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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.
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.
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).