Development of Classification Model for the Discrimination of Crohn’s Disease and Healthy Controls Using Surface-enhanced Raman Spectroscopy

Crohn’s disease (CD) is an idiopathic chronic inflammatory bowel disease without a cure. Most of the CD patients are firstly diagnosed by invasive endoscopy, clinical and pathological examination are further required to confirm the diagnosis. Hence, development of a non-invasive, rapid and accurate diagnosis method for CD patients is essential. In this study, the urine samples from 95 CD patients and 48 healthy controls (HC) were investigated by surface-enhanced Raman spectroscopy (SERS). Principal component analysis (PCA)-linear discriminant analysis (LDA) was employed to establish classification model to distinguish CD patients and HC. It showed that the classification accuracy of CD/HC based on PCA-LDA was 69.9%. It indicates that the metabolic change of patients could be identified by measuring urine with SERS. Our findings are helpful for clinicians to diagnose CD patients, monitor progress and recurrence of the disease.