Temporal convolutional network on Raman shift for human osteoblast cells fingerprint Analysisa,b,c

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 finger print assignment through Raman shift analysis, opening new avenues for advancements in physiological and biochemical studies. 1. Introduction In recent years, the growing demand to know in more detail the composition and function of cells in a particular status has led to a great expansion of physiological studies. Accurate identification and classifi cation of different cell types are prominent for understanding cellular behavior, disease stage, and drug development. Conventional methods for cell analysis, such. as fluorescence-based techniques, often involve invasive procedures and may interfere with cellular integrity, limiting their applicability in live cell studies. To overcome these limitations, Raman spectroscopy [1,2] has emerged as a promising non-invasive and label-free technique for cell analysis. Raman spectroscopy relies on the inelastic scattering of photons by molecules within a sample, resulting in characteristic Raman shifts that correspond to the vibrational modes of specific chemical bonds. This unique spectral information can be used as a "fingerprint" to differentiate different cell types and to study various cellular processes. Howe