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Raman Spectrum Classification of Cinnabar and Cinnabar-Clam White Based on Data Augmentation and Convolutional Neural Network

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Journal of Applied Spectroscopy Aims and scope

Mineral pigments are commonly used in cultural relics, which makes the analysis of mineral pigments helpful in such research. It is complicated and time-consuming work to establish the data set of mineral pigment Raman spectra, so it is necessary to study the method of data augmentation. In this paper, two methods of augmenting Raman spectra data are explored — translation transformation, adding noise; expanding the size of the data set from 20 to 320 — then a convolutional neural network model is proposed and trained with the expanded data set. Experimental results showed that the accuracy of the model can reach 100% when the SNR of the test set is not less than 40 dB.

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Authors and Affiliations

  1. Beijing Information Science and Technology University, Beijing, China

    T. Mu, W. Qi & Sh. Chen

  2. Beijing University of Posts and Telecommunications, Beijing, China

    H. Li

Authors
  1. T. Mu
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  2. W. Qi
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  3. Sh. Chen
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  4. H. Li
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Corresponding author

Correspondence to T. Mu.

Additional information

Abstract of article is published in Zhurnal Prikladnoi Spektroskopii, Vol. 90, No. 2, p. 349, March–April, 2023.

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Mu, T., Qi, W., Chen, S. et al. Raman Spectrum Classification of Cinnabar and Cinnabar-Clam White Based on Data Augmentation and Convolutional Neural Network. J Appl Spectrosc 90, 448–453 (2023). https://doi.org/10.1007/s10812-023-01552-4

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  • DOI: https://doi.org/10.1007/s10812-023-01552-4

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