Abstract
Chemometrics represents a potent tool for optimizing the experimental setup and subsequently boosting the performance of analytical methods. In particular, design of experiments (DoE) allows the experimental conditions to be optimized with high accuracy and a lower number of experiments when compared with the classical univariate approach, also known as one variable at a time (OVAT), which provides only a partial understanding on how factors affect the response. In this work, DoE was exploited, specifically a D-optimal design was used, to improve the analytical performance of a hybridization-based paper-based electrochemical biosensor, taking as target of the study the miRNA-29c (miR-29c) that is related to triple negative breast cancer. The sensing platform is composed of six variables to be optimized, including both those related to the sensor's manufacture (i.e., gold nanoparticles, immobilized DNA probe) and those related to the working conditions (i.e., ionic strength, probe-target hybridization, electrochemical parameters). The adoption of DoE allowed us to optimize the device using only 30 experiments with respect to the 486 that would have been required with the OVAT approach, and as a consequence of the more accurate optimal conditions that have been reached, the detection of miRNA was more sensitive and repeatable when compared with previous data reported using the univariate approach for optimization, leading to a 5-fold limit of detection (LOD) improvement toward miRNA. It confirms that chemometrics might be considered a fundamental tool to be used in the development of various kinds of sensors and biosensors.