Premium
One‐dimensional convolutional neural networks for spectroscopic signal regression
Author(s) -
Malek Salim,
Melgani Farid,
Bazi Yakoub
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2977
Subject(s) - convolutional neural network , pooling , pattern recognition (psychology) , computer science , artificial intelligence , regression , gaussian process , particle swarm optimization , support vector machine , kriging , gaussian , regression analysis , machine learning , mathematics , statistics , physics , quantum mechanics
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.