DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins.

To classify raw SERS Raman spectra from biological materials, we propose Background: Bacterial endotoxin, a lipopolysaccharide exuded by bacteria during their growth and infection process, serves as a valuable biomarker for bacterial identification. It is a vital component of the outer membrane layer in Gram-negative bacteria. By employing silver nanorod-based array substrates, surface-enhanced Raman scattering (SERS) spectra were obtained for two separate datasets: Eleven endotoxins produced by bacteria, each having an 8.75 pg average detection quantity per measurement, and three controls chitin, lipoteichoic acid (LTA), bacterial peptidoglycan (PGN), because their structures differ greatly from those of LPS. Objective: This study utilized various classical machine learning techniques, such as support vector machines, k-nearest neighbors, and random forests, in conjunction with a modified deep learning approach called DeepRaman. These algorithms were employed to distinguish and categorize bacterial endotoxins, following appropriate spectral pre-processing, which involved novel filtering techniques and advanced feature extraction methods. Result: Most traditional machine learning algorithms achieved distinction accuracies of over 99 percent, whereas Conclusion: We present the effectiveness of