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

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 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.

Chemometrics-assisted spectrophotometric method for simultaneous estimation of antipsychotic drugs in biological fluid

2020 ◽  
Vol 16 ◽  
Author(s):  
Mojdeh Alibakhshi ◽  
Mahmoud Reza Sohrabi ◽  
Mehran Davallo
Keyword(s):  
Simultaneous Determination ◽  
Antipsychotic Drugs ◽  
Multivariate Calibration ◽  
Biological Fluid ◽  
Principal Component Regression ◽  
Reference Method ◽  
Principal Component ◽  
Simultaneous Estimation ◽  
Calibration Methods

Background: Haloperidol (HP) and Risperidone (RIS) are antipsychotic drugs and the simultaneous determination of these drugs is important. Estimation of HP and RIS alone or in combination with other drugs has been performed in a variety of ways. Objective: The aim of this paper was to propose a rapid, simple, accurate, and robust method for the simultaneous determination of HP and RIS using artificial neural networks (ANNs), partial least squares (PLS), and principal component regression (PCR) methods along with spectrophotometry technique. Methods: The simultaneous spectrophotometric determination of HP and RIS in synthetic mixtures and biological fluid was performed by applying ANNs containing feed forward backpropagation (FFBP) and radial basis function (RBF) networks as intelligent methods, as well as PLS, and principal component regression PCR as multivariate calibration methods. The Levenberg–Marquardt (LM), Scaled conjugate gradient (SCG), and Resilient Back-propagation (RP) algorithms with different layers and neurons were used in FFBP network and obtained results were compared with each other. Results: Among various algorithms of the FFBP network, the LM algorithm was selected as the best model with a lower mean square error (MSE). MSE of the RBF model was 1.46×10-25 and 1.62×10-23 for HP and RIS, respectively. On the other hand, the mean recovery of PLS and PCR was 99.91%, 100.01% and 98.60%, 101.90% for HP and RIS, respectively. Conclusion: The proposed models and high-performance liquid chromatography (HPLC) as a reference method were compared with each other by one-way analysis of variance (ANOVA) test at the 95 % confidence level for the urine sample. It was observed that the developed methods presented comparable results for the simultaneous determination of HP and RIS.

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