One of the issues when using PCA (or POD, Karhunen-Loève expansion... whatever your field calls it) on a dataset is that it optimizes the spectral content of ea

One of the issues when using PCA (or POD, Karhunen-Loève expansion... whatever your field calls it) on a dataset is that it optimizes the spectral content of each mode to obtain the best possible orthonormal basis for the reconstruction, so you can get very different frequencies in a single mode. Should you want to restrict this, you could use DMD (Dynamic Mode Decomposition, one frequency only per each mode) or mPOD (Multi-Scale Proper Orthogonal Decomposition, a restricted set of frequencies per mode).