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Acquisition of classical absorption spectra of liquids in the mid-IR range with quantum cascade lasers (QCLs) is often limited in sensitivity by noise from the laser source. Alternatively, measurement of molecular dispersion (i.e., refractive index) spectra poses an experimental approach that is immune to intensity fluctuations and further offers a direct relationship between the recorded signal and the sample concentration. In this work, we present an external cavity quantum cascade laser (EC-QCL) based Mach–Zehnder interferometer setup to determine dispersion spectra of liquid samples. We present two approaches for acquisition of refractive index spectra and compare the qualitative experimental results. Furthermore, the performance for quantitative analysis is evaluated. Finally, multivariate analysis of a spectrally complex mixture comprising three different sugars is performed. The obtained figures of merit by partial least squares (PLS) regression modelling compare well with standard absorption spectroscopy, demonstrating the potential of the introduced dispersion spectroscopic method for quantitative chemical analysis.

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Stand‐off Raman spectroscopy offers a highly selective technique to probe unknown substances from a safe distance. Often, it is necessary to scan large areas of interest. This can be done by pointwise imaging (PI), that is, spectra are sequentially acquired from an array of points over the region of interest (point‐by‐point mapping). Alternatively, in this paper a direct hyperspectral Raman imager is presented, where a defocused laser beam illuminates a wide area of the sample and the Raman scattered light is collected from the whole field of view (FOV) at once as a spectral snapshot filtered by a liquid crystal tunable filter to select a specific Raman shift. Both techniques are compared in terms of achievable FOV, spectral resolution, signal‐to‐noise performance, and time consumption during a measurement at stand‐off distance of 15 m. The HSRI showed superior spectral resolution and signal‐to‐noise ratio, while more than doubling the FOV of the PI at laser power densities reduced by a factor of 277 at the target. Further, the output hyperspectral image data cube can be processed with state of the art chemometric algorithms like vertex component analysis in order to get a simple deterministic false color image showing the chemical composition of the target. This is shown for an artificial polymer sample, measured at a distance of 15 m.