Machine learning-based investigation of the relationship between plasma emission spectra and biological responses *

Abstract Cold atmospheric plasma (CAP) has shown promising potential across biomedical applications. However, translating these effects into predictable and reproducible outcomes remains challenging due to device variability and the complex and unknown interplay of reactive species. This study evaluates the potential of machine learning (ML) to reliably predict CAP biological results including 24 h MTT and 48 h MTT from optical emission spectroscopy (OES) data. Data-driven models correlate plasma characteristics with cell viability and metabolic activity outcomes in human dermal fibroblasts. Diverse ML models were employed for their differing capabilities in feature extraction from OES data, essential for assessing the predictive capability of ML models for the biological effects of CAP from OES data. To evaluate cross plasma-device transferability without information leakage, models were tested on two distinct CAP jet systems. While the models achieved high accuracy for the primary jet used in training, their performance degraded considerably when applied to data from the secondary jet. To assess whether the loss of cross-device predictability could in principle be restored through minimal domain calibration, we conducted a controlled fine-tuning ablation on the pre-trained models. Results show that while spectra cannot serve as a device-independent predictor for the acute (24 h MTT) outcome, they retain partial transferability for the delayed (48 h MTT) response when minimal calibration is performed and sufficiently expressive models are employed to extract the underlying transferable structure. Moreover, the analysis identified the plasma source frequency as the most influential operational predictor, followed by voltage and treatment time. In identifying the gas-phase free radicals with the highest impact on biological outcomes, spectral fingerprints show the most influential species contributed in cell viability.