Timing matters: the overlooked issue of response time mismatch in pH-dependent analyte sensing using multiple sensors

Abstract

Accurate measurement of pH-dependent analytes is crucial for a wide range of applications, including environmental monitoring, industrial processes, and healthcare diagnostics. In multi-sensor systems, combining data from multiple sensors offers the potential for more comprehensive analysis, yet it is important to be aware of the limitations of this approach. In this paper, we investigate the often-overlooked issue of response time mismatch among sensors, which can introduce significant errors in calculated sum parameters. We present a model and software application (SensinSilico) that allows predicting the error arising from a mismatch of sensor response times. The model was compared and validated using experimental results from calculations of total dissolved sulphide (TDS). These calculations were based on data from concurrent sensor measurements of hydrogen sulfide (H₂S) and pH, which had different response times. We believe that SensinSilico has the potential to be a powerful tool for researchers, professionals, and end-users, enabling them to estimate and minimize errors arising from response time mismatches, enhancing the accuracy and reliability of their results.