Parinet Julien
Université de Paris-Est, ANSES, Laboratory for Food Safety, 94700, Maisons-Alfort, France.
Heliyon. 2021 Dec 7;7(12):e08563. doi: 10.1016/j.heliyon.2021.e08563. eCollection 2021 Dec.
To be able to predict reversed phase liquid chromatographic (RPLC) retention times of contaminants is an asset in order to solve food contamination issues. The development of quantitative structure-retention relationship models (QSRR) requires selection of the best molecular descriptors and machine-learning algorithms. In the present work, two main approaches have been tested and compared, one based on an extensive literature review to select the best set of molecular descriptors (16), and a second with diverse strategies in order to select among 1545 molecular descriptors (MD), 16 MD. In both cases, a deep neural network (DNN) were optimized through a gridsearch.
能够预测污染物的反相液相色谱（RPLC）保留时间对于解决食品污染问题是一项优势。定量结构-保留关系模型（QSRR）的开发需要选择最佳的分子描述符和机器学习算法。在本研究中，测试并比较了两种主要方法，一种基于广泛的文献综述来选择最佳的分子描述符集（16个），另一种采用多种策略以便从1545个分子描述符（MD）中选择16个MD。在这两种情况下，都通过网格搜索对深度神经网络（DNN）进行了优化。
