  Convolutional neural networks (CNNs) are widely used for image recognition
and text analysis, and have been suggested for application on one-dimensional
data as a way to reduce the need for pre-processing steps. Pre-processing is an
integral part of multivariate analysis, but determination of the optimal
pre-processing methods can be time-consuming due to the large number of
available methods. In this work, the performance of a CNN was investigated for
classification and regression analysis of spectral data. The CNN was compared
with various other chemometric methods, including support vector machines
(SVMs) for classification and partial least squares regression (PLSR) for
regression analysis. The comparisons were made both on raw data, and on data
that had gone through pre-processing and/or feature selection methods. The
models were used on spectral data acquired with methods based on near-infrared,
mid-infrared, and Raman spectroscopy. For the classification datasets the
models were evaluated based on the percentage of correctly classified
observations, while for regression analysis the models were assessed based on
the coefficient of determination (R$^2$). Our results show that CNNs can
outperform standard chemometric methods, especially for classification tasks
where no pre-processing is used. However, both CNN and the standard chemometric
methods see improved performance when proper pre-processing and feature
selection methods are used. These results demonstrate some of the capabilities
and limitations of CNNs used on one-dimensional data.
