Data augmentation for machine learning assisted pesticide detection from SERS

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Machine Learning

Background:

• Surface-Enhanced Raman Spectroscopy (SERS) faces challenges in detecting low-concentration pesticides due to obscured Raman signals.
• Limited diverse spectral data hinders the training and generalization of machine learning (ML) models for SERS pesticide sensing.
• The 'needle-in-a-haystack' problem requires advanced data augmentation strategies.

Purpose of the Study:

• To develop a novel method for generating high-quality synthetic spectral data for SERS pesticide sensing.
• To address the data scarcity issue in training robust ML models for pesticide detection.
• To evaluate the efficacy of a transformer-based data synthesizer in enhancing ML model performance.

Main Methods:

• Utilized TabuLa, a transformer-based data synthesizer, to generate synthetic SERS spectral data mimicking real pesticide signals.
• Augmented existing real SERS datasets with the synthetically generated data.
• Evaluated TabuLa's performance by comparing real and synthetic datasets and by assessing ML model detection accuracy on real data.

Main Results:

• TabuLa successfully generated realistic synthetic SERS spectral data comparable to real pesticide signals.
• Augmenting real data with synthetic samples enhanced dataset diversity and improved ML model robustness.
• Supervised ML models trained on TabuLa-augmented data demonstrated significantly improved pesticide detection capabilities.

Conclusions:

• TabuLa offers a promising solution to overcome data limitations in SERS applications.
• Synthetic data generation via TabuLa can substantially enhance the performance of ML-based pesticide detection systems.
• This approach holds potential for advancing sensitive and reliable pesticide monitoring using SERS technology.