Raman spectroscopy offers a powerful, label-free way to characterize the chemical composition of biological and chemical samples. It is a relatively low-cost and non-destructive method, making it highly attractive for applications in bioprocess monitoring, chemical screening, and quality control. In recent years, significant efforts have been made to establish Raman spectroscopy as a Process Analytical Technology (PAT) tool in bioprocess engineering, where in situ and real-time measurements are critical for process understanding and control. However, models trained on Raman spectra from one device often fail to generalize across instruments from different vendors due to subtle differences in hardware and signal processing. 
This challenge asks for machine learning approaches that utilize information derived from Raman spectra in different measurement setups to predict concentrations of three analytes - glucose, acetate, and magnesium sulfate - which are commonly found in bioprocess samples. You'll tackle a real-world transfer learning problem in spectroscopy. Your goal is to build a machine learning model that generalizes across multiple Raman devices and can adapt to a new measurement setup with minimal additional data.