Assessing the Performance of 1D-Convolution Neural Networks to Predict
  Concentration of Mixture Components from Raman Spectra
Dexter Antonio Hannah O'Toole Randy Carney Ambarish Kulkarni Ahmet Palazoglu
An emerging application of Raman spectroscopy is monitoring the state of
chemical reactors during biologic drug production. Raman shift intensities
scale linearly with the concentrations of chemical species and thus can be used
to analytically determine real-time concentrations using non-destructive light
irradiation in a label-free manner. Chemometric algorithms are used to
interpret Raman spectra produced from complex mixtures of bioreactor contents
as a reaction evolves. Finding the optimal algorithm for a specific bioreactor
environment is challenging due to the lack of freely available Raman mixture
datasets. The RaMix Python package addresses this challenge by enabling the
generation of synthetic Raman mixture datasets with controllable noise levels
to assess the utility of different chemometric algorithm types for real-time
monitoring applications. To demonstrate the capabilities of this package and
compare the performance of different chemometric algorithms, 48 datasets of
simulated spectra were generated using the RaMix Python package. The four
tested algorithms include partial least squares regression (PLS), a simple
neural network, a simple convolutional neural network (simple CNN), and a 1D
convolutional neural network with a ResNet architecture (ResNet). The
performance of the PLS and simple CNN model was found to be comparable, with
the PLS algorithm slightly outperforming the other models on 83\% of the data
sets. The simple CNN model outperforms the other models on large, high noise
datasets, demonstrating the superior capability of convolutional neural
networks compared to PLS in analyzing noisy spectra. These results demonstrate
the promise of CNNs to automatically extract concentration information from
unprocessed, noisy spectra, allowing for better process control of industrial
drug production. Code for this project is available at
github.com/DexterAntonio/RaMix.