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The use of colorimetric reactions as a sensitive, selective and low-cost method for the detection of fire related gases is evaluated in this work. As the most relevant fire gas carbon monoxide CO is addressed in the first place. A rhodium-based metal-organic complex has been selected, which a colorimetric material sensitive for CO. For the optical readout a compact setup has been developed which provides the option for spectroscopy in transmission and attenuated total reflection in parallel. The detection of CO in concentrations relevant for fire detection has been demonstrated in synthetic gas mixtures as well as in real fire gas.

Assessing data analysis routines (DARs) for microplastics (MP) identification in Fourier-transform infrared (FTIR) images left the question ‘Do we overlook any MP particles in our sample?’ widely unanswered. Here, a reference image of microplastics, RefIMP, is presented to answer this question. RefIMP contains over 1200 MP and non-MP particles that serve as a ground truth that a DAR’s result can be compared to. Together with our MatLab® script for MP validation, MPVal, DARs can be evaluated on a particle level instead of isolated spectra. This prevents over-optimistic performance expectations, as testing of three hypotheses illustrates: (I) excessive background masking can cause overlooking of particles, (II) random decision forest models benefit from high-diversity training data, (III) among the model hyperparameters, the classification threshold influences the performance most. A minimum of 7.99% overlooked particles was achieved, most of which were polyethylene and varnish-like. Cellulose was the class most susceptible to over-segmentation. Most false assignments were attributed to confusion of polylactic acid for polymethyl methacrylate and of polypropylene for polyethylene. Moreover, a set of over 9000 transmission FTIR spectra is provided with this work, that can be used to set up DARs or as standard test set.