Recovering fluorescence spectra hidden by scattering signal: In search of the best smoother.

Interpolation of the scattering areas in fluorescence excitation-emission matrices is a useful preprocessing method in fluorescence spectroscopy and data modelling. Commonly used row-by-row interpolation using piecewise cubic Hermite interpolating polynomials smoother (PCHIP), however, frequently leads to artifacts because it does not make any use of the information in the other dimension. We have suggested the way of constructing the penalty matrices for Whittaker smoothing that removed one of the main sources of difference between the axis of multiparametric signal - the grid step size - thus making it possible to reduce the number of parameters to optimize. We have compared Whittaker smoother with various surface interpolation methods, including LOESS, Kriging, multilevel B-spline approximation, and PCHIP for the purpose of data preprocessing before PARAFAC modelling of fluorescence signal on a model dataset. The two leaders by signal reconstruction and reconstruction of PARAFAC loadings are LOESS and Whittaker smoothing; the latter is additionally shown to have fundamentally interpretable parameters, which are easier to optimise for the whole dataset. Moreover, Whittaker keeps the shape of the signal and is resistant to variations in data structure and noise level that is very important in numerous applications. We also tested smoothers performance for Åsmund Rinnan fluorescence dataset and the high performance of Whittaker was proved. We can recommend the Whittaker smoothing as a perfect tool for interpolation of scattering areas in florescence spectra of seawaters with low signal-to-noise ratio.

Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics.

Zooplankton identification has been the subject of many studies. They are mainly based on the analysis of photographs (computer vision). However, spectroscopic techniques can be a good alternative due to the valuable additional information that they provide. We tested the performance of several chemometric techniques (principal component analysis (PCA), non-negative matrix factorisation (NMF), and common dimensions and specific weights analysis (CCSWA of ComDim)) for the unsupervised classification of zooplankton species based on their spectra. The spectra were obtained using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. It was convenient to assess the discriminative power in terms of silhouette metrics (Sil). The LIBS data were substantially more useful for the task than the Raman spectra, although the best results were achieved for the combined LIBS + Raman dataset (best Sil = 0.67). Although NMF (Sil = 0.63) and ComDim (Sil = 0.39) gave interesting information in the loadings, PCA was generally enough for the discrimination based on the score graphs. The distinguishing between Calanoida and Euphausiacea crustaceans and Limacina helicina sea snails has proved possible, probably because of their different mineral compositions. Conversely, arrow worms (Parasagitta elegans) usually fell into the same class with Calanoida despite the differences in their Raman spectra.

A novel approach for discovering correlations between elemental and molecular composition using laser-based spectroscopic techniques.

Modern analytical techniques, including laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy, yield multidimensional data, which are most efficiently used in conjunction with chemometric techniques, including multi-block algorithms. In this study, we use several algorithms for the processing of laser-induced breakdown and Raman spectra of zooplankton organisms, which are found to accumulate lithium for an unknown reason. Correlations between elemental and molecular composition of zooplankton have been found. We studied 29 samples: crustaceans, arrow worms, and sea snails. The obtained spectra were examined by principal component analysis (PCA), non-negative matrix factorization (NMF), consensus PCA (CPCA), and analysis of common components and specific weights (CCSWA, or ComDim). LIBS spectra are more sensitive towards taxonometric differences than Raman spectra. All the algorithms gave similar results, although still differing in details. Data fusion revealed a number of relationships, including the correlation of Li with potassium (R = 0.83, n = 14), with Raman bands of carotenoids (R = 0.89, n = 11) and tryptophan (R = 0.94, n = 9). The correlations were most pronounced in light-coloured parts of the inhomogeneous biological material. Ratios of fatty acids are associated with Li concentration if above 200 mg kg-1. Valine is also related to the Li accumulation. Thus, it is shown that the combination of LIBS and Raman spectroscopy, followed by appropriate mathematical treatment, is a convenient tool for comprehensive studies of environmental objects.

Carbon determination in carbon-manganese steels under atmospheric conditions by Laser-Induced Breakdown Spectroscopy.

The most sensitive lines of carbon, used nowadays for its determination in steels by laser-induced-breakdown spectroscopy (LIBS), are at vacuum UV and, thereby, LIBS potential is significantly reduced. We suggested the use of the C I 833.51 nm line for carbon determination in low-alloy steels (c(C)~0.186-1.33 wt.%) in air. Double-pulse LIBS with the collinear scheme was performed for maximal enhancement of a carbon emission signal without substantial complication of experimental setup. Since this line is strongly broadened in laser plasma, it overlapped with the closest iron lines greatly. We implemented a PCR method for the construction of a multivariate calibration model under spectral interferences. The model provided a RMSECV = 0.045 wt.%. The predicted carbon content in the rail templet was in an agreement with the reference value obtained by a combustion analyzer within the relative error of 6%.

Automatic identification of emission lines in laser-induced plasma by correlation of model and experimental spectra.

We have applied an algorithm to automatically identify emission lines in laser-induced breakdown spectrometry (LIBS). A Q-switched Nd:YAG laser at 355 nm was used to ablate a high-alloy stainless steel sample. The algorithm was implemented by three parts: simulation of the set of spectra corresponding to different temperature (T) and electron density (N(e)), searching the best correlated pair of a model spectrum and an experimental one, and attributing the peaks with certain lines. In order to construct the model spectra, we used the parameters of atomic and ionic lines, levels, the mechanisms of the broadening of spectral lines, and the selected parameters of the spectrograph. The highest correlation coefficient between the model and the experimental spectrum was 0.943 for T = 0.675 eV and lg(N(e)) = 16.7 cm(-3). More than 40 emission lines were labeled automatically in the spectral region 393.34-413.04 nm.

Reduction of the matrix influence on analytical signal in laser-enhanced ionization spectrometry with laser sampling.

The novel approach using a slope of correlation line (laser-enhanced ionization of lithium versus laser-induced plasma emission of aluminum) as analytical signal was proposed for reduction of matrix interferences in laser-enhanced ionization spectrometric determination of Li with laser sampling.