A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

Abstract

The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

Keywords: SARS-CoV-2; machine learning; nonlinear optics; optical biosensors; photonics.

Figure 1

Biosensors assisted by ML.

Figure 2

ML categories according to the nature of the features space.

Figure 3

ANN common structure.

Figure 4

Roadmap of investigations based on NLO processes assisted by ML and soft computing [168,169,170,171,172,173,174,175,176].

Figure 5

NLO effect schemes proposed for biosensing.