A principal components algorithm for spectra normalisation

There is an increasing use of spectroscopic techniques, such as high-resolution NMR spectroscopy, to examine variations in cell metabolism and/or structure in response to numerous physical, chemical, and biological agents. In these types of studies, in order to obtain relative quantitative information, a comparison between signal intensities of control samples and treated or exposed ones is often conducted. A possible strategy is to estimate, by an opportune algorithm, a normalisation constant which takes into consideration all cell metabolites in the sample. In this paper, a new normalisation algorithm based on Principal Component Analysis (PCA) is presented. PRICONA (PRIncipal COmponent Normalisation Algorithm) is advantageous in normalising simultaneously great data sets of spectra, in individuating signals that could have been affected by the agent, and in allowing to measure their quantitative variation. The algorithm was tested by Monte Carlo simulations as well as experimentally.