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Lim M, Park KH, Hwang JS, Choi M, Shin HY, Kim HK. Enhancing spatial resolution in Fourier transform infrared spectral image via machine learning algorithms. Sci Rep 2023; 13:22699. [PMID: 38123797 PMCID: PMC10733398 DOI: 10.1038/s41598-023-50060-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023] Open
Abstract
Owing to the intrinsic signal noise in the characterization of chemical structures through Fourier transform infrared (FT-IR) spectroscopy, the determination of the signal-to-noise ratio (SNR) depends on the level of the concentration of the chemical structures. In situations characterized by limited concentrations of chemical structures, the traditional approach involves mitigating the resulting low SNR by superimposing repetitive measurements. In this study, we achieved comparable high-quality results to data scanned 64 times and superimposed by employing machine learning algorithms such as the principal component analysis and non-negative matrix factorization, which perform the dimensionality reduction, on FT-IR spectral image data that was only scanned once. Furthermore, the spatial resolution of the mapping images correlated to each chemical structure was enhanced by applying both the machine learning algorithms and the Gaussian fitting simultaneously. Significantly, our investigation demonstrated that the spatial resolution of the mapping images acquired through relative intensity is further improved by employing dimensionality reduction techniques. Collectively, our findings imply that by optimizing research data through noise reduction enhancing spatial resolution using the machine learning algorithms, research processes can be more efficient, for instance by reducing redundant physical measurements.
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Affiliation(s)
- Mina Lim
- Advanced Analysis and Data Center, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
- School of Industrial and Management Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Kyu Ho Park
- Materials and Devices Advanced Research Institute, LG Electronics, Seoul, 07796, Republic of Korea
| | - Jae Sung Hwang
- Materials and Devices Advanced Research Institute, LG Electronics, Seoul, 07796, Republic of Korea
| | - Mikyung Choi
- Materials and Devices Advanced Research Institute, LG Electronics, Seoul, 07796, Republic of Korea
| | - Hui Youn Shin
- Materials and Devices Advanced Research Institute, LG Electronics, Seoul, 07796, Republic of Korea
| | - Hong-Kyu Kim
- Advanced Analysis and Data Center, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
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2
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Koziol P, Kosowska K, Korecki P, Wrobel TP. Scattering correction for samples with cylindrical domains measured with polarized infrared spectroscopy. Anal Chim Acta 2023; 1278:341722. [PMID: 37709463 DOI: 10.1016/j.aca.2023.341722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 07/14/2023] [Accepted: 08/14/2023] [Indexed: 09/16/2023]
Abstract
Scattering artifacts are one of the most common effects distorting transmission spectra in Fourier-Transform Infrared spectroscopy. Their increased impact, strongly diminishing the quantitative and qualitative power of IR spectroscopy, is especially observed for structures with a size comparable to the radiation wavelength. To tackle this problem, a wide range of preprocessing techniques based on the Extended Multiplicative Scattering Correction method was developed, using physical properties to remove scattering presence in the spectra. However, until recently those algorithms were mostly focused on spherically shaped samples, for example, cells. Here, an algorithm for samples with cylindrical domains is described, with additional implementation of a linearly polarized light case, which is crucial for the growing field of polarized IR imaging and spectroscopy. An open-source code with GPU based implementation is provided, with a calculation time of several seconds per spectrum. Optimizations done to improve the throughput of this algorithm allow the application of this method into the standard preprocessing pipeline of small datasets.
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Affiliation(s)
- Paulina Koziol
- SOLARIS National Synchrotron Radiation Centre, Jagiellonian University, Czerwone Maki 98, 30-392, Krakow, Poland; Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Lojasiewicza 11, 30-348, Krakow, Poland
| | - Karolina Kosowska
- SOLARIS National Synchrotron Radiation Centre, Jagiellonian University, Czerwone Maki 98, 30-392, Krakow, Poland
| | - Pawel Korecki
- SOLARIS National Synchrotron Radiation Centre, Jagiellonian University, Czerwone Maki 98, 30-392, Krakow, Poland; Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Lojasiewicza 11, 30-348, Krakow, Poland
| | - Tomasz P Wrobel
- SOLARIS National Synchrotron Radiation Centre, Jagiellonian University, Czerwone Maki 98, 30-392, Krakow, Poland.
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3
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Rehman HU, Tafintseva V, Zimmermann B, Solheim JH, Virtanen V, Shaikh R, Nippolainen E, Afara I, Saarakkala S, Rieppo L, Krebs P, Fomina P, Mizaikoff B, Kohler A. Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach. Molecules 2022; 27:2298. [PMID: 35408697 PMCID: PMC9000438 DOI: 10.3390/molecules27072298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/28/2022] [Accepted: 03/30/2022] [Indexed: 02/04/2023] Open
Abstract
Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.
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Affiliation(s)
- Hafeez Ur Rehman
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway; (V.T.); (B.Z.); (J.H.S.); (A.K.)
| | - Valeria Tafintseva
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway; (V.T.); (B.Z.); (J.H.S.); (A.K.)
| | - Boris Zimmermann
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway; (V.T.); (B.Z.); (J.H.S.); (A.K.)
| | - Johanne Heitmann Solheim
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway; (V.T.); (B.Z.); (J.H.S.); (A.K.)
| | - Vesa Virtanen
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, 90570 Oulu, Finland; (V.V.); (S.S.); (L.R.)
| | - Rubina Shaikh
- Department of Applied Physics, University of Eastern Finland, 70210 Kuopio, Finland; (R.S.); (E.N.); (I.A.)
- Department of Orthopedics, Traumatology, Hand Surgery, Kuopio University Hospital, 70210 Kuopio, Finland
| | - Ervin Nippolainen
- Department of Applied Physics, University of Eastern Finland, 70210 Kuopio, Finland; (R.S.); (E.N.); (I.A.)
| | - Isaac Afara
- Department of Applied Physics, University of Eastern Finland, 70210 Kuopio, Finland; (R.S.); (E.N.); (I.A.)
| | - Simo Saarakkala
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, 90570 Oulu, Finland; (V.V.); (S.S.); (L.R.)
| | - Lassi Rieppo
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, 90570 Oulu, Finland; (V.V.); (S.S.); (L.R.)
| | - Patrick Krebs
- Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89081 Ulm, Germany; (P.K.); (P.F.); (B.M.)
| | - Polina Fomina
- Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89081 Ulm, Germany; (P.K.); (P.F.); (B.M.)
| | - Boris Mizaikoff
- Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89081 Ulm, Germany; (P.K.); (P.F.); (B.M.)
| | - Achim Kohler
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway; (V.T.); (B.Z.); (J.H.S.); (A.K.)
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Meyvisch P, Gurdebeke PR, Vrielinck H, Neil Mertens K, Versteegh G, Louwye S. Attenuated Total Reflection (ATR) Micro-Fourier Transform Infrared (Micro-FT-IR) Spectroscopy to Enhance Repeatability and Reproducibility of Spectra Derived from Single Specimen Organic-Walled Dinoflagellate Cysts. APPLIED SPECTROSCOPY 2022; 76:235-254. [PMID: 34494488 DOI: 10.1177/00037028211041172] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The chemical composition of recent and fossil organic-walled dinoflagellate cyst walls and its diversity is poorly understood and analyses on single microscopic specimens are rare. A series of infrared spectroscopic experiments resulted in the proposition of a standardized attenuated total reflection micro-Fourier transform infrared-based method that allows the collection of robust data sets consisting of spectra from individual dinocysts. These data sets are largely devoid of nonchemical artifacts inherent to other infrared spectrochemical methods, which have typically been used to study similar specimens in the past. The influence of sample preparation, specimen morphology and size and spectral data processing steps is also assessed within this methodological framework. As a result, several guidelines are proposed which facilitate the collection and qualitative interpretation of highly reproducible and repeatable spectrochemical data. These, in turn, pave the way for a systematic exploration of dinocyst chemistry and its assessment as a chemotaxonomical tool or proxy.
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Affiliation(s)
| | | | - Henk Vrielinck
- Department of Solid-State Sciences, Ghent University, Ghent, Belgium
| | | | - Gerard Versteegh
- Marine Biochemistry Group, Alfred-Wegener-Institute, Bremerhaven, Germany
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5
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Spadea A, Denbigh J, Lawrence MJ, Kansiz M, Gardner P. Analysis of Fixed and Live Single Cells Using Optical Photothermal Infrared with Concomitant Raman Spectroscopy. Anal Chem 2021; 93:3938-3950. [PMID: 33595297 PMCID: PMC8018697 DOI: 10.1021/acs.analchem.0c04846] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 01/26/2021] [Indexed: 12/24/2022]
Abstract
This paper reports the first use of a novel completely optically based photothermal method (O-PTIR) for obtaining infrared spectra of both fixed and living cells using a quantum cascade laser (QCL) and optical parametric oscillator (OPO) laser as excitation sources, thus enabling all biologically relevant vibrations to be analyzed at submicron spatial resolution. In addition, infrared data acquisition is combined with concomitant Raman spectra from exactly the same excitation location, meaning the full vibrational profile of the cell can be obtained. The pancreatic cancer cell line MIA PaCa-2 and the breast cancer cell line MDA-MB-231 are used as model cells to demonstrate the capabilities of the new instrumentation. These combined modalities can be used to analyze subcellular structures in both fixed and, more importantly, live cells under aqueous conditions. We show that the protein secondary structure and lipid-rich bodies can be identified on the submicron scale.
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Affiliation(s)
- Alice Spadea
- NorthWest
Centre for Advanced Drug Delivery (NoWCADD), School of Health Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, U.K.
- Division
of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre Oxford
Road, Manchester M13 9PL, U.K.
| | - Joanna Denbigh
- Seda
Pharmaceutical Development Services, Alderley Park, Alderley
Edge, Cheshire SK10 4TG, U.K.
- School
of Science, Engineering and Environment, University of Salford, Salford, M5 4WT, U.K.
| | - M. Jayne Lawrence
- NorthWest
Centre for Advanced Drug Delivery (NoWCADD), School of Health Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, U.K.
- Division
of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre Oxford
Road, Manchester M13 9PL, U.K.
| | - Mustafa Kansiz
- Photothermal
Spectroscopy Corp. 325
Chapala Street, Santa Barbara, California 93101, United States
| | - Peter Gardner
- Manchester
Institute of Biotechnology, University of
Manchester, 131 Princess Street, Manchester M1 7DN, U.K.
- Department
of Chemical Engineering and Analytical Science, School of Engineering, University of Manchester, Oxford Road, Manchester M13 9PL, U.K.
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Magnussen EA, Solheim JH, Blazhko U, Tafintseva V, Tøndel K, Liland KH, Dzurendova S, Shapaval V, Sandt C, Borondics F, Kohler A. Deep convolutional neural network recovers pure absorbance spectra from highly scatter-distorted spectra of cells. JOURNAL OF BIOPHOTONICS 2020; 13:e202000204. [PMID: 32844585 DOI: 10.1002/jbio.202000204] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/21/2020] [Accepted: 08/21/2020] [Indexed: 06/11/2023]
Abstract
Infrared spectroscopy of cells and tissues is prone to Mie scattering distortions, which grossly obscure the relevant chemical signals. The state-of-the-art Mie extinction extended multiplicative signal correction (ME-EMSC) algorithm is a powerful tool for the recovery of pure absorbance spectra from highly scatter-distorted spectra. However, the algorithm is computationally expensive and the correction of large infrared imaging datasets requires weeks of computations. In this paper, we present a deep convolutional descattering autoencoder (DSAE) which was trained on a set of ME-EMSC corrected infrared spectra and which can massively reduce the computation time for scatter correction. Since the raw spectra showed large variability in chemical features, different reference spectra matching the chemical signals of the spectra were used to initialize the ME-EMSC algorithm, which is beneficial for the quality of the correction and the speed of the algorithm. One DSAE was trained on the spectra, which were corrected with different reference spectra and validated on independent test data. The DSAE outperformed the ME-EMSC correction in terms of speed, robustness, and noise levels. We confirm that the same chemical information is contained in the DSAE corrected spectra as in the spectra corrected with ME-EMSC.
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Affiliation(s)
| | | | - Uladzislau Blazhko
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, Minsk, Belarus
| | - Valeria Tafintseva
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Kristin Tøndel
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Kristian Hovde Liland
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Simona Dzurendova
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Volha Shapaval
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | | | | | - Achim Kohler
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
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