Automatic Raman Measurements in a High-Throughput Bioprocess Development Lab
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917986261008384 |
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| author | Lange, Christoph Seidel, Simon Altmann, Madeline Stors, Daniel Kemmer, Annina Cai, Linda Born, Stefan Neubauer, Peter Bournazou, M. Nicolas Cruz |
| author_facet | Lange, Christoph Seidel, Simon Altmann, Madeline Stors, Daniel Kemmer, Annina Cai, Linda Born, Stefan Neubauer, Peter Bournazou, M. Nicolas Cruz |
| contents | This study presents a collection of physical devices and software services that fully automate Raman spectra measurements for liquid samples within a robotic facility. This method is applicable to various fields, with demonstrated efficacy in biotechnology, where Raman spectroscopy monitors substrates, metabolites, and product-related concentrations. Our system specifically measures 50 $\micro L$ samples using a liquid handling robot capable of taking 8 samples simultaneously. We record multiple Raman spectra for 10s each. Furthermore, our system takes around 20s for sample handling, cleaning, and preparation of the next measurement. All spectra and metadata are stored in a database, and we use a machine learning model to estimate concentrations from the spectra. This automated approach enables gathering spectra for various applications under uniform conditions in high-throughput fermentation processes, calibration procedures, and offline evaluations. This allows data to be combined to train sophisticated machine learning models with improved generalization. Consequently, we can develop accurate models more quickly for new applications by reusing data from prior applications, thereby reducing the need for extensive calibration data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11234 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic Raman Measurements in a High-Throughput Bioprocess Development Lab Lange, Christoph Seidel, Simon Altmann, Madeline Stors, Daniel Kemmer, Annina Cai, Linda Born, Stefan Neubauer, Peter Bournazou, M. Nicolas Cruz Quantitative Methods This study presents a collection of physical devices and software services that fully automate Raman spectra measurements for liquid samples within a robotic facility. This method is applicable to various fields, with demonstrated efficacy in biotechnology, where Raman spectroscopy monitors substrates, metabolites, and product-related concentrations. Our system specifically measures 50 $\micro L$ samples using a liquid handling robot capable of taking 8 samples simultaneously. We record multiple Raman spectra for 10s each. Furthermore, our system takes around 20s for sample handling, cleaning, and preparation of the next measurement. All spectra and metadata are stored in a database, and we use a machine learning model to estimate concentrations from the spectra. This automated approach enables gathering spectra for various applications under uniform conditions in high-throughput fermentation processes, calibration procedures, and offline evaluations. This allows data to be combined to train sophisticated machine learning models with improved generalization. Consequently, we can develop accurate models more quickly for new applications by reusing data from prior applications, thereby reducing the need for extensive calibration data. |
| title | Automatic Raman Measurements in a High-Throughput Bioprocess Development Lab |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2504.11234 |