Effects of Polarizer-Analyzer Configurations for FTIR Spectroscopy: Implications for Multiple Polarization Algorithms.

Polarized Fourier transform infrared (p-FTIR) spectroscopy is a widely used technique for determining orientational information in thin organic materials. Conventionally, a single polarizer is placed in the path of the incident light (termed the polarizer). Occasionally, a second polarizer is also placed after the sample (referred to as the analyzer). However, this polarizer-analyzer configuration has the potential to induce polarization-dependent variances in the final spectra beyond those that are expected, i.e., the squared-cosine relationship of absorptance with respect to polarization angle is no longer accurate. These variances are due to changes in the polarization state of the transmitted light induced by the sample and have yet to be explored in the context of p-FTIR. Consequently, this study employs both theoretical and experimental approaches to identify the effects of including a second polarizer in p-FTIR analyses of anisotropic organic samples. For thin samples, the most significant spectral variance arising from only birefringence is observed on the shoulders of the dichroic peaks. By adopting a crossed polarizer configuration, it is shown that there is potential to identify anisotropy of samples that are generally considered too thick for p-FTIR analysis by exploiting this feature. Furthermore, the squared-cosine relationship of absorptance with respect to the polarization angle is also shown to be inapplicable when a second parallel-oriented polarizer is included. Accordingly, a function that accounts for the second polarizer is proposed for multiple polarization techniques.

Linearly Polarized Infrared Spectroscopy for the Analysis of Biological Materials.

The analysis of biological samples with polarized infrared spectroscopy (p-IR) has long been a widely practiced method for the determination of sample orientation and structural properties. In contrast to earlier works, which employed this method to investigate the fundamental chemistry of biological systems, recent interests are moving toward "real-world" applications for the evaluation and diagnosis of pathological states. This focal point review provides an up-to-date synopsis of the knowledge of biological materials garnered through linearly p-IR on biomolecules, cells, and tissues. An overview of the theory with special consideration to biological samples is provided. Different modalities which can be employed along with their capabilities and limitations are outlined. Furthermore, an in-depth discussion of factors regarding sample preparation, sample properties, and instrumentation, which can affect p-IR analysis is provided. Additionally, attention is drawn to the potential impacts of analysis of biological samples with inherently polarized light sources, such as synchrotron light and quantum cascade lasers. The vast applications of p-IR for the determination of the structure and orientation of biological samples are given. In conclusion, with considerations to emerging instrumentation, findings by other techniques, and the shift of focus toward clinical applications, we speculate on the future directions of this methodology.

Visible microspectrophotometry coupled with machine learning to discriminate the erythrocytic life cycle stages of P. falciparum malaria parasites in functional single cells.

Malaria was regarded as the most devastating infectious disease of the 21st century until the COVID-19 pandemic. Asexual blood staged parasites (ABS) play a unique role in ensuring the parasite's survival and pathogenesis. Hitherto, there have been no spectroscopic reports discriminating the life cycle stages of the ABS parasite under physiological conditions. The identification and quantification of the stages in the erythrocytic life cycle is important in monitoring the progression and recovery from the disease. In this study, we explored visible microspectrophotometry coupled to machine learning to discriminate functional ABS parasites at the single cell level. Principal Component Analysis (PCA) showed an excellent discrimination between the different stages of the ABS parasites. Support Vector Machine Analysis provided a 100% prediction for both schizonts and trophozoites, while a 92% and 98% accuracy was achieved for predicting control and ring staged infected RBCs, respectively. This work shows proof of principle for discriminating the life cycle stages of parasites in functional erythrocytes using visible microscopy and thus eliminating the drying and fixative steps that are associated with other optical-based spectroscopic techniques.