RSPSSL:A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization  

作　　者：Jiaqi Hu Gina Jinna Chen Chenlong Xue Pei Liang Yanqun Xiang Chuanlun Zhang Xiaokeng Chi Guoying Liu Yanfang Ye Dongyu Cui De Zhang Xiaojun yu Hong Dang Wen Zhang Junfan Chen Quan Tang Penglai Guo Ho-Pui Ho Yuchao Li Longqing Cong Perry Ping Shum 

机构地区：[1]State Key Laboratory of Optical Fiber and Cable Manufacture Technology,Guangdong Key Laboratory of Integrated Optoelectronics Intellisense,Department of EEE,Southern University of Science and Technology,Shenzhen 518055,China [2]College of Optical and Electronic Technology,China Jiliang University,Hangzhou 310018,China [3]Department of Nasopharyngeal Carcinoma,Sun Yat-sen University Cancer Center,State Key Laboratory of Oncology in South China,Collaborative Innovation Center for Cancer Medicine,Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy,Guangzhou 510060,China [4]Department of Ocean Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,China [5]Department of Nephrology,Chaozhou People’s Hospital,Chaozhou 521011,China [6]Clinical Research Design Division,Sun Yatsen Memorial Hospital,Guangzhou,Guangdong 510120,China [7]School of Automation,Northwestern Polytechnical University,Xi’an,Shaanxi 710072,China [8]Department of Biomedical Engineering,The Chinese University of Hong Kong,Hong Kong,China [9]Guangdong Provincial Key Laboratory of Nanophotonic Manipulation,Institute of Nanophotonics,Jinan University,Guangzhou 511443,China

出　　处：《Light(Science & Applications)》2024年第2期366-386,共21页光（科学与应用)（英文版）

基　　金：This work was supported by National Natural Science Foundation of China(62220106006);Shenzhen Science and Technology Program(SGDX20211123114001001,JSGGKQTD20221101115656030);Guangdong Basic and Applied Basic Research Foundation(2021B1515120013).

摘　　要：Raman spectroscopy has tremendous potential for material analysis with its molecular fingerprinting capability in many branches of science and technology.It is also an emerging omics technique for metabolic profiling to shape precision medicine.However,precisely attributing vibration peaks coupled with specific environmental,instrumental,and specimen noise is problematic.Intelligent Raman spectral preprocessing to remove statistical bias noise and sample-related errors should provide a powerful tool for valuable information extraction.Here,we propose a novel Raman spectral preprocessing scheme based on self-supervised learning(RSPSSL)with high capacity and spectral fidelity.It can preprocess arbitrary Raman spectra without further training at a speed of~1900 spectra per second without human interference.The experimental data preprocessing trial demonstrated its excellent capacity and signal fidelity with an 88%reduction in root mean square error and a 60%reduction in infinite norm(L__(∞))compared to established techniques.With this advantage,it remarkably enhanced various biomedical applications with a 400%accuracy elevation(ΔAUC)in cancer diagnosis,an average 38%(few-shot)and 242%accuracy improvement in paraquat concentration prediction,and unsealed the chemical resolution of biomedical hyperspectral images,especially in the spectral fingerprint region.It precisely preprocessed various Raman spectra from different spectroscopy devices,laboratories,and diverse applications.This scheme will enable biomedical mechanism screening with the label-free volumetric molecular imaging tool on organism and disease metabolomics profiling with a scenario of high throughput,cross-device,various analyte complexity,and diverse applications.

关 键 词：SPECTRAL SCHEME RESOLUTION 

分 类 号：R318[医药卫生—生物医学工程] O657.37[医药卫生—基础医学]