Autoencoding Raman spectra to predict analyte concentrations

Machine learning analysis has been applied to Raman data obtained in both nuclear and biopharmaceutical industrial applications. A 785‐nm Raman instrument using a spatial heterodyne spectrometer (SHS) was used to acquire Raman spectra for the nuclear dataset, whilst a new deep UV resonant SHS system, featuring a 228.5‐nm diode‐pumped solid‐state laser, was used to capture Raman spectra of biological macromolecule samples for the biopharmaceutical dataset. A key focus is on the practical challenges faced in the design of data processing tasks and machine learning architectures due to real‐world limitations in data collection. A fully connected (FC) autoencoder is employed as part of a regression task, which generates predictions on analyte concentrations in mixed substances. The method was shown to outperform industry standard regression tools, principal component regression (PCR) and partial least squares (PLS) regression, each used as comparative benchmarks, by over 50% in a test of model precision across the nuclear and biopharmaceutical datasets investigated in this work. Advancements in the precision, speed and effectiveness of such tools are of critical importance in an industrial environment. This is driven by compelling motivations to reduce not only the costs associated with these processes but also to increase the quality of resulting products or to reduce the risks within industrial operations, where applicable.