RamanFormer: A Transformer-Based Quantification Approach for Raman Mixture Components

Abstract

Raman spectroscopy is a noninvasive technique to identify materials by their unique molecular vibrational fingerprints. However, distinguishing and quantifying components in mixtures present challenges due to overlapping spectra, especially when components share similar features. This study presents "RamanFormer", a transformer-based model designed to enhance the analysis of Raman spectroscopy data. By effectively managing sequential data and integrating self-attention mechanisms, RamanFormer identifies and quantifies components in chemical mixtures with high precision, achieving a mean absolute error of 1.4% and a root mean squared error of 1.6%, significantly outperforming traditional methods such as least squares, MLP, VGG11, and ResNet50. Tested extensively on binary and ternary mixtures under varying conditions, including noise levels with a signal-to-noise ratio of up to 10 dB, RamanFormer proves to be a robust tool, improving the reliability of material identification and broadening the application of Raman spectroscopy in fields, such as material science, forensics, and biomedical diagnostics.

Figure 1

Diagram illustrates the sequential composition of layers in the proposed model for robust spectral analysis and precise component ratio prediction. Here, N stands for the number of samples (eq 5).

Figure 2

Raman spectra presented here showcase a specific ternary mixture and its individual components: (a) methanol (M), (b) isopropyl alcohol (IPA), and (c) ethanolamine (E) and (d) Raman spectrum of a ternary mixture composed of M, IPA, and E. The respective ratios of these components in the mixture are methanol at 78.75%, isopropyl alcohol at 8.75%, and ethanolamine at 12.50%.

Figure 3

Prediction errors of methanol (M), isopropyl alcohol (IPA), and ethanolamine (E) in the mixtures of (a) isopropyl alcohol and ethanolamine, (b) methanol and ethanolamine, and (c) methanol and isopropyl alcohol. Actual mixture percentages of M, IPA, and E are specified under each bar plot, respectively.

Figure 4

Prediction errors of methanol (M), isopropyl alcohol (IPA), and ethanolamine (E) in the ternary mixtures. Actual mixture percentages of M, IPA, and E are specified under each bar plot, respectively.

Figure 5

Prediction errors of methanol (M), isopropyl alcohol (IPA), and ethanolamine (E) in the binary mixtures of challenging scenarios, where one chemical is dominant and the other chemical is present in very low amounts.

Figure 6

Demonstration that when exposed to a noisy training set characterized by diverse signal-to-noise ratio (SNR) values, the model exhibits the capability to achieve convergence even with a signal-to-noise ratio (SNR) of 0 dB, though with a somewhat higher error. Training instances are augmented with additive Gaussian noise as in eq 6, while evaluation of the error is conducted on the clean, untouched test data.

Figure 7

Evaluation of the RamanFormer model’s performance across different levels of additive noise, as measured by varying signal-to-noise ratios (SNR), showcasing its durability against noise in test data. Notably, RamanFormer remains effective against noise up to a 10 dB SNR threshold, after which error rates start to increase gradually.