Dataset and response surface modeling tools for data-driven optimization of a functional cupuassu (Theobroma grandiflorum) cremogenate.

Authors

Paola A García-Rincón, Andrés F Bahamón-Monje, Gentil A Collazos-Escobar, Nelson Gutiérrez-Guzmán

Published in

Data in brief. Volume 69. Pages 113305. Epub Oct 01, 2026.

Abstract

This article reports an experimental dataset for the optimization of a functional cupuassu (Theobroma grandiflorum) cremogenate formulated with Lactobacillus casei ATCC 393 and inulin. The data were obtained using a central composite design and computational Response Surface Methodology (RSM) tools to assess the multivariate effects of probiotic biomass (2.0, 2.25, and 2.5%), inulin concentration (2, 3, and 4%), and homogenization time (5, 7.5, and 10 min) on key quality attributes of the cupuassu-based cremogenate. The measured response variables included moisture content (%wet basis), water activity, viscosity (cP), hydrogen potential (pH), soluble solids content (°Brix), particle-size distribution (D10, D50, and D90; µm), zeta potential (mV), and probiotic viability determined by colony counting (CC, log CFU/g). The dataset provides a robust experimental framework for elucidating the interactions between formulation and processing variables affecting the quality attributes of a functional cupuassu-based cremogenate. Furthermore, this work presents a computational workflow implemented in MATLAB for response surface modeling, statistical prediction, and process optimization. The scripts support the fitting of second-order polynomial models, visualization of response surfaces, analysis of factor interactions, surface response optimization, and prediction of optimal formulation conditions. These computational tools enhance reproducibility and offer a flexible framework for extending optimization strategies to other functional food systems. The integration of the experimental and computational datasets constitutes a reliable basis for the rational design of probiotic fruit-based products and for the application of advanced statistical optimization approaches in food process engineering.

PMID:
42893246
Bibliographic data and abstract were imported from PubMed on 18 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.