Adaptive compressed sensing of Raman spectroscopic profiling data for discriminative tasks

Yinsheng Zhang; Zhengyong Zhang; Yaju Zhao; Rong Dian; Yongbo Cheng; Xiaolin Qin; Haiyan Wang

doi:10.1016/j.talanta.2019.120681

Adaptive compressed sensing of Raman spectroscopic profiling data for discriminative tasks

Talanta. 2020 May 1:211:120681. doi: 10.1016/j.talanta.2019.120681. Epub 2019 Dec 28.

Authors

Yinsheng Zhang¹, Zhengyong Zhang², Yaju Zhao³, Rong Dian³, Yongbo Cheng², Xiaolin Qin⁴, Haiyan Wang⁵

Affiliations

¹ School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, 310018, China; School of Information Sciences, University of Illinois at Urbana Champaign, Champaign, IL, 61820-6211, USA. Electronic address: zhangys@illinois.edu.
² School of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing, 210023, China.
³ School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, 310018, China.
⁴ College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
⁵ School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, 310018, China. Electronic address: njue2010@163.com.

PMID: 32070569
DOI: 10.1016/j.talanta.2019.120681

Abstract

Raman spectroscopy is widely used in discriminative tasks. It provides a wide-range physio-chemical fingerprint in a rapid and non-invasive way. The Raman spectrometry uses a sensor array to convert photon signals into digital spectroscopic data. This analog-to-digital process can benefit from the compressed sensing (CS) technique. The major benefits include less memory usage, shorter acquisition time, and more cost-efficient sensor. Traditional compressed sensing and reconstruction is a series of mathematical operations performed on the signal. Meanwhile, for discriminative tasks, both the signal and the categorical information are involved. For such scenarios, this paper proposes a method that uses both domain signal and categorical information to optimize CS hyper-parameters, including 1) the sampling ratio or the sensing matrix, 2) the basis matrix for the sparse transform, and 3) the regularization rate or shrinkage factor for L1-norm minimization. A case study of formula milk brand identification proves the proposed method can generate effective compressed sensing while preserving enough discriminative power in the reconstructed signal. Under the optimized hyper-parameters, a 100% classification accuracy is retained by only sampling 20% of the original signal.

Keywords: Basis selection; Compressed sensing; Discriminative task; Hyper-parameter optimization; Least absolute shrinkage and selection operator; Raman spectroscopy.