A classification method for fluorescence emission spectra of anionic surfactants with few-shot learning.

Abstract

Context: The unregulated use of anionic surfactants poses significant environmental risks, necessitating methods for their rapid and accurate identification. While fluorescence spectroscopy is a powerful tool, its application faces a critical challenge: existing analytical strategies either rely on complex and costly sensor arrays to acquire rich data, or they apply traditional machine learning to simpler, single-spectrum data, which often requires pre-processing steps like PCA that risk information loss. Furthermore, standard deep learning approaches are often unsuitable due to the high cost and effort required to acquire the large datasets they need for training. To address this gap, we propose an end-to-end, few-shot learning method (CNN-PN) for the classification of anionic surfactant fluorescence emission spectra. Our approach leverages a one-dimensional convolutional neural network (1D-CNN) to automatically extract features from the full, raw spectrum, thus avoiding lossy pre-processing. It then employs a prototypical network to perform robust, similarity-based classification, a strategy highly effective for limited sample sizes. We validated our method on our FESS dataset (53 surfactant categories) and a public metal oxides dataset. In our experiments, the CNN-PN method consistently outperformed traditional techniques like LDA, SVM, and KNN. It achieved 76.36% accuracy when trained with only a single sample per class, 95.90% in a multi-sample scenario on our FESS dataset, and 84.86% on the public dataset. This work provides a powerful and data-efficient framework for spectral analysis, facilitating the development of more accessible and rapid fluorescence sensing technologies, particularly for applications where data collection is expensive or constrained.

Methods: A few-shot learning classification method based on prototypical networks was employed. A one-dimensional convolutional neural network (1D-CNN) was utilized to extract spectral features from the full fluorescence emission spectra. Classification was then performed within the prototypical network framework using Euclidean distance as the similarity metric between features in the learned latent space. The Python programming language and the PyTorch library were used for all model implementations and data analysis.