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UV-induced transformation of dissolved
organic matter (DOC) is often accompanied by reduction
of molecular weight and aromaticity and an increase of
low-molecular weight (LMW) matter that can be utilized as
a substrate by heterotrophic bacteria. Moreover, the generation
of reactive transients and mineralization of DOC
occurs. For a better understanding of the modification that
starts after irradiation and to distinguish between possible
chemical and biological modifications, we selected different
natural organic matter (NOM) from Norway and
Germany. The aqueous solutions were treated by UV
irradiation and divided into two aliquot samples. NaN3
anti-bacterial treatment was applied to one sample, and
high-pressure size-exclusion chromatography (HPSEC)
analysis was used for both. In all samples, we found typical
modifications of NOM after UV irradiation. Incubation
(>7 days) of UV-irradiated NOM samples resulted in
lower levels of LMW matter and increased aromaticity.
Parallel to these changes of carbon fractions, an increase in
bacterial cell numbers was observed. Addition of NaN3 to
NOM, however, inhibited the reduction of LMW matter,
indicating that microbial activity accounted for the
observed changes in NOM. Analysis of the bacterial
community composition by denaturing gradient gel electrophoresis
(DGGE) of the amplified 16S rRNA genes
revealed that bacterial communities of non-irradiated and
UV-irradiated NOM were different and that UV selected
for specific members of α-proteobacteria, β-proteobacteria,
and Bacteriodetes. Our results imply that after UV-irradiation
of NOM, specific bacterial members are well adapted
to low pH, high LMW DOC concentrations, and oxidative
stress, and therefore thrive well on UV-irradiated humic
matter.
Supernatants from a fermentation process of Pichia pastoris were investigated by Raman spectroscopy. Using partial least squares regression, the principal substrates glycerol and methanol could be predicted, however not the expressed protein. To gain further insight, a priori prepared calibration samples were studied by vibrational-, UV/Vis-, and fluorescence spectroscopy. For the quantification of glycerol and methanol, Raman spectroscopy was identified as the most sensitive technique, and superior to near-infrared spectroscopy, but not for protein contents below 1 g L–1. Both UV/Vis absorption and fluorescence spectroscopy are well suited for the quantification of protein, however, best results were obtained with UV/Vis absorption.
Abstract. Biodiesel quality control is a relevant issue as biodiesel properties influence diesel engine performance and integrity. Within the European Metrology Research Program (EMRP) ENG09 project “Metrology for Biofuels”, an on-line /at-site suitable near-infrared spectroscopy (NIRS) method has been developed in parallel with an improved EN14110 headspace GC analysis method for methanol in biodiesel. Both methods have been optimized for a methanol content of 0.2 mass% as this represents the maximum limit of methanol content in FAME according to EN 14214:2009. The NIRS method is based on a mobile NIR spectrometer equipped with a fiber-optic coupled probe. Due to the high volatility of methanol, a tailored air-tight adaptor was constructed to prevent methanol evaporation during measurement. The methanol content of biodiesel was determined from evaluation of NIRS spectra by Partial Least Squares Regression (PLS). Both GC analysis and NIRS exhibited a significant dependence on biodiesel feedstock. The NIRS method is applicable to a content range of 0.1 % (m/m) to 0.4 % (m/m) of methanol with uncertainties at around 6% relative for the different feedstocks. A direct comparison of headspace GC and NIRS for samples of FAMEs yielded that the results of both methods are fully compatible within their stated uncertainties.
Due to the increasing presence of microplastic particles (MP) in the Environment and the unknown risks arising from them, there is an urgent need for analytical methods that allow for an efficient identification and quantification of microplastics (MP), i.e. particles < 5 mm, in environmental samples. So far, mostly timeconsuming (micro) infrared or micro-Raman spectroscopic methods are applied. Here an faster alternative Approach is presented based on a Raman processspectrometer with fiber-optical probes in combination with multivariate data analysis.
A process spectroscopic approach for the monitoring of microplastics in environmental samples
(2018)
The potential of Raman and NIR process-spectroscopic as a rapid approach for the estimation of microplastics (MP) in soil matrix were tested. For this purpose, artificial MP/soil mixture samples consisting of standard soils or sand with defined ratios of MP (0,5 – 10 mass% polymer) from polyethylene (PE), polypropylene (PP), polystyrene (PS) and polyethylene terephthalate (PET) were prepared. MP particles with diameters < 2 mm and < 125 µm were obtained from industrial polymer pellets after cryo-milling. Spectra of these mixtures were collected by (i) a process FT-NIR spectrometer equipped with a reflection probe, (ii) by a cw-process Raman spectrometer and (iii) by a time-gated Raman spectrometer using fiber-optic probes. The evaluation of process-spectra was performed by chemometric methods. Whereas MP could be detected by Raman spectroscopy in coastal sand at 0.5 mass%, in standard soil detection of MP was limited to 10 – 5 mass% with the large fraction, but samples containing particles of the 125µm mass-fraction yielded no positive result at all. One reason for the lacking sensitivity could be fluorescence by soil organic matter and thus, in a next test time-gated Raman spectroscopy was applied. However, although being indeed more sensitive to the small particles this method failed at MP < 5 mass% indicating that fluorescence was not the major problem. Finally, FT-NIR was tested. Depending on the polymer, MP contents of 0,5 or 1 mass%, respectively, could be detected in standard soils and polymers identified. Furthermore, this approach could be used for the investigation of mixtures of up to four polymers and in real-world samples from bio-waste fermenter residues.
The increasing enrichment of water bodies and soils with plastic waste leads to the accumulation of microscopic plastic particles, so-called microplastics (MP). There is an urgent need for analytical methods that help to identify and quantify MP. At present, mainly thermo-analytical and microscopic methods such as micro-infrared spectroscopy or micro-Raman are used for this purpose. The latter are usually tied to time-consuming sample enrichment and preparation, only small sample quantities (micrograms) can be examined and the evaluation of the obtained spectra can be demanding. In the context of this presentation, two approaches are presented which, via the multi-variate analysis of spectroscopic data, allow i) a new methodological approach to screening MP in contaminated soils and ii) an alternative evaluation of large (micro)-spectroscopic data sets.
First, a NIR spectroscopic method is presented which allows MP consisting of polyethylene, polyethylene terephthalate, polypropylene and polystyrene to be detected in the range of up to 0.5 mass percent. Due to short measurement times and robust technology, this approach has the potential, in contrast to thermo-analytical and micro-spectroscopic methods, to examine larger sample quantities with minimal pre-treatment.
The second approach deals with the evaluation of large data sets, as typically obtained as a result of micro-FTIR using modern FPA detectors. The micro-FTIR technique is based on the spectral recording, imaging and subsequent identification of vibration bands typical of synthetic polymers. The image data sets are large and contain spectra of numerous particles of natural and synthetic origin. Exploratory multivariate data analysis has been tested to complement existing approaches based on e.g. spectrum library searches. The core concept used was dimensionality reduction. The results not only represent an orthogonal method for checking the results obtained by an automated library search, but also revealed a group of spectra that were not recorded in the existing spectrum libraries.
Multivariate data analysis is a universal tool for the evaluation of process spectroscopic data. In process analytics, huge amounts of information are produced i) by the many variables contained in one spectrum often exceeding 1000 (wavenumbers, wavelength, shifts…), and ii) the high number of spectra that is generated within the measurement period. Multivariate data analysis, often also called Chemometrics, help to extract the relevant information which is needed to examine and even control processes. Therefore, calibration models must be precise and robust, and moreover, must cover a wide range of variation of factors posing an influence on the process.
In this pre-conference course an introduction to both, explorative data analysis by PCA (Principal Component Analysis), and regression analysis by the most frequently used method, i.e. PLSR (Partial Least Squares Regression) is given. In principal, all optical spectroscopic methods are suited for multivariate evaluation. It will be demonstrated that under certain preconditions, even process NMR spectra can be predicted by PLSR models.
At first, basic principles of multivariate data analysis will be provided. This includes a short introduction into the concept of model building and interpretation of results. Detailed aspects of data pretreatment and calibration & validation strategies for chemometric models will be provided with own data from NIR, Raman and NMR spectroscopy.
In a first example, the development of an online compatible method for the quantification of methanol in biodiesel by PLSR is presented. This also includes the classification of biodiesel feedstocks by PCA and statistical tools which allow for the estimation of a full uncertainty budget.
The Raman spectroscopic prediction of Hydroformylation reaction in a miniplant is used to discuss shortcomings and pitfalls which may occur with the transfer of off-line models to the real processes. Design of experiment and strategies for suitable lab-scale experiments are presented as a possible way to overcome problems.
In a third application the prediction of the reactants of an esterification reaction based on process NMR data is demonstrated.
The interaction of microcroplastics (MP) with dissolved organic matter, especially humic substances, is of great importance in understanding the behavior of microplastics in aquatic ecosystems. Surface modification by humic substances plays an essential role in transport and interaction of MP with abiotic and biotic components. Previous studies on the interaction between MP and humic substances were largely based on a model compound, humic acid (Sigma-Aldrich). In our work, we therefore investigated the interaction of natural organic matter (NOM) sampled from a German surface water with low-density polyethylene particles (LDPE). Highpressure size exclusion chromatography (HPSEC) and UV/vis absorption and fluorescence spectroscopy were used to characterize the incubation solutions after modifications due to the presence of LDPE, and Raman spectroscopy was used to characterize the incubated microplastics. While the studies of the solutions generally showed only very small effects, Raman spectroscopic studies allowed clear evidence of the binding of humic fractions to MP. The comparison of the incubation of NOM and a lignite fulvic acid which also was tested further showed that specific signatures of the humic substances used could be detected by Raman spectroscopy. This provides an elegant opportunity to conduct broader studies on this issue in the future.
The growing need to implement sensors such as NIR or Raman spectroscopy for the in-situ monitoring of bioprocesses which follows the standards of Quality by Design is either restricted by the impact of the huge water signal or by a disturbing fluorescence background originating from compounds in the culture media. Furthermore, the characterization of the bioprocess samples is challenging due to changing conditions in course of cultivation.
Here we evaluate two different process-suitable Raman spectroscopic approaches, namely time-gated Raman which bears the potential to extract the Raman signal from the fluorescence background, and cw- Raman with NIR excitation in combination with Surface Enhanced Raman Spectroscopy- (SERS) to investigate cell-free supernatants of Escherichia coli sampled over the course of a cultivation. A confocal Raman microscope was used as a reference for the process devices. The concentration of the analytes, glucose, acetate as well as metabolites such as cAMP, AMP and amino-acids were determined by offline by High-Performance Liquid Chromatography (HPLC) to serve as reference for the calibration of the Raman and SERS spectral data.
Multivariate evaluation of the Raman and SERS spectra by Partial Least Squares Regression (PLSR) yielded for most of the analytes robust correlations at each sampling point. Repeated investigation of the off-line samples over a larger experimental period suggested not only a high reliability of the Raman data in general but also a high repeatability of the SERS experiments. Similar spectral features in different quality and signal/noise ratios were measured with all three set-ups. Major results of the comparison of the different Raman spectroscopic approaches and their combination with SERS are summarized and conclusions are drawn on which approach provides the most accurate concentration data among the target analytes.
Acknowledgement
The authors kindly thank Mario Birkholz (IHP, Frankfurt (Oder), Germany) for the opportunity to use a confocal Raman microscope, Alex Bunker and Tapani Viitala (Division of Pharmaceutical Biosciences, Centre for Drug Research, University of Helsinki, Finland).
Spectroscopic methods possess important advantages for the observation of bioprocesses: they are robust, gain many data within a short time scale, and allow for the measurement of multiple relevant information at the same time. Next to Near-infrared spectroscopy, Raman spectroscopy is increasingly applied as a powerful online tool to monitor bioprocesses. However, Raman spectra may be obscured by straylight, cosmic rays and fluorescence. To achieve the best performance, optimal conditions for spectral quality and stability should be pursued. This comprises not only the choice of highly-sensitive equipment, but also the way of implementation into the process. Here we compared the potential of introducing a sterilized fiber optical Raman probe, and a flow-through cell into the bypass of a stirred tank reactor to monitor an autotrophic cultivation of Ralstonia eutropha where gasses are the energy and carbon sources. Raman spectra were continuously recorded over seven days and 22 samples were taken for the reference analytics of biomass, optical density, and protein. In addition, data for of phosphate and ammonium were available. The quantitative Evaluation of Raman spectra by Partial Least Squares Regression (PLSR) yielded comparable results for both approaches, however, there was a general trend of lower prediction errors with the immersion probe.
The better performance of the non-contact optic may be either related to a more representative interaction of the measurement optics with the probe or simply to the absence of probe-fouling, which was observed with the immersion optic in the interstice between window and metal frame.