Hierarchical principal component analysis (PCA) and projection to latent structure (PLS) technique on spectroscopic data as a data pretreatment for calibration

• Spectroscopic data consists of several hundred to some thousand variables, wherein most of the variables are: autocorrelated. When PCA and PLS techniques are used for the interpretation of these kinds of data, the loading plots are usually complex due to

Nyckelord

hierarchical; multiplicative scattering correction (MSC); near infrared spectroscopy (NIR); infrared spectroscopy (IR); principal component analysis (PCA); projection to latent structures; chemometrics; qualimetrics; MODELS

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