Hyperspectral imaging for chemicals identification: a human-inspired machine learning approach.

ABSTRACT: Data analysis has increasingly relied on machine learning in recent years. Since machines implement mathematical algorithms without knowing the physical nature of the problem, they may be accurate but lack the flexibility to move across different domains. This manuscript presents a machine-educating approach where a machine is equipped with a physical model, universal building blocks, and an unlabeled dataset from which it derives its decision criteria. Here, the concept of machine education is deployed to identify thin layers of organic materials using hyperspectral imaging (HSI). The measured spectra formed a nonlinear mixture of the unknown background materials and the target material spectra. The machine was educated to resolve this nonlinear mixing and identify the spectral signature

SUBMITTER: Kendler S 

PROVIDER: S-EPMC9584913 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature