Extending the spectral database of laser-induced breakdown spectroscopy with generative adversarial nets

Area of Science:

• Spectroscopy
• Machine Learning
• Data Science

Background:

• Laser-induced breakdown spectroscopy (LIBS) is crucial for substance detection but is destructive.
• Acquiring sufficient spectral data for LIBS classification models can be challenging due to sample limitations and experimental constraints.
• Accurate classification models are vital for qualitative analysis in LIBS.

Purpose of the Study:

• To propose a novel spectral generation method for extending LIBS spectral databases.
• To enhance the training dataset for LIBS classification models using generative adversarial nets (GANs).
• To improve the accuracy of substance identification in LIBS, especially when experimental data is scarce.

Main Methods:

• A generative adversarial network (GAN) was employed to synthesize LIBS spectra.
• Unsupervised clustering methods, including Principal Component Analysis (PCA) and K-means, were used to evaluate the generated spectra.
• Support Vector Machine (SVM) models were trained using extended spectral databases to assess classification performance.

Main Results:

• Generated spectra were visually and statistically indistinguishable from experimental LIBS spectra.
• Unsupervised clustering demonstrated that generated spectra were effectively integrated with real data.
• Classification models trained with the extended database showed improved identification accuracy, particularly with limited experimental spectra.

Conclusions:

• The proposed GAN-based method is effective for extending LIBS spectral databases.
• Synthetic spectra generated by GANs can significantly enhance the performance of LIBS classification models.
• This approach offers a viable solution for building robust LIBS qualitative analysis models with limited sample data.