Analytische Chemie
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- Animal species differentiation (1)
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Organisationseinheit der BAM
In this paper, we present a collection of machine learning assisted distributed fiber optic sensors (DFOS) for applications in the field of infrastructure monitoring. We employ advanced signal processing based on artificial neural networks (ANNs) to enhance the performance of the dynamic DFOS for strain and vibration sensing. Specifically, ANNs in comparison to conventional and computationally expensive correlation and linearization algorithms, deliver lower strain errors and speed up the signal processing allowing real time strain monitoring. Furthermore, convolutional neural networks (CNNs) are used to denoise the dynamic DFOS signal and enable useable sensing lengths of up to 100 km. Applications of the machine learning assisted dynamic DFOS in road traffic and railway infrastructure monitoring are demonstrated. In the field of static DFOS, machine learning is applied to the well-known Brillouin optical frequency domain analysis (BOFDA) system. Specifically, CNN are shown to be very tolerant against noisy spectra and contribute towards significantly shorter measurement times. Furthermore, different machine learning algorithms (linear and polynomial regression, decision trees, ANNs) are applied to solve the well-known problem of cross-sensitivity in cases when temperature and humidity are measured simultaneously. The presented machine learning assisted DFOS can potentially contribute towards enhanced, cost effective and reliable monitoring of infrastructures.
A big problem with the chemistry literature is that it is not standardized with respect to precise operational parameters, and real time corrections are hard to make without expert knowledge. This lack of context means difficult reproducibility because many steps are ambiguous, and hence depend on tacit knowledge. Here we present the integration of online NMR into an automated chemical synthesis machine (CSM aka. “Chemputer” which is capable of small-molecule synthesis using a universal programming language) to allow automated analysis and adjustment of reactions on the fly. The system was validated and benchmarked by using Grignard reactions which were chosen due to their importance in synthesis. The system was monitored in real time using online-NMR, and spectra were measured continuously during the reactions. This shows that the synthesis being done in the Chemputer can be dynamically controlled in response to feedback optimizing the reaction conditions according to the user requirements.
In the field of animal species differentiation, work on standardizing methods has been intensified in Germany in recent years, not least due to the horsemeat scandal in 2013. Even though there are now hardly ever any positive findings anymore in examinations to detect horse adulterations in foods such as lasagne, animal species differentiation altogether ranks high in detecting adulteration of foods. This article, therefore, summarises the current status of analytical techniques used in Germany with standardization at German level. It has been established by the working group "Biochemical and Molecular Biological Analytics" of the Lebensmittelchemische Gesellschaft (Food Chemistry Society within the German Chemical Society) with support of experts in the working group “Molecular biology techniques for differentiating plant and animal species" (§64 of the German Food and Feed Code - LFGB) and the "Immunology and molecular biology" task force of the food hygiene and food of animal origin working group (ALTS), both from Germany.