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Fluorescent materials are now a critical field of research due to their unique excitation and emission properties that can be tailored to specific fluorescence detection technologies. In this work, a procedure is described to approximate the emission spectral data of fluorescent materials of different types from their excitation spectral data using...
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... fluorescence phenomenon is absorption of light or other electromagnetic radiations by a substance and the emission of light in a longer wavelength (Fig. 1). The flu- orescence typically occurs by aromatic molecules and emission spectra of fluorescent materials are depending on their chemical structure. 1,2 Over the recent years, development of fluorescent mol- ecules have been progressing remarkably due to their potential applications in the field of optoelectronic devi- ces, such as ...
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Citations
... Their results indicated that the former is superior to the latter. They also predicted the emission spectra for fluorescence materials [12], which investigated the relationship between excitation and emission spectra for different fluorescence materials. In addition, it is also essential to have more discipline to reconstruct the spectra. ...
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Background
Today, artificial intelligence-based computational approach is facilitating multitasking and interdisciplinary analytical research. For example, the data gathered during an analytical research project such as spectral and chromatographic data can be used in predictive experimental research. The spectral and chromatographic information plays crucial role in pharmaceutical research, especially use of instrumental analytical approaches and it consume time, man power, and money. Hence, predictive analysis would be beneficial especially in resource-limited settings.
Main body
Computational approaches verify data at an early phase of study in research process. Several in silico techniques for predicting analyte’s spectral and chromatographic characteristics have recently been developed. Understanding of these tools may help researchers to accelerate their research with boosted confidence and prevent researchers from being misled by incorrect analytical data. In this communication, the properties of chemical compounds and its relation to chromatographic retention will be discussed, as well as the prediction technique for UV/IR/Raman/NMR spectrograms. This review looked at the reference data of chemical compounds to compare the predictive ability in silico tools along with the percentage error, limitations, and advantages.
Conclusion
The computational prediction of analytical characteristics offers a wide range of applications in academic research, bioanalytical method development, computational chemistry, analytical method development, data analysis approaches, material characterization, and validation process.
The best way to describe a color is to study its reflectance spectrum, which provide the most useful information. Different methods were purposed for reflectance spectra reconstruction from CIE tristimulus values such as principal components analysis. In this study, the training samples were first divided into 3, 6, 9, and 12 subgroups by creating a competitive neural network. To do that, L*a*b*, L*C*h or L*a*b*C*h were introduced to neural network as input elements. In order to investigate the performance of reflectance spectra reconstruction, the color difference and RMS between actual and reconstructed data were obtained. The reconstruction of reflectance spectra were improved by using a six or nine-neuron layer with L*a*b* input elements. © 2016 Wiley Periodicals, Inc. Col Res Appl, 2016
