Efficient Raman data analysis

However, the data sets obtained are very complex and depending on the conditions, the spectroscopic fingerprint varies only marginally.
By combining the analytical process with artificial intelligence (AI), the spectral data can nevertheless be made useful for effective and rapid diagnosis. The use of AI allows enormous amounts of data to be handled in just a short time, enabling, for example, microorganisms or chemical components within process flows to be precisely identified and classified, and concentration and behavioural analysis to be carried out.

Spectroscopic databases

The basis for this is formed by spectroscopic databases in which the spectral fingerprints of individual microorganisms with different characteristics or chemical components are stored. These are processed by specific machine-learning algorithms and compared with the measurement data obtained. This machine-learning mechanism is not limited to diagnostics, but is transferable to many other areas where microbes play an important role, such as in drug production, the food industry or agriculture, and can also be used for process analysis in the chemical sector.

Application spectrum of AI in data analysis

Furthermore, AI is not only useful for spectroscopic data. For example, visual data such as microscope images can also be processed and a database created based on them to analyse and categorise images and shapes. Due to a constant increase in information in the database systems, even unknown bacteria and microbes can be identified in this way.

Basics and measuring principle

Similar to infrared spectroscopy, information acquisition is based on the interaction of electromagnetic radiation with the sample under investigation. In the case of Raman spectroscopy, the sample is excited by monochromatic light from a laser source, which leads to elastic and inelastic scattering processes in the sample, whereby the inelastic scattering contains important information on substance- and molecule-specific vibrations. This information can be detected in the form of wavelength changes of the reflected light and allows conclusions to be drawn about the material composition of a sample as well as statements about crystallinity, doping, pressure and temperature dependence.

In biophotonics in particular, Raman spectroscopy offers enormous potential, as it allows extensive conclusions to be drawn about the sample composition and information to be obtained about the spectral fingerprint of individual components within a very short time with only a minimal amount of sample and little effort for sample preparation....

Advantages for bioprocess analysis

For example, species such as bacteria, yeasts and somatic cells can be easily identified. Furthermore, due to its non-destructive nature, Raman spectroscopy enables non-invasive tracking of biological process sequences, such as metabolic processes, and subsequent verification of the results by other analytical methods on the same sample.