In-process Estimation of Ascorbic Acid Assay Using Machine Learning Algorithms

Abstract

Abstract Demand for greater volume of pediatric vitamin C syrups become higher every year whilst maintaining an expected high quality Current laboratory techniques employ costly time-consuming and destructive testing which inhibit manufacturers to attain higher process efficiency In this study machine learning methods: random forest RF support vector machine SVM artificial neural network ANN and adaptive neuro-fuzzy inference system ANFIS are used for the estimation of percentage ascorbic acid %AA assay Likewise these methods are compared to conventional linear regression LR model and to each other pH specific gravity viscosity and %AA assay measurements were used for the training of the network Preprocessing technique involving data smoothing was employed on each nonlinear main effect relationship to reduce the noise and achieve better prediction accuracy Upon training it was found the ANN coupled with Bayeasian Regularization exhibited 02314 MSE a higher accuracy among other algorithms Furthermore it generally pushed ANN to be relatively more accurate than ANFIS with a minimal MSE of 030810