Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Investigation of Seasonal Variation of 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
ORCID logo ,
Margherita Addis
ORCID logo ,
Monica Vacca
,
Cristina Manis
ORCID logo ,
Pierluigi Caboni
ORCID logo
Version 1 : Received: 30 June 2023 / Approved: 30 June 2023 / Online: 30 June 2023 (14:57:36 CEST)

A peer-reviewed article of this Preprint also exists.

Sibono, L.; Grosso, M.; Tronci, S.; Errico, M.; Addis, M.; Vacca, M.; Manis, C.; Caboni, P. 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. Metabolites 2023, 13, 877. Sibono, L.; Grosso, M.; Tronci, S.; Errico, M.; Addis, M.; Vacca, M.; Manis, C.; Caboni, P. 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. Metabolites 2023, 13, 877.

Abstract

The 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 profile. 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 acids concentration for the sample in which they were absent. The levels of rumenic acid, vac-cenic 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 ac-curacy in cross-validation equal to 98 %. Then, when the model was tasked to impute the lacking Fatty acid concentration values, the optimal number of principal components resulted in an R2 value in cross-validation of 99.53%

Keywords

Metabolomics; Cheese seasonality; Pecorino Romano PDO; Conjugated linoleic acids; Omega-3; Fatty acid; Mineral; Probabilistic Principal Component Analysis; Linear Discriminant Analysis; Cross validation

Subject

Biology and Life Sciences, Food Science and Technology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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