Deep attention for interpretable detection of organic pollutants in water using colloidal SERS
Year: 2025
Type: article
Abstract: This paper presents an innovative approach to the interpretable detection of organic pollutants dissolved in water – carbendazim, thiacloprid, and acetamiprid – by leveraging self-attention mechanisms to within the deep neural network (DNN) used for detection. The core contribution of our work is demonstrating how attention mechanisms can significantly enhance the interpretability and performance of pollutant detection in water using colloidal Surface-Enhanced Raman Spectroscopy (SERS) measurements. The cornerstone of our methodology is the optimization of the measurement process, aimed not merely at acquiring high-quality signals but at securing a high volume of data that embodies the full spectrum of measurement variability. This optimization includes the development of a measurement protocol that involves (i) the fabrication of colloidal silver nanoparticles utilizing the method proposed by Leopold and Lendl, (ii) the aging of the colloidal mixture with the analytes for a predetermined period, and (iii) the SERS measurement settings. Each step is carefully calibrated to maximize the SERS response sensitivity and reproducibility for the detection of the targeted analytes. Building upon this optimized measurement framework, the paper introduces a deep learning algorithm with an embedded attention mechanism designed to focus on the most relevant spectral features for pollutant detection. Unlike traditional machine learning methods, which often lack interpretability, the proposed attention model provides clear insights into which features are deemed most important for the detection task, thereby offering a direct interpretation of the decision-making process of the neural network.
Language: English
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,440
OpenAlex ID: W4417073197