Hyperspectral microscopy as a particle exposure assessment tool

Abstract

Hyperspectral imaging combines the characteristics of computer vision and point spectroscopy by obtaining an image with both spatial and spectral information. Therefore, in combination with microscopy, it can increase material discrimination possibilities with respect to regular microscopy imaging. We explore this increased discrimination potential to assess exposure to particle contamination, since workplace exposure to specific particle materials poses wellknown health hazards. In this respect, we are focusing on discriminating more health relevant particles such as silica in the respirable size fraction. For this purpose, a particle sampling protocol has been proposed and hyperspectral imaging in combination with transmission microscopy is used for particle material identification. We use a Snapscan visual nearinfrared (VNIR) camera providing high spectral and spatial resolution in the 460-900 nm range, 150 spectral bands and up to 7 Mpixels of spatial resolution and high acquisition speed. The hyperspectral microscopy system has been tested for discrimination of fifteen different particle materials, such as silica, coal, dolomite, barite, or rutile, among others. The combined analysis of spatial and spectral information shows potential to accurately discriminate the 15 tested particle materials so far by means of a random forest classifier. In addition, a band relevance analysis is performed showing that only a few specific bands are needed to provide accurate discrimination of the tested materials. The hyperspectral hardware and method presented could lead to a faster exposure assessment than traditional techniques used for occupational exposure estimation.