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. 2023 Jul 24;13(7):877.
doi: 10.3390/metabo13070877.

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  1 , Massimiliano Grosso  1 , Stefania Tronci  1 , Massimiliano Errico  2 , Margherita Addis  3 , Monica Vacca  4 , Cristina Manis  5 , Pierluigi Caboni  5
Affiliations

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 et al. Metabolites. .

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 R2 value in cross-validation of 99.53%.

Keywords: Omega-3; Pecorino Romano PDO; cheese seasonality; conjugated linoleic acids; cross validation; fatty acids; linear discriminant analysis; metabolomics; mineral; missing data; probabilistic principal component analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Score plot obtained from PPCA, and the relative boundaries obtained from LDA delimiting the regions to which the three different classes belong: Pecorino Romano produced in January, April, and June.
Figure 2
Figure 2
External cross-validation results. The performance regarding PPCA is shown for each PC (a), while the results of PLS-DA are presented with respect to the number of LVs (b). The accuracy of classification is expressed as the percentage of correctly classified samples.
Figure 3
Figure 3
PPCA Biplot: Scores related to Pecorino Romano samples produced in January, April, and June are reported in blue, green, and red, respectively. Loadings related to the metabolites are reported in black, whereas those related to minerals are in orange.
Figure 4
Figure 4
Root mean square error of cross-validation for PPCA and PLS models while varying the number of PCs and LVs, respectively.
See this image and copyright information in PMC

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