It is common to transfer analytical methods from the original location to other laboratories when scaling up clinical processes. However, it must be ensured that the receiving laboratories can obtain results with the same accuracy, precision and sensitivity as the source laboratory. In the field of ML based models, this step is critical, as differences in data collection, preprocessing, and confounding variables can significantly impact model performance, particularly in regression and classification tasks. The domain adaptation discipline aims to develop models capable of predicting samples outside the calibration space . This is crucial for translating clinical spectroscopic models into real-world applications, as models should generalize effectively without requiring re-calibration for different clinics, instruments, or patient populations, which would be impractical and extremely costly.