Multivariate analysis extracts the most important features on SIMS images. The example shows the shampoo/conditioner ingredients automatically identified on human hair.

Secondary Ion Mass Spectrometry (SIMS) is a powerful technique capable of generating surface spectra, images and depth profiles, each containing large volumes of molecular chemical information. For example, SIMS imaging generates a 3D 'datacube' containing a complete SIMS spectrum at each pixel. Multivariate methods provide a fast, objective and statistically valid approach to exploit the wealth of information obtained from SIMS. NPL is developing robust and validated methodologies for identification, classification and quantification of SIMS data using multivariate methods. This includes principal component analysis (PCA), multivariate curve resolution (MCR), partial least squares regression (PLS) and discriminant analysis (DA). We provide guidance in choosing the appropriate multivariate method, suitable data scaling, and strategies to deal with additional data complexities including sample topography and detector saturation. The aim is to enhance the application of multivariate methods to cope with complex 'real-world' data, and provide statistically robust insight into the key surface chemistries.

Selected Publications

• The application of multivariate data analysis techniques in surface analysis
  J L S Lee and I S Gilmore
  Surface Analysis – The Principal Techniques 2nd Edition (Eds: J C Vickerman, I S Gilmore), Wiley, Chichester, UK
• Multivariate image analysis strategies for ToF-SIMS images with topography
  J L S Lee, I S Gilmore, I W Fletcher and M P Seah
  Surf. Interface Anal. Surf. Interface Anal. 41(8) (2009) 653-665
• Quantification and methodology issues in multivariate analysis of ToF-SIMS data for mixed organic systems
  J L S Lee, I S Gilmore and M P Seah
  Surf. Interface Anal. 40 (2008) 1-14

NESAC/BIO Multivariate Surface Analysis website