Dataset for functional, sensory, rheological, physicochemical, and economic optimization of premium and standard cocoa blends for dark chocolate formulation

Description

This dataset contains the experimental data used to evaluate and optimize dark chocolate formulations prepared with different proportions of premium- and standard-quality cocoa beans. The formulations were developed at three cocoa liquor levels (60%, 70%, and 80%) and five premium cocoa proportions in the blend (0%, 20%, 50%, 80%, and 100%). The dataset includes physicochemical, functional, rheological, colorimetric, methylxanthine, and sensory response variables measured in the chocolate samples. The physicochemical data include moisture, pH, titratable acidity, fat content, and instrumental color parameters. Functional quality was assessed through total polyphenol content and antioxidant capacity. The methylxanthine profile includes theobromine and caffeine concentrations determined in the different formulations. Rheological behavior was characterized using viscosity-related parameters, while sensory data include descriptive attributes associated with bitterness, astringency, acidity, fruity notes, cocoa/chocolate character, and overall quality. The dataset also supports the development of response surface models and multi-response optimization based on the Derringer–Suich desirability function. Three independent optimization scenarios are represented: an economic scenario focused on reducing the proportion of premium cocoa, a functional scenario aimed at maximizing bioactive-related responses, and a sensory scenario focused on maximizing overall sensory quality and balanced flavor expression. These data provide a reproducible basis for analyzing how cocoa liquor percentage and the proportion of premium cocoa influence the technological, functional, sensory, and economic performance of dark chocolate formulations. The dataset may be useful for researchers, chocolate manufacturers, and product developers interested in cocoa blending, functional food formulation, response surface methodology, and multi-criteria optimization of chocolate quality.

Institutions

• Colombian Corporation for Agricultural Research - AGROSAVIA

  Cundinamarca, Mosquera
• Universidad Surcolombiana

  Huila Department, Neiva

Categories

Funders

• Sistema General de Regalías de Colombia

  Bogotá

  Grant ID: 2021000100166

Licence