Convolutional neural networks for vibrational spectroscopic data analysis.

@article{Acquarelli2017ConvolutionalNN,
  title={Convolutional neural networks for vibrational spectroscopic data analysis.},
  author={Jacopo Acquarelli and Twan van Laarhoven and Jan Gerretzen and Thanh N. Tran and Lutgarde M. C. Buydens and Elena Marchiori},
  journal={Analytica chimica acta},
  year={2017},
  volume={954},
  pages={22-31}
}
In this work we show that convolutional neural networks (CNNs) can be efficiently used to classify vibrational spectroscopic data and identify important spectral regions. CNNs are the current state-of-the-art in image classification and speech recognition and can learn interpretable representations of the data. These characteristics make CNNs a good candidate for reducing the need for preprocessing and for highlighting important spectral regions, both of which are crucial steps in the analysis… CONTINUE READING

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