Assessing the effect of season, montanera length and sampling location on Iberian pig fat by compositional data analysis and standard multivariate statistics. (15th October 2019)

• Record Type: Journal Article
• Authors: Garrido-Fernández, Antonio; León-Camacho, Manuel
• Is Part Of: Food chemistry. Volume 295(2019)
• Page Start: 377
• Page End: 386
• Publication Date: 2019-10-15
• Subjects: Fatty acid (FA) -- Pig fat -- Ham -- Rump -- Season -- Compositional data (CoDa) analysis -- Hierarchical clustering analysis (HCA) -- Discriminant analysis (DA); Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2019.05.123
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Formally, Compositional Data (CoDa) Analysis is appropriate to study Iberian pig fat. Variation array and biplot showed the greatest variances for C18:3, C17:1, and C20:0. Environmental factors explained only partially the changes in pig fat composition. Besides montanera feeding, phenotype may play sensible role in pig fat composition. Full fatty acid (FA) profile led to better/similar results than with the major ones. Abstract: The work studies the effect of season, montanera length, and sampling location on the Iberian pig fat, using compositional data (CoDa) analysis and standard statistics. CoDa variation array and logratios involving C18:3: (C18:3/C17:1), (C18:3/C20:0), and (C18:3/18:0) as well as the ilr balances (coordinates), based on C18:3, C17:1, and C20:0, showed the highest variances. Discriminant Analysis (DA) led to similar (conventional/coordinates) correct assignations regarding seasons (69/70%), montanera length (71/70%) and sampling location (68/67%). Re-analyzing the subcomposition of only the major fatty acids (FAs), led to slightly poorer results; therefore, the removed FAs might play a role in segregation. Results are in line with those from other authors and could indicate a partial capacity of pigs to control their FA profiles. Overall, CoDa analysis provided useful information on data variability, the effects of factors and, after conversion into coordinates, allowed applying standard statistics while being respectful with the compositional sampling space.