Prediction range estimation from noisy Raman spectra with robust optimization

摘要

Inferences need to be drawn in biological systems using experimental multivariate data. The number ofnsamples collected in many such experiments is small, and the data are noisy. We present and study thenperformance of a robust optimization (RO) model for such situations. We adapt this model to generatena minimum and a maximum estimation of analyte concentration for a given sample, producingna prediction range. The calibration model was applied to sets of Raman spectra. In particular we usednnormal Raman measurements of pyridine/deuterated pyridine mixtures and spectra from a morencomplex glucose detection system based on surface-enhanced Raman spectroscopy. The results fromnthe RO model were compared with prediction intervals estimated from partial least squares (PLS)nmethod. We find that the RO prediction ranges included the actual concentration value of the samplenmore consistently than the 99% prediction intervals built with PLS methods.