Bahamón-Monje Andrés F, García-Rincón Paola A, Collazos-Escobar Gentil A, Amorocho-Cruz Claudia M, Gutiérrez-Guzmán Nelson
Centro Surcolombiano de Investigación en Café (CESURCAFÉ), Departamento de Ingeniería Agrícola, Universidad Surcolombiana, Neiva-Huila 410001, Colombia.
Departamento de Ingeniería Agroindustrial, Facultad de Ingeniería, Universidad Surcolombiana, Neiva, Huila, Colombia.
Data Brief. 2025 May 28;61:111720. doi: 10.1016/j.dib.2025.111720. eCollection 2025 Aug.
This study presents a comprehensive dataset of water desorption isotherms and infrared spectral data for cupuassu pulp, a by-product with significant potential for value-added applications in the food industry. The dataset includes experimentally determined desorption isotherms within a water activity range of 0.1-1 at 25 °C, obtained using the Dynamic Dewpoint Isotherm (DDI) method. Infrared spectra were acquired via Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy, covering the wavenumber range of 4000-650 cm with a resolution of 8 cm. Additionally, mathematical modeling and multivariate statistical tools were applied to analyze water desorption behavior, optimizing low-temperature energy consumption and ensuring maximum storage stability. Preprocessing techniques, including baseline correction, Standard Normal Variate (SNV), and Multiplicative Scatter Correction (MSC), were employed to enhance spectral data quality. Principal Component Analysis (PCA), based on scores-loadings computation, was utilized for exploratory analysis of the cupuassu pulp spectral fingerprint. The dataset is provided in Excel format, organized by experimental conditions and replicates. Moreover, MATLAB® R2023a (The MathWorks Inc., Natick, MA, USA) scripts for multivariate statistical analysis were implemented to facilitate model-based assessment of water desorption and infrared properties. In this regard, two MATLAB scripts detail the step-by-step mathematical modeling of isotherms using Peleg, Caurie, and White & Eiring sorption models, as well as Gibbs free energy computation for moisture stability optimization. Furthermore, two MATLAB scripts are dedicated to spectral preprocessing and PCA: one prompts the user to select preprocessing techniques, including the option for sequential application, and visualizes the resulting infrared spectra; the other enables PCA calibration based on the selected preprocessed data. This research provides valuable insights for the food industry, supporting informed decision-making in cupuassu pulp processing. By improving food-processing strategies, ensuring product consistency, and optimizing dehydration processes, this dataset contributes to the development of value-added products from cupuassu by-products, promoting sustainability and resource efficiency.
本研究展示了一组关于可可李果肉的水脱附等温线和红外光谱数据的综合数据集，可可李果肉是一种在食品工业中具有显著增值应用潜力的副产品。该数据集包括在25℃下使用动态露点等温线（DDI）方法在0.1 - 1的水分活度范围内通过实验测定的脱附等温线。红外光谱通过衰减全反射傅里叶变换红外（ATR - FTIR）光谱法获得，覆盖4000 - 650 cm的波数范围，分辨率为8 cm。此外，应用数学建模和多元统计工具来分析水脱附行为，优化低温能耗并确保最大储存稳定性。采用了包括基线校正、标准正态变量变换（SNV）和多元散射校正（MSC）在内的预处理技术来提高光谱数据质量。基于得分 - 载荷计算的主成分分析（PCA）用于可可李果肉光谱指纹的探索性分析。数据集以Excel格式提供，按实验条件和重复进行组织。此外，还实现了用于多元统计分析的MATLAB® R2023a（美国马萨诸塞州纳蒂克市MathWorks公司）脚本，以促进基于模型的水脱附及红外特性评估。在这方面，两个MATLAB脚本详细介绍了使用佩莱格、考里以及怀特和艾林吸附模型对等温线进行逐步数学建模，以及用于水分稳定性优化的吉布斯自由能计算。此外，还有两个MATLAB脚本专门用于光谱预处理和PCA：一个提示用户选择预处理技术，包括顺序应用选项，并可视化所得红外光谱；另一个基于所选预处理数据进行PCA校准。本研究为食品工业提供了有价值的见解，支持可可李果肉加工中的明智决策。通过改进食品加工策略、确保产品一致性以及优化脱水过程，该数据集有助于从可可李副产品开发增值产品，促进可持续性和资源效率。
