Description:
As the project grows, simple univariate analysis or peak fitting is often not enough to understand complex spectral datasets (especially for mapping/imaging data or large batches of spectra). We need to implement Principal Component Analysis (PCA) and potentially other Multivariate Analysis (MVA) techniques (such as K-Means clustering or PLS-DA) to help users identify hidden patterns, group similar spectra, and reduce data dimensionality.
Description:
As the project grows, simple univariate analysis or peak fitting is often not enough to understand complex spectral datasets (especially for mapping/imaging data or large batches of spectra). We need to implement Principal Component Analysis (PCA) and potentially other Multivariate Analysis (MVA) techniques (such as K-Means clustering or PLS-DA) to help users identify hidden patterns, group similar spectra, and reduce data dimensionality.