Data Augmentation Scheme for Raman Spectra with Highly Correlated
  Annotations
Christoph Lange Isabel Thiele Lara Santolin Sebastian L. Riedel Maxim Borisyak Peter Neubauer M. Nicolas Cruz Bournazou
In biotechnology Raman Spectroscopy is rapidly gaining popularity as a
process analytical technology (PAT) that measures cell densities, substrate-
and product concentrations. As it records vibrational modes of molecules it
provides that information non-invasively in a single spectrum. Typically,
partial least squares (PLS) is the model of choice to infer information about
variables of interest from the spectra. However, biological processes are known
for their complexity where convolutional neural networks (CNN) present a
powerful alternative. They can handle non-Gaussian noise and account for beam
misalignment, pixel malfunctions or the presence of additional substances.
However, they require a lot of data during model training, and they pick up
non-linear dependencies in the process variables. In this work, we exploit the
additive nature of spectra in order to generate additional data points from a
given dataset that have statistically independent labels so that a network
trained on such data exhibits low correlations between the model predictions.
We show that training a CNN on these generated data points improves the
performance on datasets where the annotations do not bear the same correlation
as the dataset that was used for model training. This data augmentation
technique enables us to reuse spectra as training data for new contexts that
exhibit different correlations. The additional data allows for building a
better and more robust model. This is of interest in scenarios where large
amounts of historical data are available but are currently not used for model
training. We demonstrate the capabilities of the proposed method using
synthetic spectra of Ralstonia eutropha batch cultivations to monitor
substrate, biomass and polyhydroxyalkanoate (PHA) biopolymer concentrations
during of the experiments.