Gayathri K B, Roy S
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632 014, India.
Sci Rep. 2025 Jul 8;15(1):24557. doi: 10.1038/s41598-025-08002-5.
Food preservatives play a crucial role in extending the shelf life of food products. Understanding their physicochemical properties can help in designing more effective and safer preservatives. In this study, we use a Quantitative Structure Property Relationship (QSPR) approach based on topological indices to develop a predictive model for certain physicochemical properties of food preservatives. We compare the performance of linear and curvilinear regression models to understand which provides the best prediction model. Among the tested models, the cubic regression model demonstrated superior predictive performance. Of all the models tested, the cubic regression model had the best predictive capabilities such as [Formula: see text] for vapour density and [Formula: see text] for molecular weight. To validate our findings, we employ the developed model to estimate the properties of an existing food preservative, the propionic acid. Our results offer valuable insights that can aid in the development of new and improved food preservatives.
食品防腐剂在延长食品保质期方面起着至关重要的作用。了解它们的物理化学性质有助于设计出更有效、更安全的防腐剂。在本研究中，我们使用基于拓扑指数的定量结构-性质关系（QSPR）方法来开发食品防腐剂某些物理化学性质的预测模型。我们比较线性和曲线回归模型的性能，以了解哪种模型能提供最佳预测模型。在测试的模型中，三次回归模型表现出卓越的预测性能。在所有测试模型中，三次回归模型具有最佳的预测能力，例如蒸汽密度的[公式：见正文]和分子量的[公式：见正文]。为了验证我们的发现，我们使用开发的模型来估计现有食品防腐剂丙酸的性质。我们的结果提供了有价值的见解，有助于开发新的和改进的食品防腐剂。
