Application of Gaussian process regression and asymmetric least squares baseline algorithm on the determination of electrochemical sensor characteristics: A case study on SARS-CoV-2 glucometer
Abstract
In this study, it was aimed to determine limit of detection, limit of quantification, and signal to noise ratio by the use of SARS-CoV-2 glucometer response data in "Singh, N·K., Ray, P., Carlin, A.F., Morgan, S·C., Magallanes, C., Laurent, L.C., Aronoff-Spencer, E.S., Hall, D.A., 2021b. Dataset on optimization and development of a point-of-care glucometer-based SARS-CoV-2 detection assay using aptamers. Data Brief 38, 107,278.” and by Bayesian optimization and asymmetric least squares baseline algorithm which was used for prediction of response curve and baseline. It was seen that the predicted limit of detection and limit of quantification reached a certain level after a certain number of training observations and ranged between 0.9-16 pM and 2.8–44 pM respectively.
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