Deep learning mitigates batch effects in Raman spectroscopy
Abstract
Raman spectroscopy is widely used for its ability to provide rich molecular-level information from biological samples. However, the high sensitivity of Raman spectra can also unintentionally encode extraneous information, such as patient identity, instrument-specific signatures, environmental conditions, and sample handling protocols. This hidden structure, referred to as the batch effect, poses increasing challenges in machine learning applications, where it can lead to model overfitting, poor generalization, and even privacy risks when datasets are shared. In this work, we investigate the presence and impact of the batch effect in a Raman spectroscopy dataset derived from glioma tumor samples. We show that deep learning can accurately predict patient identity from individual spectra, revealing strong bias patterns in the data. To mitigate this issue, we explore several approaches and introduce an algorithmic framework based on adversarial deep learning. Our method effectively reduces the influence of confounding sample-specific signals while preserving information relevant to tumor classification. These findings highlight the need for careful bias control in Raman-based AI pipelines and provide tools to support more robust applications in biomedical spectroscopy.
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BibTeX
@article{sjoberg2026deep,
title = {{Deep learning mitigates batch effects in Raman spectroscopy}},
author = {Joel Sjöberg and Adrian Lita and Mioara Larion and Ion Petre},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-62086-1},
url = {https://www.nature.com/articles/s41598-026-62086-1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Deep learning mitigates batch effects in Raman spectroscopy
AU - Joel Sjöberg
AU - Adrian Lita
AU - Mioara Larion
AU - Ion Petre
PY - 2026
DA - 2026/10/26
JO - Scientific Reports
DO - 10.1038/s41598-026-62086-1
UR - https://www.nature.com/articles/s41598-026-62086-1
AB - Raman spectroscopy is widely used for its ability to provide rich molecular-level information from biological samples. However, the high sensitivity of Raman spectra can also unintentionally encode extraneous information, such as patient identity, instrument-specific signatures, environmental conditions, and sample handling protocols. This hidden structure, referred to as the batch effect, poses increasing challenges in machine learning applications, where it can lead to model overfitting, poor generalization, and even privacy risks when datasets are shared. In this work, we investigate the presence and impact of the batch effect in a Raman spectroscopy dataset derived from glioma tumor samples. We show that deep learning can accurately predict patient identity from individual spectra, revealing strong bias patterns in the data. To mitigate this issue, we explore several approaches and introduce an algorithmic framework based on adversarial deep learning. Our method effectively reduces the influence of confounding sample-specific signals while preserving information relevant to tumor classification. These findings highlight the need for careful bias control in Raman-based AI pipelines and provide tools to support more robust applications in biomedical spectroscopy.
SN - 2045-2322
ER -