Characterization of Biological Particles Using an integrated Hyperspectral Imaging and Machine Learning

Characterization of Biological Particles Using an integrated Hyperspectral Imaging and Machine Learning

Hyperspectral imaging (HSI) is a promising modality in medicine with many potential applications. This study focuses on developing a label-free lipid nanoparticle characterization method using a convolutional neural network (CNN) analysis of HSI images. The HSI data, hypercube, consists of a series of images acquired at different wavelengths for the same field of view, providing continuous spectra information for each pixel. Three distinct liposome samples were collected for analysis. Advanced image preprocessing and classification methods for HSI data were developed to differentiate liposomes based on their material compositions. Our machine learning-based classification method was able to distinguish different liposome types (i.e., drug loaded and empty) without the need of labeling. This study suggests that label-free hyperspectral imaging has great potential for characterizing drug delivery systems during or after the manufacturing process. Further development in hyperspectral imaging technology and AI-driven characterization methods are essential to unlock the full potential of its application in pharmaceutical studies.