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Steinmann SN, Wang Q, Seh ZW. How machine learning can accelerate electrocatalysis discovery and optimization. MATERIALS HORIZONS 2023; 10:393-406. [PMID: 36541226 DOI: 10.1039/d2mh01279k] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Advances in machine learning (ML) provide the means to bypass bottlenecks in the discovery of new electrocatalysts using traditional approaches. In this review, we highlight the currently achieved work in ML-accelerated discovery and optimization of electrocatalysts via a tight collaboration between computational models and experiments. First, the applicability of available methods for constructing machine-learned potentials (MLPs), which provide accurate energies and forces for atomistic simulations, are discussed. Meanwhile, the current challenges for MLPs in the context of electrocatalysis are highlighted. Then, we review the recent progress in predicting catalytic activities using surrogate models, including microkinetic simulations and more global proxies thereof. Several typical applications of using ML to rationalize thermodynamic proxies and predict the adsorption and activation energies are also discussed. Next, recent developments of ML-assisted experiments for catalyst characterization, synthesis optimization and reaction condition optimization are illustrated. In particular, the applications in ML-enhanced spectra analysis and the use of ML to interpret experimental kinetic data are highlighted. Additionally, we also show how robotics are applied to high-throughput synthesis, characterization and testing of electrocatalysts to accelerate the materials exploration process and how this equipment can be assembled into self-driven laboratories.
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Affiliation(s)
| | - Qing Wang
- Univ Lyon, ENS de Lyon, CNRS, Laboratoire de Chimie UMR 5182, Lyon, France.
| | - Zhi Wei Seh
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis, 138634, Singapore.
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2
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Zhou H, Xu L, Ren Z, Zhu J, Lee C. Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics. NANOSCALE ADVANCES 2023; 5:538-570. [PMID: 36756499 PMCID: PMC9890940 DOI: 10.1039/d2na00608a] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/06/2022] [Indexed: 06/17/2023]
Abstract
The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.
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Affiliation(s)
- Hong Zhou
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
| | - Liangge Xu
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
- National Key Laboratory of Special Environment Composite Technology, Harbin Institute of Technology Harbin 150001 China
| | - Zhihao Ren
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
| | - Jiaqi Zhu
- National Key Laboratory of Special Environment Composite Technology, Harbin Institute of Technology Harbin 150001 China
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
- NUS Suzhou Research Institute (NUSRI) Suzhou 215123 China
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3
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Stiebing C, Post N, Schindler C, Göhrig B, Lux H, Popp J, Heutelbeck A, Schie IW. Revealing the Chemical Composition of Birch Pollen Grains by Raman Spectroscopic Imaging. Int J Mol Sci 2022; 23:ijms23095112. [PMID: 35563504 PMCID: PMC9101400 DOI: 10.3390/ijms23095112] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/26/2022] [Accepted: 04/28/2022] [Indexed: 02/01/2023] Open
Abstract
The investigation of the biochemical composition of pollen grains is of the utmost interest for several environmental aspects, such as their allergenic potential and their changes in growth conditions due to climatic factors. In order to fully understand the composition of pollen grains, not only is an in-depth analysis of their molecular components necessary but also spatial information of, e.g., the thickness of the outer shell, should be recorded. However, there is a lack of studies using molecular imaging methods for a spatially resolved biochemical composition on a single-grain level. In this study, Raman spectroscopy was implemented as an analytical tool to investigate birch pollen by imaging single pollen grains and analyzing their spectral profiles. The imaging modality allowed us to reveal the layered structure of pollen grains based on the biochemical information of the recorded Raman spectra. Seven different birch pollen species collected at two different locations in Germany were investigated and compared. Using chemometric algorithms such as hierarchical cluster analysis and multiple-curve resolution, several components of the grain wall, such as sporopollenin, as well as the inner core presenting high starch concentrations, were identified and quantified. Differences in the concentrations of, e.g., sporopollenin, lipids and proteins in the pollen species at the two different collection sites were found, and are discussed in connection with germination and other growth processes.
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Affiliation(s)
- Clara Stiebing
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), Albert-Einstein-Straße 9, 07745 Jena, Germany; (C.S.); (J.P.)
| | - Nele Post
- Department of Medical Engineering and Biotechnology, University of Applied Sciences Jena, Carl-Zeiss-Promenade 2, 07745 Jena, Germany;
| | - Claudia Schindler
- Institute of Occupational, Social and Environmental Medicine, Jena University Hospital, Erlanger Allee 103, 07747 Jena, Germany; (C.S.); (B.G.); (H.L.); (A.H.)
| | - Bianca Göhrig
- Institute of Occupational, Social and Environmental Medicine, Jena University Hospital, Erlanger Allee 103, 07747 Jena, Germany; (C.S.); (B.G.); (H.L.); (A.H.)
| | - Harald Lux
- Institute of Occupational, Social and Environmental Medicine, Jena University Hospital, Erlanger Allee 103, 07747 Jena, Germany; (C.S.); (B.G.); (H.L.); (A.H.)
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Brandenburg Medical School, 16816 Neuruppin, Germany
| | - Jürgen Popp
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), Albert-Einstein-Straße 9, 07745 Jena, Germany; (C.S.); (J.P.)
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
| | - Astrid Heutelbeck
- Institute of Occupational, Social and Environmental Medicine, Jena University Hospital, Erlanger Allee 103, 07747 Jena, Germany; (C.S.); (B.G.); (H.L.); (A.H.)
| | - Iwan W. Schie
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), Albert-Einstein-Straße 9, 07745 Jena, Germany; (C.S.); (J.P.)
- Department of Medical Engineering and Biotechnology, University of Applied Sciences Jena, Carl-Zeiss-Promenade 2, 07745 Jena, Germany;
- Correspondence:
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4
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Cialla-May D, Krafft C, Rösch P, Deckert-Gaudig T, Frosch T, Jahn IJ, Pahlow S, Stiebing C, Meyer-Zedler T, Bocklitz T, Schie I, Deckert V, Popp J. Raman Spectroscopy and Imaging in Bioanalytics. Anal Chem 2021; 94:86-119. [PMID: 34920669 DOI: 10.1021/acs.analchem.1c03235] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Dana Cialla-May
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Christoph Krafft
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Petra Rösch
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Tanja Deckert-Gaudig
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Torsten Frosch
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Izabella J Jahn
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Susanne Pahlow
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Clara Stiebing
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Tobias Meyer-Zedler
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Thomas Bocklitz
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Iwan Schie
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Ernst-Abbe-Hochschule Jena, University of Applied Sciences, Department of Biomedical Engineering and Biotechnology, Carl-Zeiss-Promenade 2, 07745 Jena, Germany
| | - Volker Deckert
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Jürgen Popp
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
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5
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Automated Raman Micro-Spectroscopy of Epithelial Cell Nuclei for High-Throughput Classification. Cancers (Basel) 2021; 13:cancers13194767. [PMID: 34638253 PMCID: PMC8507544 DOI: 10.3390/cancers13194767] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/15/2021] [Accepted: 09/16/2021] [Indexed: 11/25/2022] Open
Abstract
Simple Summary We demonstrate an automated Raman cytology system designed for high-throughput and reproducibility. The system uses a Raman spectroscopy system integrated into a conventional microscope, all controlled electronically via and open source software, Micro-Manager. The system can automatically identify and probe epithelial cell nuclei for Raman spectroscopy. 6426 HT1197 (high-grade bladder cancer) cell spectra, and 7499 RT112 (low-grade bladdercancer) cell spectra were recorded. The data was subsequently culled and processed for denoising and artifact removal. We demonstrate, using multivariate statistical analysis, that the cells can be distinguished, using a variety of approaches with accuracy, sensitivity and specificity in excess of 95%. Abstract Raman micro-spectroscopy is a powerful technique for the identification and classification of cancer cells and tissues. In recent years, the application of Raman spectroscopy to detect bladder, cervical, and oral cytological samples has been reported to have an accuracy greater than that of standard pathology. However, despite being entirely non-invasive and relatively inexpensive, the slow recording time, and lack of reproducibility have prevented the clinical adoption of the technology. Here, we present an automated Raman cytology system that can facilitate high-throughput screening and improve reproducibility. The proposed system is designed to be integrated directly into the standard pathology clinic, taking into account their methodologies and consumables. The system employs image processing algorithms and integrated hardware/software architectures in order to achieve automation and is tested using the ThinPrep standard, including the use of glass slides, and a number of bladder cancer cell lines. The entire automation process is implemented, using the open source Micro-Manager platform and is made freely available. We believe that this code can be readily integrated into existing commercial Raman micro-spectrometers.
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Shi L, Fung AA, Zhou A. Advances in stimulated Raman scattering imaging for tissues and animals. Quant Imaging Med Surg 2021; 11:1078-1101. [PMID: 33654679 PMCID: PMC7829158 DOI: 10.21037/qims-20-712] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 08/25/2020] [Indexed: 12/14/2022]
Abstract
Stimulated Raman scattering (SRS) microscopy has emerged in the last decade as a powerful optical imaging technology with high chemical selectivity, speed, and subcellular resolution. Since the invention of SRS microscopy, it has been extensively employed in life science to study composition, structure, metabolism, development, and disease in biological systems. Applications of SRS in research and the clinic have generated new insights in many fields including neurobiology, tumor biology, developmental biology, metabolomics, pharmacokinetics, and more. Herein we review the advances and applications of SRS microscopy imaging in tissues and animals, as well as envision future applications and development of SRS imaging in life science and medicine.
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Affiliation(s)
- Lingyan Shi
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Anthony A Fung
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Andy Zhou
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
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7
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Resolving complex phenotypes with Raman spectroscopy and chemometrics. Curr Opin Biotechnol 2020; 66:277-282. [DOI: 10.1016/j.copbio.2020.09.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 09/10/2020] [Accepted: 09/15/2020] [Indexed: 12/30/2022]
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8
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Korinth F, Mondol AS, Stiebing C, Schie IW, Krafft C, Popp J. New methodology to process shifted excitation Raman difference spectroscopy data: a case study of pollen classification. Sci Rep 2020; 10:11215. [PMID: 32641779 PMCID: PMC7343813 DOI: 10.1038/s41598-020-67897-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 06/15/2020] [Indexed: 02/07/2023] Open
Abstract
Shifted excitation Raman difference spectroscopy (SERDS) is a background correction method for Raman spectroscopy. Here, the difference spectra were directly used as input for SERDS-based classification after an optimization procedure to correct for photobleaching of the autofluorescence. Further processing included a principal component analysis to compensate for the reduced signal to noise ratio of the difference spectra and subsequent classification by linear discriminant analysis. As a case study 6,028 Raman spectra of single pollen originating from plants of eight different genera and four different growth habits were automatically recorded at excitation wavelengths 784 and 786 nm using a high-throughput screening Raman system. Different pollen were distinguished according to their growth habit, i.e. tree versus non-tree with an accuracy of 95.9%. Furthermore, all pollen were separated according to their genus, providing also insight into similarities based on their families. Classification results were compared using spectra reconstructed from the differences and raw spectra after state-of-art baseline correction as input. Similar sensitivities, specificities, accuracies and precisions were found for all spectra with moderately background. Advantages of SERDS are expected in scenarios where Raman spectra are affected by variations due to detector etaloning, ambient light, and high background.
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Affiliation(s)
- F Korinth
- Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745, Jena, Germany
| | - A S Mondol
- Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745, Jena, Germany
| | - C Stiebing
- Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745, Jena, Germany
| | - I W Schie
- Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745, Jena, Germany.,Department of Medical Engineering and Biotechnology, University of Applied Sciences, Carl-Zeiss-Promenade 2, 07745, Jena, Germany
| | - C Krafft
- Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745, Jena, Germany.
| | - J Popp
- Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745, Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743, Jena, Germany
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9
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Diehn S, Zimmermann B, Tafintseva V, Bağcıoğlu M, Kohler A, Ohlson M, Fjellheim S, Kneipp J. Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains. Anal Bioanal Chem 2020; 412:6459-6474. [PMID: 32350580 PMCID: PMC7442581 DOI: 10.1007/s00216-020-02628-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/11/2020] [Accepted: 03/28/2020] [Indexed: 02/06/2023]
Abstract
Fourier-transform infrared (FTIR) spectroscopy enables the chemical characterization and identification of pollen samples, leading to a wide range of applications, such as paleoecology and allergology. This is of particular interest in the identification of grass (Poaceae) species since they have pollen grains of very similar morphology. Unfortunately, the correct identification of FTIR microspectroscopy spectra of single pollen grains is hindered by strong spectral contributions from Mie scattering. Embedding of pollen samples in paraffin helps to retrieve infrared spectra without scattering artifacts. In this study, pollen samples from 10 different populations of five grass species (Anthoxanthum odoratum, Bromus inermis, Hordeum bulbosum, Lolium perenne, and Poa alpina) were embedded in paraffin, and their single grain spectra were obtained by FTIR microspectroscopy. Spectra were subjected to different preprocessing in order to suppress paraffin influence on spectral classification. It is shown that decomposition by non-negative matrix factorization (NMF) and extended multiplicative signal correction (EMSC) that utilizes a paraffin constituent spectrum, respectively, leads to good success rates for the classification of spectra with respect to species by a partial least square discriminant analysis (PLS-DA) model in full cross-validation for several species. PLS-DA, artificial neural network, and random forest classifiers were applied on the EMSC-corrected spectra using an independent validation to assign spectra from unknown populations to the species. Variation within and between species, together with the differences in classification results, is in agreement with the systematics within the Poaceae family. The results illustrate the great potential of FTIR microspectroscopy for automated classification and identification of grass pollen, possibly together with other, complementary methods for single pollen chemical characterization.
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Affiliation(s)
- Sabrina Diehn
- Department of Chemistry, Humboldt-Universität zu Berlin, Brook-Taylor-Straße 2, 12489, Berlin, Germany
| | - Boris Zimmermann
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432, Ås, Norway
| | - Valeria Tafintseva
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432, Ås, Norway
| | - Murat Bağcıoğlu
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432, Ås, Norway
| | - Achim Kohler
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432, Ås, Norway
| | - Mikael Ohlson
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, 1432, Ås, Norway
| | - Siri Fjellheim
- Faculty of Biosciences, Norwegian University of Life Sciences, 1432, Ås, Norway
| | - Janina Kneipp
- Department of Chemistry, Humboldt-Universität zu Berlin, Brook-Taylor-Straße 2, 12489, Berlin, Germany.
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10
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Kenđel A, Zimmermann B. Chemical Analysis of Pollen by FT-Raman and FTIR Spectroscopies. FRONTIERS IN PLANT SCIENCE 2020; 11:352. [PMID: 32296453 PMCID: PMC7136416 DOI: 10.3389/fpls.2020.00352] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 03/10/2020] [Indexed: 05/13/2023]
Abstract
Pollen studies are important for the assessment of present and past environment, including biodiversity, sexual reproduction of plants and plant-pollinator interactions, monitoring of aeroallergens, and impact of climate and pollution on wild communities and cultivated crops. Although information on chemical composition of pollen is of importance in all of those research areas, pollen chemistry has been rarely measured due to complex and time-consuming analyses. Vibrational spectroscopies, coupled with multivariate data analysis, have shown great potential for rapid chemical characterization, identification and classification of pollen. This study, comprising 219 species from all principal taxa of seed plants, has demonstrated that high-quality Raman spectra of pollen can be obtained by Fourier transform (FT) Raman spectroscopy. In combination with Fourier transform infrared spectroscopy (FTIR), FT-Raman spectroscopy is obtaining comprehensive information on pollen chemistry. Presence of all the main biochemical constituents of pollen, such as proteins, lipids, carbohydrates, carotenoids and sporopollenins, have been identified and detected in the spectra, and the study shows approaches to measure relative and absolute content of these constituents. The results show that FT-Raman spectroscopy has clear advantage over standard dispersive Raman measurements, in particular for measurement of pollen samples with high pigment content. FT-Raman spectra are strongly biased toward chemical composition of pollen wall constituents, namely sporopollenins and pigments. This makes Raman spectra complementary to FTIR spectra, which over-represent chemical constituents of the grain interior, such as lipids and carbohydrates. The results show a large variability in pollen chemistry for families, genera and even congeneric species, revealing wide range of reproductive strategies, from storage of nutrients to variation in carotenoids and phenylpropanoids. The information on pollen's chemical patterns for major plant taxa should be of outstanding value for various studies in plant biology and ecology, including aerobiology, palaeoecology, forensics, community ecology, plant-pollinator interactions, and climate effects on plants.
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Affiliation(s)
- Adriana Kenđel
- Division of Analytical Chemistry, Department of Chemistry, Faculty of Science, University of Zagreb, Zagreb, Croatia
| | - Boris Zimmermann
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
- Division of Organic Chemistry and Biochemistry, Ruđer Bošković Institute, Zagreb, Croatia
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