Supervised data analysis and reliability estimation with exemplary application for spectral data

2009 ◽  
Vol 72 (16-18) ◽  
pp. 3590-3601 ◽  
Author(s):  
Frank-Michael Schleif ◽  
Thomas Villmann ◽  
Matthias Ongyerth
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Reliability Estimation

Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine

2021 ◽  
Vol 87 (6) ◽  
pp. 1079-1086
Author(s):  
M. De Luca ◽  
G. Ioele ◽  
G. Ragno
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Drug Mixture ◽  
Potassium Canrenoate ◽  
Spectral Data Analysis ◽  
Complex Drug

Chemical components of the rhizomes of Drynaria fortunei (KUNZE) J. Sm. (polypodiaceae) in Vietnam

2011 ◽  
Vol 76 (9) ◽  
pp. 1133-1139 ◽  
Author(s):  
Pham Thi Nhat Trinh ◽  
Nguyen Cong Hao ◽  
Phan Thanh Thao ◽  
Le Tien Dung
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Phenolic Acid ◽  
Ethanol Extract ◽  
Chemical Components ◽  
Spectroscopic Methods ◽  
Phenylpropanoid Glycoside ◽  
Spectral Data Analysis ◽  
First Time

From the ethanol extract of Drynaria fortunei (KUNZE) J. Sm., a new phenylpropanoid glycoside, fortunamide (1), was isolated and characterized by spectroscopic methods. Together with a new glycoside, 9 known compounds, including three curcuminoids (2–4), two isoprenylated flavonoids (5, 6), two flavonoids (7, 8), one monoterpenoid (9) and one phenolic acid (10) were isolated and identified by spectral data analysis from the rhizomes of Drynaria fortunei (KUNZE) J. Sm. Eight of them were isolated from Drynaria fortunei (KUNZE) J. Sm. for the first time.

scholarly journals Quantitative Spectral Data Analysis Using Extreme Learning Machines Algorithm Incorporated with PCA

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 18
Author(s):  
Michael Li ◽  
Santoso Wibowo ◽  
Wei Li ◽  
Lily D. Li
Keyword(s):  
Data Analysis ◽  
Conceptual Framework ◽  
Spectral Data ◽  
Learning Algorithm ◽  
Structural Parameters ◽  
Inverse Function ◽  
Principal Component ◽  
Learning Task ◽  
Classification Problems ◽  
Spectral Data Analysis

Extreme learning machine (ELM) is a popular randomization-based learning algorithm that provides a fast solution for many regression and classification problems. In this article, we present a method based on ELM for solving the spectral data analysis problem, which essentially is a class of inverse problems. It requires determining the structural parameters of a physical sample from the given spectroscopic curves. We proposed that the unknown target inverse function is approximated by an ELM through adding a linear neuron to correct the localized effect aroused by Gaussian basis functions. Unlike the conventional methods involving intensive numerical computations, under the new conceptual framework, the task of performing spectral data analysis becomes a learning task from data. As spectral data are typical high-dimensional data, the dimensionality reduction technique of principal component analysis (PCA) is applied to reduce the dimension of the dataset to ensure convergence. The proposed conceptual framework is illustrated using a set of simulated Rutherford backscattering spectra. The results have shown the proposed method can achieve prediction inaccuracies of less than 1%, which outperform the predictions from the multi-layer perceptron and numerical-based techniques. The presented method could be implemented as application software for real-time spectral data analysis by integrating it into a spectroscopic data collection system.

Coumarins and a Naphthyl Labdanoate Diarabinoside from the Fruits of Peucedanum grande C. B. Clarke

2012 ◽  
Vol 67 (11-12) ◽  
pp. 580-586 ◽  
Author(s):  
Mohammad Aslam ◽  
Mohammed Ali ◽  
Rameshwar Dayal ◽  
Kalim Javed
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Chemical Reactions ◽  
Wistar Rats ◽  
Methanolic Extract ◽  
Nephroprotective Activity ◽  
Coumarin 1 ◽  
Spectral Data Analysis

Phytochemical investigations of the methanolic extract of the fruits of Peucedanum grande C. B. Clarke (Apiaceae) led to the identification of three coumarins and a naphthyl labdanoate diarabinoside characterized as 5-hydroxy-6-isopranyl coumarin (1), 5,6-furanocoumarin (2), 7-methoxy-5,6-furanocoumarin (3), and labdanyl-3α-ol-18-(3’’’-methoxy-2’’’- naphthyl-oate)-3α-L-arabinofuranosyl-(2’→1’’)-α-L-arabinofuranoside (4). The structures of these compounds were identified on the basis of spectral data analysis and chemical reactions. The methanolic extract and 4 showed nephroprotective activity against gentamicininduced nephrotoxicity in Wistar rats.

Detecting Levels and Innovative Applications for the Detection of Aromatic Compounds Using Multivariate Curve Analysis and Spectroscopy Data

2021 ◽  
Vol 19 (3) ◽  
pp. 46-55
Author(s):  
Al-Ibadi Zeyad ◽  
Muthana Alboedam ◽  
Ilya Katanov ◽  
Al-Zubaidi Sura
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Aromatic Compounds ◽  
Curve Analysis ◽  
Spectral Bands ◽  
Technological Developments ◽  
Benzene Toluene ◽  
Technical Solutions ◽  
Spectral Mixture ◽  
Specific Degree

Rapid technological developments and the increasing complexity of matrix work are increasing interest in finding robust technical solutions and fast and assertive data analysis. The data can be filtered to obtain spectral data in more useful spectral bands, the developed algorithms allow quantification of the spectral mixture, and it can be measured with or without titration. We focused in this study is on samples within the range of wavelengths (459,466,462,464 nm), different strategies are utilized to check Aromatic compounds. These enable us to survey a specific degree of ordinary compounds, much the same as benzene, toluene and xylene, and so on. In this study compares data analysis, received from sensor between A polynomial approximation PolyFit method and Processors Gases method, and compares the results of each, a method with the multivariate curve resolve - alternate least squares (MCR - ALS) regression strategy of analyzing spectral data of information with a different concentration. Additionally, adequately moo deviations of the anticipated values were accomplished from the genuine values, the standard deviation. And, the amended range was normalized to their region and to some degrees smooth. The autofluorescence establishment was subtracted, for the pure extend investigation, by utilizing logical approaches.

Spectral data analysis approaches for improved provenance classification

2015 ◽  
Author(s):  
Kellen J. Sorauf ◽  
Amy J. R. Bauer ◽  
Andrzej W. Miziolek ◽  
Frank C. De Lucia
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Spectral Data Analysis
Sign in / Sign up

Export Citation Format

Share Document