Analytical and experimental solutions for Fourier transform infrared microspectroscopy measurements of microparticles: A case study on Quercus pollen.

Background: FTIR microspectroscopy is a popular non-destructive technique for chemical analysis and identification of microparticles, such as microplastics, pollen, spores, microplankton organisms, sediments and microfossils. Unfortunately, measured spectra of microparticles are usually distorted by Mie-type scattering interferents thus hindering the analysis of spectral data. To retrieve chemical absorbance spectra, two different approaches are regularly employed: analytical (application of scatter-correction preprocessing methods), and experimental (measurement in an embedding matrix). The comparative studies of preprocessing spectral strategies are needed to determine pros and cons of these approaches, and when they are most suitable for use.

Results: We conducted the first-ever comparative study on 12 different analytical and experimental approaches for FTIR measurements of microparticles, as demonstrated on classification and chemical characterisation of pollen of four Quercus species. Individual pollen grains were measured on 1) microscope slides and 2) embedded in a paraffin-polyethylene (PEP) matrix. For analytical approaches, we have applied simple model-based algorithm (EMSC: extended multiplicative signal correction), Mie-theory model-based algorithm (ME-EMSC: Mie-extinction EMSC) and deep learning-based algorithm (DCNN: deep convolutional neural network). Moreover, we applied algorithms for the correction of the embedded spectra: fringe-correction EMSC and two different paraffin-correction EMSC algorithms. The best classification accuracy is obtained for simple preprocessing, where scattering information is not completely removed, as well as for complex algorithms where scattering information is parameterized and retained. In chemical characterisation studies, strong scattering signals hinder valuable chemical information, and it is imperative to suppress them either by embedding or by an analytical approach.

Significance: The results show that scattering spectral interferents are not necessarily detrimental for classification studies of biological microparticles. In fact, they have considerable diagnostic value even in closely related microorganisms due to species-specific physical properties. The results clearly show that analytical and experimental solutions for FTIR measurements of microparticles should be carefully selected, taking into account the origin of the microparticles (i.e., biological or artificial) and purpose of the study (classification or chemical characterisation).