Neural Network-Based Filter Design for Compressive Raman Classification of Cells

Abstract

Cell-based therapies are bound to revolutionize medicine, but significant technical hurdles must be overcome before wider adoption. In particular, nondestructive, label-free methods to characterize cells in real time are needed to optimize the production process and improve quality control. Raman spectroscopy, which provides a fingerprint of a cell's chemical composition, would be an ideal modality but is too slow for high-throughput applications. Compressive Raman techniques, which measure only linear combinations of Raman intensities, can be fast but require careful optimization to deliver high performance. Here, we develop a neural network model to identify optimal parameters for a compressive sensing scheme that reduces measurement time by 2 orders of magnitude. In a data set containing Raman spectra of three different cell types, it achieves up to 90% classification accuracy using only five linear combinations of Raman intensities. Our method thus unlocks the power of Raman spectroscopy for the characterization of cell products.