A Portable Nitrate Biosensing Device Using Electrochemistry and Spectroscopy

So far, several enzyme-based electrochemical biosensors have been developed for nitrate concentration determination. Generally, nitrate measurement in these methods is based on the catalytic current obtained from cyclic voltammetry or amperometry carried out in laboratory conditions. However, voltammetric response cannot be directly used to measure nitrate in portable biosensors because operating conditions of the device and samples' characteristics significantly affect the biosensor's response. In this paper, a portable biosensor is introduced in which, operating features of the device and samples' characteristics along with electrochemical and spectroscopic features of the samples are considered for nitrate concentration determination. Electrochemical, spectroscopic, operating and sample's features included the results of cyclic voltammetry in an unstirred three-electrode system, spectral absorption in the range of 150-1000 nm, storage time, temperature of the prepared working electrode, and sample's type and pH, respectively. A machine learning method was used to learn the features of train samples to predict nitrate concentration in liquid samples, including drinking water, juice, plant extract, and human saliva, with a wide range of pH (between 3.5 and 8.5). Results showed that the device containing plant-based nitrate reductase as the biocatalyst, anthraquinone-2-sulfonate as the mediator, and support vector machine as the machine learning method was usable up to 10 days after the enzyme immobilization and it was able to predict nitrate concentration in more than 200 samples without the need for enzyme replacement.