PCA is a method for reducing the 100 variables (wavelength data) in each spectrum down to just a few important variables. These variables are often referred to as latent variables, principal components, factors, eigenvectors, etc, and are vectors. This manual will refer to them as PC’s. The dot product of these vectors with the spectral data yields scalars called “PC scores”. Unknowns can be identified by comparing the PC scores of unknown materials to those of the model.
As an alternative to PCA, Spectral Matching may be used as a material identification method. This is particularly useful for large numbers of categories. Spectral Matching compares the shape of each spectrum with each spectrum in the library and assigns a “degree of match” value ranging from ‑1 (perfectly anti-matched) to +1 (perfect match) using a proprietary algorithm. The library entries that have the highest match values to the unknown sample are then used to identify the unknown.
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