Key points are not available for this paper at this time.
Chilacayote (Cucurbita ficifolia Bouché) is recognized as a rich source of nutrients and bioactive compounds, making it a promising ingredient for fortifying staple foods such as corn tortillas. While fortification can improve nutritional properties, it may also alter sensory characteristics that determine consumer acceptance. Therefore, a rigorous and structurally grounded assessment of these sensory modifications is required. In this study, sensory evaluations were conducted with regular tortilla consumers using Check-All-That-Apply (CATA) questionnaires to examine six attributes (color, smell, texture, taste, mouthfeel, and aftertaste) in tortillas made with nixtamalized dough and commercial flour, both with and without chilacayote powder. Then, a structured framework for dimensionality reduction and sensory profile identification of tortillas is proposed. In this framework, three classical feature extraction methods (Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and a combination of both (PCA+LDA)) were compared with an evolutionary discriminant approach (Differential Evolutionary Linear Discriminant Analysis for Feature Extraction and Visualization (DE-LDAFE)). The projection quality of these methods was evaluated using a multi-scale separability index that integrates global, semi-global, and local metrics, and the experiments were conducted considering global and attribute-based analyses. Beyond quantitative discrimination, the optimized projections enabled a geometric interpretation that allows the identification of sensory profiles for the tortilla variants. The proposed methodology bridges evolutionary optimization, structural separability assessment, and interpretable sensory characterization, offering a robust and adaptable strategy for multivariate food analysis and other complex discrimination problems and insights into the sensory impact of chilacayote fortification for the development of nutritionally enhanced tortillas that preserve consumer appeal.
Building similarity graph...
Analyzing shared references across papers
Loading...
Adriana-Laura López-Lobato
Universidad Veracruzana
Héctor-Gabriel Acosta-Mesa
Universidad Veracruzana
Efrn Mezura-Montes
Universidad Veracruzana
Mathematical and Computational Applications
Universidad Veracruzana
Universidad Autónoma del Estado de Hidalgo
Universidad de Xalapa
Building similarity graph...
Analyzing shared references across papers
Loading...
López-Lobato et al. (Sun,) studied this question.
synapsesocial.com/papers/6a15ae8ea2f71238514ea331 — DOI: https://doi.org/10.3390/mca31030082