Prediction range estimation from noisy Raman spectra with robust optimization

Olga Lyandres, Richard P. Van Duyne, Joseph T. Walsh, Matthew R. Glucksberg, Sanjay Mehrotra*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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

Original languageEnglish (US)
Pages (from-to)2111-2118
Number of pages8
Issue number8
StatePublished - Aug 2010

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Spectroscopy
  • Electrochemistry
  • Environmental Chemistry


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