One-dimensional convolutional neural networks for spectroscopic signal regression

This paper proposes a novel approach for driving chemometric analyses from spectroscopic data and based on a convolutional neural network (CNN) architecture. For such purpose, the well-known 2-D CNN is adapted to the monodimensional nature of spectroscopic data. In particular, filtering and pooling operations as well as equations for training are revisited. We also propose an alternative to train the resulting 1D-CNN by means of particle swarm optimization. The resulting trained CNN architecture is successively exploited to extract features from a given 1D spectral signature to feed any regression method. In this work, we resorted to 2 advanced and effective methods, which are support vector machine regression and Gaussian process regression. Experimental results conducted on 3 real spectroscopic datasets show the interesting capabilities of the proposed 1D-CNN methods. The objective of this work is to develop a 1-dimensional convolutional neural network for chemometric data analysis. Particle swarm optimization is used to estimate the weights of the different layers. The final estimation is performed by means of support vector machine regression or Gaussian process regression.