Modern analytical methods such as SIMS and ESCA provide researchers with a vast amount of data in a short time. Traditional data reduction methods are not only inefficient but also make it difficult to identify patterns in and relationships between variables. Multivariate statistical methods can permit a more efficient use of the data collected, as they provide tools to assist in the optimization, interpretation, and extension of surface analytical techniques. Current emphasis is on applying PCA analysis to SIMS spectra from protein mixtures to correlate spectral information with the amino acids present in the mixtures. Other areas of application being investigated include determination of spectral similarity applied to ESCA and TOF SIMS; quantitation of two overlapped components; computer spectral recognition; and correlation of surface properties with biological responses.
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