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The authenticity of objects and artifacts is often
the focus of forensic analytic chemistry. In document fraud
cases, the most important objective is to determine the
origin of a particular ink. Here, we introduce a new
approach which utilizes the combination of two analytical
methods, namely Raman spectroscopy and laser-induced
breakdown spectroscopy (LIBS). The methods provide
complementary information on both molecular and elemental
composition of samples. The potential of this hyphenation
of spectroscopic methods is demonstrated for ten blue
and black ink samples on white paper. LIBS and Raman
spectra from different inks were fused into a single data
matrix, and the number of different groups of inks was
determined through multivariate analysis, i.e., principal
component analysis, soft independent modelling of class
analogy, partial least-squares discriminant analysis, and
support vector machine. In all cases, the results obtained
with the combined LIBS and Raman spectra were found to
be superior to those obtained with the individual Raman or
LIBS data sets.
A major industrial reaction based on homogeneous catalysis is hydroformylation for the production of aldehydes from alkenes and syngas. Hydroformylation in microemulsions, which is currently under investigation at Technische Universität Berlin on a mini-plant scale, was identified as a cost efficient approach which also enhances product selectivity. Herein, we present the application of online Raman spectroscopy on the reaction of 1-dodecene to 1-tridecanal within a microemulsion. To achieve a good representation of the operation range in the mini-plant with regard to concentrations of the reactants a design of experiments was used. Based on initial Raman spectra partial least squares regression (PLSR) models were calibrated for the prediction of 1-dodecene and 1-tridecanal. Limits of predictions arise from nonlinear correlations between Raman intensity and mass fractions of compounds in the microemulsion system. Furthermore, the prediction power of PLSR models becomes limited due to unexpected by-product formation. Application of the lab-scale derived calibration spectra and PLSR models on online spectra from a mini-plant operation yielded promising estimations of 1-tridecanal and acceptable predictions of 1-dodecene mass fractions suggesting Raman spectroscopy as a suitable technique for process analytics in microemulsions.
Medium-resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and processmonitoring. In contrast to high-resolution onlineNMR (HR-NMR),MR-NMRcan be operated under rough environmental conditions. A continuous re-circulating stream of reaction mixture fromthe reaction vessel to the NMR spectrometer enables a non-invasive, volume integrating online analysis of reactants and products. Here, we investigate the esterification of 2,2,2-trifluoroethanol with acetic acid to 2,2,2-trifluoroethyl acetate both by 1H HR-NMR (500MHz) and 1H and 19F MRNMR (43MHz) as amodel system. The parallel online measurement is realised by splitting the flow,which allows the adjustment of quantitative and independent flow rates, both in the HR-NMR probe as well as in the MR-NMR probe, in addition to a fast bypass line back to the reactor. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra are treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprise (i) direct integration, (ii) automated line fitting, (iii) indirect hard modelling (IHM) and (iv) partial least squares regression (PLS-R). To assess the potential of these evaluation strategies for MR-NMR, prediction results are compared with the line fitting data derived from the quantitative HR-NMR spectroscopy. Although, superior results are obtained from both IHM and PLS-R for 1H MR-NMR, especially the latter demands for elaborate data pretreatment, whereas IHM models needed no previous alignment.
The increasing pollution of terrestrial and aquatic ecosystems with plastic debris leads to the accumulation of microscopic plastic particles of still unknown amount. To monitor the degree of contamination analytical methods are urgently needed, which help to quantify microplastics (MP). Currently, time-costly purified materials enriched on filters are investigated both by micro-infrared spectroscopy and/or micro-Raman. Although yielding precise results, these techniques are time consuming, and are restricted to the analysis of a small part of the sample in the order of few micrograms. To overcome these problems, here we tested a macroscopic dimensioned NIR process-spectroscopic method in combination with chemometrics. For calibration, artificial MP/soil mixtures containing defined ratios of polyethylene, polyethylene terephthalate, polypropylene, and polystyrene with diameters < 125 µm were prepared and measured by a process FT-NIR spectrometer equipped with a fiber optic reflection probe. The resulting spectra were processed by chemometric models including support vector machine regression (SVR), and partial least squares discriminant analysis (PLS-DA). Validation of models by MP mixtures, MP-free soils and real-world samples, e.g. and fermenter residue, suggest a reliable detection and a possible classification of MP at levels above 0.5 to 1.0 mass% depending on the polymer. The benefit of the combined NIRS chemometric approach lies in the rapid assessment whether soil contains MP, without any chemical pre-treatment. The method can be used with larger sample volumes and even allows for an online prediction and thus meets the demand of a high-throughput method.