scholarly journals Simultaneous Spectrophotometric Kinetic Determination of Four Flavor Enhancers in Foods with the Aid of Chemometrics

2011 ◽  
Vol 94 (4) ◽  
pp. 1210-1216 ◽  
Author(s):  
Yongnian Ni ◽  
Jinfeng Chen ◽  
Serge Kokot
Keyword(s):  
Spectral Data ◽  
Principal Component Regression ◽  
Principal Component ◽  
Food Samples ◽  
Hplc Method ◽  
Ethyl Vanillin ◽  
Sulfuric Acid Medium ◽  
Kinetic Spectrophotometric ◽  
Four Flavor

Abstract A sensitive kinetic spectrophotometric method was developed for the determination of four flavor enhancers—maltol, ethyl maltol, vanillin, and ethyl vanillin—in food samples. The method was based on the reduction of iron(III) by the four analytes in a sulfuric acid medium (0.012 mol/L), and the subsequent interaction of iron(II) with hexacyanoferrate(III) to form the strongly colored Prussian blue complex, which exhibited an absorption maximum at 800 nm. The optimized method had linear calibrations over the concentration ranges of 0.2–2.8 mg/L for maltol, ethyl maltol, and vanillin, as well as 0.2–1.8 mg/L for ethyl vanillin; the corresponding detection limits were 0.07, 0.07, 0.06, and 0.06 mg/L, respectively. Calibration models were constructed from the original and frst-derivative spectral data with the use of partial least-squares (PLS) and principal component regression chemometrics methods. Ultimately, the proposed analytical procedure was successively applied for the determination of the four compounds in commercial food samples with the use of a PLS calibration based on the frst-derivative spectral data. The results were comparable with those from a reference HPLC method.

Determination of Enantiomeric Composition of Dopa by Using UV Spectroscopy Combined with Principal Component Regression

2012 ◽  
Vol 622-623 ◽  
pp. 1451-1455
Author(s):  
Qiang Deng ◽  
Long Jiao ◽  
Yi Qing Ge ◽  
Yun Xia Wang
Keyword(s):  
Spectral Data ◽  
Uv Spectroscopy ◽  
Principal Component Regression ◽  
Principal Component ◽  
High Sensitivity ◽  
Enantiomeric Composition ◽  
Calibration Models ◽  
Host Chemistry

A method which combines UV spectroscopy, guest–host chemistry and principal component regression (PCR) was proposed for determining the enantiomeric composition of DOPA samples. The calibration models were developed from UV spectral data of a series of samples containing DOPA with different known enantiomeric compositions by using PCR. The obtained model was subsequently validated by determining the enantiomeric composition of a set of independently prepared samples. This method shows high sensitivity for determining the enantiomeric composition of DOPA. When there is 5.00 μM DOPA in the samples, the enantiomeric composition of DOPA can be accurately determined.

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 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 New Kinetic Spectrophotometric Method for Determination of Folic Acid in Pharmaceutical Formulations

2015 ◽  
Vol 50 ◽  
pp. 169-178
Author(s):  
Mouhammed Khateeb ◽  
Basheer Elias ◽  
Fatema Al Rahal
Keyword(s):  
Folic Acid ◽  
Spectrophotometric Method ◽  
Pharmaceutical Formulations ◽  
Fixed Time ◽  
Rate Data ◽  
Sulfuric Acid Medium ◽  
Significant Difference ◽  
Kinetic Spectrophotometric ◽  
Comparison Of The Results

A simple and sensitive kinetic spectrophotometric method has been developed for the determination of folic acid (FA) in bulk and pharmaceutical Formulations. The method is based on the oxidation of FA by Fe (III) in sulfuric acid medium. Fe (III) subsequently reduces to Fe (II) which is coupled with potassium ferricyanide to form Prussian blue. The reaction is followed spectrophotometrically by measuring the increase in absorbance at λmax 725 nm. The rate data and fixed time methods were adopted for constructing the calibration curves. The linearity range was found to be 1–20 μg mL-1 for each method. The correlation coefficient was 0.9978 and 0.9993, and LOD was found to be 0.91 and 0.09 μg mL-1 for rate data and fixed time methods, respectively. The proposed method has been successfully applied to the determination of FA in formulations with no interference from the excipients. Statical comparison of the results shows that there is no significant difference between the proposed and pharmacopoeial methods

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.

Near-IR Spectroscopic Determination of NaCl in Aqueous Solution

1992 ◽  
Vol 46 (12) ◽  
pp. 1809-1815 ◽  
Author(s):  
Jie Lin ◽  
Chris W. Brown
Keyword(s):  
Aqueous Solution ◽  
Temperature Effects ◽  
Continuous Monitoring ◽  
Principal Component Regression ◽  
Principal Component ◽  
Standard Errors ◽  
Near Ir ◽  
Linear And Nonlinear ◽  
Ir Spectroscopic

The concentrations of NaCl in aqueous solutions have been determined with the use of near-IR spectra between 1100 and 1900 nm. Models expressing the concentration of NaCl are developed with linear and nonlinear regression with the use of the absorbances at selected wavelengths and with principal component regression (PCR) using entire spectra. Temperature perturbations on water bands interfere with the measurement of NaCl but can be removed by linear or nonlinear regressions using the absorbances at the wavelengths where the temperature effects are zero, or they can be accounted for by PCR. Standard errors of 5 mM and a detection limit of IS mM are obtained for NaCl. This technique can be applied for quantitative analysis of NaCl in the laboratory or can be readily adapted for continuous monitoring in process control.

High Pressure Liquid Chromatographic Determination of 5-(Hydroxymethyl)-2-Furaldehyde in Caramel Solution

1980 ◽  
Vol 63 (6) ◽  
pp. 1310-1313
Author(s):  
Felipe C Alfonso ◽  
Glenn E Martin ◽  
Randolph H Dyer
Keyword(s):  
Chromatographic Determination ◽  
Hplc Method ◽  
Uv Detector ◽  
High Pressure Liquid Chromatographic ◽  
Ethyl Vanillin ◽  
Caramel Color ◽  
Water Gradient ◽  
Liquid Chromatographic Determination ◽  
Liquid Chromatographic

Abstract An HPLC method is described for the detection of caramel color by measuring the level of 5-(hydroxymethyl)-2-furaldehyde (5-HMF). For the several products of caramelization examined, 5-HMF was the most sensitive indicator of the presence of caramel. The method specifies a reverse phase C18 column, a UV detector set at 277 nm, and a methanol-water gradient to separate 5-HMF from interfering substances. Other flavor compounds resolved by the same gradient are vanillin, ethyl vanillin, coumarin, benzaldehyde, caffeine, anethole, theobromine, and cinnamaldehyde.

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