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The project InPROMPT aims to establish a robust hydroformylation reaction process of the alkene n-dodecene to the aldehyde n-tridecanal using a rhodium complex as catalyst in the presence of syngas. This industrial relevant production process demands further research for optimization and expansion. For that purpose, the fiber optic coupled probe of a process Raman spectrometer is directly introduced in the product stream, which enables the recording of spectra within short control intervals.
An experimental investigation has been carried out to characterize and discriminate seven saffron samples and to verify their declared geographical origin using a voltammetric electronic tongue (VE-tongue). The ability of multivariable analysis methods such as Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) to classify the saffron samples according to their geographical origin have been investigated. A good discrimination has reached using PCA and HCA in the VE-tongue characterization case. Furthermore, cross validation and Partial Least Square (PLS) techniques were applied in order to build suitable management and prediction models for the determination of safranal concentration in saffron samples based on SPME-GC-MS and UV-Vis Spectrophotometry. The obtained results reveals that some relationships were established between the VE-tongue signal, SPMEGC-MS and UV-Vis spectrophotometry methods to predict safranal concentration levels in saffron samples by using the PLS model. In the light of these results, we can say that the proposed electronic system offer a fast, simple and efficient tool to recognize the declared geographical origin of the saffron samples.
There is a high demand of monitoring in the era of QbD in industrial scale require new approaches to gain data rapidly and of sufficient quality in real time. Raman spectroscopy technology has great potential but not yet shown it fully in process on-line monitoring due to limitations such as i) uncomplete separation between cells and growth media alone, ii) general weak Raman signals of analytes in complex solutions and iii) strong background signals such as the auto-fluorescence, cosmic rays and surrounding lights overlapping the weak Raman signals. Here we demonstrate a Proof-of-Concept on an the example lactic acid bacteria process using a Streptococcus thermophiles fermentation. Results from three different Raman approaches are presented: 1) Time-Gated Raman Spectroscopy (TG-Raman), 2) Surface Enhanced Raman Spectroscopy (SERS) and 3) Raman process spectroscopy with NIR excitation combined with multivariate data analysis (MVDA) using Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR).
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.
LIBS matures to a quantitative method for elemental analysis rather than a qualitative diagnostic tool. Numerous real world applications for bulk and microanalysis profit from instrumental and methodical advances in the last decade. Today, recording of numerous spectra from samples can be done with low experimental efforts and low cost per spectrum. Not surprisingly, LIBS data, with a high spectral resolution and a broad spectral range, become “big data” and can be utilized in different ways beyond elemental analysis. But, what is the generic or best approach for quantitative LIBS analysis? What techniques can be employed to gain new insights into data? Emergent information can arise through data fusion of LIBS data with other data, i.e. orthogonal spectroscopic or other information related to the sample. But are fused data better than data from a single method?
This talk will provide an in-depth overview what chemometric tools can do for LIBS. For quantitative analysis, pre-processing tools are essential to improve precision, accuracy, and reproducibility but at the same time their application to data is still based on phenomenological criteria. Multivariate analysis seems to dominate LIBS, but are there drawbacks on using all information from spectra. For different data sets from real applications, the use of multivariate calibration and (un)supervised pattern recognition will be discussed in comparison with reference analytical methods and possible improvements through plasma diagnostics and modelling.
For the damage assessment of reinforced concrete structures the quantified ingress profiles of harmful species like chlorides, sulfates and alkali need to be determined. In order to provide on-site analysis of concrete a fast and reliable method is necessary. Low transition probabilities as well as the high ionization energies for chlorine and sulfur in the near-infrared range makes the detection of Cl I and S I in low concentrations a difficult task. For the on-site analysis a mobile LIBS-system (k = 1064 nm, Epulse ≤ 3 mJ, t = 1.5 ns) with an automated scanner has been developed at BAM. Weak chlorine and sulfur signal intensities do not allow classical univariate analysis for process data derived from the mobile system. In order to improve the analytical performance multivariate analysis like PLS-R will be presented in this work. A comparison to standard univariate analysis will be carried out and results covering important parameters like detection and quantification limits (LOD, LOQ) as well as processing variances will be discussed (Allegrini and Olivieri, 2014 [1]; Ostra et al., 2008 [2]). It will be shown that for the first time a low cost mobile system is capable of providing reproducible chlorine and sulfur analysis on concrete by using a low sensitive system in combination with multivariate evaluation.
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.
Raman spectroscopy is becoming a powerful process analytical technology (PAT) tool. Until now Raman technology has not shown its full potential in bioprocess on-line monitoring due to several technical challenges. Because of only small? interference from water molecules, Raman-spectroscopy is in contrast to IR spectroscopy able to follow changes of metabolite concentrations in dilute aqueous solutions. Results from common CCD-based process Raman-spectrometers reveal only barely identifiable peaks with a dominating (fluorescence) background. To solve this, Raman spectroscopy needs: (A) an enhancement to increase the limit of detection (LOD), and (B) a reliable method to distinguish the Raman signal from the sample-related auto-fluorescence.
SERS (surface enhanced Raman spectroscopy) acts as an optical "nano-antenna"-effect. It causes a dipolar localized surface Plasmon resonance effect due to noble metallic nanoparticles or roughened metal and improves the limit of detection (LOD) significantly. Another new process-monitoring technique, which removes the fluorescence background in Raman-measurements is called time-gated Raman spectroscopy. It uses a picosecond pulsed Nd:YVO4-laser as emission source (exc= 532 nm) and a gated SPAD-array (Single Photon Avalanche Detector) detector instead of commonly used CCD (Charged Coupled Device)-detectors and CW (Continues Wave) laser emission. Time-Gate can measure the Raman-signal before the stronger fluorescence signal reaches the detector.
In this study we utilized both SERS (Surface Enhanced Raman Spectroscopy) and time-gated Raman spectroscopy (TG-Raman) in combination on cell-free supernatant samples of an Escherichia coli cultivation with mineral salt media and a lactic acid bacteria fermentation with complex media. As a reference method for the estimation of amino acids and other metabolites, HPLC-RID and HPLC-FLD were used to evaluate the Raman-based detection. The quantitative evaluation of Raman data was performed by multivariate data analysis such as principal component analysis (PCA) and partial least squares regression (PLSR). For the first time, we can show that both qualitative and quantitative measurements are conducted successfully with both, SERS and time-gated Raman methods in industrially relevant media, so that a fast and reliable in situ or bypassed concentration measurement becomes feasible.
For reaction monitoring and process control using NMR instruments, in particular, after acquisition of the FID the data needs to be corrected in real-time for common effects using fast interfaces and automated methods. When it comes to NMR data evaluation under industrial process conditions, the shape of signals can change drastically due to nonlinear effects. Additionally, the multiplet structure becomes more dominant because of the comparably low-field strengths which results in overlapping of multiple signals. However, the structural and quantitative information is still present but needs to be extracted by applying predictive models.
We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modeling). 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.
Proceedings of 4th European Conference on Process Analytics and Control Technology (EuroPACT 2017)
(2017)
EuroPACT 2017 is the fourth European Conference on Process Analytics and Control Technology.
The conference covered new technologies in process analytics, the implementation of these technologies in various fields and the transformation of data into knowledge. The conference was supported by an exhibition of instrumentation, applications and data evaluation tools.
EUROPACT 2017 provided a meeting and a discussion forum for scientists and users of process analytics from academia and industry. The conference programme includes plenary lectures and discussion during poster sessions.
The conference took place 10-12 May 2017 in Potsdam (nearby Berlin), Germany.