Raman spectrum model transfer method based on Cycle-GAN.

Area of Science:

• Spectroscopy
• Chemometrics
• Machine Learning

Background:

• Raman spectrometer hardware variations lead to inconsistent spectral data.
• Models trained on one instrument often perform poorly on others.
• Traditional chemometric methods struggle with accurate spectral data transformation.

Purpose of the Study:

• To develop a novel deep learning approach for transforming Raman spectral data between different instruments.
• To overcome the limitations of conventional model transfer techniques.
• To enable direct, parameter-free conversion of spectral data.

Main Methods:

• A deep learning network based on Cycle-Consistent Adversarial Networks (Cycle-GAN) was adapted for vector-to-vector transformation.
• The network directly converts spectral data from a source domain to a target domain.
• No parameter adjustments or additional operations were required for the transformation.

Main Results:

• The developed deep learning network successfully transformed spectral data between different domains.
• The method demonstrated superior intelligence and efficiency compared to traditional chemometric approaches.
• Cosine similarity between source and transformed target domain data exceeded 99%.

Conclusions:

• The Cycle-GAN-based deep learning network offers an effective solution for Raman spectral data transfer.
• This approach enhances model applicability across different Raman spectrometer instruments.
• The method represents a significant advancement in intelligent and efficient spectral data processing.