Spectral encoder to extract the efficient features of Raman spectra for reliable and precise quantitative analysis

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Machine Learning

Background:

• Raman spectroscopy is vital for analyzing microorganisms, food, environment, and pharmaceuticals.
• Quantitative analysis in Raman spectroscopy relies heavily on effective spectral feature extraction.
• Complex samples and poor signal quality pose challenges for traditional methods.

Purpose of the Study:

• To develop a novel method for extracting effective spectral features using a spectral encoder.
• To enhance the reliability and precision of quantitative analysis in Raman spectroscopy.
• To build and validate a latent encoded feature regression model for component concentration prediction.

Main Methods:

• Utilized an autoencoder to reconstruct spectrometer output and extract latent features from the bottleneck layer.
• Developed a deep regression model to predict component concentrations using these latent features.
• Conducted ablation and comparative experiments on single- and multi-component datasets.

Main Results:

• The proposed model significantly improved regression precision compared to common methods.
• The method demonstrated robustness against noise and eliminated interference from irrelevant information.
• Latent encoded features showed strong nonlinear representation, low computational cost, and wide adaptability.

Conclusions:

• The spectral encoder offers a novel, effective, and parameter-free feature extraction approach for spectral analysis.
• The method is adaptable to various techniques and suitable for diverse real-world measurement scenarios.
• Latent encoded features hold potential for broader applications beyond spectral regression.