The study focuses on predicting polymer sizes from Raman spectra using machine learning workflows. Three alternative methods are proposed, including diffusion maps and conformal autoencoder neural networks. The Y-shaped autoencoder outperforms other methods in accuracy and efficiency.
The research compares the proposed nonlinear approaches with state-of-the-art benchmark methods for predicting particle sizes. Results show that the Y-shaped autoencoder achieves superior prediction accuracy, similar to established size measurement devices like DLS. The study highlights the potential of data-driven approaches for accurate and efficient size prediction from in-line measurements.
Key points include the application of machine learning workflows to predict polymer sizes directly from untreated Raman spectra, circumventing labor-intensive processes like DLS. The study emphasizes the importance of dimensionality reduction and specific latent variables in achieving accurate predictions of polymer sizes.
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