Multiple-output multivariate optical computing for spectrum recognition

We describe a multivariate optical computer that can implement multiple spectral filters simultaneously. By parallel detection of multiple outputs, our proposed approach is capable of identifying more than two spectra simultaneously, and therefore could significantly speed up spectrum recognition based on optical computing. We demonstrate our approach by recognizing two rare-earth-doped glass samples and a third white light sample spectrum with a fidelity of at least 0.83.