Emission Spectral Reconstruction Based on Principal Component Analysis Applied to Fluorescence Full-Color Prints

2019 ◽  
Vol 11 (10) ◽  
pp. 1349-1356
Author(s):  
Mi Shang ◽  
Ling Yang ◽  
Danfei Liu ◽  
Zijie Cui ◽  
Yunfei Zhong
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Emission ◽  
Emission Spectra ◽  
Principal Component ◽  
Color Difference ◽  
Component Analysis ◽  
Fluorescence Emission Spectrum ◽  
Color Science ◽  
Full Color ◽  
Spectral Reconstruction

Color reproduction of fluorescent full-color prints depends on many factors, such as preparation of luminescent inks, ratio of luminescent inks to each other, printing technology and so on. In order to make color expression more abundant on fluorescent full-color prints, reconstruction of fluorescence emission spectrum is particularly significant. As opposed to custom methods, principal component analysis has been applied to color science permanently. The method was applied to emission spectral reconstruction in this work and the up-conversion luminescent inks were selected. 336 samples were composed of single ink halftone at a quarter, half, 75%, and 100% surface coverages. The samples were firstly superimposed in one ink and two inks on the blank paper. Moreover, their emission spectral was measured and the procedure for principal component analysis was also performed. The emission spectral was reconstructed by using 1 nm interval from 351 nm to 748 nm. Ultimately, the accuracy of recovery spectral was evaluated through CIEDE2000 color difference evaluation. The obtained results indicated that principal component analysis can be used to reconstruct emission spectra. Besides, the method can also be used for color estimation between different printing materials.

Are the classical markers sufficient to describe uremic solute accumulation in dialyzed patients? Hippurates reconsidered.

1988 ◽  
Vol 34 (6) ◽  
pp. 1022-1029 ◽  
Author(s):  
A C Schoots ◽  
J B Dijkstra ◽  
S M Ringoir ◽  
R Vanholder ◽  
C A Cramers
Keyword(s):  
Principal Component Analysis ◽  
Renal Function ◽  
Patient Group ◽  
Fluorescence Emission ◽  
Principal Component ◽  
Component Analysis ◽  
Residual Renal Function ◽  
Tubular Secretion ◽  
Multivariate Statistical ◽  
Renal Tubular

Abstract Interdependencies of accumulated solutes, analyzed by liquid chromatography in dialyzed and non-dialyzed patients, were studied by multivariate statistical analysis. In principal component analysis, three principal components (PC1-PC3) were retained from the data on 22 accumulated compounds in dialyzed patients, whereas only one principal component was retained from analogous data of a non-dialyzed patient group. PC1 in the dialyzed patient group comprises concentrations of hippuric acid, p-hydroxyhippuric acid, tryptophan, and five unidentified fluorescent solutes in serum. Concentrations of the classical markers urea, uric acid, creatinine, and phosphate were closely related to PC2 in these patients. Indoleacetic acid and two unidentified fluorescent compounds constitute PC3. The compounds associated with the groups found by principal component analysis may be characterized by chemical structure and by the mechanism of their excretion via the remaining nephrons of dialyzed patients. In the non-dialyzed group, most of the solutes could be described by a single PC. This PC and PC1 from the dialyzed group correlated significantly with residual renal function, and with total ultraviolet absorbance and total fluorescence emission. The data suggest that it is of value to introduce a marker of uremic solute retention in addition to urea, to account for renal-function-related "organic-acid-like" compounds that are excreted by renal tubular secretion in dialyzed patients. The hippurates may serve this purpose.

Multi-channel Raman Spectral Reconstruction Based on Gaussian Kernel Principal Component Analysis

2020 ◽  
Vol 49 (3) ◽  
pp. 330001-330001
Author(s):  
王昕 Xin WANG ◽  
康哲铭 Zhe-ming KANG ◽  
刘龙 Long LIU ◽  
范贤光 Xian-guang FAN
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Gaussian Kernel ◽  
Spectral Reconstruction ◽  
Raman Spectral

scholarly journals Characterization of the Lateral Distribution of Fluorescent Lipid in Binary-Constituent Lipid Monolayers by Principal Component Analysis

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
István P. Sugár ◽  
Xiuhong Zhai ◽  
Ivan A. Boldyrev ◽  
Julian G. Molotkovsky ◽  
Howard L. Brockman ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Linear Combination ◽  
Emission Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
Lipid Monolayer ◽  
Lateral Organization ◽  
Binary Constituent ◽  
Fluorescent Lipid

Lipid lateral organization in binary-constituent monolayers consisting of fluorescent and nonfluorescent lipids has been investigated by acquiring multiple emission spectra during measurement of each force-area isotherm. The emission spectra reflect BODIPY-labeled lipid surface concentration and lateral mixing with different nonfluorescent lipid species. Using principal component analysis (PCA) each spectrum could be approximated as the linear combination of only two principal vectors. One point on a plane could be associated with each spectrum, where the coordinates of the point are the coefficients of the linear combination. Points belonging to the same lipid constituents and experimental conditions form a curve on the plane, where each point belongs to a different mole fraction. The location and shape of the curve reflects the lateral organization of the fluorescent lipid mixed with a specific nonfluorescent lipid. The method provides massive data compression that preserves and emphasizes key information pertaining to lipid distribution in different lipid monolayer phases. Collectively, the capacity of PCA for handling large spectral data sets, the nanoscale resolution afforded by the fluorescence signal, and the inherent versatility of monolayers for characterization of lipid lateral interactions enable significantly enhanced resolution of lipid lateral organizational changes induced by different lipid compositions.

Research on Reconstruction of Spectral Reflectance Based on Principal Component Analysis

2012 ◽  
Vol 262 ◽  
pp. 53-58 ◽  
Author(s):  
Yan Zhang ◽  
Shi Sheng Zhou
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Spectral Reflectance ◽  
Principal Component ◽  
Component Analysis ◽  
Spectral Reconstruction ◽  
Training Samples ◽  
Variance Contribution ◽  
Color Sample ◽  
Different Color

Traditional color reproduction technology based on the Metamerism principle, the disadvantage is that different observer condition leads to different color appearance.To fulfill the color consistency, the spectrum reflectance of the object color sample need to be reconstructed. The principal component analysis makes use of the linear combination of a few principal components to reconstruct the spectral reflectance of sample. This paper analyzes the 31*31 matrix of Munsell spectral data by the principle component analyze method and achieves the principal component for spectrum reflectance. The numbers of principal components are identified as six by discussing the variance contribution rate. Spectral reconstruction of four Munsell testing samples makes use of first six principal components, which has met the accuracy requirements. Research shows that the reconstruction of spectral accuracy decreased when training samples and testing samples belong to the different database.

Sign in / Sign up

Export Citation Format

Share Document