Use of Multivariate Analysis to Determine Temperature from Low-Resolution Infrared Spectra of Carbon Dioxide

2000 ◽  
Vol 54 (2) ◽  
pp. 197-201 ◽  
Author(s):  
Michael P. Szczepanski ◽  
Augustus W. Fountain
Keyword(s):  
Carbon Dioxide ◽  
Chemical Species ◽  
Principal Component Regression ◽  
Principal Component ◽  
Industrial Applications ◽  
Low Resolution ◽  
Band Shape ◽  
Temperature Spectra ◽  
Plume Temperature ◽  
Industrial Monitoring

The remote optical monitoring of gaseous contaminants is important for both military and industrial applications. An important parameter for quantifying chemical species and for predicting plume dynamics is the temperature. While in some industrial monitoring situations it may be practical to independently measure the temperature of stack emissions, for compliance monitoring and military chemical reconnaissance a remote optical means of estimating gas plume temperature is required. It was noticed that the band shape of low-resolution spectra of carbon dioxide in equilibrium with an exhaust plume was very sensitive to temperature. Spectra of carbon dioxide were acquired under controlled laboratory conditions in 5° increments from 20 to 200 °C. Various multivariate models were used to predict the temperature. It was found that partial least-squares (PLS) was unable to effectively model the simultaneous changes in amplitude and bandwidth with temperature. However, principal component regression (PCR) was found to be well correlated with temperature and allowed cross-validated prediction within 4% error.

Determination of Chemical Species in MDEA – Carbon Dioxide – Water System by Raman Spectroscopy

2015 ◽  
Vol 1113 ◽  
pp. 261-266 ◽  
Author(s):  
Humbul Suleman ◽  
Muhammad Zubair Shahid ◽  
Abdulhalim Shah Maulud ◽  
Zakaria Man ◽  
Mohammad Azmi Bustam Khalil
Keyword(s):  
Carbon Dioxide ◽  
Raman Spectroscopy ◽  
Chemical Speciation ◽  
Water System ◽  
Chemical Species ◽  
Principal Component Regression ◽  
Principal Component ◽  
Carbon Dioxide Absorption

Alkanolamines based carbon dioxide absorption from flue gases remains the most industrially implemented technique. The effective design of absorbers and associated equipment requires robust thermodynamic and kinetic models thus, instigating research efforts in chemical speciation and characterization of CO2loaded alkanolamine solutions. In this study, the potential of Raman spectroscopy has been investigated to determine the in situ chemical speciation in MDEA – CO2– Water system. The Raman spectra have been fitted to thermodynamic values using principal component regression. Results are in good agreement for carbonate, bicarbonate, MDEA and protonated MDEA chemical species.

Monitoring of Chemical Speciation of DEA – CO2 – Water System by Raman Spectroscopy

2015 ◽  
Vol 1113 ◽  
pp. 358-363 ◽  
Author(s):  
Muhammad Zubair Shahid ◽  
Humbul Suleman ◽  
Adulhalim Shah Maulud ◽  
Mohammad Azmi Bustam Khalil ◽  
Zakaria Man
Keyword(s):  
Raman Spectroscopy ◽  
Water System ◽  
Chemical Species ◽  
Principal Component Regression ◽  
Principal Component ◽  
Carbon Dioxide Separation ◽  
Key Factor ◽  
Kinetics Of ◽  
Respective Characteristic ◽  
Good Agreement

Carbon dioxide separation has gained immense importance since its detrimental effects towards our environment has been realized. Commercially, CO2has been captured by absorption in alkanolamines such as diethanolamine (DEA), since many years. The thermodynamics and kinetics of the process is a key factor towards its efficiency and significantly depends on its qualitative and quantitative speciation. In this work, the analysis of speciation for CO2loaded aqueous DEA has been performed by Raman spectroscopy. Experimentally determined CO2loading data and modified Kent Eisenberg equation was used to quantify the chemical species present. The speciation results were fitted with the respective characteristic Raman peaks of (CO3-, HCO3-, DEACOO-, DEA, DEA+, CO2) by Principal Component Regression (PCR). The fitted results showed good agreement with thermodynamically predicted chemical species.

Nonlinear Optimization Algorithm for Multivariate Optical Element Design

2002 ◽  
Vol 56 (4) ◽  
pp. 477-487 ◽  
Author(s):  
Olusola O. Soyemi ◽  
Frederick G. Haibach ◽  
Paul J. Gemperline ◽  
Michael L. Myrick
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Algorithm ◽  
Optical Computing ◽  
Chemical Species ◽  
Principal Component Regression ◽  
Principal Component ◽  
Optical Element ◽  
Optical Elements ◽  
Design Algorithm ◽  
Nonlinear Optimization Algorithm

A new algorithm for the design of optical computing filters for chemical analysis, otherwise known as multivariate optical elements (MOEs), is described. The approach is based on the nonlinear optimization of the MOE layer thicknesses to minimize the standard error in sample prediction for the chemical species of interest using a modified version of the Gauss–Newton nonlinear optimization algorithm. The design algorithm can either be initialized with random layer thicknesses or with layer thicknesses derived from spectral matching of a multivariate principal component regression (PCR) vector for the constituent of interest. The algorithm has been successfully tested by using it to design various MOEs for the determination of Bismarck Brown dye in a binary mixture of Crystal Violet and Bismarck Brown.

Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Khairunnisa Khairunnisa ◽  
Rizka Pitri ◽  
Victor P Butar-Butar ◽  
Agus M Soleh
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Principal Component Regression ◽  
Circulation Model ◽  
Principal Component ◽  
Meteorological Station ◽  
Grid Size ◽  
Partial Least Square ◽  
Least Square ◽  
Output Data

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

scholarly journals Application of Derivative, Derivative Ratio, and Multivariate Spectral Analysis and Thin-Layer Chomatography-Densitometry for Determination of a Ternary Mixture Containing Drotaverine Hydrochloride, Caffeine, and Paracetamol

2007 ◽  
Vol 90 (2) ◽  
pp. 391-404 ◽  
Author(s):  
Fadia H Metwally ◽  
Yasser S El-Saharty ◽  
Mohamed Refaat ◽  
Sonia Z El-Khateeb
Keyword(s):  
Thin Layer ◽  
Least Squares ◽  
Ternary Mixture ◽  
Principal Component Regression ◽  
Pharmaceutical Preparations ◽  
Principal Component ◽  
Standard Addition ◽  
Drotaverine Hydrochloride ◽  
Assay Precision

Abstract New selective, precise, and accurate methods are described for the determination of a ternary mixture containing drotaverine hydrochloride (I), caffeine (II), and paracetamol (III). The first method uses the first (D1) and third (D3) derivative spectrophotometry at 331 and 315 nm for the determination of (I) and (III), respectively, without interference from (II). The second method depends on the simultaneous use of the first derivative of the ratio spectra (DD1) with measurement at 312.4 nm for determination of (I) using the spectrum of 40 μg/mL (III) as a divisor or measurement at 286.4 and 304 nm after using the spectrum of 4 μg/mL (I) as a divisor for the determination of (II) and (III), respectively. In the third method, the predictive abilities of the classical least-squares, principal component regression, and partial least-squares were examined for the simultaneous determination of the ternary mixture. The last method depends on thin-layer chromatography-densitometry after separation of the mixture on silica gel plates using ethyl acetatechloroformmethanol (16 + 3 + 1, v/v/v) as the mobile phase. The spots were scanned at 281, 272, and 248 nm for the determination of (I), (II), and (III), respectively. Regression analysis showed good correlation in the selected ranges with excellent percentage recoveries. The chemical variables affecting the analytical performance of the methodology were studied and optimized. The methods showed no significant interferences from excipients. Intraday and interday assay precision and accuracy values were within regulatory limits. The suggested procedures were checked using laboratory-prepared mixtures and were successfully applied for the analysis of their pharmaceutical preparations. The validity of the proposed methods was further assessed by applying a standard addition technique. The results obtained by applying the proposed methods were statistically analyzed and compared with those obtained by the manufacturer's method.

scholarly journals Principal component regression in GAMLSS applied to Greek–German government bond yield spreads

2021 ◽  
pp. 1471082X2110229
Author(s):  
D. Stasinopoulos Mikis ◽  
A. Rigby Robert ◽  
Georgikopoulos Nikolaos ◽  
De Bastiani Fernanda
Keyword(s):  
Additive Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Complex Nature ◽  
Government Bond ◽  
German Government ◽  
Yield Spreads ◽  
Explanatory Variables ◽  
Interaction Terms ◽  
Bond Yield

A solution to the problem of having to deal with a large number of interrelated explanatory variables within a generalized additive model for location, scale and shape (GAMLSS) is given here using as an example the Greek–German government bond yield spreads from 25 April 2005 to 31 March 2010. Those were turbulent financial years, and in order to capture the spreads behaviour, a model has to be able to deal with the complex nature of the financial indicators used to predict the spreads. Fitting a model, using principal components regression of both main and first order interaction terms, for all the parameters of the assumed distribution of the response variable seems to produce promising results.

scholarly journals Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form

2021 ◽  
Vol 19 (1) ◽  
pp. 205-213
Author(s):  
Hany W. Darwish ◽  
Abdulrahman A. Al Majed ◽  
Ibrahim A. Al-Suwaidan ◽  
Ibrahim A. Darwish ◽  
Ahmed H. Bakheit ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simultaneous Determination ◽  
Predictive Power ◽  
Principal Component Regression ◽  
Selection Procedure ◽  
Principal Component ◽  
Chemometric Methods ◽  
Full Spectrum ◽  
Azilsartan Medoxomil

Abstract Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted.

scholarly journals A Study of Overripe Seed Byproducts from Sun-Dried Grapes by Dispersive Raman Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 483
Author(s):  
Francisco J. Rivero ◽  
Leonardo Ciaccheri ◽  
M. Lourdes González-Miret ◽  
Francisco J. Rodríguez-Pulido ◽  
Andrea A. Mencaglia ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Spectroscopic Data ◽  
Principal Component ◽  
Industrial Applications ◽  
Phenolic Composition ◽  
Grape Seeds ◽  
Detection Scheme ◽  
Sun Drying ◽  
Dried Grapes ◽  
Excitation Laser

Overripe seeds from sun-dried grapes submitted to postharvest dehydration constitute a scarcely investigated class of vinification byproduct with limited reports on their phenolic composition and industrial applications. In this study, Raman spectroscopy was applied to characterize a selection of overripe seed byproducts from different white grapes (cv. Moscatel, cv. Pedro Ximénez and cv. Zalema) submitted to postharvest sun drying. The Raman measurements were taken using a 1064 nm excitation laser in order to mitigate the fluorescent effect and the dispersive detection scheme allowed a compactness of the optical system. Spectroscopic data were processed by a principal component analysis to reduce the dimensionality and partner recognition. The evolution of the Raman spectrum during the overripening process was compared with the phenolic composition of grape seeds, which was determined by rapid resolution liquid chromatography/mass spectrometry (RRLC/MS). A multivariate processing of the spectroscopic data allowed the classification of overripe seeds according to the grape variety and the monitoring of stages of the postharvest sun drying process.

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