Developing Synthetic Spectroscopy Noise and Chemometric Database for Computational Classification

TL;DR

This paper introduces a framework for generating synthetic spectroscopy noise and expanding chemometric databases to improve the robustness of chemical classification algorithms.

Contribution

It presents a novel method to create realistic noise data from limited experimental spectra, enhancing chemometric model training.

Findings

Generated diverse synthetic noise datasets.

Improved classification robustness with expanded data.

Framework adaptable to various spectroscopy instruments.

Abstract

There has been little to no work in the area of spectroscopy noise in order to create data sets for analytical algorithms to be challenged on the ability to separate chemicals. We present a framework on how to build off of a sparse about of experimental data in order to expand your chemometric database and create realistic instrumentation noise. The combination of various interactions of chemicals combined with various random permutations of spectroscopy noises enables researchers to better capture and model the multitude of types of signals and variations that can be present within an experimental reading.