Comparing the performance of neural networks to well-established methods of multivariate data analysis: the classification of mass spectral data

1992 ◽  
Vol 344 (4-5) ◽  
pp. 186-189 ◽  
Author(s):  
H. Lohninger ◽  
F. Stancl
Keyword(s):  
Neural Networks ◽  
Data Analysis ◽  
Spectral Data ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Mass Spectral Data ◽  
Mass Spectral

Attribution of fentanyl analogue synthesis routes by multivariate data analysis of orthogonal mass spectral data

Talanta ◽  
2019 ◽  
Vol 203 ◽  
pp. 122-130 ◽  
Author(s):  
Lina Mörén ◽  
Johanna Qvarnström ◽  
Magnus Engqvist ◽  
Robin Afshin-Sander ◽  
Xiongyu Wu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Mass Spectral Data ◽  
Mass Spectral ◽  
Synthesis Routes

Classification of Edible Vegetable Oil Degradation Using Multivariate Data Analysis From Electrochemical Techniques

2021 ◽  
Author(s):  
Sandra Ramirez-Montes ◽  
Eva M. Santos ◽  
Carlos A. Galan-Vidal ◽  
J. Andres Tavizon-Pozos ◽  
Jose A. Rodriguez
Keyword(s):  
Data Analysis ◽  
Vegetable Oil ◽  
Multivariate Data Analysis ◽  
Electrochemical Techniques ◽  
Multivariate Data ◽  
Oil Degradation ◽  
Edible Vegetable Oil

Classification of iron-based inks by means of micro-Raman spectroscopy and multivariate data analysis

2013 ◽  
Vol 44 (9) ◽  
pp. 1299-1305 ◽  
Author(s):  
Giovanna Piantanida ◽  
Eva Menart ◽  
Marina Bicchieri ◽  
Matija Strlič
Keyword(s):  
Raman Spectroscopy ◽  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Micro Raman Spectroscopy ◽  
Iron Based

scholarly journals Spectral Characterization of Copper and Iron Sulfide Combustion: A Multivariate Data Analysis Approach for Mineral Identification on the Blend

Metals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 1017 ◽  
Author(s):  
Walter Díaz ◽  
Carlos Toro ◽  
Eduardo Balladares ◽  
Victor Parra ◽  
Pablo Coelho ◽  
...  
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Iron Sulfide ◽  
Multivariate Data ◽  
Copper Production ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Drop Tube

The pyrometallurgical processes for primary copper production have only off-line and time-demanding analytical techniques to characterize the in and out streams of the smelting and converting steps. Since these processes are highly exothermic, relevant process information could potentially be obtained from the visible and near-infrared radiation emitted to the environment. In this work, we apply spectral sensing and multivariate data analysis methodologies to identify and classify copper and iron sulfide minerals present in the blend from spectra measured during their combustion in a laboratory drop-tube setup, in which chemical reactions that take place in flash smelting furnaces can be reproduced. Controlled combustion experiments were conducted with two industrial concentrates and with high-grade mineral species as well, with a focus on pyrite and chalcopyrite. Exploratory analysis by means of Principal Component Analysis (PCA) applied on the spectral data depicted high correlation features among species with similar elemental compositions. Classification algorithms were tested on the spectral data, and a classification accuracy of 95.3% with a support vector machine (SVM) algorithm with a Gaussian kernel was achieved. The results obtained by the described procedures are shown to be very promising as a first step in the development of a predictive and analytical tool in search of fitting the current need for real-time control of pyrometallurgical processes.

scholarly journals A numerical method for mass spectral data analysis

2005 ◽  
Vol 18 (12) ◽  
pp. 1412-1417 ◽  
Author(s):  
Anthony J. Kearsley ◽  
William E. Wallace ◽  
Javier Bernal ◽  
Charles M. Guttman
Keyword(s):  
Data Analysis ◽  
Numerical Method ◽  
Spectral Data ◽  
Mass Spectral Data ◽  
Mass Spectral ◽  
Spectral Data Analysis

Modelling phase shifts, peak shifts and peak width variations in spectral data sets: its value in multivariate data analysis

2001 ◽  
Vol 432 (1) ◽  
pp. 113-124 ◽  
Author(s):  
H. Witjes ◽  
M. Pepers ◽  
W.J. Melssen ◽  
L.M.C. Buydens
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Phase Shifts ◽  
Data Sets ◽  
Peak Width

Multivariate data analysis in classification of must and wine from chemical measurements

2000 ◽  
Vol 212 (1) ◽  
pp. 100-107 ◽  
Author(s):  
G. González ◽  
E. M. Peña-Méndez
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Chemical Measurements
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