Microplastic particles have been found in drinking water sources worldwide and, thus, also in our food and beverages. Especially small microplastics, with sizes of 1 mm and less, cannot be identified reliably without spectroscopic means such as Fourier transform infrared spectroscopy (FTIR) or Raman spectroscopy, usually applied to the particles extracted from the samples. However, for drinking and tap water, with its comparatively low biological loads, direct observation may be possible and allows a point-of-entry monitoring for beverages and food to ensure uncontaminated drinking water is being used. In a proof of concept, we apply Raman spectroscopy to observe individual microplastic particles in tap water with added particulate and fluorescent contaminants streaming with 1 L/h through a custom-made flow cell. We evaluated several tubing materials for compatibility with microplastic suspensions containing three different polymers widely found in microplastic surveys worldwide. The experiment promises the monitoring of streaming tap water and even clear surface waters for microplastics smaller than 0.1 mm.

Resonance Raman microspectroscopy in combination with hierarchical cluster analysis (HCA) is one of the most promising tools for the rapid examination of complex biological and medical samples. HCA is a ready, computerized tool for examining large sets of data for common characteristics, and a multitude of algorithms for this purpose have been developed over the years. However, resonance Raman spectra obtained from complex biological samples may originate from different chromophores as well as from a common chromophore found in different host environments, i.e., bacteria. Therefore, algorithms applied to resonance Raman spectra must handle data of high intrinsic similarity, i.e., spectra originating from a common chromophore, and data with highly dissimilar features, i.e., spectra from different chromophores, in the same unsupervised analysis. We examined the performance of eight widely used algorithms for hierarchical cluster analysis in clustering resonance Raman spectra of bacteria: Single-Linkage (Nearest-Neighbor), Complete-Linkage (Farthest-Neighbor), Average-Linkage, Weighted-Average-Linkage, Centroid, Median, and the Ward algorithm. Algorithm performance was evaluated by comparing the results of clustering a set of high-quality reference spectra with the results obtained when clustering a set of spectra recorded from single cells. References were formed by averaging 100 spectra of individual cells. While all algorithms returned highly similar results when clustering the reference spectra, their performance differed significantly when applied to single spectra. The best-performing algorithm, Weighted-Average-Linkage, correctly grouped single spectra with a reliability of above 95% while the spectral distances between the clusters deviated less than 10% from the results obtained with reference spectra. In contrast, the algorithm performing worst showed no similarity to the reference clustering at all. The widely used Ward algorithm deviated up to 30% from the reference in the spectral distances and returned a different spectral relation between bacteria expressing the same chromophore.

The temperature-sensitive gating of human Connexin 26 (hCx26) was analyzed with confocal Raman microscopy. High-resolution Raman spectra covering the spectral range between 400 and 1500 rel. cm⁻¹ with a spectral resolution of 1 cm⁻¹ were fully annotated, revealing notable differences between the spectrum recorded from solubilized hCx26 in Ca²⁺-buffered POPC at 10°C and any other set of protein conditions (temperature, Ca²⁺ presence, POPC presence). Spectral components originating from specific amino acids show that the TM1/EL1 parahelix and probably the TM4 trans-membrane helix and the plug domain are involved in the gating process responsible for fully closing the hemichannel.