SmartSPR sensor: Machine learning approaches to create intelligent surface plasmon based sensors

Abstract

Surface plasmon resonance (SPR) based sensors allow the evaluation of aqueous and gaseous solutions from real-time measurements of molecular interactions. The reliability of the response generated by a SPR sensor must be guaranteed, especially in substance detection, diagnoses, and other routine applications since poorly handled samples, instrumentation noise features, or even molecular tampering manipulations can lead to wrong interpretations. This work investigates the use of different machine learning (ML) techniques to deal with these issues, and aim to improve and attest to the quality of the real-time SPR responses so-called sensorgrams. A new strategy to describe a SPR-sensorgram is shown. The results of the proposed ML-approach allow the creation of intelligent SPR sensors to give a safe, reliable, and auditable analysis of sensorgram responses. Our arrangement can be embedded in an Intelligence Module that can classify sensorgrams and identify the substances presents in it. Also made it possible to order and analyze interest areas of sensorgrams, standardizing data, and supporting eventual audit procedures. With those intelligence features, the new generation of SPR-intelligent biosensors is qualifying to perform automated testing. A properly protocol for Leishmaniasis diagnosis with SPR was used to verify this new feature.