Autoregressive modeling of near-IR spectra and MLR to predict RON values of gasolines

Abstract

A new calibration method that accurately predicts the Research Octane Number (RON) values of gasoline fractions, based on their infrared spectra, is presented. This model combines Linear Predictive Coding (LPC) and multiple linear regression (MLR) as an integrated estimation technique. Spectral information from the 4800–3520 cm −1 range was initially encoded into Linear Predictive (LP) coefficients, which were used as predictor variables in the MLR model against RON values. The model was trained and tested on an extensive data set (384 gasoline samples) and found to ensure prediction accuracy of 0.3 RON Root Mean Squared Error (RMSE). The LPC technique was found to be efficient in capturing spectral features of the entire range, related to the RON characteristics of the gasoline samples, without the need of any pretreatment on the experimental raw data. The small number of input variables in the regression model ensures a robust, easy-to-use and high accuracy prediction model.