The justified continuous emerging of new β-lactam antibiotics provokes the need for developing suitable analytical methods that accelerate and facilitate their analysis. A face central composite experimental design was adopted using different levels of phosphate buffer pH, acetonitrile percentage at zero time and after 15 min in a gradient program to obtain the optimum chromatographic conditions for the elution of 31 β-lactam antibiotics. Retention factors were used as the target property to build two QSRR models utilizing the conventional forward selection and the advanced nature-inspired firefly algorithm for descriptor selection, coupled with multiple linear regression. The obtained models showed high performance in both internal and external validation indicating their robustness and predictive ability. Williams-Hotelling test and student’s t-test showed that there is no statistical significant difference between the models’ results. Y-randomization validation showed that the obtained models are due to significant correlation between the selected molecular descriptors and the analytes’ chromatographic retention. These results indicate that the generated FS-MLR and FFA-MLR models are showing comparable quality on both the training and validation levels. They also gave comparable information about the molecular features that influence the retention behavior of β-lactams under the current chromatographic conditions. We can conclude that in some cases simple conventional feature selection algorithm can be used to generate robust and predictive models comparable to that are generated using advanced ones.

有理由不断涌现出新的β-内酰胺类抗生素，因此需要开发合适的分析方法以加速和促进其分析。采用面部中心复合实验设计，在梯度程序中使用不同水平的磷酸盐缓冲液pH，乙腈百分比（在零时间和15分钟后），以获得洗脱31种β-内酰胺抗生素的最佳色谱条件。保留因子被用作目标属性，以利用传统的前向选择和先进的自然启发性萤火虫算法进行描述符选择，以及多重线性回归，建立两个QSRR模型。获得的模型在内部和外部验证中均显示出高性能，表明它们的鲁棒性和预测能力。Williams-Hotelling考试和学生的t检验表明，模型结果之间没有统计学上的显着差异。Y随机验证表明，所获得的模型是由于所选分子描述符与分析物的色谱保留率之间的显着相关性所致。这些结果表明，所生成的FS-MLR和FFA-MLR模型在训练和验证水平上均显示出可比的质量。他们还提供了有关影响当前色谱条件下β-内酰胺保留行为的分子特征的可比信息。我们可以得出结论，在某些情况下，可以使用简单的常规特征选择算法来生成可与使用高级模型生成的模型相媲美的鲁棒和预测模型。