Spectral classification by generative adversarial linear discriminant analysis.

Area of Science:

• Analytical Chemistry
• Chemometrics
• Machine Learning

Background:

• Overfitting is a significant challenge in spectrochemical analysis, leading to reduced classification accuracy.
• Existing methods for minimizing overfitting often rely on feature extraction or data reduction.
• Generative adversarial neural networks (GANs) have shown success in reducing overfitting in artificial neural networks.

Purpose of the Study:

• To introduce Generative Adversarial Linear Discriminant Analysis (GALDA) as a novel tool for spectrochemical analysis.
• To enhance classification accuracy and reduce overfitting in spectral data.
• To evaluate GALDA's performance against established dimension reduction and classification methods.

Main Methods:

• GALDA was developed using a linear algebra framework, distinct from GANs.
• GALDA augments data by identifying and excluding irrelevant spectral regions.
• The method was tested on simulated spectra from the Romanian Database of Raman Spectroscopy (RDRS) and real-world samples.

Main Results:

• GALDA demonstrated improved classification accuracy compared to non-adversarial methods.
• Dimension reduction via GALDA resulted in smoothed loading plots with more prominent spectral features.
• Analysis of clopidogrel bisulfate and aspirin tablet constituents showed GALDA's practical applicability.

Conclusions:

• GALDA is a broadly applicable and effective tool for improving classification accuracy in spectrochemical analysis.
• The method offers advantages over traditional approaches by performing data augmentation and reducing overfitting.
• GALDA shows significant potential for various spectrochemical applications, including microscopy and imaging.