Characterizing the Uncertainty in Near-Infrared Spectroscopic Prediction of Mixed-Oxygenate Concentrations in Gasoline: Sample-Specific Prediction Intervals

Abstract

Oxygenates are added to gasoline to reduce exhaust emission levels of carbon monoxide and to boost octane. The U.S. National Institute of Standards and Technology (NIST) provides 12 Standard Reference Materials (SRMs) for single oxygenates in reference gasoline. A previous study demonstrated the feasibility of nondestructively quantifying oxygenate concentration in SRM gasoline ampules using near-infrared (near-IR) spectroscopy combined with multivariate calibration techniques. A drawback of this approach has been that an average prediction uncertainty, rather than a sample-specific one, is obtained. Recent developments in multivariate calibration theory for prediction error variance cure this problem. This report characterizes the significant sources of uncertainties in multivariate calibration using principal component regression and partial least-squares, validating near-IR and other multivariate spectroscopic techniques for use in assigning certified values (expected value with specified uncertainty) to selected materials. This report interprets prediction results in terms of multivariate analytical figures of merit, enabling the visualization of complex multivariate models as univariate graphs.