scholarly journals Development of Advanced Chemometric-Assisted Spectrophotometric Methods for the Determination of Cromolyn Sodium and Its Alkaline Degradation Products

Molecules ◽  
2020 ◽  
Vol 25 (24) ◽  
pp. 5953
Author(s):  
Noha M. El Zahar ◽  
Mariam M. Tadros ◽  
Bassam M. Ayoub
Keyword(s):  
Degradation Products ◽  
Principal Component Regression ◽  
Principal Component ◽  
Pharmaceutical Products ◽  
Partial Least Square ◽  
Cromolyn Sodium ◽  
Least Square ◽  
Alkaline Degradation ◽  
Stability Indicating

Advanced and sensitive spectrophotometric and chemometric analytical methods were successfully established for the stability-indicating assay of cromolyn sodium (CS) and its alkaline degradation products (Deg1 and Deg2). Spectrophotometric mean centering ratio spectra method (MCR) and chemometric methods, including principal component regression (PCR) and partial least square (PLS-2) methods, were applied. Peak amplitudes after MCR at 367.8 nm, 373.8 nm and 310.6 nm were used within linear concentration ranges of 2–40 µg mL−1, 5–40 µg mL−1 and 10–100 µg mL−1 for CS, Deg1 and Deg2, respectively. For PCR and PLS-2 models, a calibration set of eighteen mixtures and a validation set of seven mixtures were built for the simultaneous determination of CS, Deg1 and Deg2 in the ranges of 5–13 µg mL−1, 8–16 µg mL−1, and 10–30 µg mL−1, respectively. The authors emphasize the importance of a stability-indicating strategy for the investigation of pharmaceutical products.

scholarly journals Determination of acid, peroxide, and saponification value in patin fish oil by FTIR spectroscopy combined with chemometrics

Food Research ◽  
2020 ◽  
Vol 4 (5) ◽  
pp. 1758-1766
Author(s):  
A.R. Putri ◽  
A. Rohman ◽  
W. Setyaningsih ◽  
S. Riyanto
Keyword(s):  
Fish Oil ◽  
Ftir Spectroscopy ◽  
Principal Component Regression ◽  
Principal Component ◽  
Acid Value ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Saponification Value

Simple, rapid, and reproducible methods for determining the acid value (AV), peroxide value (PV), and saponification value (SV) of patin fish oil (PFO) were developed using Fourier Transform Infrared (FTIR) spectroscopy combined with chemometrics of Principal Component Regression (PCR) and Partial Least Square (PLS). The relationship between actual values was determined using AOCS method and predicted value was determined with FTIR spectroscopy and chemometrics. From the validation work, the high coefficient of determination (R2 ) reached up to > 0.99. This study concluded that by means of FTIR spectra that combined with PCR and PLS technique can be used to determine AV, PV, and SV of PFO.

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 Different spectrophotometric and TLC-densitometric methods for determination of olmesartan medoxomil and hydrochlorothiazide and their degradation products

2018 ◽  
Vol 9 (4) ◽  
pp. 400-407 ◽  
Author(s):  
Selvia Maged Adly ◽  
Maha Mohamed Abdelrahman ◽  
Nada Sayed Abdelwahab ◽  
Nourudin Wageh Ali
Keyword(s):  
Multivariate Calibration ◽  
Degradation Products ◽  
External Validation ◽  
Olmesartan Medoxomil ◽  
Partial Least Square ◽  
Least Square ◽  
Pharmaceutical Dosage Form ◽  
Calibration Models ◽  
Significant Difference

In this work, multivariate calibration models and TLC-densitometric methods have been developed and validated for quantitative determination of olmesartan medoxomil (OLM) and hydrochlorothiazide (HCZ) in presence of their degradation products, olmesartan (OL) and salamide (SAL), respectively. In the first method, multivariate calibration models including principal component regression (PCR) and partial least square (PLS) were applied. The wavelength range 210-343 nm was used and data was auto-scaled and mean centered as pre-processing steps for PCR and PLS models, respectively. These models were tested by application to external validation set with mean percentage recoveries 99.78, 100.01, 100.41 and 100.46% for OLM, HCZ, OL and SAL, respectively, for PLS model and also, 100.22, 100.40, 102.25 and 100.13% for them, respectively, for PCR model. The second method is TLC-densitometry at which the chromatographic separation was carried out using silica gel 60F254 TLC plates and the developing system consisted of a mixture of ethyl acetate:chloroform:methanol: formic acid:tri-ethylamine (60:40:4:4:1, by volume) with UV-scanning at 254 nm. The developed methods were successfully applied for determination of OLM and HCZ in their pharmaceutical dosage form. Also, statistical comparison was made between the developed methods and the reported method using student’s-t test and F-test and results showed that there was no significant difference between them concerning both accuracy and precision.

scholarly journals Application of FTIR Spectroscopy and HPLC Combined with Multivariate Calibration for Analysis of Xanthones in Mangosteen Extracts

2020 ◽  
Vol 88 (3) ◽  
pp. 35
Author(s):  
Endjang Prebawa Tejamukti ◽  
Widiastuti Setyaningsih ◽  
Irnawati ◽  
Budiman Yasir ◽  
Gemini Alam ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Ethanolic Extract ◽  
Ftir Spectra ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Garcinia Mangostana

Mangosteen, or Garcinia mangostana L., has merged as an emerging fruit to be investigated due to its active compounds, especially xanthone derivatives such as α -mangostin (AM), γ-mangostin (GM), and gartanin (GT). These compounds had been reported to exert some pharmacological activities, such as antioxidant and anti-inflammatory, therefore, the development of an analytical method capable of quantifying these compounds should be investigated. The aim of this study was to determine the correlation between FTIR spectra and HPLC chromatogram, combined with chemometrics for quantitative analysis of ethanolic extract of mangosteen. The ethanolic extract of mangosteen pericarp was prepared using the maceration technique, and the obtained extract was subjected to measurement using instruments of FTIR spectrophotometer at wavenumbers of 4000–650 cm−1 and HPLC, using a PDA detector at 281 nm. The data acquired were subjected to chemometrics analysis of partial least square (PLS) and principal component regression (PCR). The result showed that the wavenumber regions of 3700–2700 cm−1 offered a reliable method for quantitative analysis of GM with coefficient of determination (R2) 0.9573 in calibration and 0.8134 in validation models, along with RMSEC value of 0.0487% and RMSEP value 0.120%. FTIR spectra using the second derivatives at wavenumber 3700–663 cm−1 with coefficient of determination (R2) >0.99 in calibration and validation models, along with the lowest RMSEC value and RMSEP value, were used for quantitative analysis of GT and AM, respectively. It can be concluded that FTIR spectra combined with multivariate are accurate and precise for the analysis of xanthones.

scholarly journals COMPARATIVE STUDY OF FATTY ACID PROFILES IN PATIN (PANGASIUS MICRONEMUS) AND GABUS (CHANNA STRIATA) FISH OIL AND ITS AUTHENTICATION USING FTIR SPECTROSCOPY COMBINED WITH CHEMOMETRICS

2019 ◽  
pp. 55-60
Author(s):  
ANGGITA ROSIANA PUTRI ◽  
ABDUL ROHMAN ◽  
SUGENG RIYANTO
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Fish Oil ◽  
Ftir Spectroscopy ◽  
Principal Component Regression ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Channa Striata

Objective: The aims of this research were to analyse the fatty acids contained in Patin (Pangasius micronemus) and Gabus (Channa striata) fish oils also its authentication using FTIR spectroscopy combined with chemometrics. Methods: Patin fish oil (PFO) was extracted from patin flesh using the maceration method with petroleum benzene as the solvent, while gabus fish oil (GFO) was purchased from the market in Yogyakarta. The analysis of fatty acid was done using gas chromatography–flame ionization detector (GC-FID). The authentication was performed using FTIR spectrophotometer and chemometrics methods. Principal component analysis (PCA) was used to determine the proximity of oils based on the characteristic similarity. The quantification of adulterated PFO was performed using multivariate calibrations, partial least square (PLS) and principal component regression (PCR). The classification between authentic oils and those adulterated used discriminant analysis (DA). Results: The level of saturated and polyunsaturated fatty acids in PFO is higher than in GFO. The PLS and PCR methods using the second derivative spectra at wavenumbers of 666–3050 cm-1 offered the highest values of coefficient of determination (R2) and lowest root means the square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). Conclusion: The PCA method was successfully used to determine the proximity of oils. Among oils studied, PFO has a similarity fatty acid composition with GFO. The DA method was able to screen pure PFO from adulterated PFO without any misclassification reported. FTIR spectroscopy in combined with chemometrics can be used for authentication and quantification.

scholarly journals PENERAPAN KEMOMETRIK PADA METODE CITRA DIGITAL UNTUK ANALISIS ION MERKURI (II) DENGAN INDIKATOR NANOPARTIKEL PERAK

Alotrop ◽  
2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Angga Aprian Dinata ◽  
M. Lutfi Firdaus ◽  
Rina Elvia
Keyword(s):  
Quantitative Analysis ◽  
Digital Image ◽  
Limit Of Detection ◽  
Principal Component Regression ◽  
Image Data ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Image Method ◽  
Rgb Values

Digital image method in quantitative analysis usually uses one of the RGB primary color components (Red, Green, Blue), so that not all digital image data can be extracted. Then needed a method that can render the whole RGB values as variables in quantitative analysis are known as chemometric. This research aims to know the influence of the application of chemometric against the sensitivity of the digital image. Chemometry method used is the Principal Component Regression (PCR) and Partial Least Square (PLS) using Unscramber X software from Camo software, USA.. This method is applied for the quantitative analysis of Mercury (II) ion with silver nanoparticles (NPP) immobilization on filter paper indicator. The research results showed that chemometric has a good influence against the level of the Limit of Detection (LOD) of the digital image, where the level of LOD with chemometric application of the Principal Component Regression (PCR) is 0.4311 ppb, and Partial Least Square (PLS) is  0.4310 ppb smaller than without the application of chemometric Single Linear Regression (SLR) at 0.837 ppb. 

scholarly journals Simultaneous Determination of Diloxanide Furoate and Metronidazole in Presence of Diloxanide Furoate Degradation Products

2011 ◽  
Vol 94 (5) ◽  
pp. 1427-1439
Author(s):  
Samah S Abbas ◽  
Nour E Wagieh ◽  
Mohamed Abdelkawy ◽  
Maha M Abdelrahman
Keyword(s):  
Simultaneous Determination ◽  
Mobile Phase ◽  
Orthophosphoric Acid ◽  
Degradation Products ◽  
Potassium Phosphate ◽  
Principal Component Regression ◽  
Principal Component ◽  
Pharmaceutical Formulations ◽  
Dibasic Potassium Phosphate

Abstract Three methods are presented for the simultaneous determination of diloxanide furoate (DLX) and metronidazole (MTR), used for their antiprotozoal and antiamoebic effect, in the presence of DLX alkaline degradates and in pharmaceutical formulations, without previous separation. The first method is chemometric-assisted spectrophotometry, in which principal component regression and partial least squares were applied. These two approaches were successfully applied to quantify each drug in the mixture using the information included in the absorption spectra in the range of 225–320 nm. The second method is TLC-densitometry, in which the binary mixture and degradates were separated on silica gel plates using a chloroform–acetone–glacial acetic acid (9.5 + 0.5 + 0.07, v/v/v) mobile phase and the bands were scanned at 254 nm. The last method is HPLC, in which DLX, MTR, and degradates were separated using the mobile phase acetonitrile–0.05 M dibasic potassium phosphate (25 + 75, v/v), adjusted to pH 4 with orthophosphoric acid, at a flow rate of 1 mL/min, on a C18 analytical column. Detection was at 254 nm. The proposed methods were successfully applied for the analysis of DLX and MTR in pharmaceutical formulations, and the results were statistically compared with a reported spectrophotometric method.

scholarly journals Multivariate Validated Models for Simultaneous Determination of Ibuprofen and Famotidine in the Presence of Related Substances

2018 ◽  
Vol 101 (4) ◽  
pp. 1001-1007
Author(s):  
Eman S Elzanfaly ◽  
Hala E Zaazaa ◽  
Aya T Soudi ◽  
Maissa Y Salem
Keyword(s):  
Simultaneous Determination ◽  
Multivariate Calibration ◽  
Degradation Products ◽  
Principal Component Regression ◽  
Principal Component ◽  
Standard Addition ◽  
Spectrophotometric Methods ◽  
Related Substances ◽  
Calibration Methods

Abstract Two multivariate validated spectrophotometric methods, namely partial least-squares (PLS) and principal component regression (PCR), were developed and validated for the determination of ibuprofen and famotidine in presence of famotidine degradation products and ibuprofen impurity (4-isobutylacetophenone). A calibration set was prepared in which the two drugs together with the degradation products and impurity were modeled using a multilevel multifactor design. This calibration set was used to build the PLS and PCR models. The proposed models successfully predicted the concentrations of both drugs in validation samples, with low root mean square error of cross validation (RMSECV) percentage. The method was validated by the estimate of the figures of merit depending on the net analyte signal. The results of the two models showed that the simultaneous determination of both drugs could be performed in the concentration ranges of 100–500 µg/mL for ibuprofen and 5–25 µg/mL for famotidine. The proposed multivariate calibration methods were applied for the determination of ibuprofen and famotidine in their pharmaceutical formulation, and the results were verified by the standard addition technique.

Mechanical Parameter Analysis of Rock Material via PLSR Model

2011 ◽  
Vol 467-469 ◽  
pp. 1826-1831 ◽  
Author(s):  
Zao Bao Liu ◽  
Wei Ya Xu ◽  
Fei Xu ◽  
Lin Wei Wang
Keyword(s):  
Regression Model ◽  
Principal Component ◽  
Partial Least Square ◽  
Rock Material ◽  
Least Square ◽  
Parameter Analysis ◽  
Partial Least Square Regression ◽  
Mechanical Parameter ◽  
Least Square Regression

Mechanical parameter analysis is a complicated issue since it is influenced by many factors. Closely related with the influencing factors of compressibility coefficients of rock material (sandstone), this article first introduces the way to process partial least square regression (PLSR) analysis. The process of carrying out PLSR is divided into six steps as for analysis and prediction of the regression model, which are data preparation, principle collection, regression model for first principle component, secondary principle analysis, establishment of final regression model and number determination of principal component l. And then introduces PLSR for application of analysis and prediction of compressibility coefficients with 30 experiment samples. Seven prediction samples are carried out by PLSR with the training process of 30 samples. The result shows PLSR has good accuracy in prediction under the condition that the model is properly deprived based on certain experimental samples. Finally, some conclusions are made for further study on both mechanical parameters and partial least square regression method.

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