Electronic noses (eNoses) are an emerging class of experimental diagnostic tools. They are based on the detection of volatile organic compounds. Urine is used as sample medium in several publications but neither the effect of chronic kidney disease (CKD) on the analysis nor the potential to detect CKD has been explored. Materials & methods: We utilized an eNose based on field asymmetric ion mobility spectrometry (FAIMS) technology to classify urine samples from CKD patients and controls. Results: We were able to differentiate extremes of kidney function with an accuracy of 81.4%. Conclusion: In this preliminary study, applying eNose technology we were able to distinguish the patients with impaired kidney function from those with normal kidney function.
Urine headspace analysis with field asymmetric ion mobility spectrometry for detection of chronic kidney disease
