A new approach to prediction riboflavin absorbance using imprinted polymer and ensemble machine learning algorithms.

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.