Multivariate characterization of the nutritional diversity of traditional Ecuadorian foods.

Authors

Orlando Meneses Quelal

Published in

Frontiers in nutrition. Volume 13. Pages 1913247. Epub Aug 06, 2026.

Abstract

The availability of food composition databases is a fundamental tool for nutritional research, public health, and food policy design. However, the multivariate structure of the nutritional diversity of traditional Ecuadorian foods remains understudied. The objective of this research was to characterize the nutritional composition of traditional Ecuadorian foods using descriptive, inferential, and multivariate analyses. The Food Composition Table of Cuenca, Ecuador, comprising 129 foods distributed across ten food groups, was used. Because this database was compiled from foods sampled primarily in the southern Andean region of Ecuador, the findings characterize the nutritional structure of this regional database rather than Ecuador as a whole. Ten nutritional variables related to energy, macronutrients, and minerals were analyzed. Differences between groups were evaluated using one-way ANOVA, while relationships between nutrients were studied using Pearson correlations. Additionally, principal component analysis (PCA) and hierarchical cluster analysis were applied to identify nutritional patterns. The results showed significant differences between food groups for energy (F = 23.71; η² = 0.614), protein (F = 20.38; η² = 0.641), carbohydrates (F = 18.61; η² = 0.560), fat (F = 8.97; η² = 0.491), calcium (F = 3.00; η² = 0.178), and zinc (F = 11.15; η² = 0.446) (p < 0.05). The strongest correlation was observed between energy and water (r = -0.972), followed by energy and carbohydrates (r = 0.783). The PCA showed that the first two components explained 49.6% of the total variability and revealed two main gradients: energy density and protein-mineral profile. Cluster analysis identified four distinct nutritional profiles. The results demonstrate that the nutritional diversity of Ecuadorian foods is organized around well-defined compositional patterns, providing a scientific basis for future applications in nutrition, epidemiology, and sustainable food systems.

PMID:
42626006
Bibliographic data and abstract were imported from PubMed on 21 Aug 2026.

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