Data of analysis of the influence of microparticle morphology on the qualitative state of spray-dried fruit with the use of machine learning

The dataset comprises analyses of the physical properties of currant powders obtained through dehumidified air-assisted spray drying. The numerical data pertains to various datasets of physical properties of these powders, including moisture content and water activity, Lab color and Fourier Transform Infrared (FTIR) spectroscopy. Individual learning sets describing currant powders with the smallest (30%) and the largest (50%) proportion and the selected carrier type were deposited. Learning sets for color analysis were deposited, i.e. Multi-Layer Perceptron Networks (MLPNs) based on color descriptor for currant powders. A summary of MLPN models identifying currant powders in relation to ratio and content of the carrier was included.

本数据集包含对经除湿空气辅助喷雾干燥制备的黑加仑粉物理性质的分析结果。数值数据涉及该类粉体的多种物理性质数据集，包括水分含量、水分活度、Lab颜色参数及傅里叶变换红外光谱（FTIR）数据。本数据集还存储了描述载体比例最小（30%）、最大（50%）及选定载体类型的黑加仑粉的独立学习集。此外，存储了用于颜色分析的学习集，即基于黑加仑粉颜色描述符构建的多层感知器网络（MLPNs）模型相关数据集。数据集还包含了基于载体比例及含量识别黑加仑粉的MLPN模型总结报告。