Light-matter interaction is a very complicated process. In particular reflectance, the amount of light reflected away from a material surface, is difficult to describe quantitatively since it depends on the complex refractive index of the materials components and the local morphology. Spheres made of an absorbing medium scatter light differently than those made of a lossless dielectric, and small spheres scatter differently from large spheres. Therefore, it is rather difficult to determine particle properties from their reflectance spectra. Many areas of science, however, are in need of a database that allows inferring particle properties from a reflectance spectrum, since it is often not possible or impractical to get a sample into a laboratory with advanced analysis tools (think of Mars or other NASA expeditions). In the past, most studies have thus focused on geological samples, but with the advent of reflectance spectroscopy in other applied sensing applications, e.g. food or security screening, a broader database for quantitative material properties from reflectance spectra would be highly useful.

A detailed approach towards a quantitative classification of reflectance and particle size is therefore much needed. Myers et al. in this Applied Optics article report a comprehensive data set on the effect of different particle size and material compositions on the reflectance in the infrared wavelength range, where most molecules are uniquely identifiable. They chose a range of samples that represents a cross-section among organic and inorganic materials of interest, for example lactose or sodium sulfate and find – not too surprisingly – that the reflectance indeed varies strongly as a function of the particle size. The results show that it is possible to detect very small size variations, e.g. differences on the 15 µm-range for the investigated materials.

The empirical approach by Myers et al. can clearly only be a first step towards a deeper quantitative understanding of the reflectance–particle size correlation in different materials, but it is an important one. Further research should not only broaden the database but also show that it can be indeed applied to sensing, e.g., show that trace amounts of molecules can be detected in a broader matrix.

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