Towards calibration-invariant spectroscopy using deep learning.

ABSTRACT: The interaction between matter and electromagnetic radiation provides a rich understanding of what the matter is composed of and how it can be quantified using spectrometers. In many cases, however, the calibration of the spectrometer changes as a function of time (such as in electron spectrometers), or the absolute calibration may be different between different instruments. Calibration differences cause difficulties in comparing the absolute position of measured emission or absorption peaks between different instruments and even different measurements taken at different times on the same instrument. Present methods of avoiding this issue involve manual feature extraction of the original signal or qualitative analysis. Here we propose automated feature extraction using deep convolutional n

SUBMITTER: Chatzidakis M 

PROVIDER: S-EPMC6376024 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature