Principal component analysis (PCA) was one of the earliest methods used for pump–probe data analysis, as it is commonly used for hyperspectral data analysis. PCA decomposes the data into orthogonal components. In melanoma studies, the principal components have shown good agreement with the signals obtained from the different forms of melanin. An advantage of PCA is that noise can be reduced by keeping only the principal components that account for majority of the variance in the data. However, the principal components do not necessarily reflect actual properties of the underlying chemical species, which are typically non-orthogonal. Therefore, a limitation is that the number of unique chemical species cannot be inferred using PCA.