Novel Approach to Freshwater Diatom Profiling and Identification Using Raman Spectroscopy and Chemometric Analysis

Abstract

(1) An approach with great potential for fast and cost-effective profiling and identification of diatoms in lake ecosystems is presented herein. This approach takes advantage of Raman spectroscopy. (2) The study was based on the analysis of 790 Raman spectra from 29 species, belonging to 15 genera, 12 families, 9 orders and 4 subclasses, which were analysed using chemometric methods. The Raman data were first analysed by a partial least squares regression discriminant analysis (PLS-DA) to characterise the diatom species. Furthermore, a method was developed to streamline the integrated interpretation of PLS-DA when a high number of significant components is extracted. Subsequently, an artificial neural network (ANN) was used for taxa identification from Raman data. (3) The PLS interpretation produced a Raman profile for each species reflecting its biochemical composition. The ANN models were useful to identify various taxa with high accuracy. (4) Compared to studies in the literature, involving huge datasets one to four orders of magnitude larger than ours, high sensitivity was found for the identification of Achnanthidium exiguum (67%), Fragilaria pararumpens (67%), Amphora pediculus (71%), Achnanthidium minutissimum (80%) and Melosira varians (82%).

A Digital Light Microscopic Method for Diatom Surveys Using Embedded Acid-Cleaned Samples

“…Automated methods for large-scale microscopic image acquisition for diatom preparations have been developed in the past, as early as 2000 with ADIAC [16][17][18][19]. In most cases, the aim was to use a large-scale imaging workflow as part of an (envisaged, though hitherto not fully realised) entirely automated diatom analysis workflow that would also include automated taxonomic identification.…”

A Digital Light Microscopic Method for Diatom Surveys

“…Automated methods for large-scale microscopic image acquisition for diatom preparations have been developed in the past, as early as 2000 with ADIAC [13][14][15][16][17]. In most cases, the aim was to use a large-scale imaging workflow as part of an (envisaged, though hitherto not fully realised) entirely automated diatom analysis workflow that would also include automated taxonomic identification.…”