I have vector spectral data that I collected from samples, and I want to compare with USGS spectral library. The goal is to figure out what are the major minerals in the samples that dominate the spectral signal and what is the approximate percentage of the major end-members.
The collected spactral data were vectors with 2151 x 1. The USGS library also provide vector data with 2151 x 1.With the selected potential N end-member minerals. I was thinking about creating a matrix of 2151 x N and compare with the samples' spectral vectors.
I tried to find the related function online but most of them were related to image unmixing process. I think what I am doing is close to PCA, eigenvalue, or SVD. Such as: 60% of mineral A, 30% pf mineral B, and 5% of mineral C ca explain the collected spectrum. Is there any function that could possibily support my analysis between vector and matrix? Thanks for any guidence or suggestion of starting points.