Smartphone based optical biosensor for the detection of urea in saliva

• Published in: Sensors and actuators. B, Chemical Vol. 269; pp. 346 - 353
• Main Authors: Soni, Anuradha, Surana, Rajat Kumar, Jha, Sandeep Kumar
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 15.09.2018; Elsevier Science Ltd
• Subjects: Biosensors; Body fluids; Calibration; Color; Filter paper; Non-invasive biosensor; Optical biosensor; Pixels; Response time; Saliva; Salivary urea; Smartphone based biosensor; Smartphones; Ureas; Salivary urea; Non-invasive biosensor; Smartphone based biosensor; Optical biosensor
• DOI: 10.1016/j.snb.2018.04.108
• ISSN: 0925-4005

•We developed a noninvasive smartphone based biosensor for urea using saliva as sample, which is first such report.•The sensor was fabricated by co-immobilization of urease & pH indicator on filter paper strip which changed color with reaction to salivary urea.•This color change can be used to deduce urea concentration using smartphone based application by reading RGB levels.•Clinical validation carried out on spiked or clinical saliva samples show great possibility of using the biosensor for diagnosis of uremia and CKD. In the present study, we have developed a smartphone based handheld optical biosensor for determination of urea in saliva. A simple strategy was adopted by immobilization of urease enzyme along with a pH indicator on a filter paper based strip. The strip changed color upon the reaction with urea present in saliva and the color change can be estimated using our smartphone based application based on RGB profiling. Calibration of the biosensor was carried out using spiked saliva samples and an exponentially decreasing calibration curve has been obtained for green pixel intensity in the broad range (10–1000 mgdL−1) with a linear detection range of 10–260 mgdL−1 and a response time of 20 s. The sensitivity reported for the biosensor in the clinically significant range was −0.005 average pixels sec−1/mgdL−1 with a LOD of 10.4 mgdL−1. Studies carried out on spiked saliva samples showed a good correlation between salivary urea estimated using our biosensor against phenol-hypochlorite based spectroscopic procedure. Development of a smartphone based biosensor for urea estimation eliminates the need for procuring a dedicated instrument as well as trained technician for daily monitoring and saves time as compared to traditional laboratory methods of analysis.