the spring season, while the length of the saturated fatty acids increased throughout the production seasons. Concerning the classification performances, the optimal number of principal components (i.e., 5) achieved an accuracy in cross-validation equal to 98%. Then, when the model was tasked with imputing the lacking fatty acid concentration values, the optimal number of principal components resulted in an

Indexed as

cheese seasonalityconjugated linoleic acidscross validationfatty acidslinear discriminant analysismetabolomicsmineralmissing dataOmega-3Pecorino Romano PDOprobabilistic principal component analysis

Identifiers

PMID37512584
PMCPMC10386313

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