Lake snow refers to mucilaginous suspended material in lakes comprising of extracellular polysaccharides (EPS) and other components. The present study employed Raman and infrared (IR) spectroscopy to discriminate lake snow produced by the diatom Lindavia intermedia from different lakes (W anaka, Wakatipu and H awea) in New Zealand and from mucilaginous samples associated with other algae. It is possible to distinguish algal material including extracellular polymeric substances produced by L. intermedia and the genera Didymosphenia, Zygnema, Spirogyra and Nostoc. Furthermore, the study also explored the use of Raman spectroscopy for quantitative detection of lake snow suspended in a water column using partial least squares regression (PLSR). Thirty-three (33) samples (lake snow, 21; Didymosphenia, 3; Zygnema, 3; Spirogyra, 3; Nostoc, 3) were analysed using Raman and IR spectroscopy. The data analysis was carried out through support vector machine (SVM) and principal component analysis-linear discriminate analysis (PCA-LDA)-based classification methods. The SVM classification model provided better accuracy (100%) in species discrimination for both the calibration and full cross-validation sets compared to the accuracy (92%) obtained by the PCA-LDA model. The PCA analysis separated lake snow based on both sampling location and sampling depth. A partial least squares regression (PLSR) model was constructed using different dilutions (0.0001 to 0.0284 mg/ml) of lake snow suspension with two different spectral preprocessing methods (PP1, smoothing + SNV transformation; PP2, smoothing + RBC + SNV transformation) to investigate the ability of 1064 nm Raman in the quantification of suspended algal loading in the Abbreviations: PCA, principal component analysis; SVM, support vector machine; LDA, linear discriminate analysis; PLSR, partial least square regression analysis; EPS, extracellular polymeric substance; TEPs, transparent exopolymer particles; RMSE C , root-mean-square of error of calibration; RMSE V , root-mean-square of error of cross-validation.

The investigation of time since exposure and taphonomic alteration of bone in marine environments is crucial in forensic sciences. In this study, we explored the diagenetic changes to juvenile porcine bone in two different environmental marine contexts (submerged and intertidal) and how seasonal variation at time of deposition impacted on the pattern of taphonomic alteration in bone during early stages of exposure, from 6 to 24 weeks. The analysis was conducted using Raman spectroscopy combined with chemometrics. Principal component analysis of the Raman data of the recovered bones (either summer or winter season in 2014) showed that the main chemical changes occurred in the bioapatite (phosphate band, ν1(PO43−) at 961 cm−1) and organic constituents in the bones and depended on the exposure environment. Support vector machine (SVM) classification analysis classified the samples based on bone type, exposed environment and season, with high accuracy (>80%), but exposure time was less accurate (56%), although still higher than chance. When applying a SVM regression, time since exposure could be predicted with a ±5‐week uncertainty. This study illustrated the potential and limitations of using Raman spectroscopy to detect structural degradation of bone in different marine environments for forensic purposes.

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The cover image is based on the Special Issue – Research Article Lake snow caused by the invasive diatom Lindavia intermedia can be discriminated from different sites and from other algae using vibrational spectroscopy by Fatema Ahmmed et al., https://doi.org/10.1002/jrs.6161.