Yarahmadi Bita, Hashemianzadeh Seyed Majid, Milani Hosseini Seyed Mohammad-Reza
Real Samples Analysis Laboratory, Department of Chemistry, Iran University of Science and Technology, Tehran, Iran.
Molecular Simulation Research Laboratory, Department of Chemistry, Iran University of Science and Technology, Tehran, Iran.
Heliyon. 2023 Jul 12;9(7):e17953. doi: 10.1016/j.heliyon.2023.e17953. eCollection 2023 Jul.
The molecularly imprinted polymer (MIP) is useful for measuring the amount of riboflavin (vitamin B2), in various samples using UV/Vis instruments. The practical optimization of the MIP synthesis conditions has a number of drawbacks, like the need to spend money, the need to spend time, the use of the compounds that cause contamination, needing laboratory equipment and tools. Using machine learning (ML) to predict the amount of riboflavin absorbance is a creative solution to overcome the problems and shortcomings of optimizing polymer synthesis conditions. In fact, by using the model without needing real work in the laboratory, the optimum laboratory conditions are determined, and as a result the maximized absorption of the riboflavin is obtained. In this paper, MIP was synthesized for selective extraction of the riboflavin, and UV/Vis spectrophotometry was used to quantitatively measure riboflavin absorbance. Various factors affect the performance of the polymer. The effect of six important factors, including the molar ratio of the template, the molar ratio of monomer, the molar ratio of cross-linker, loading time, stirring rate, and pH, were investigated. Then, using ensemble ML algorithms, like gradient boosting (GB), extra trees (ET), random forest (RF), and Ada boost (Ada) algorithms, an accurate model was created to predict the riboflavin absorption. Also, the mutual information feature selection method was used to determine the important features. The results of using feature selection method was shown that variables such as the molar ratio of the template, the molar ratio of the monomer, and the molar ratio of the cross-linker had a high effect on riboflavin absorbance. The GB and Ada boost algorithms performed better than ET and RF algorithms. After tuning the n-estimator hyper parameter (n-estimator = 300), the GB algorithm was shown an excellent performance in predicting the absorbance of riboflavin and the maximum R-scoring of the model was obtained at 0.965995, the minimum of the mean absolute error (MAE), and mean square error (MSE) of the model respectively were obtained -0.003711 and -0.000078. Therefore, by using the proposed model, it is possible to predict riboflavin absorbance theoretically, and with high accuracy by changing the inputs of model, and using the model instead of working in the lab saves time, money, chemical compounds, and lab ware.
分子印迹聚合物（MIP）可用于使用紫外/可见仪器测量各种样品中核黄素（维生素B2）的含量。MIP合成条件的实际优化存在许多缺点，如需要花钱、需要花费时间、使用会造成污染的化合物、需要实验室设备和工具。使用机器学习（ML）预测核黄素吸光度是克服聚合物合成条件优化问题和缺点的一种创造性解决方案。事实上，通过使用该模型而无需在实验室进行实际操作，即可确定最佳实验室条件，从而获得核黄素的最大吸收。本文合成了用于选择性提取核黄素的MIP，并使用紫外/可见分光光度法定量测量核黄素吸光度。各种因素会影响聚合物的性能。研究了六个重要因素的影响，包括模板的摩尔比、单体的摩尔比、交联剂的摩尔比、负载时间、搅拌速率和pH值。然后，使用集成ML算法，如梯度提升（GB）、极端随机树（ET）、随机森林（RF）和Ada Boost（Ada）算法，创建了一个准确的模型来预测核黄素吸收。此外，使用互信息特征选择方法来确定重要特征。使用特征选择方法的结果表明，模板的摩尔比、单体的摩尔比和交联剂的摩尔比等变量对核黄素吸光度有很大影响。GB和Ada Boost算法的性能优于ET和RF算法。在调整n估计器超参数（n估计器 = 300）后，GB算法在预测核黄素吸光度方面表现出色，模型的最大R得分达到0.965995，模型的平均绝对误差（MAE）和均方误差（MSE）的最小值分别为 -0.003711和 -0.000078。因此，通过使用所提出的模型，可以从理论上预测核黄素吸光度，并且通过改变模型输入可以实现高精度预测，使用该模型代替在实验室工作可节省时间、金钱、化合物和实验室器具。
