Reliable simulation of retention factor (k) is crucial in high-performance liquid chromatography (HPLC) method development. In this research, three different Artificial intelligence (AI) based models, namely the multi-layer perceptron (MLP), Support vector machine (SVM) and Hammerstein–Weiner (HW) models, were employed as well as three ensemble techniques, i.e., neural network ensemble (NNE), weighted average ensemble (WAE) and simple average ensemble (SAE) to predict k for HPLC method development. In this context, the pH and composition of the mobile phase (methanol) are used as the input variables with the corresponding Methyclothiazide (M) and Amiloride (A) as antihypertensive target analytes. The performance efficiency of the models was evaluated using mean square error (MSE), determination coefficient (R2), and correlation coefficient (R). The results obtained from the single models showed that MLP outperformed the other two models and increased the prediction accuracy up to 1% and 3% for the HW and SVM models, respectively, for the prediction of M. However, for the prediction of A, SVM outperformed the other two models and increased the prediction accuracy up to 7% and 6% for HW and MLP, respectively. In the ensemble technique, the results obtained for the prediction of both M and A demonstrated that NNE increased the performance accuracy by 14% of the single models. Also, NNE proved to be superior to the two linear ensembles and improved the prediction accuracy up to 14% and 2% for SAE and WAE, respectively, for the simulation of M with R2 = 0.9962 and 0.9949 for both calibration and verification, and up to 9% and 6% for A with R2 = 0.9606 and 0.9569 for both calibration and verification phases respectively. The overall results depicted the reliability and robustness of both the AI-based models and justified the enhancement capability for ensemble techniques for both the two analytes.

中文翻译：

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一种用于预测 HPLC 方法开发中保留因子的新型多模型数据驱动集成技术

保留因子 (k) 的可靠模拟对于高效液相色谱 (HPLC) 方法开发至关重要。在这项研究中，采用了三种不同的基于人工智能 (AI) 的模型，即多层感知器 (MLP)、支持向量机 (SVM) 和 Hammerstein-Weiner (HW) 模型以及三种集成技术，即，神经网络集合 (NNE)、加权平均集合 (WAE) 和简单平均集合 (SAE) 来预测 HPLC 方法开发的 k。在这种情况下，流动相（甲醇）的 pH 值和组成用作输入变量，相应的甲噻嗪 (M) 和阿米洛利 (A) 作为抗高血压目标分析物。使用均方误差 (MSE)、决定系数 (R2) 和相关系数 (R) 评估模型的性能效率。从单个模型获得的结果表明，MLP 优于其他两个模型，并且对于 HW 和 SVM 模型，对于 M 的预测，预测精度分别提高了 1% 和 3%。 然而，对于 A 的预测， SVM 优于其他两种模型，并将 HW 和 MLP 的预测精度分别提高了 7% 和 6%。在集成技术中，M 和 A 的预测结果表明，NNE 将性能精度提高了 14% 的单个模型。此外，NNE 被证明优于两个线性集成，并将 SAE 和 WAE 的预测精度分别提高了 14% 和 2%，对于 M 的模拟，R2 = 0.9962 和 0.9949，用于校准和验证，以及更高对于 A，R2 = 0.9606 和 0，分别为 9% 和 6%。9569 分别用于校准和验证阶段。总体结果描述了基于 AI 的模型的可靠性和稳健性，并证明了这两种分析物的集成技术的增强能力是合理的。