MULTIVARIATE ANALYSIS AND ARTIFICIAL NEURONAL NETWORKS TO DEFINE THE IDENTITY TRAITS AND QUALITY PROFILE OF MANDARIN CLEMENULES: MVA AND ANN FOR THE IDENTITY AND QUALITY OF CLEMENTINE MANDARINS - Canelo Barrado, Carlos (2025)
This study analyzes the physical, organoleptic, and nutritional characteristics of the clemenules mandarin, produced in the Plana Baja region of Castellón (Spain). Through tasting tests and statistical analysis, consumers' perception of quality was assessed, identifying the determining factors of the product's quality and identity. More than 200 scientific articles from the last 30 years were reviewed. This study is distinguished by its innovative and objective methodology. While conventional sensory analyses are typically performed in producing areas, this study was conducted in Ávila, a city distant from the production area and with no citrus-growing tradition, minimizing biases associated with geographical proximity to obtain more impartial assessments. In addition to integrating qualitative and quantitative approaches compared to previous studies that have addressed the quality of clemenules from an agronomic, genetic, or sustainability perspective, this work introduces a multi-methodological analysis that combines Content Analysis, inferential methods, and the use of Artificial Neural Networks (ANN). This approach allows for the identification of complex patterns in sensory perception and consumer behavior, providing deeper and more predictive information. The study seeks to identify the sensory characteristics appreciated by consumers, develop a distinctive organoleptic profile, and evaluate its nutritional value in relation to product acceptance and its impact on healthy eating.