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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.
An alternative spectroscopic approach for the monitoring of microplastics in environmental samples
(2017)
The increasing pollution of terrestrial and aquatic ecosystems with plastic debris leads to the accumulation of microscopic plastic particles of still unknown fate. To monitor the degree of contamination and to understand the underlying processes of turnover, analytical methods are urgently needed, which help to identify and quantify microplastic (MP). Currently, costly collected and purified materials enriched on filters are investigated both by micro-infrared spectroscopy and micro-Raman. Although yielding precise results, these techniques are time consuming and restricted to sample aliquots in the order of micrograms precluding prompt and representative information on both, larger sample numbers and realistic material volumes. To overcome these problems, here we tested Raman and NIR process-spectroscopic methods in combination with multivariate data analysis.
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, polypropylene, and polystyrene were prepared. MP particles with diameters < 2 mm and < 125 µm were obtained from industrial polymer pellets after cryomilling. 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. For the calibration of chemometric models (partial least squares regression, PLSR) 5 – 10 spectra of defined MP/soil mixtures (consisting of 1 – 4 g material each) were collected. The obtained PLSR models served for the prediction of both, polymer type and content based on the spectra of “unknown” test samples.
Whereas MP could be detected by Raman spectroscopy in coastal sand at 0.5 mass%, in standard soils detection of MP was limited to 10 – 5 mass%. The sensitivity of Raman spectroscopy could be improved by mild treatment with hydrogen peroxide. FT-NIR was suitable for the investigation of MP in standard soils in the range of 5 – 1 mass%, however, here a non-linear effect was observed at higher polymer concentrations. When mixtures of several polymers at low concentration levels were milled together, FT-NIR spectroscopy yielded false positive polymers together with unprecise quantitative information. Recently, the investigation of “real-world” samples shall be tested and compared to the results obtained by micro-FTIR and micro-Raman.
Within the Collaborative Research Center InPROMPT a novel process concept for the hydroformylation of long-chained olefins is studied in a mini-plant, using a rhodium complex as catalyst in the presence of syngas. Recently, the hydroformylation in micro¬emulsions, which allows for the efficient recycling of the expensive rhodium catalyst, was found to be feasible. However, the high sensitivity of this multi-phase system with regard to changes in temperature and composition demands a continuous observation of the reaction to achieve a reliable and economic plant operation. For that purpose, we tested the potential of both online NMR and Raman spectroscopy for process control. The lab-scale experiments were supported by off-line GC-analysis as a reference method.
A fiber optic coupled probe of a process Raman spectrometer was directly integrated into the reactor. 25 mixtures with varying concentrations of olefin (1-dodecene), product (n-tridecanal), water, n-dodecane, and technical surfactant (Marlipal 24/70) were prepared according to a D-optimal design. Online NMR spectroscopy was implemented by using a flow probe equipped with 1/16” PFA tubing serving as a flow cell. This was hyphenated to the reactor within a thermostated bypass to maintain process conditions in the transfer lines.
Partial least squares regression (PLSR) models were established based on the initial spectra after activation of the reaction with syngas for the prediction of unknown concentrations of 1-dodecene and n-tridecanal over the course of the reaction in the lab-scale system. The obtained Raman spectra do not only contain information on the chemical composition but are further affected by the emulsion properties of the mixtures, which depend on the phase state and the type of micelles. Based on the spectral signature of both Raman and NMR spectra, it could be deduced that especially in reaction mixtures with high 1-dodecene content the formation of isomers as a competitive reaction was dominating. Similar trends were also observed during some of the process runs in the mini-plant. The multivariate calibration allowed for the estimation of reactants and products of the hydroformylation reaction in both laboratory setup and mini-plant.
Monitoring chemical reactions is the key to chemical process control. Today, mainly
optical online methods are applied. NMR spectroscopy has a high potential for direct
loop process control. Compact NMR instruments based on permanent magnets
are robust and relatively inexpensive analysers, which feature advantages like low
cost, low maintenance, ease of use, and cryogen-free operation. Instruments for
online NMR measurements equipped with a flow-through cell, possessing a good
signal-to-noise-ratio, sufficient robustness, and meeting the requirements for
integration into industrial plants (i.e., explosion safety and fully automated data
analysis) are currently not available off the rack.
Intensified continuous processes are in focus of current research. Flexible (modular)
chemical plants can produce different products using the same equipment with short
down-times between campaigns and quick introduction of new products to the
market. In continuous flow processes online sensor data and tight closed-loop control
of the product quality are mandatory. If these are not available, there is a huge risk of
producing large amounts of out-of-spec (OOS) products. This is addressed in the
European Unionʼs Research Project CONSENS (Integrated Control and Sensing)
by development and integration of smart sensor modules for process monitoring and
control within such modular plant setups.
The presented NMR module is provided in an explosion proof housing of 57 x 57 x
85 cm module size and involves a compact 43.5 MHz NMR spectrometer together
with an acquisition unit and a programmable logic controller for automated data
preparation (phasing, baseline correction) and evaluation. Indirect Hard Modeling
(IHM) was selected for data analysis of the low-field NMR spectra. A set-up for
monitoring continuous reactions in a thermostated 1/8” tubular reactor using
automated syringe pumps was used to validate the IHM models by using high-field
NMR spectroscopy as analytical reference method.
Hydroformylation of short-chained olefins has been established as a standard industrial process for the production of C2 to C6 aldehydes. Using aqueous solutions of transition metal complexes these processes are carried out homogeneously catalyzed. A biphasic approach allows for highly efficient catalyst recovery. Regarding renewable feedstocks, the hydroformylation of long-chained alkenes (> C10) in a biphasic system, using highly selective rhodium catalysts has yet not been shown. Therefore, the Collaborative Research Center SFB/TR 63 InPROMPT develops new process concepts, involving innovative tuneable solvent systems to enable rather difficult or so far nonviable synthesis paths. One possible concept is the hydroformylation of long-chained alkenes in microemulsions. For this, a modular mixer-settler concept was proposed, combining high reaction rates and efficient catalyst recycling via the application of technical grade surfactants. The feasibility of such a concept is evaluated in a fully automated, modular mini-plant system within which the characteristics of such a multiphase system pose several obstacles for the operation. Maintaining a stable phase separation for efficient product separation and catalyst recycling is complicated by small and highly dynamic operation windows as well as poor measurability of component concentrations in the liquid phases. In this contribution, a model-based strategy is presented to enable concentration tracking and phase state control within dynamic mini-plant experiments. Raman spectroscopy is used as an advanced process analytical tool, which allows for online in-situ tracking of concentrations. Combined with optical and conductivity analysis optimal plant trajectories can be calculated via the solution of dynamic optimization problem under uncertainty. Applying these, a stable reaction yield of 40 % was achieved, combined with an oil phase purity of 99,8 % (total amount of oily components in the oil phase) and catalyst leaching below 0.1 ppm.
With a more and more general acceptance of accreditation in the field of reference materials production, and an ever increasing number of RMP accredited, accreditation bodies face applications from RMP active in the field of qualitative RM production.
While (the still valid and used for accreditation purposes) ISO Guides 34 and 35 describe in detail requirements applicable to RMP dealing with quantitative RM, accreditation bodies normally claim lack of normative requirements with respect to quantitative RM. The new ISO 17034:2016 which will be introduced in the accreditation practice over the next two to three years solves the problem pragmatically, allowing strategies for RM certification other than those for (purely) quantitative materials.
In fact, both ISO Guide 34 and the new ISO 17034 are written in a form that, at least for the overwhelming majority of requirements, may be applied to RMP of both qualitative and quantitative RM. Peculiarities may occur in homogeneity and stability testing, and the uncertainty of a purely qualitative result is still under discussion. The problem of traceability might be solved for most of the application fields considered.
The talk gives an overview of the specific problems encountered, and provides some possible solutions both for homogeneity and stability testing, the expression of uncertainty, and the statement of traceability in certificates.
Qualitative reference materials (RM) cover a wide range of the overall RM market. Proficiency testing providers attract up to a thousand of participants in PT schemes purely oriented on qualitative results. The RM used for these kinds of PT are poorly regulated, nevertheless with a more and more general acceptance of accreditation in the field of RM production and PT provision, there is an ever increasing interest in assessing producers and providers according to rules already well accepted in the field of quantitative analysis.
The basic governing document, ISO 17034:2016, is written in a form that, at least for the overwhelming majority of requirements, may be applied to both qualitative and quantitative RM. However, problems remain. In particular, the expression of uncertainty of a purely qualitative result is still unresolved, and under discussion, the latter now lasting already dozens of years.
Some handles would be needed. In the poster, existing approaches and some pragmatic, new ways to tackle the problem are displayed and discussed.
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).
Monitoring chemical reactions is the key to chemical process control. Today, mainly optical online methods are applied, which require excessive calibration effort. NMR spectroscopy has a high potential for direct loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and harsh environments for advanced process Monitoring and control, as demonstrated within the European Union’s Horizon 2020 project CONSENS.
We present a range of approaches for the automated spectra analysis moving from conventional multivariate statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process Analytical Technology) regarding low calibration effort/calibration free methods, fast adaptions for new reactants or derivatives and robust automation schemes.