PCA and Exploratory Analysis

Principal Component Analysis reduces a spectral matrix into latent variables that explain major sources of variation.

Use PCA For

• checking sample clustering
• spotting outliers
• assessing preprocessing
• inspecting loadings
• finding instrument or batch drift
• deciding whether calibration or classification is plausible

Outputs to Inspect

• scores: sample positions in PC space
• loadings: spectral variables contributing to each PC
• explained variance / scree
• Hotelling T2 and Q residual diagnostics when available

Practical Interpretation

Scores show sample relationships. Loadings explain which spectral regions drive those relationships. A good PCA review looks at both, not only the score plot.