This paper presents definitions of concepts related to speciation of elements, more particularly speciation analysis and chemical species. Fractionation is distinguished from speciation analysis, and a general outline of fractionation procedures is given. We propose a categorization of species according to isotopic composition of the element, its oxidation and electronic states, and its complex and molecular structure. Examples are given of methodological approaches used for speciation analysis. A synopsis of the methodology of dynamic speciation analysis is also presented.

Raman and Infrared (IR) spectroscopies provide information about the structure, functional groups and environment of the molecules in the sample. In combination with a microscope, these techniques can also be used to study molecular distributions in heterogeneous samples. Over the past few decades Raman and IR microspectroscopy based techniques have been extensively used to understand fundamental biology and responses of living systems under diverse physiological and pathological conditions. The spectra from biological systems are complex and diverse, owing to their heterogeneous nature consisting of bio-molecules such as proteins, lipids, nucleic acids, carbohydrates etc. Sometimes minor differences may contain critical information. Therefore, interpretation of the results obtained from Raman and IR spectroscopy is difficult and to overcome these intricacies and for deeper insight we need to employ various data mining methods. These methods must be suitable for handling large multidimensional data sets and for exploring the complete spectral information simultaneously. The effective implementation of these multivariate data analysis methods requires the pretreatment of data. The preprocessing of raw data helps in the elimination of noise (unwanted signals) and the enhancement of discriminating features. This review provides an outline of the state-of-the-art data processing tools for multivariate analysis and the various preprocessing methods that are widely used in Raman and IR spectroscopy including imaging for better qualitative and quantitative analysis of biological samples.

The abundance of plastic products in modern society has resulted in a proliferation of small plastic particles called “microplastics” in the global environment. Currently, spectroscopic techniques such as Fourier‐transform infrared and spontaneous (i.e., conventional) Raman spectroscopy are widely employed for the identification of the plastic microparticles, but these are rather time consuming. Stimulated Raman scattering (SRS) microscopy, based on the coherent interaction of 2 different laser beams with vibrational levels in the molecules of the sample, would enable much faster detection and identification of microplastics. Here, we present for the first time an SRS‐based method for identifying 5 different high production‐volume polymer types in microplastics extracted from environmental or consumer product samples. The particles from the extracts were collected on a flat alumina filter, and 6 SRS images were acquired at specifically chosen wavenumbers. Next, we decomposed these spectral data into specific images for the 5 polymers selected for calibration. We tested the approach on an artificial mixture of plastic particles and determined the signal‐to‐noise and level of cross talk for the 5 polymer types. As a proof of principle, we identified polyethylene terephthalate particles extracted from a commercial personal care product, demonstrating also the thousand‐fold higher speed of mapping with SRS compared with conventional Raman. Furthermore, after density separation of a Rhine estuary sediment sample, we scanned 1 cm² of the filter surface in less than 5 hr and detected and identified 88 microplastics, which corresponds to 12,000 particles per kilogram dry weight. We conclude that SRS can be an efficient method for monitoring microplastics in the environment and potentially many other matrices of interest.