Multi-block chemometric approaches to the unsupervised spectral classification of geological samples

In this paper, the potential use of multi-block chemometric methods to provide improved unsupervised classification of compositionally complex materials through the integration of multi-modal spectrometric data sets (one XRF, two NIR, and two FT-Raman) was tested. We concluded that multi-block HPLS models are effective at combining multi-modal spectrometric data to provide a more comprehensive classification of compositionally complex samples, and VIP can reduce HPLS model complexity, while increasing its data interpretability.