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Updated: Jan 13, 2026

A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants

A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants

Published on: February 19, 2016

Data augmentation for machine learning assisted pesticide detection from SERS.

Thwahira Shirin Alampara¹, Abhishek Jayachandran², Shraddha Ramakrishna Bhat¹

¹School of Chemistry, Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM), Vithura, Thiruvananthapuram 695551, Kerala, India.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy

|October 28, 2025

Summary

This summary is machine-generated.

This study introduces TabuLa, a transformer-based synthesizer, to create synthetic spectral data for Surface-Enhanced Raman Spectroscopy (SERS) pesticide detection. Augmenting real data with synthetic samples significantly improves machine learning model performance for identifying low-concentration pesticides.

Keywords:

Acetamiprid Data augmentation Generative adversarial networks (GAN)Large language model (LLM)Machine learning (ML)Pesticide detection Surface Enhanced Raman Spectroscopy (SERS)Transformer

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Last Updated: Jan 13, 2026

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Published on: February 19, 2016

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Published on: March 16, 2019

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.