Enantioselective Raman spectroscopy (esR) for distinguishing between the enantiomers of 2-butanol

Multimodal Imaging Using Raman Spectroscopy and FTIR in a Single Analytical Instrument with a Microscope (Infrared Raman Microscopy AIRsight, Shimadzu): Opportunities and Applications

Raman spectroscopy and Fourier transform infrared (FTIR) spectroscopy are powerful analytical techniques widely used separately in different fields of study. Integrating these two powerful spectroscopic techniques into one device represents a groundbreaking advance in multimodal imaging. This new combination which merges the molecular vibrational information from Raman spectroscopy with the ability of FTIR to study polar bonds, creates a unique and complete analytical tool. Through a detailed examination of the microscope's operation and case studies, this article illustrates how this integrated analytical instrument can provide more thorough and accurate analysis than traditional methods, potentially revolutionising analytical sample characterisation. This article aims to present the features and possible uses of a unified instrument merging FTIR and Raman spectroscopy for multimodal imaging. It particularly focuses on the technological progress and collaborative benefits of these two spectroscopic techniques within the microscope system. By emphasising this approach's unique benefits and improved analytical capabilities, the authors aim to illustrate its applicability in diverse scientific and industrial sectors.

Identification of Enantiomers Using Low-Frequency Raman Spectroscopy

Distinguishing between d and l enantiomers is of important scientific interest, especially for the pharmaceutical industry. Enantiomeric differentiation in the solid form is repeatedly presented as a challenge in the research community. Raman spectroscopy is a nondestructive tool, widely used for the characterization of different materials by probing their vibrational modes. The low-frequency region of the Raman spectrum reveals lattice-level interactions and global fluctuations in the molecule. Lower frequencies correspond to vibrations arising from weaker bonds and long-range interactions and hence are very susceptible to polarization changes. This work presents low-frequency Raman (LFR) spectroscopy as a facile technique to identify enantiomers. The optical setup of conventional Raman spectroscopy is engineered such that the excitation and collection geometries use an asymmetrical focal cone. In addition, a half-wave retarder is added to the excitation path and a Glan-Taylor polarizer is added to the collection path, and these modifications allow us to select the polarization plane for both excitation and collection geometries. The asymmetry in the foci when using a polarized beam for excitation provides different intensities of the collected signal for each polarization plane. In a calibrated system, one can define the chirality of an analyte by comparing the intensity of the LFR signal along orthogonal sets of polarization planes. For nonchiral molecules, the spectral intensity is always higher in the co-polarized plane when compared to the orthogonally depolarized plane, as expected. This contrast in the intensity of Raman spectra serves as a distinct tool for identifying enantiomers.

Chiral discrimination of amino acids using phosphorene assisted graphene-enhanced Raman spectroscopy

Discrimination of enantiomers poses a scientific challenge as the chemical and physical properties of enantiomers are nearly identical. The chiral analysis is usually performed by separation techniques, including chromatography, electrophoresis, or optical instrumentation based on an interaction of the analyzed sample with a polarized beam of light. Here we present a novel method for a chiral screening based on a combination of the black phosphorus@Graphene nanocomposite and Raman spectroscopy. The nanocomposite allows to enhance the Raman signal with factors higher than 100 asymmetrically and provide altered signals for mixtures containing varying enantiomeric ratios of target compounds. Tryptophan, Phenylalanine, DOPA, Isoleucine, and Leucine were selected as model compounds; the method allows us to discriminate between mixtures with 10, 25, 50, 75, and 100% enantiomeric purity.

Chemometric analysis of enantioselective Raman spectroscopy data enables enantiomeric ratio determination

In-line determination of the enantiomeric ratio is still a challenge in process analytical technology (PAT). This study combines enantioselective Raman (esR) spectroscopy with partial least-squares regression (PLSR) to determine the enantiomeric fraction of the chiral molecule (5,6)-diphenyl-morpholin-2-one diluted in dimethyl sulfoxide (DMSO) as a proof-of-concept. Morpholinone derivates are potential candidates for pharmaceutical applications. The PLS weights were carefully analyzed in order to avoid misleading regression results, e.g. caused by sample impurities. A suitable PLSR model was found with two components and it was validated by a leave-one-out cross-validation. The enantiomeric fraction ef₍₊₎ could be calculated with deviations from the prepared ef₍₊₎ in the range of -0.031 and +0.052 from the esR spectra recorded at a half-wave retarder angle of 30.0°.

Principal component analysis to enhance enantioselective Raman spectroscopy

Enantioselective Raman (esR) spectroscopy is an innovative technique with a high potential for online process monitoring in chiral media, e.g. in the pharmaceutical industry. A prerequisite for an effective application is to combine the experimental approach with suitable concepts for data analysis. In this work, we present a chemometric approach to analyze the esR spectra recorded in an automatized polarization-resolved Raman set-up. It is demonstrated that the proposed method is capable of distinguishing between the enantiomers of the chiral alcohol 4-methylpentan-2-ol in a fully unsupervised fashion. Furthermore, it is shown that the difficulty of facing only small intensity differences between the esR spectra of the enantiomers can be overcome by feeding difference spectra between the pure enantiomers and the racemate into the principal component analysis (PCA) algorithm. The enantiomers are clearly discriminable along the first principal component.