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.

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.

In situ determination of dissolution kinetics of d-limonene in supercritical carbon dioxide by Raman spectroscopy

2017 ◽  
Vol 41 (22) ◽  
pp. 13929-13934 ◽  
Author(s):  
C. N. Buzolin ◽  
M. Espina Palanco ◽  
O. F. Wendt ◽  
I. Rodriguez-Meizoso
Keyword(s):  
Carbon Dioxide ◽  
Raman Spectroscopy ◽  
Supercritical Carbon Dioxide ◽  
Dissolution Kinetics ◽  
Kinetics Of ◽  
Supercritical Carbon

Dissolution kinetics is shown to be dependent on density of the medium and thus significant to consider when optimizing supercritical processes.

scholarly journals Correction: In situ determination of dissolution kinetics of d-limonene in supercritical carbon dioxide by Raman spectroscopy

2018 ◽  
Vol 42 (8) ◽  
pp. 6548-6548
Author(s):  
C. N. Buzolin ◽  
M. Espina Palanco ◽  
O. F. Wendt ◽  
I. Rodriguez-Meizoso
Keyword(s):  
Carbon Dioxide ◽  
Raman Spectroscopy ◽  
Supercritical Carbon Dioxide ◽  
Dissolution Kinetics ◽  
Kinetics Of ◽  
Supercritical Carbon

Correction for ‘In situ determination of dissolution kinetics of d-limonene in supercritical carbon dioxide by Raman spectroscopy’ by C. N. Buzolin et al., New J. Chem., 2017, 41, 13929–13934.

Method for In-Situ Determination of Concentration of Components in Hydrothermal Environments

2009 ◽  
Author(s):  
Rei Arai ◽  
Natsumi Iwasa ◽  
Naoki Nakatani ◽  
Tetsuo Yamazaki
Keyword(s):  
High Resolution ◽  
Principal Component Regression ◽  
Principal Component ◽  
Determination Method ◽  
Absorbance Spectrum ◽  
Massive Sulfides ◽  
The One ◽  
Hydrothermal Environments

In order to take measures against environmental impacts during the process of mining seafloor massive sulfides (SMS), it is important to measure some of the components of hydrothermal origin with high resolution in time and space on-site as well as to understand the ecosystem in the hydrothermal environments. The adoption of spectrophotometry for measuring concentrations of the components, such as H2S and Fe, is proposed in the study. It is necessary to extract the absorbance spectrum of each component from the one of seawater for the first step. Applying a determination method referred to as principal component regression (PCR), the fundamental solution is obtained.

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.

Multivariate Analysis Aided Surface-Enhanced Raman Spectroscopy (MVA-SERS) Multiplex Quantitative Detection of Trace Fentanyl in Illicit Drug Mixtures Using a Handheld Raman Spectrometer

2021 ◽  
pp. 000370282110329
Author(s):  
Ling Wang ◽  
Mario O. Vendrell-Dones ◽  
Chiara Deriu ◽  
Sevde Doğruer ◽  
Peter de B. Harrington ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Principal Component ◽  
Surface Enhanced Raman Spectroscopy ◽  
Quantitative Detection ◽  
Least Squares Regression ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Sers Detection ◽  
Current Screening

Recently there has been upsurge in reports that illicit seizures of cocaine and heroin have been adulterated with fentanyl. Surface-enhanced Raman spectroscopy (SERS) provides a useful alternative to current screening procedures that permits detection of trace levels of fentanyl in mixtures. Samples are solubilized and allowed to interact with aggregated colloidal nanostars to produce a rapid and sensitive assay. In this study, we present the quantitative determination of fentanyl in heroin and cocaine using SERS, using a point-and-shoot handheld Raman system. Our protocol is optimized to detect pure fentanyl down to 0.20 ± 0.06 ng/mL and can also distinguish pure cocaine and heroin at ng/mL levels. Multiplex analysis of mixtures is enabled by combining SERS detection with principal component analysis and super partial least squares regression discriminate analysis (SPLS-DA), which allow for the determination of fentanyl as low as 0.05% in simulated seized heroin and 0.10% in simulated seized cocaine samples.

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.

Nondestructive NIR and NIT Determination of Protein, Fat, and Water in Plastic-Wrapped, Homogenized Meat

1992 ◽  
Vol 46 (11) ◽  
pp. 1685-1694 ◽  
Author(s):  
Tomas Isaksson ◽  
Charles E. Miller ◽  
Tormod Næs
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Prediction Errors ◽  
Optimal Test ◽  
Meat Samples ◽  
Water Contents

In this work, the abilities of near-infrared diffuse reflectance (NIR) and transmittance (NIT) spectroscopy to noninvasively determine the protein, fat, and water contents of plastic-wrapped homogenized meat are evaluated. One hundred homogenized beef samples, ranging from 1 to 23% fat, wrapped in polyamide/polyethylene laminates, were used. Results of multivariate calibration and prediction for protein, fat, and water contents are presented. The optimal test set prediction errors (root mean square error of prediction, RMSEP), obtained with the use of the principal component regression method with NIR data, were 0.45, 0.29 and 0.50 weight % for protein, fat, and water, respectively, for plastic-wrapped meat (compared to 0.40, 0.28 and 0.45 wt % for unwrapped meat). The optimal prediction errors for the NIT method were 0.31, 0.52 and 0.42 wt % for protein, fat, and water, respectively, for plastic-wrapped meat samples (compared to 0.27, 0.38, and 0.37 wt % for unwrapped meat). We can conclude that the addition of the laminate only slightly reduced the abilities of the NIR and NIT method to predict protein, fat, and water contents in homogenized meat.

scholarly journals The Determination of Parkinson’s Drugs in Human Urine by Applying Chemometric Methods

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Guzide Pekcan Ertokus
Keyword(s):  
Urine Sample ◽  
Human Urine ◽  
Principal Component Regression ◽  
Principal Component ◽  
Healthy Person ◽  
Drug Analysis ◽  
Uv Spectrophotometry ◽  
Least Squares Regression ◽  
Chemometric Methods

The spectrophotometric-chemometric analysis of levodopa and carbidopa that are used for Parkinson’s disease was analyzed without any prior reservation. Parkinson’s drugs in the urine sample of a healthy person (never used drugs in his life) were analyzed at the same time spectrophotometrically. The chemometric methods used were partial least squares regression (PLS) and principal component regression (PCR). PLS and PCR were successfully applied as chemometric determination of levodopa and carbidopa in human urine samples. A concentration set including binary mixtures of levodopa and carbidopa in 15 different combinations was randomly prepared in acetate buffer (pH 3.5).). UV spectrophotometry is a relatively inexpensive, reliable, and less time-consuming method. Minitab program was used for absorbance and concentration values. The normalization values for each active substance were good (r2>0.9997). Additionally, experimental data were validated statistically. The results of the analyses of the results revealed high recoveries and low standard deviations. Hence, the results encouraged us to apply the method to drug analysis. The proposed methods are highly sensitive and precise, and therefore they were implemented for the determination of the active substances in the urine sample of a healthy person in triumph.

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