A recent study published in Spectrochimica Acta at the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, has leveraged a Siamese network to tackle the issue of inter-instrument variation in Raman spectroscopy. The study introduces a modular design that facilitates the fusion training and mutual prediction of Raman spectra, enhancing the accuracy and reliability of spectral analysis across different instruments. Raman spectroscopy has become a widely used technique for spectral analysis, finding applications in various fields such as biological analysis, real-time monitoring of chemical reactions, and material identification. However, a significant challenge in Raman spectroscopy is the variation in spectra collected by different instruments, which can impact the accuracy and reproducibility of results. Previous methods to address this issue, such as environmental parameter correction and the use of standard Raman spectral databases, have been insufficient in fully mitigating inter-instrument variation.
To address this challenge, the research team assembled three Raman spectral datasets, each representing data collected from different bacterial strains using different Raman spectrometers. The datasets were preprocessed to remove spikes, subtract baselines, and normalize the spectra. The team then utilized three classification models with different architectures—ResNet, Transformer, and LSTM—to evaluate their performance in classifying the Raman spectra.
However, instead of directly training these models for classification, the researchers adopted a Siamese network approach. The Siamese network compares whether Raman spectra collected by two instruments belong to the same class, outputting a feature distance between the two spectra that is then mapped into a similarity value. This approach allows for the determination of the unknown spectrum's class by comparing its similarity values with the reference spectra of each known class.The Siamese network was implemented with multiple projection layers to enable a modular design, allowing for the decoupling of the spectral encoding layer from the classifier. This design makes it easier to plug and play different spectral encoders, facilitating the fusion training and mutual prediction of Raman spectra of different lengths.
The research found that the Siamese network approach significantly improved the classification accuracy of Raman spectra across different instruments, especially when dealing with spectra of the same resolution. The Transformer-based Siamese network demonstrated the best performance, attributed to its ability to process sequential data in parallel through its attention mechanism.
The study's findings have important practical implications for the field of Raman spectroscopy. By addressing the issue of inter-instrument variation, the Siamese network approach enables more accurate and reproducible spectral analysis across different instruments. This is particularly significant in fields such as biological analysis and material identification, where the ability to obtain consistent and reliable results is crucial.
Moreover, the modular design of the Siamese network facilitates the easy integration of new spectral encoders, allowing for the continuous improvement and adaptation of the system. This makes the approach highly versatile and adaptable to various applications and scenarios.
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