Adaptive compressed sensing of Raman spectroscopic profiling data for discriminative tasks
Adaptive compressed sensing of Raman spectroscopic profiling data for discriminative tasks
复制标题
用于判别任务的拉曼光谱分析数据的自适应压缩感知
DOI：
10.1016/j.talanta.2019.120681
复制
发表时间：
2020-05-01
期刊：
影响因子：
6.1
通讯作者：
Wang, Haiyan
中科院分区：
文献类型：
--
作者：
Zhang, Yinsheng;Zhang, Zhengyong;Wang, Haiyan
拉曼光谱广泛应用于判别任务。它以快速、无创的方式提供了大范围的理化指纹图谱。拉曼光谱法使用传感器阵列将光子信号转换为数字光谱数据。这种模数转换过程可以受益于压缩感知（CS）技术。主要优点包括更少的内存使用，更短的采集时间，更经济的传感器。传统的压缩感知与重构是对信号进行一系列的数学运算。同时，对于判别任务，信号信息和分类信息都涉及到。针对这种情况，本文提出了一种同时使用域信号和分类信息来优化CS超参数的方法，包括1)采样比或感知矩阵，2)稀疏变换的基矩阵，3)l-范数最小化的正则化率或收缩因子。以配方奶品牌识别为例，证明了该方法能够产生有效的压缩感知，同时在重构信号中保持足够的判别能力。在优化后的超参数下，只对原始信号进行20%的采样，就能保持100%的分类准确率。
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 Ll-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.