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[2402.00851] Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations
Summary
This article presents a data augmentation scheme for Raman spectra, enhancing model training by generating additional data points with independent labels, improving the robustness of convolutional neural networks in biotechnology applications.
Why It Matters
The proposed data augmentation technique addresses the challenge of limited training data in machine learning, particularly in complex biological processes. By enabling the reuse of existing spectra data, it enhances model performance and applicability in diverse contexts, which is crucial for advancing analytical technologies in biotechnology.
Key Takeaways
- The new data augmentation scheme improves CNN training by generating statistically independent labels.
- This method allows for better model performance in scenarios with different correlation structures in data.
- Utilizing historical data effectively can lead to more robust models in biotechnology applications.
Computer Science > Machine Learning arXiv:2402.00851 (cs) [Submitted on 1 Feb 2024 (v1), last revised 19 Feb 2026 (this version, v2)] Title:Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations Authors:Christoph Lange, Isabel Thiele, Lara Santolin, Sebastian L. Riedel, Maxim Borisyak, Peter Neubauer, M. Nicolas Cruz Bournazou View a PDF of the paper titled Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations, by Christoph Lange and 5 other authors View PDF HTML (experimental) Abstract:In biotechnology Raman Spectroscopy is rapidly gaining popularity as a process analytical technology (PAT) that measures cell densities, substrate- and product concentrations. As it records vibrational modes of molecules it provides that information non-invasively in a single spectrum. Typically, partial least squares (PLS) is the model of choice to infer information about variables of interest from the spectra. However, biological processes are known for their complexity where convolutional neural networks (CNN) present a powerful alternative. They can handle non-Gaussian noise and account for beam misalignment, pixel malfunctions or the presence of additional substances. However, they require a lot of data during model training, and they pick up non-linear dependencies in the process variables. In this work, we exploit the additive nature of spectra in order to generate additional data points from a given dataset that have statistically indep...
