THz spectral data analysis and components unmixing based on non-negative matrix factorization methods

2017 ◽  
Vol 177 ◽  
pp. 49-57 ◽  
Author(s):  
Yehao Ma ◽  
Xian Li ◽  
Pingjie Huang ◽  
Dibo Hou ◽  
Qiang Wang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Matrix Factorization ◽  
Factorization Methods ◽  
Spectral Data Analysis ◽  
Non Negative Matrix Factorization

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.

scholarly journals Supervised non-negative matrix factorization methods for MALDI imaging applications

2018 ◽  
Vol 35 (11) ◽  
pp. 1940-1947 ◽  
Author(s):  
Johannes Leuschner ◽  
Maximilian Schmidt ◽  
Pascal Fernsel ◽  
Delf Lachmund ◽  
Tobias Boskamp ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Maldi Imaging ◽  
Factorization Methods ◽  
Non Negative Matrix Factorization

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.

scholarly journals Application of non-negative matrix factorization to multispectral FLIM data analysis

2012 ◽  
Vol 3 (9) ◽  
pp. 2244 ◽  
Author(s):  
Paritosh Pande ◽  
Brian E. Applegate ◽  
Javier A. Jo
Keyword(s):  
Data Analysis ◽  
Matrix Factorization ◽  
Non Negative Matrix Factorization

scholarly journals Peak picking NMR spectral data using non-negative matrix factorization

2014 ◽  
Vol 15 (1) ◽  
pp. 46 ◽  
Author(s):  
Suhas Tikole ◽  
Victor Jaravine ◽  
Vladimir Rogov ◽  
Volker Dötsch ◽  
Peter Güntert
Keyword(s):  
Spectral Data ◽  
Matrix Factorization ◽  
Peak Picking ◽  
Non Negative Matrix Factorization
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