Dataset on storage stability and quality changes of achira (Canna edulis K.) biscuits: water adsorption isotherms and machine learning-based shelf-life monitoring
Description
This dataset provides comprehensive information on the storage stability and quality changes of achira (Canna edulis K.) biscuits, incorporating experimental data and digital models for in-depth analysis. It includes initial characterization data, covering color properties, moisture content, water activity, and texture. Additionally, the dataset contains mid-infrared spectra obtained using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy to characterize the biscuits spectrally. The water adsorption isotherms were determined using the Dynamic Dewpoint Isotherm (DDI) method, considering a water activity range from 0.1 to 0.8 at temperatures of 25, 35, and 45 ºC. To enhance predictive modeling, the dataset includes MATLAB code for Support Vector Machine (SVM) modeling, which was used to model adsorption isotherms and optimize the predictive capability of this technique. Additionally, storage assessment data track quality changes over time by analyzing variations in color, moisture content, water activity, texture, and sensory quality of packaged biscuits. Finally, MATLAB-based digital models have been developed to describe and optimize quality changes during storage, providing a model-based approach for understanding and improving product stability. This dataset serves as a valuable resource for researchers studying food stability, predictive modeling, and shelf-life optimization strategies.
Files
Institutions
- Universidad Surcolombiana
- Universitat Politecnica de Valencia