HTRamanBioCatalysisAXP Dataset

Dataset Overview

This dataset consists of Raman spectra tailored for the real-time monitoring of biocatalytic reactions. A key feature of this data is the use of Deep Eutectic Solvents (DES) as the reaction medium. While DES offers significant advantages for green chemistry and enzyme stability, it presents a challenge for analytical monitoring because the solvent contributes a dominant background signal, requiring robust regression models (like PLS) to extract the analyte concentrations.

Biocatalytic Use Case: Adenosine Phosphate Monitoring

The dataset facilitates the quantification of the adenosine signaling and energy cycle. In biocatalysis, monitoring the conversion of these molecules is vital for understanding reaction kinetics and energy charge in cell-free systems.

The primary challenge addressed here is the simultaneous quantification of four structurally similar molecules within a complex matrix. This is particularly useful for researchers developing PAT (Process Analytical Technology) tools for automated bioreactors or enzymatic cascades where traditional HPLC does not give adequate results.

Target Parameters and Concentration Ranges

The concentrations represent the actual values achieved via a Tecan Liquid Handling Robot, ensuring high precision across the 344 samples.

Analyte	Full Name	Unit
Adenosine	Adenosine	[g / L]
ATP	Adenosine Triphosphate	[g / L]
ADP	Adenosine Diphosphate	[g / L]
AMP	Adenosine Monophosphate	[g / L]

Data Acquisition

Raman spectra were recorded with a focus on reproducibility in a high-throughput environment:

• Instrument: Metrohm i-Raman Plus
• Laser Wavelength: 785 nm
• Exposure Time: 25 s
• Laser Power: 450 mW
• Scans per Sample: 5 (averaged)
• Automation: Fully automated pipetting and measurement via Tecan Liquid Handling Robot.

Potential Applications

• Chemometrics: Developing baseline correction and signal decoupling algorithms.
• Bioprocess Control: Training models for real-time enzymatic reaction monitoring.
• Transfer Learning: Testing if models trained on DES-based Raman data can be adapted to aqueous or other ionic liquid environments.

Citation

If you utilize this dataset in your research, please cite:

BibTeX:

@article{lange2025setup,
  title={A Setup for Automatic Raman Measurements in High-Throughput Experimentation},
  author={Lange, Christoph and Seidel, Simon and Altmann, Madeline and Stors, Daniel and Kemmer, Annina and Cai, Linda and Born, Stefan and Neubauer, Peter and Bournazou, M Nicolas Cruz},
  journal={Biotechnology and Bioengineering},
  volume={122},
  number={10},
  pages={2751--2769},
  year={2025},
  publisher={Wiley Online Library}
}