Siamese network for classification of Raman spectroscopy with inter-instrument variation for biological applications.

Raman spectroscopy has emerged as a highly sensitive, rapid, and label-free detection method, extensively utilized in biological research. Presently, it is frequently paired with artificial intelligence (AI) algorithms to facilitate identification and classification tasks. However, variations in the settings across different Raman spectrometers, along with the sensitive and continuous nature of biological Raman signals, can subtly alter the acquisition of these signals. This can potentially impact the classification outcomes of the spectra. Moreover, Raman spectra with disparate resolutions pose challenges for effective model training. In this study, we introduce a modularized Siamese neural network, equipped with multiple projection layers to segregate the model components. This design allows our model to support the core module spectral encoder's pluggability. The model determines the classification results by extracting the features of Raman spectra with inter-instrument variation, mapping these feature distances into spectral similarities, and finally, comparing a set of similarities. Our experimental results demonstrate the feasibility of training the model with only 10 spectra per category, using bacterial datasets we created. We compared the classification outcomes of three distinct spectral encoders, with the most effective model achieving a classification accuracy exceeding 90%. Furthermore, we successfully implemented the fusion training and prediction of Raman spectra with different resolutions. In conclusion, our model enhances the validity and comparability of Raman spectral acquisition for biological applications and diversifies the methods of Raman spectral acquisition.