Infrared and Raman Instrumentation for Mapping and Imaging

Griffiths, Peter R.; Miseo, Ellen V.

doi:10.1002/9783527628230.ch1

Cited by 27 publications

(9 citation statements)

References 72 publications

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“…In the past years, Confocal Raman Microscopy (CRM) has gained great interest as characterization technique for biological materials due to the high lateral resolution [ 1 ]. Depending on the numerical aperture of the objective and the excitation wavelength of the laser, a CRM set up can reach a spatial resolution down to 250 nm [ 2 , 3 ]. The advantages of CRM are many since one can acquire a chemical profile of the sample in a non-destructive and fast way, without time consuming sample preparation and staining.…”

Section: Introductionmentioning

confidence: 99%

Multivariate unmixing approaches on Raman images of plant cell walls: new insights or overinterpretation of results?

et al. 2018

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BackgroundPlant cell walls are nanocomposites based on cellulose microfibrils embedded in a matrix of polysaccharides and aromatic polymers. They are optimized for different functions (e.g. mechanical stability) by changing cell form, cell wall thickness and composition. To reveal the composition of plant tissues in a non-destructive way on the microscale, Raman imaging has become an important tool. Thousands of Raman spectra are acquired, each one being a spatially resolved molecular fingerprint of the plant cell wall. Nevertheless, due to the multicomponent nature of plant cell walls, many bands are overlapping and classical band integration approaches often not suitable for imaging. Multivariate data analysing approaches have a high potential as the whole wavenumber region of all thousands of spectra is analysed at once.ResultsThree multivariate unmixing algorithms, vertex component analysis, non-negative matrix factorization and multivariate curve resolution–alternating least squares were applied to find the purest components within datasets acquired from micro-sections of spruce wood and Arabidopsis. With all three approaches different cell wall layers (including tiny S1 and S3 with 0.09–0.14 µm thickness) and cell contents were distinguished and endmember spectra with a good signal to noise ratio extracted. Baseline correction influences the results obtained in all methods as well as the way in which algorithm extracts components, i.e. prioritizing the extraction of positive endmembers by sequential orthogonal projections in VCA or performing a simultaneous extraction of non-negative components aiming at explaining the maximum variance in NMF and MCR-ALS. Other constraints applied (e.g. closure in VCA) or a previous principal component analysis filtering step in MCR-ALS also contribute to the differences obtained. ConclusionsVCA is recommended as a good preliminary approach, since it is fast, does not require setting many input parameters and the endmember spectra result in good approximations of the raw data. Yet the endmember spectra are more correlated and mixed than those retrieved by NMF and MCR-ALS methods. The latter two give the best model statistics (with lower lack of fit in the models), but care has to be taken about overestimating the rank as it can lead to artificial shapes due to peak splitting or inverted bands. Electronic supplementary materialThe online version of this article (10.1186/s13007-018-0320-9) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Multivariate unmixing approaches on Raman images of plant cell walls: new insights or overinterpretation of results?

et al. 2018

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“…Another important difference is the spatial resolution obtainable. The spatial resolution of a light microscope is ultimately determined by the diffraction of the radiation which implies that it depends on the wavelength of the light and the numerical aperture of the objective (Griffiths 2009). For woodrelated applications, this means that selective acquisition of spectra from the cell wall and its different layers or of the cell corners and rays is only possible using either UV-VIS microscopy or Raman microscopy.…”

Section: Principles Of Infrared Raman and Uv Microspectroscopy With mentioning

confidence: 99%

“…The small apertures required for IR microspectroscopy with diffraction limited spatial resolution let less photons arrive at the detector. Therefore, the most sensitive detectors of the type narrow band mercury cadmium telluride (MCT) are required, which are only sensitive down to 700 cm -1 (Griffiths 2009). The spectral range of current multichannel focal plane array (FPA) detectors is even smaller (3,600-900 cm -1 ) and excludes prominent wood-related IR bands between 900 and 700 cm -1 .…”

Section: Principles Of Infrared Raman and Uv Microspectroscopy With mentioning

confidence: 99%

Microspectroscopy as applied to the study of wood molecular structure

Fackler

Thygesen

2012

Wood Sci Technol

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Microspectroscopy gives access to spatially resolved information on the molecular structure and chemical composition of a material. For a highly heterogeneous and anisotropic material like wood, such information is essential when assessing structure/property relationships such as moisture-induced dimensional changes, decay resistance or mechanical properties. It is, however, important to choose the right technique for the purpose at hand and to apply it in a suitable way if any new insights are to be gained. This review presents and compares three different microspectroscopic techniques: infrared, Raman and ultraviolet. Issues such as sample preparation, spatial resolution, data acquisition and extraction of knowledge from the spectral data are discussed. Additionally, an overview of applications in wood science is given for each method. Lastly, current trends and challenges within microspectroscopy of wood are discussed.

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“…In this study, none of these indices were able to detect and discriminate the types of rust. However, the anthocyanin reflectance index can be used to detect yellow rust, and the transformed chlorophyll absorption and reflectance index can be used to detect leaf rust [21]. In another study conducted by Frank and Menz, hyperspectral and leaf multispectral data were used to estimate the severity of wheat leaf rust [22].…”

Section: Resultsmentioning

confidence: 99%