Machine Learning for Deconvolution and Segmentation of Hyperspectral Imaging Data from Biopharmaceutical Resins

Area of Science:

• Analytical Chemistry
• Materials Science
• Biotechnology

Background:

• Biopharmaceutical resins are critical inert matrices for drug purification, delivery, and biocatalysis.
• Current methods for analyzing resin properties are often destructive, require sample alteration, and yield limited information.
• A need exists for advanced, non-destructive techniques to characterize these essential materials.

Purpose of the Study:

• To develop and validate a novel, rapid, and non-destructive method for elucidating the molecular and spatial properties of biopharmaceutical resins.
• To apply unsupervised machine learning algorithms, non-negative matrix factorization (NMF) and k-means clustering, in conjunction with Raman hyperspectral imaging.
• To demonstrate the capability of this integrated approach for comprehensive resin analysis, using Immobead 150P as a representative example.

Main Methods:

• Utilized Raman hyperspectral imaging to collect spectral data from biopharmaceutical resins.
• Applied non-negative matrix factorization (NMF) for spectral and spatial deconvolution of resin and substrate components.
• Employed k-means clustering for image segmentation following NMF deconvolution across multiple excitation wavelengths (532, 638, and 785 nm).

Main Results:

• Successfully deconvoluted and spatially resolved chemical species within the Immobead 150P resin and glass substrate using NMF.
• Achieved accurate image segmentation of resin components via k-means clustering across all tested excitation wavelengths.
• Demonstrated the effectiveness of the combined NMF and k-means approach for comprehensive, data-rich characterization of biopharmaceutical resins.

Conclusions:

• The integration of unsupervised machine learning (NMF and k-means) with Raman hyperspectral imaging offers a powerful, rapid, and non-destructive tool for analyzing biopharmaceutical resins.
• This methodology provides detailed molecular and spatial insights, overcoming limitations of traditional analytical techniques.
• The developed approach represents a significant advancement for research and development in biopharmaceuticals and related multidisciplinary fields.