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

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

doi:10.1038/s41377-024-01394-5

Your Location：

Home >

Browse articles >

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

Article

- RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization
- RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization
- LSA 2024年13卷第2期页码：366-386
- Affiliations：
- Author bio：
  
  bchenjn@sustech.edu.cn
  xshum@ieee.org
- Funds：
  
  Shenzhen Science and Technology Innovation Commission;National Natural Science Foundation of China (National Science Foundation of China);Guangdong Basic and Applied Basic Research Foundation, 2021B1515120013;Government of Guangdong Province;Guangdong Basic and Applied Basic Research Foundation,2021B1515120013
- DOI：10.1038/s41377-024-01394-5
  中图分类号：
- 收稿日期：2023-08-18，
  
  修回日期：2024-01-02，
  
  录用日期：2024-01-23，
  
  网络出版日期：2024-02-20，
  
  纸质出版日期：2024-12
- Accepted：
Scan QR Code
RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization[J]. LSA, 2024,13(2):366-386.

Jiaqi Hu, Gina Jinna Chen, Chenlong Xue, et al. RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization[J]. Light: science & applications, 2024, 13(2): 366-386.
RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization[J]. LSA, 2024,13(2):366-386. DOI： 10.1038/s41377-024-01394-5.

Jiaqi Hu, Gina Jinna Chen, Chenlong Xue, et al. RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization[J]. Light: science & applications, 2024, 13(2): 366-386. DOI： 10.1038/s41377-024-01394-5.

摘要

Abstract

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 ~1 900 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 (

$$${L}_{{\infty }}$$$

) 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) a

nd 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.

A novel generic high-fidelity Raman spectral denoising and baseline correction strategy to enhance diverse cross-device/specimen biomedical applications and hyperspectral image chemical resolution visualization to reveal spatial features of cancer tissue.

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=70082609&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=70082609&type=small

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=70082609&type=middle

关键词

Keywords

references

K.S. Lee et al. , Raman microspectroscopy for microbiology . Nat. Rev. Methods Prim. 1 , 80 ( 2021 ). http://doi.org/10.1038/s43586-021-00075-6 http://doi.org/10.1038/s43586-021-00075-6

Y.H. Wang et al. , In situ Raman spectroscopy reveals the structure and dissociation of interfacial water . Nature 600 , 81 - 85 ( 2021 ). http://doi.org/10.1038/s41586-021-04068-z http://doi.org/10.1038/s41586-021-04068-z

J. Xiong et al. , Surface defect engineering in 2D nanomaterials for photocatalysis . Adv. Funct. Mater. 28 , 1801983 ( 2018 ). http://doi.org/10.1002/adfm.201801983 http://doi.org/10.1002/adfm.201801983

G. Cutshaw et al. , The emerging role of Raman spectroscopy as an omics approach for metabolic profiling and biomarker detection toward precision medicine . Chem. Rev. 123 , 8297 - 8346 ( 2023 ). http://doi.org/10.1021/acs.chemrev.2c00897 http://doi.org/10.1021/acs.chemrev.2c00897

Z.Y. Wu et al. , Non‐invasive detection, precise localization, and perioperative navigation of in vivo deep lesions using transmission Raman spectroscopy . Adv. Sci. 10 , 2301721 ( 2023 ). http://doi.org/10.1002/advs.202301721 http://doi.org/10.1002/advs.202301721

G.Y. Chen et al. , Revealing unconventional host–guest complexation at nanostructured interface by surface-enhanced Raman spectroscopy . Light Sci. Appl. 10 , 85 ( 2021 ). http://doi.org/10.1038/s41377-021-00526-5 http://doi.org/10.1038/s41377-021-00526-5

M. Zhang et al. , Deep oxidative desulfurization of dibenzothiophene with POM-based hybrid materials in ionic liquids . Chem. Eng. J. 220 , 328 - 336 ( 2013 ). http://doi.org/10.1016/j.cej.2012.11.138 http://doi.org/10.1016/j.cej.2012.11.138

Y.P. Li et al. , Super-multiplex nonlinear optical imaging unscrambles the statistical complexity of cancer subtypes and tumor microenvironment . Adv. Sci. 9 , 2104379 ( 2022 ). http://doi.org/10.1002/advs.202104379 http://doi.org/10.1002/advs.202104379

L.P. Huang et al. , Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning . Nat. Commun. 14 ,( 2023 ). http://doi.org/10.1038/s41467-022-35696-2 http://doi.org/10.1038/s41467-022-35696-2

W. Yang et al. , Real-time molecular imaging of near-surface tissue using Raman spectroscopy . Light Sci. Appl. 11 , 90 ( 2022 ). http://doi.org/10.1038/s41377-022-00773-0 http://doi.org/10.1038/s41377-022-00773-0

S.L. Dong et al. , Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning . eLight 3 , 17 ( 2023 ). http://doi.org/10.1186/s43593-023-00051-5 http://doi.org/10.1186/s43593-023-00051-5

X.X. Han et al. , Surface-enhanced Raman spectroscopy . Nat. Rev. Methods Prim. 1 , 87 ( 2022 ). http://doi.org/10.1038/s43586-021-00083-6 http://doi.org/10.1038/s43586-021-00083-6

W. Peterson , K. Hiramatsu , K. Goda , The marriage of coherent Raman scattering imaging and advanced computational tools . Light Sci. Appl. 12 , 113 ( 2023 ). http://doi.org/10.1038/s41377-023-01160-z http://doi.org/10.1038/s41377-023-01160-z

M. Jermyn et al. , Intraoperative brain cancer detection with Raman spectroscopy in humans . Sci. Transl. Med. 7 , 274ra19 ( 2015 ). http://doi.org/10.1126/scitranslmed.aaa2384 http://doi.org/10.1126/scitranslmed.aaa2384

H.B. Hu et al. , Improved baseline correction method based on polynomial fitting for Raman spectroscopy . Photonic Sens. 8 , 332 - 340 ( 2018 ). http://doi.org/10.1007/s13320-018-0512-y http://doi.org/10.1007/s13320-018-0512-y

Y.L. Chen , L.K. Dai , An automated baseline correction method based on iterative morphological operations . Appl. Spectrosc. 72 , 731 - 739 ( 2018 ). http://doi.org/10.1177/0003702817752371 http://doi.org/10.1177/0003702817752371

F. Zhang et al. , Baseline correction for infrared spectra using adaptive smoothness parameter penalized least squares method . Spectrosc. Lett. 53 , 222 - 233 ( 2020 ). http://doi.org/10.1080/00387010.2020.1730908 http://doi.org/10.1080/00387010.2020.1730908

X. Wang et al. , Development of weak signal recognition and an extraction algorithm for Raman imaging . Anal. Chem. 91 , 12909 - 12916 ( 2019 ). http://doi.org/10.1021/acs.analchem.9b02887 http://doi.org/10.1021/acs.analchem.9b02887

F. Ehrentreich , L. Sümmchen , Spike removal and denoising of Raman spectra by wavelet transform methods . Anal. Chem. 73 , 4364 - 4373 ( 2001 ). http://doi.org/10.1021/ac0013756 http://doi.org/10.1021/ac0013756

H.N. Lin et al. , Microsecond fingerprint stimulated Raman spectroscopic imaging by ultrafast tuning and spatial-spectral learning . Nat. Commun. 12 ,( 2021 ). http://doi.org/10.1038/s41467-021-23202-z http://doi.org/10.1038/s41467-021-23202-z

Kung, B. H. et al. Baseline correction and denoising of Raman spectra by deep residual CNN. 2020 Conference on Lasers and Electro-Optics (CLEO). San Jose, CA, USA: IEEE, 2020, 1-2.

Y.J. Liu , Adversarial nets for baseline correction in spectra processing . Chemometr. Intell. Lab. Syst. 213 , 104317 ( 2021 ). http://doi.org/10.1016/j.chemolab.2021.104317 http://doi.org/10.1016/j.chemolab.2021.104317

T.J. Chen et al. , Baseline correction using a deep-learning model combining ResNet and UNet . Analyst 147 , 4285 - 4292 ( 2022 ). http://doi.org/10.1039/D2AN00868H http://doi.org/10.1039/D2AN00868H

G.Y. Wang et al. , Deep-learning-enabled protein–protein interaction analysis for prediction of SARS-CoV-2 infectivity and variant evolution . Nat. Med. 29 , 2007 - 2018 ( 2023 ). http://doi.org/10.1038/s41591-023-02483-5 http://doi.org/10.1038/s41591-023-02483-5

J. Schmidhuber , Deep learning in neural networks: An overview . Neural Netw. 61 , 85 - 117 ( 2015 ). http://doi.org/10.1016/j.neunet.2014.09.003 http://doi.org/10.1016/j.neunet.2014.09.003

Kazemzadeh, M. et al. Deep learning as an improved method of preprocessing biomedical Raman spectroscopy data. Print at https://doi.org/10.36227/techrxiv.19435718 10.36227/techrxiv.19435718 (2022).

X.J. Yu et al. , A generative adversarial network with multi-scale convolution and dilated convolution res-network for OCT retinal image despeckling . Biomed. Signal Process. Control 80 , 104231 ( 2023 ). http://doi.org/10.1016/j.bspc.2022.104231 http://doi.org/10.1016/j.bspc.2022.104231

S.K.J. Rizvi , M.A. Azad , M.M. Fraz , Spectrum of advancements and developments in multidisciplinary domains for generative adversarial networks (GANs) . Arch. Comput. Methods Eng. 28 , 4503 - 4521 ( 2021 ). http://doi.org/10.1007/s11831-021-09543-4 http://doi.org/10.1007/s11831-021-09543-4

M. Matinfar , N. Khaji , G. Ahmadi , Deep convolutional generative adversarial networks for the generation of numerous artificial spectrum‐compatible earthquake accelerograms using a limited number of ground motion records . Comput.-Aided Civ. Infrastruct. Eng. 38 , 225 - 240 ( 2022 ). http://doi.org/10.1111/mice.12852 http://doi.org/10.1111/mice.12852

I. Goodfellow et al. , Generative adversarial networks . Commun. ACM 63 , 139 - 144 ( 2020 ). http://doi.org/10.1145/3422622 http://doi.org/10.1145/3422622

C.S. Ho et al. , Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning . Nat. Commun. 10 ,( 2019 ). http://doi.org/10.1038/s41467-019-12898-9 http://doi.org/10.1038/s41467-019-12898-9

Z.Q. Zhu et al. , Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI . Inf. Fusion 91 , 376 - 387 ( 2023 ). http://doi.org/10.1016/j.inffus.2022.10.022 http://doi.org/10.1016/j.inffus.2022.10.022

Szegedy, C. et al. Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA: IEEE, 2015, 1-9.

T. Priemel et al. , Microfluidic-like fabrication of metal ion–cured bioadhesives by mussels . Science 374 , 206 - 211 ( 2021 ). http://doi.org/10.1126/science.abi9702 http://doi.org/10.1126/science.abi9702

W. Shen et al. , Reliable quantitative SERS analysis facilitated by core-shell nanoparticles with embedded internal standards . Angew. Chem. Int. Ed. 54 , 7308 - 7312 ( 2015 ). http://doi.org/10.1002/anie.201502171 http://doi.org/10.1002/anie.201502171

H.Y. Chen et al. , Large-scale hot spot engineering for quantitative SERS at the single-molecule scale . J. Am. Chem. Soc. 137 , 13698 - 13705 ( 2015 ). http://doi.org/10.1021/jacs.5b09111 http://doi.org/10.1021/jacs.5b09111

H.L. Wang et al. , Advances of surface-enhanced Raman and IR spectroscopies: from nano/microstructures to macro-optical design . Light Sci. Appl. 10 , 161 ( 2021 ). http://doi.org/10.1038/s41377-021-00599-2 http://doi.org/10.1038/s41377-021-00599-2

W.H. Park , Z.H. Kim , Charge transfer enhancement in the SERS of a single molecule . Nano Lett. 10 , 4040 - 4048 ( 2010 ). http://doi.org/10.1021/nl102026p http://doi.org/10.1021/nl102026p

J.X. Cheng , X.S. Xie , Vibrational spectroscopic imaging of living systems: An emerging platform for biology and medicine . Science 350 , aaa8870 ( 2015 ). http://doi.org/10.1126/science.aaa8870 http://doi.org/10.1126/science.aaa8870

O. Ilchenko , Y. Pilhun , A. Kutsyk , Towards Raman imaging of centimeter scale tissue areas for real-time opto-molecular visualization of tissue boundaries for clinical applications . Light Sci. Appl. 11 , 143 ( 2022 ). http://doi.org/10.1038/s41377-022-00828-2 http://doi.org/10.1038/s41377-022-00828-2

H.N. Lin , J.X. Cheng , Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science . eLight 3 , 6 ( 2023 ). http://doi.org/10.1186/s43593-022-00038-8 http://doi.org/10.1186/s43593-022-00038-8

Z. Movasaghi , S. Rehman , I. Rehman , Raman spectroscopy of biological tissues . Appl. Spectrosc. Rev. 42 , 493 - 541 ( 2007 ). http://doi.org/10.1080/05704920701551530 http://doi.org/10.1080/05704920701551530

W.T. Cheng et al. , Micro-Raman spectroscopy used to identify and grade human skin pilomatrixoma . Microsc. Res. Tech. 68 , 75 - 79 ( 2005 ). http://doi.org/10.1002/jemt.20229 http://doi.org/10.1002/jemt.20229

F. Bonnier , H.J. Byrne , Understanding the molecular information contained in principal component analysis of vibrational spectra of biological systems . Analyst 137 , 322 - 332 ( 2012 ). http://doi.org/10.1039/C1AN15821J http://doi.org/10.1039/C1AN15821J

R.J. Lakshmi et al. , Tissue Raman spectroscopy for the study of radiation damage: brain irradiation of mice . Radiat. Res. 157 , 175 - 182 ( 2002 ). http://doi.org/10.1667/0033-7587(2002)157[0175:TRSFTS]2.0.CO;2 http://doi.org/10.1667/0033-7587(2002)157[0175:TRSFTS]2.0.CO;2

Lafuente, B. et al. The power of databases: The RRUFF project. in Highlights in Mineralogical Crystallography (eds Armbruster, T. & Danisi, R. M.) (Berlin: De Gruyter (O), 2015), 1-30.

Y.J. Ai et al. , Rapid qualitative and quantitative determination of food colorants by both Raman spectra and Surface-enhanced Raman Scattering (SERS) . Food Chem. 241 , 427 - 433 ( 2018 ). http://doi.org/10.1016/j.foodchem.2017.09.019 http://doi.org/10.1016/j.foodchem.2017.09.019

Z.L. Huang et al. , Homogeneous multiplex immunoassay for one-step pancreatic cancer biomarker evaluation . Anal. Chem. 92 , 16105 - 16112 ( 2020 ). http://doi.org/10.1021/acs.analchem.0c03780 http://doi.org/10.1021/acs.analchem.0c03780

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Light | "拨云见日"重塑化学图像分辨率

Light | “拨云见日”重塑化学图像分辨率

Light | "拨云见日"重塑化学图像分辨率

暂无数据