Modelling of multilinear gradient retention time of bio-sweetener rebaudioside A in HPLC analysis

Abstract

Artificial neural network (ANN), as one of the artificial intelligence methods, has been widely using in HPLC studies for modelling purposes. Stevia rebaudiana is an important industrial plant due to its bio-sweetener molecule, rebaudioside-a, in its leaves. Although rebaudioside-a is up to 300-fold sweeter than sucrose, its calorie is almost zero. In this study, HPLC optimization of rebaudioside-a was studied and the optimization data based on multilinear gradient retention times were modelled by ANN. The input parameters were selected as concentrations, column temperatures, initial acetonitrile percentage for the first step of gradient elution, initial acetonitrile percentage for the second step of gradient elution, slope of acetonitrile, wavelengths, flow rates. The retention time was the output. Also, dried S. rebaudiana leaves were extracted and the concentrations were evaluated by HPLC. According to the ANN results, the most effective parameters on the prediction of non-linear gradient retention time for rebaudioside-a were found as flow rate and initial acetonitrile percentage for the second step of gradient. The best back propagation was selected as Levenberg-Marquardt algorithm. The highest rebaudioside-a level was found as 96.53 ± 6.36 μg mL-1. ANN modelling methods can be used in preparative HPLC applications to estimate the retention time of steviol glycosides.