Temporal convolutional network on Raman shift for human osteoblast cells fingerprint analysis

The physiological state and biological characteristics of cells play a crucial role in the study of several biological mechanisms that are at the basis of the life. Raman spectroscopy, a powerful non-destructive technique, has shown promise in providing unique molecular fingerprints of cells based on their vibrational states. However, the high-dimensional and noisy nature of Raman spectra poses significant challenges in precise cell classification. In this study, we present a novel deep learning algorithm tailored for human cells fingerprint assignment through Raman shift analysis. The proposed deep learning framework harnesses the power of Temporal Convolutional Networks (TCN) to efficiently extract and process Raman spectra information. Leveraging a dataset of labeled Raman spectra, the model is trained to learn discriminative features that capture the subtle differences in cell composition and molecular structures in differential states. Additionally, the proposed model enables real-time cell fingerprint prediction, making it highly applicable for high-throughput analysis in large-scale experiments. Experimental results demonstrate a peak accuracy of 99 %, showcasing the effectiveness and precision of the approach. Overall, the developed deep learning algorithm offers a robust and efficient solution for cell fingerprint assignment through Raman shift analysis, opening new avenues for advancements in physiological and biochemical studies.