Raman spectroscopy is an optical technique that can assess a sample’s molecular content by probing its vibrational modes and has been used over the last decades to diagnose multiple types of cancer. The standard method used to build the classification models, based on machine learning algorithms, is the source of two majors limitations: the small size of the collected training datasets and the issue of portability of statistical models across imaging systems and medical centers. Model portability can be adressed by using a spectrum processing method that totally removes the hardware influence from the processed Raman measurements. We focus here on the results of two experiments conducted to evaluate the reproductibility of Raman measurements made with nine different point-probe systems. For the first experiment, we used a nylon phantom to assess inter-systems differences and applied the data processing method which lowered the inter-systems deviation for the processed nylon peaks under 3%. Furthermore, system #1 was used in vivo in a human brain surgery to acquire 15 Raman measurements from normal and tumor tissue. We evaluated the deviation between classes and found that it was superior to the 3% inter-systems reproductibility for 10 Raman peaks associated with proteins, lipids and nucleic acids. The second experiment was done with the system #1 as a master system and systems #2 to #9 as slave systems. The master system was used to build a Support Vector Machine classification model to discriminate white matter from grey matter on fixed ex vivo monkey brain slices. The model was exported from master to slaves performing a diagnosis accuracy consistently over 95%. The reported results indicate the possibility to succesfully export statistical model from one system to another and to greatly increase the size of dataset using multiple imaging systems.