SIMULTANEOUS SPECTROPHOTOMETRIC DETERMINATION OF PHENYLEPHRINE HYDROCHLORIDE AND NAPHAZOLINE HYDROCHLORIDE IN EYE DROPS BY CHEMOMETRIC TECHNIQUES AND ARTIFICIAL NEURAL NETWORK

INDIAN DRUGS ◽  
2021 ◽  
Vol 58 (09) ◽  
pp. 38-46
Author(s):  
Satish A. Patel ◽  
Dharmendrasinh A. Baria ◽  
Keyword(s):  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Drug Analysis ◽  
Data Matrix ◽  
Eye Drops ◽  
Validation Data ◽  
Phenylephrine Hydrochloride ◽  
Squared Prediction Error ◽  
Prediction Techniques

Three multivariate calibration-prediction techniques, partial least squares (PLS), principal component regression (PCR) and artifi cial neural networks (ANN), have been applied without separation in the spectrophotometric multi-component analysis of phenylephrine hydrochloride and naphazoline hydrochloride. A set of 25 synthetic mixtures of phenylephrine hydrochloride and naphazoline hydrochloride has been evaluated to determine the predictability of PLS, PCR and ANN. The absorbance data matrix was obtained by measuring zero-order absorbances between 230-300 nm at intervals of 3 nm. The suitability of the models was determined on the basis of root mean square error (RMSE), root mean squared cross validation error (RMSECV) and root mean squared prediction error (RMSEP) values of calibration and validation data. The results showed a very good correlation between true values and the predicted concentration values. Therefore, the methods developed can be used for routine drug analysis without chemical pre-treatment.

Development and Validation of Chemometric-Assisted Spectrophotometric Methods for Simultaneous Determination of Phenylephrine Hydrochloride and Ketorolac Tromethamine in Binary Combinations

2016 ◽  
Vol 99 (5) ◽  
pp. 1247-1251 ◽  
Author(s):  
Hamed M Elfatatry ◽  
Mokhtar M Mabrouk ◽  
Sherin F Hammad ◽  
Fotouh R Mansour ◽  
Amira H Kamal ◽  
...  
Keyword(s):  
Least Squares ◽  
Simultaneous Determination ◽  
Principal Component Regression ◽  
Principal Component ◽  
Data Matrix ◽  
Ketorolac Tromethamine ◽  
Concentration Data ◽  
Phenylephrine Hydrochloride ◽  
Spectrophotometric Methods

Abstract The present work describes new spectrophotometric methods for the simultaneous determination of phenylephrine hydrochloride and ketorolac tromethamine in their synthetic mixtures. The applied chemometric techniques are multivariate methods including classical least squares, principal component regression, and partial least squares. In these techniques, the concentration data matrix was prepared by using the synthetic mixtures containing these drugs dissolved in distilled water. The absorbance data matrix corresponding to the concentration data was obtained by measuring the absorbances at 16 wavelengths in the range 244–274 nm at 2 nm intervals in the zero-order spectra. The spectrophotometric procedures do not require any separation steps. The accuracy, precision, and linearity ranges of the methods have been determined, and analyzing synthetic mixtures containing the studied drugs has validated them. The developed methods were successfully applied to the synthetic mixtures and the results were compared to those obtained by a reported HPLC method.

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.

scholarly journals Prediction of Total Phenolic Content in Extracts of Prunella Species from HPLC Profiles by Multivariate Calibration

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Saliha Sahin ◽  
Esra Isik ◽  
Cevdet Demir
Keyword(s):  
High Performance ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Total Phenol ◽  
Total Phenolic ◽  
Total Phenol Content ◽  
Phenol Content ◽  
Calibration Methods ◽  
Hplc Profiles

The multivariate calibration methods—principal component regression (PCR) and partial least squares (PLSs)—were employed for the prediction of total phenol contents of four Prunella species. High performance liquid chromatography (HPLC) and spectrophotometric approaches were used to determine the total phenol content of the Prunella samples. Several preprocessing techniques such as smoothing, normalization, and column centering were employed to extract the chemically relevant information from the data after alignment with correlation optimized warping (COW). The importance of the preprocessing was investigated by calculating the root mean square error (RMSE) for the calibration set of the total phenol content of Prunella samples. The models developed based on the preprocessed data were able to predict the total phenol content with a precision comparable to that of the reference of the Folin-Ciocalteu method. PLS model seems preferable, because of its predictive and describing abilities and good interpretability of the contribution of compounds to the total phenol content. Multivariate calibration methods were constructed to model the total phenol content of the Prunella samples from the HPLC profiles and indicate peaks responsible for the total phenol content successfully.

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 Liquid Chromatographic and Spectrophotometric Determination of Diflucortolone Valerate and Isoconazole Nitrate in Creams

2011 ◽  
Vol 94 (1) ◽  
pp. 128-135 ◽  
Author(s):  
Elif Karacan ◽  
Mehmet Gokhan Çaġlayan ◽  
İsmail Murat Palabiyik ◽  
Feyyaz Onur
Keyword(s):  
Principal Component Regression ◽  
Principal Component ◽  
Derivative Spectrophotometry ◽  
Data Matrix ◽  
Concentration Data ◽  
Spectrophotometric Methods ◽  
First Derivative ◽  
First Derivative Spectrophotometry ◽  
Liquid Chromatographic

Abstract A new RP-LC method and two new spectrophotometric methods, principal component regression (PCR) and first derivative spectrophotometry, are proposed for simultaneous determination of diflucortolone valerate (DIF) and isoconazole nitrate (ISO) in cream formulations. An isocratic system consisting of an ACE® C18 column and a mobile phase composed of methanol–water (95+5, v/v) was used for the optimal chromatographic separation. In PCR, the concentration data matrix was prepared by using synthetic mixtures containing these drugs in methanol–water (3+1, v/v). The absorbance data matrix corresponding to the concentration data matrix was obtained by measuring the absorbances at 29 wavelengths in the range of 242–298 nm for DIF and ISO in the zero-order spectra of their combinations. In first derivative spectrophotometry, dA/dλ values were measured at 247.8 nm for DIF and at 240.2 nm for ISO in first derivative spectra of the solution of DIF and ISO in methanol–water (3+1, v/v). The linear ranges were 4.00–48.0 μg/mL for DIF and 50.0–400 μg/mL for ISO in the LC method, and 2.40–40.0 μg/mL for DIF and 60.0–260 μg/mL for ISO in the PCR and first derivative spectrophotometric methods. These methods were validated by analyzing synthetic mixtures. These three methods were successfully applied to two pharmaceutical cream preparations.

Spectrophotometric determination of mixtures of iron(III) and manganese(II) by complexation with 3-indolylacetohydroxamic acid and principal component regression multivariate calibration

The Analyst ◽  
1994 ◽  
Vol 119 (7) ◽  
pp. 1537-1540 ◽  
Author(s):  
Mercedes Jiménez Arrabal ◽  
Pablo Valiente González ◽  
Concepción Caro Gámez ◽  
Antonio Sánchez Misiego ◽  
Arsenio Muñoz de la Peña
Keyword(s):  
Spectrophotometric Determination ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component

Multivariate Calibration of Infrared Spectra for Quantitative Analysis Using Designed Experiments

1988 ◽  
Vol 42 (5) ◽  
pp. 865-872 ◽  
Author(s):  
Frederick Cahn ◽  
Senja Compton
Keyword(s):  
Infrared Spectra ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Experimental System ◽  
Weight Fraction ◽  
Quantitative Prediction ◽  
Residual Spectrum ◽  
Lattice Design ◽  
Simplex Lattice

The principal component regression (PCR) and partial least-squares (PLS) methods are used to calibrate and validate models for quantitative prediction of the composition of mixtures from FT-IR spectra. An experimental system of two- and three-component mixtures of xylene isomers was sampled with the use of statistical experimental designs. For two-component mixtures, the prediction error of independent validation samples decreased with increasing numbers of design points in the calibration. Four design points were needed to achieve a prediction accuracy of 0.0013 weight fraction. For three-component mixtures, a Scheffé {3,3} simplex lattice design, which has ten design points, achieved an equivalent accuracy of 0.002 weight fraction. There was little difference in performance between PLS and PCR computations. The results demonstrate the application of statistical methodology to the calibration of infrared spectra and show the importance of including an adequate number of samples in the calibration. The F test on the residual spectrum is shown to be a valuable tool for the identification of spurious data.

Sign in / Sign up

Export Citation Format

Share Document