---
tags:
- raman-spectroscopy
- biotechnology
- bioprocess
- fermentation
- metabolites
- spectroscopy
- Glucose
- Glycerol
- Acetate
- EnPump
- Nitrate
- Yeast Extract
- Phosphate
- Sulfate
license: cc-by-4.0
language: en
pretty_name: Substrate Mix Raman Spectroscopy Dataset
task_categories:
- tabular-regression
configs:
- config_name: default
  data_files:
  - split: train
    path: "train.csv"
  - split: validation
    path: "val.csv"
  - split: test
    path: "test.csv"
size_categories:
- 1K<n<10K
---

## Dataset Overview

This dataset, designed for biotechnological applications, provides a valuable resource for calibrating models used in high-throughput bioprocess development, particularly for bacterial fermentations. It features **Raman spectra** of samples containing varying, **statistically independent concentrations** of eight key metabolites, along with mineral salt medium and antifoam.

The primary motivation behind this dataset is to offer a **calibration resource** with **uncorrelated metabolite concentrations**, addressing a common challenge in biotechnological applications. By ensuring the independence of substance concentrations, the dataset is ideal for training robust models and for integration with use-case specific labeled spectra.

## Substances and Concentration Ranges

The dataset includes the following eight pure substances (metabolites) chosen for their relevance to fermentation processes:

* **Glucose**
* **Glycerol**
* **Acetate**
* **EnPump**
* **Nitrate**
* **Yeast Extract**
* **Phosphate**
* **Sulfate**

Concentrations for each substance were randomly sampled from a uniform distribution within ranges relevant for bacterial fermentations. This approach ensures that all possible combinations of concentrations have an equal chance of appearing in the dataset, effectively preventing correlations among the substances. Lower concentrations were deliberately oversampled to enhance predictive accuracy for models trained on data within that range.

## Additional Components and Their Impact

To better mimic real-world conditions encountered during bacterial fermentations, varying concentrations of **mineral salt medium** and **antifoam** were also included in the samples.

* **Antifoam** significantly impacts signal intensity; increased amounts lead to greater turbidity and a decrease in overall signal. By incorporating varying antifoam concentrations, the dataset helps ensure that models trained on this data are **agnostic to changing concentrations of antifoam or mineral salt medium**, improving their applicability in diverse experimental setups.

## Data Acquisition

Samples were prepared using a Liquid Handler (LiHa) system. The process involved:

1.  **Determining exact concentrations** for each substance in a sample.
2.  **Creating the sample** with the specified concentrations.
3.  **Recording Raman spectra** of the prepared sample.

More details on the creation process can be found in the paper: https://dx.doi.org/10.2139/ssrn.5239248
As soon as this paper is published in a peer-reviewed journal, we will add the link.