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Single-Step Preprocessing of Raman Spectra using Convolutional Neural Networks
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics. (Experimental Mechanics)
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.ORCID iD: 0000-0003-4879-8261
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.ORCID iD: 0000-0003-3268-1691
2020 (English)In: Applied Spectroscopy, ISSN 0003-7028, E-ISSN 1943-3530, ISSN 0003-7028Article in journal (Refereed) Epub ahead of print
Abstract [en]

Preprocessing of Raman spectra is generally done in three separate steps: (1) cosmic ray removal, (2) signal smoothing, and(3) baseline subtraction. We show that a convolutional neural network (CNN) can be trained using simulated data tohandle all steps in one operation. First, synthetic spectra are created by randomly adding peaks, baseline, mixing of peaksand baseline with background noise, and cosmic rays. Second, a CNN is trained on synthetic spectra and known peaks.The results from preprocessing were generally of higher quality than what was achieved using a reference based onstandardized methods (second-difference, asymmetric least squares, cross-validation). From 105 simulated observations,91.4% predictions had smaller absolute error (RMSE), 90.3% had improved quality (SSIM), and 94.5% had reduced signal-tonoise(SNR) power. The CNN preprocessing generated reliable results on measured Raman spectra from polyethylene,paraffin and ethanol with background contamination from polystyrene. The result shows a promising proof of concept forthe automated preprocessing of Raman spectra.

Place, publisher, year, edition, pages
Sage Publications, 2020.
Keywords [en]
Raman spectroscopy, convolutional neural network, CNN, preprocessing, simulated data, chemometrics
National Category
Signal Processing
Research subject
Experimental Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-77138DOI: 10.1177/0003702819888949OAI: oai:DiVA.org:ltu-77138DiVA, id: diva2:1377289
Funder
Swedish Foundation for Strategic Research , ITM17-0056Swedish Research Council, 2016-04220Available from: 2019-12-11 Created: 2019-12-11 Last updated: 2020-02-21
In thesis
1. Development of Methods to Investigate Pulmonary Arterial Smooth Muscle Cells under Hypoxia
Open this publication in new window or tab >>Development of Methods to Investigate Pulmonary Arterial Smooth Muscle Cells under Hypoxia
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Hypoxic pulmonary vasoconstriction (HPV) is a physiological response to localized alveolarhypoxia that is intrinsic to the pulmonary circulation. By hypoxia-induced contractionof pulmonary arterial smooth muscle cells (PASMCs), the pulmonary capillary bloodflow is redirected to alveolar areas of high oxygen partial pressure, thus maintaining theventilation-perfusion ratio. Although the principle of HPV was recognized decades agothe underlying pathway remains elusive. The patch clamp technique, imaging and Ramanspectroscopy are methods that can be used to investigate parts of the mechanisms. Toenable measurements at controlled oxygen concentrations a gas-tight microfluidic systemwas developed. In this thesis preparatory experiments to couple the gas-tight systemto a microscope that enabled simultaneous measurements with patch clamp, imagingand Raman spectroscopy are discussed. The patch clamp technique is to be used formeasurements on the dynamics of the ion-channels in the cellular membrane as well aschanges in membrane potential as a response to hypoxia. Imaging of PASMCs is requiredto successfully apply the patch clamp technique. Further, imaging will also reveal whetherthe mechanical response of HPV has been triggered, for this purpose image analysis forestimation of optical flow can be used. Raman spectroscopy enables measurements ofbiochemical changes in redox biomarkers, cytochrome c and NADH, of the mitochondrialelectron transport chain. This thesis shows that the gas-tight microfluidic system providesoptimal control of the oxygen content, in an experimantal setting where the patch clamptechnique can be applied. Raman measurements showed significantly larger variationsin spectra compared to an open fluidic system, which is the conventional approach.However, the results showed a need for improved Raman preprocessing. For this purposea Convolutional Neural Network (CNN) was trained using synthetic spectra that providedoptimal reconstruction of the Raman signal. Finally, simultaneous imaging and Ramanspectroscopy of red blood cells were performed in a home built microscope. The resultspave the way for measurements on PASMCs.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2019
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Medical Engineering Applied Mechanics
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-77140 (URN)978-91-7790-513-4 (ISBN)978-91-7790-514-1 (ISBN)
Presentation
2020-01-24, E237, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2019-12-11 Created: 2019-12-11 Last updated: 2020-03-12Bibliographically approved

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Wahl, JoelSjödahl, MikaelRamser, Kerstin

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