Screening of Blood Serum Samples With Elevated Creatinine Levels From Renal Failure Patients Employing SERS Along With Multivariate Data Analysis Techniques

Renal failure (RF) is a progressive disorder characterized by incurability, elevated morbidity, and increased mortality, frequently affecting the adult population, particularly individuals diagnosed with diabetes and hypertension. In this paper, a rapid, easy, and non‐invasive diagnostic technique, surface‐enhanced Raman spectroscopy (SERS), is discussed for the screening of RF using blood serum samples with elevated creatinine concentrations. Silver nanoparticles (AgNPs) were employed as SERS substrates to enhance the Raman signals of biomolecules present in the serum samples. For this purpose, serum samples from healthy/control individuals and RF patients with varying creatinine concentrations were used for SERS spectral measurements. The diagnostic capability of SERS was further improved by applying multivariate analysis techniques such as principal component analysis (PCA) and partial least‐squares discriminant analysis (PLS‐DA) to evaluate the potential of SERS for differentiating and classifying healthy and RF blood serum samples. Moreover, partial least‐squares regression (PLSR) was used to predict unknown creatinine concentrations in RF serum samples, yielding coefficients of determination of 0.9395 mg/dL for calibration and 0.9561 mg/dL for prediction/validation. The associated errors included a root mean squared error of calibration (RMSEC) of 0.6977 mg/dL and a root mean squared error of prediction (RMSEP) of 0.8595 mg/dL. This study demonstrates that SERS, combined with multivariate analysis, is a valuable tool for the effective diagnosis of RF.