Advances in Near Infrared Spectroscopy and Related Computational Methods
In the last few decades, near-infrared (NIR) spectroscopy has distinguished itself as one of the most rapidly advancing spectroscopic techniques. Mainly known as an analytical tool useful for sample characterization and content quantification, NIR spectroscopy is essential in various other fields, e...
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MDPI - Multidisciplinary Digital Publishing Institute
2020
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| 005 | 20210211 | ||
| 020 | |a books978-3-03928-053-7 | ||
| 020 | |a 9783039280520 | ||
| 020 | |a 9783039280537 | ||
| 024 | 7 | |a 10.3390/books978-3-03928-053-7 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Bec, Krzysztof B. |4 auth | |
| 700 | 1 | |a Huck, Christian |4 auth | |
| 245 | 1 | 0 | |a Advances in Near Infrared Spectroscopy and Related Computational Methods |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
| 300 | |a 1 electronic resource (496 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a In the last few decades, near-infrared (NIR) spectroscopy has distinguished itself as one of the most rapidly advancing spectroscopic techniques. Mainly known as an analytical tool useful for sample characterization and content quantification, NIR spectroscopy is essential in various other fields, e.g. NIR imaging techniques in biophotonics, medical applications or used for characterization of food products. Its contribution in basic science and physical chemistry should be noted as well, e.g. in exploration of the nature of molecular vibrations or intermolecular interactions. One of the current development trends involves the miniaturization and simplification of instrumentation, creating prospects for the spread of NIR spectrometers at a consumer level in the form of smartphone attachments-a breakthrough not yet accomplished by any other analytical technique. A growing diversity in the related methods and applications has led to a dispersion of these contributions among disparate scientific communities. The aim of this Special Issue was to bring together the communities that may perceive NIR spectroscopy from different perspectives. It resulted in 30 contributions presenting the latest advances in the methodologies essential in near-infrared spectroscopy in a variety of applications. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
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| 653 | |a photonics | ||
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| 653 | |a image processing | ||
| 653 | |a colorectal cancer | ||
| 653 | |a test set validation | ||
| 653 | |a deep convolutional neural network | ||
| 653 | |a near-infrared fluorescence | ||
| 653 | |a classification | ||
| 653 | |a variety discrimination | ||
| 653 | |a near-infrared hyperspectral imaging | ||
| 653 | |a ensemble learning | ||
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| 653 | |a origin traceability | ||
| 653 | |a Paris polyphylla var. yunnanensis | ||
| 653 | |a Fourier transform mid-infrared spectroscopy | ||
| 653 | |a dry matter | ||
| 653 | |a Fourier transform infrared spectroscopy | ||
| 653 | |a hyperspectral imaging | ||
| 653 | |a FT-NIR spectroscopy | ||
| 653 | |a proximal sensing | ||
| 653 | |a perfusion measurements | ||
| 653 | |a near-infrared spectroscopy | ||
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| 653 | |a object-wise | ||
| 653 | |a water-mirror approach | ||
| 653 | |a Chrysanthemum | ||
| 653 | |a bootstrapping soft shrinkage | ||
| 653 | |a FTIR | ||
| 653 | |a PLS-R | ||
| 653 | |a multivariate data analysis | ||
| 653 | |a combination bands | ||
| 653 | |a binary dragonfly algorithm | ||
| 653 | |a geographical origin | ||
| 653 | |a Vitis vinifera L. | ||
| 653 | |a glucose | ||
| 653 | |a detection | ||
| 653 | |a di-(2-picolyl)amine | ||
| 653 | |a non-destructive sensor | ||
| 653 | |a splanchnic | ||
| 653 | |a adulteration | ||
| 653 | |a animal origin | ||
| 653 | |a melamine | ||
| 653 | |a artemether | ||
| 653 | |a MicroNIR™ | ||
| 653 | |a brain | ||
| 653 | |a fluorescent probes | ||
| 653 | |a Folin-Ciocalteu | ||
| 653 | |a SCiO | ||
| 653 | |a support vector machine | ||
| 653 | |a anharmonic quantum mechanical calculations | ||
| 653 | |a PLSR | ||
| 653 | |a Zn(II) | ||
| 653 | |a RMSEP | ||
| 653 | |a overtones | ||
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| 653 | |a FT-IR | ||
| 653 | |a partial least squares calibration | ||
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| 653 | |a auxiliary diagnosis | ||
| 653 | |a handheld near-infrared spectroscopy | ||
| 653 | |a precision viticulture | ||
| 653 | |a partial least squares | ||
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| 653 | |a near infrared | ||
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| 653 | |a quantum chemical calculation | ||
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| 653 | |a Trichosanthis Fructus | ||
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| 653 | |a Raman spectroscopy | ||
| 653 | |a NIR spectroscopy | ||
| 653 | |a calibration transfer | ||
| 653 | |a imaging | ||
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| 653 | |a lumefantrine | ||
| 653 | |a BRAF V600E mutation | ||
| 653 | |a wavelength selection | ||
| 653 | |a bone cancer | ||
| 653 | |a imaging visualization | ||
| 653 | |a near infrared spectroscopy | ||
| 653 | |a raisins | ||
| 653 | |a chemometric techniques | ||
| 653 | |a data fusion | ||
| 653 | |a prepared slices | ||
| 653 | |a Ewing sarcoma | ||
| 653 | |a biomonitoring | ||
| 653 | |a Rubus fructicosus | ||
| 653 | |a VIS/NIR hyperspectral imaging | ||
| 653 | |a combinations bands | ||
| 653 | |a quantitative analysis model | ||
| 653 | |a partial least square regression | ||
| 653 | |a DFT calculations | ||
| 653 | |a TreeBagger | ||
| 653 | |a antimalarial tablets | ||
| 653 | |a accelerated aging | ||
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| 653 | |a spectroscopy | ||
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| 653 | |a isotopic substitution | ||
| 653 | |a multivariate calibration | ||
| 653 | |a phytoextraction | ||
| 653 | |a Fourier-transform near-infrared spectroscopy | ||
| 653 | |a phenolics | ||
| 653 | |a deparaffinized | ||
| 653 | |a near-infrared (NIR) spectroscopy | ||
| 653 | |a SIMCA | ||
| 653 | |a counter propagation artificial neural network | ||
| 653 | |a fructose | ||
| 653 | |a PLS-DA | ||
| 653 | |a ultra-high performance liquid chromatography | ||
| 653 | |a aquaphotomics | ||
| 653 | |a support vector machine-discriminant analysis | ||
| 653 | |a hier-SVM | ||
| 653 | |a DNA | ||
| 653 | |a NIR | ||
| 653 | |a support vector machine model | ||
| 653 | |a API | ||
| 653 | |a principal component analysis | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://www.mdpi.com/books/pdfview/book/1917 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/40321 |7 0 |z DOAB: description of the publication |