In recent years, multivariate modelling techniques have been employed with the aim of analysing, describing, and generally interpreting multidimensional data obtained from experiments. The objective of this study was to evaluate the applicability of multiple linear regression, nonlinear regression, piecewise linear regression, and artificial neural network modelling for the prediction of the physical properties (total dissolved solids, extraction yield), and chemical properties (total phenolic content and antioxidant activity) of the aqueous extracts of nine medicinal plants (dandelion, camomile, lavender, lemon balm, marigold, mint, nettle, plantain, and yarrow), prepared in dynamic experiments based on the extraction conditions (time and temperature), and plant species. Results indicated that simple multivariate regression models could be used for prediction of physical and chemical properties of medicinal plants aqueous extracts (the highest R² were obtained for total phenolic content), while the artificial neural network proved a very effective tool (R² > 0.9) for simultaneous prediction of both physical and chemical properties of medicinal plants aqueous extracts.

近年来，已采用多变量建模技术来分析，描述和一般解释从实验中获得的多维数据。这项研究的目的是评估多元线性回归，非线性回归，分段线性回归和人工神经网络建模的适用性，以预测物理性质（总溶解固体，提取产率）和化学性质（总酚含量）并根据提取条件（时间和温度）在动态实验中制备的九种药用植物（蒲公英，甘菊，薰衣草，柠檬香脂，万寿菊，薄荷，荨麻，车前草和欧arrow草）的水提取物，以及抗氧化活性），以及植物品种。R ²获得了总酚含量），而人工神经网络证明了非常有效的工具（R ² > 0.9），用于同时预测药用植物水提取物的理化性质。