Chemical Analysis of Mimic Urine in Pathogenic Conditions using ATR-FTIR Spectroscopy

¹ Graduate School, Khon Kaen University, Khon Kaen 40002, Thailand
² Center for Research and Development of Medical Diagnostic Laboratories (CMDL), Faculty of Associated Medical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand
³ Center for Innovation and Standards for Medical Technology and Physical Therapy (CISMaP), Faculty of Associated Medical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand
⁴ School of Medical Technology, Faculty of Associated Medical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand
⁵ Faculty of Public Health, Khon Kaen University, Khon Kaen 40002, Thailand

^* Corresponding author: moliwo@kku.ac.th

Abstract

Urine is commonly used for the diagnosis and prognosis of several illnesses, particularly renal diseases. Quantitative analysis specifying urinary compounds provides valuable information for clinical applications. ATR-FTIR spectroscopy is a non-destructive method based on vibrational bonding that is currently being explored as a potential point-of-care test (POCT) for detailed analysis of both compound types and their concentrations. This study aimed to identify chemical compounds in simulated urine samples spiked with glucose, albumin, and hemoglobin using an ATR-FTIR spectrometer. The method involved measuring urine samples spiked with glucose, albumin, and hemoglobin at concentrations ranging from 1.000 – 0.005 g/dL, followed by multivariate statistical analysis. The study found that spectral patterns between 1073 cm^–1 and 983 cm^–1 (C-O stretching), 1390–1370 cm^–1 (N-H stretching), and 1550–1540 cm^–1 (N-H bending) were specific for urine spiked with glucose, albumin, and hemoglobin, respectively. The lower detectable concentrations of glucose, albumin, and hemoglobin in spiked urine were 0.029, 0.079, and 0.071 g/dL, respectively. This detection capability could be applied to identify abnormal urine, such as that found in CKD patients. Therefore, ATR-FTIR spectroscopy shows potential for use in distinguishing biomolecular differences without requiring reagents or complex sample preparation steps. However, this method requires further validation for clinical applications, particularly for monitoring and disease progression.