Fast and accurate classification and identification of mass spectra using hybrid optical-electronic convolutional neural networks

Si Ma; Huarong Gu; Zheng Ouyang

doi:10.1117/12.2537184

18 November 2019 Fast and accurate classification and identification of mass spectra using hybrid optical-electronic convolutional neural networks

Si Ma, Huarong Gu, Zheng Ouyang

Author Affiliations +

Proceedings Volume 11188, Holography, Diffractive Optics, and Applications IX; 1118808 (2019) https://doi.org/10.1117/12.2537184
Event: SPIE/COS Photonics Asia, 2019, Hangzhou, China

Abstract

Mass spectrometer is one of the most important instruments in the field of modern analysis. Despite efforts to increase efficiency, it remains a challenge to deploy convolutional neural networks in mass spectrometer due to tight power budgets. In this paper, we propose a hybrid optical-electronic convolutional neural network to achieve fast and accurate classification and identification of mass spectra. The optical convolutional layer is realized by a folded 4f system. Our prototype with one single convolutional layer achieves 96.5% classification accuracy in an experimentally-acquired lipid dataset. A more complicated prototype adding one fully-connected layer achieves 100% accuracy. The proposed hybrid optical-electronic convolutional neural networks might enable non-professionals to avoid the accumulation of experimental experience and complicated calculations.

Citation Download Citation

Si Ma, Huarong Gu, and Zheng Ouyang "Fast and accurate classification and identification of mass spectra using hybrid optical-electronic convolutional neural networks", Proc. SPIE 11188, Holography, Diffractive Optics, and Applications IX, 1118808 (18 November 2019); https://doi.org/10.1117/12.2537184

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