An imaging platform based on broadband coherent anti-Stokes Raman scattering has been developed that provides an advantageous combination of speed, sensitivity and spectral breadth. The system utilizes a configuration of laser sources that probes the entire biologically relevant Raman window (500–3,500 cm^–1) with high resolution (<10 cm^–1). It strongly and efficiently stimulates Raman transitions within the typically weak ‘fingerprint’ region using intrapulse three-colour excitation, and utilizes the non-resonant background to heterodyne-amplify weak Raman signals. We demonstrate high-speed chemical imaging in two- and three-dimensional views of healthy murine liver and pancreas tissues as well as interfaces between xenograft brain tumours and the surrounding healthy brain matter.

Excitation with entangled photon pairs may lead to an increase in the efficiency of two-photon absorption at low photon flux. The corresponding process, entangled two-photon absorption (E2PA), has been investigated in numerous theoretical and experimental studies. However, significant ambiguity and inconsistency remain in the literature about the absolute values of E2PA cross sections. Here, we use a fluorescence-based registration scheme to experimentally determine upper bounds on the cross sections for six fluorophores. These bounds are up to 4 orders of magnitude lower than the smallest published cross section. For two samples that have been studied by others, Rhodamine 6G and 9R-S, we measure upper bounds 4 and 5 orders of magnitude lower than the previously reported cross sections.

PyMCR is a new open-source software library for performing multivariate curve resolution (MCR) analysis with an alternating regression scheme (MCR-AR). MCR is a chemometric method for elucidating measurement signatures of analytes and their relative abundance from a series of mixture measurements, without any knowledge of these values a priori. This software library, written in Python, enables users to perform MCR analysis with their choice of error functions for minimization, constraints, and regressors. Further, users can apply different constraints and regressors for signature and abundance calculations. Finally, this library enables users to develop their own constraints, regressors, and error functions or import them from existing libraries.