scholarly journals Determination of Cefoperazone Sodium in Presence of Related Impurities by Linear Support Vector Regression and Partial Least Squares Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Ibrahim A. Naguib ◽  
Eglal A. Abdelaleem ◽  
Hala E. Zaazaa ◽  
Essraa A. Hussein
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Partial Least Squares ◽  
Multivariate Calibration ◽  
Drug Product ◽  
Support Vector ◽  
Control Analysis ◽  
Least Squares Regression ◽  
Significant Difference ◽  
Cefoperazone Sodium

A comparison between partial least squares regression and support vector regression chemometric models is introduced in this study. The two models are implemented to analyze cefoperazone sodium in presence of its reported impurities, 7-aminocephalosporanic acid and 5-mercapto-1-methyl-tetrazole, in pure powders and in pharmaceutical formulations through processing UV spectroscopic data. For best results, a 3-factor 4-level experimental design was used, resulting in a training set of 16 mixtures containing different ratios of interfering moieties. For method validation, an independent test set consisting of 9 mixtures was used to test predictive ability of established models. The introduced results show the capability of the two proposed models to analyze cefoperazone in presence of its impurities 7-aminocephalosporanic acid and 5-mercapto-1-methyl-tetrazole with high trueness and selectivity (101.87 ± 0.708 and 101.43 ± 0.536 for PLSR and linear SVR, resp.). Analysis results of drug products were statistically compared to a reported HPLC method showing no significant difference in trueness and precision, indicating the capability of the suggested multivariate calibration models to be reliable and adequate for routine quality control analysis of drug product. SVR offers more accurate results with lower prediction error compared to PLSR model; however, PLSR is easy to handle and fast to optimize.

scholarly journals Linear Support Vector Regression and Partial Least-Squares for Determination of Dapoxetine Hydrochloride and Tadalafil in Binary Pharmaceutical Mixtures

2020 ◽  
Vol 103 (1) ◽  
pp. 132-139 ◽  
Author(s):  
Basma H Anwar ◽  
Nessreen S Abdelhamid ◽  
Maimana A Magdy ◽  
Ibrahim A Naguib
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Partial Least Squares ◽  
Multivariate Calibration ◽  
Pharmaceutical Formulations ◽  
Support Vector ◽  
Good Precision ◽  
Advantages And Disadvantages ◽  
Phosphodiesterase Type 5 Inhibitor ◽  
Significant Difference

Abstract Background: Dapoxetine (DAP) is a serotonin-norepinephrine reuptake inhibitor, and Tadalafil (TAD) is a phosphodiesterase type-5 inhibitor. Both are coformulated as tablets called Erectafil® for treatment of erectile ejaculation. Objective: DAP and TAD were analyzed in their binary mixtures and pharmaceutical formulations using two multivariate calibration chemometric models. Methods: Partial least-squares (PLS) and linear support vector regression (SVR) models were applied using two factor-four level experimental design and UV-spectrophotometric data. They were compared to each other, and their advantages and disadvantages were discussed. Results: The developed methods succeeded to determine DAP and TAD in different ratios with good results regarding International Conference on Harmonization guidelines. Linearity ranges were 2–15 μg/mL and 3–30 μg/mL for DAP and TAD, respectively, with good accuracy of 100 ± 0.37 for DAP and 100 ± 0.8 for TAD regarding PLS model and 100.04 ± 0.32 for DAP and 99.89 ± 0.77 for TAD regarding SVR model. Good precision values of 0.787 for DAP and 0.793 for TAD regarding PLS model and 1.105 for DAP and 0.930 for TAD regarding SVR model were obtained. The two models were applied on the dosage forms and statistically compared with the published HPLC method with no significant difference regarding accuracy and precision. Conclusions: The two models can be utilized for routine analysis and QC of DAP and TAD in their bulk and pharmaceutical formulations. The SVR model gives better results and generalization ability than those of the PLS model regarding accuracy and prediction error, while the latter is better for being simpler and faster.

Anwendung spektroskopischer Messverfahren und Multivariater Datenanalyse zur Bewertung und Beobachtung von Bioprozessen

2017 ◽  
Author(s):  
Jan-Patrick Voß
Keyword(s):  
Pichia Pastoris ◽  
Least Squares ◽  
Support Vector Regression ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Process Analytical Technology ◽  
Support Vector ◽  
Least Squares Regression ◽  
Analytical Technology

Die vorliegende Arbeit befasst sich mit dem Bioprozessmonitoring unter Verwendung spektroskopischer Messverfahren und multivariater Datenanalyse nach den Grundsätzen von PAT – Process Analytical Technology. Mit NIR-Spektroskopie und dem Verfahren Soft Independent Modelling of Class Analogy (SIMCA) wurde eine Quali¬tätsbewertung von Hefeextrakten realisiert. Im Vordergrund stand jedoch die Quanti¬fizierung nicht direkt messbarer Größen aus NIR-, Raman- und 2D-Fluoreszenzspektren in pharmazeutischen Produktionsprozessen mit Pichia pastoris. Eine entsprechende Online-Bestimmung mit der Methode Partial Least Squares Regression (PLSR) kam weiterführend zur Regelung der Glycerolkonzentration zum Einsatz. Darüber hinaus wurde die Verwendung nichtspektraler Online-Daten zur Prozessbeobachtung erprobt. Dabei gelang mit Hilfe des nichtlinearen Verfahrens Support Vector Regression (SVR) unter anderem die Bestimmung zellspezifischer Reaktionsraten. ...

Partial Least-Squares and Linear Support Vector Regression Chemometric Methods for Simultaneous Determination of Amoxicillin Trihydrate and Dicloxacillin Sodium in the Presence of Their Common Impurity

2016 ◽  
Vol 99 (4) ◽  
pp. 972-979
Author(s):  
Ibrahim A Naguib ◽  
Eglal A Abdelaleem ◽  
Hala E Zaazaa ◽  
Essraa A Hussein
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Partial Least Squares ◽  
Dosage Form ◽  
Support Vector ◽  
Prediction Ability ◽  
Pharmaceutical Dosage Form ◽  
Significant Difference ◽  
Amoxicillin Trihydrate ◽  
Plsr Model

Abstract Two multivariate chemometric models, namely, partial least-squares regression (PLSR) and linear support vector regression (SVR), are presented for the analysis of amoxicillin trihydrate and dicloxacillin sodium in the presence of their common impurity (6-aminopenicillanic acid) in raw materials and in pharmaceutical dosage form via handling UV spectral data and making a modest comparison between the two models, highlighting the advantages and limitations of each. For optimum analysis, a three-factor, four-level experimental design was established, resulting in a training set of 16 mixtures containing different ratios of interfering species. To validate the prediction ability of the suggested models, an independent test set consisting of eight mixtures was used. The presented results show the ability of the two proposed models to determine the two drugs simultaneously in the presence of small levels of the common impurity with high accuracy and selectivity. The analysis results of the dosage form were statistically compared to a reported HPLC method, with no significant difference regarding accuracy and precision, indicating the ability of the suggested multivariate calibration models to be reliable and suitable for routine analysis of the drug product. Compared to the PLSR model, the SVR model gives more accurate results with a lower prediction error, as well as high generalization ability; however, the PLSR model is easy to handle and fast to optimize.

scholarly journals Eco-Friendly Pharmaceutical Analysis of Multicomponent Drugs Coformulated in Different Dosage Forms Using Multivariate Curve Resolution and Partial Least Squares: A Comparative Study

2019 ◽  
Vol 102 (2) ◽  
pp. 465-472 ◽  
Author(s):  
Ahmed Mostafa ◽  
Heba Shaaban ◽  
Mishal Almousa ◽  
Mishal Al Sheqawi ◽  
Muntdher Almousa
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Dosage Forms ◽  
Multivariate Curve Resolution ◽  
Control Analysis ◽  
Least Squares Regression ◽  
Pharmaceutical Dosage Forms ◽  
Curve Resolution ◽  
Target Analytes ◽  
Significant Difference

Abstract Background: Considering the environmental impact of analytical procedures necessitates replacing the polluting analytical methods with green alternatives. Objective: This study aims to develop and validate a multivariate curve resolution–alternating least-squares (MCR-ALS) method with correlation constraint for the simultaneous determination of theophylline, ambroxol, and guaifenesin as target analytes in the presence of methylparaben and propylparaben as interfering components. In addition, a partial least-squares regression (PLSR) method was also developed andoptimized. Method: The developed methods were validated according to International Conference on Harmonization guidelines and successfully applied for the quantification of the target analytes in different pharmaceutical dosage forms. Results: Figures of merit such as root mean square error of prediction, bias, standard error of prediction, and relative error of prediction for both models were calculated, and they showedsimilar and satisfactory results. Correlation coefficients ranged between 0.9988 and 0.9992, reflectinghigh predictive ability. The optimized methods werecompared with a reported HPLC method using one-wayanalysis of variance and showed no significant difference regarding accuracy and precision. Conclusions: The proposed chemometrics methods can be used as an eco-friendly alternative for chromatographic techniques for the quality control analysis of the studied mixture in different pharmaceutical dosage forms. Highlights: An MCR-ALS model was developed. The developed model was compared with a PLSR model. Both models were validated and successfully used for the determinationof a multicomponent pharmaceutical mixture. The developed method is eco-friendly, fast, reliable, and cost-effective.

A study on prediction performance of the mechanical properties of rubber using Fourier-transform near infrared spectroscopy

2018 ◽  
Vol 26 (6) ◽  
pp. 351-358 ◽  
Author(s):  
Rattapol Pornprasit ◽  
Philaiwan Pornprasit ◽  
Pruet Boonma ◽  
Juggapong Natwichai
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Prediction Models ◽  
Scatter Correction ◽  
Support Vector ◽  
Least Squares Regression ◽  
Multiplicative Scatter Correction ◽  
Orthogonal Signal Correction

Near infrared spectroscopy is a spectroscopic method used for quality and quantity analysis of agriculture products and industry materials. Rubber is a mostly raw material of any products. NIR spectroscopy had been using to analyze the mechanical properties of rubber and polymer materials. Prediction models were built from the correlation between the NIR spectra and mechanical strength values (hardness and tensile strength). Raw data were pretreated to improve the prediction models, where the prediction models were based on partial least squares regression and support vector regression. In the case of hardness prediction, the raw dataset was pretreated with standard normal variate transformation or a combination of Savitzky–Golay smoothing and multiplicative scatter correction, following which orthogonal signal correction and uninformative variable elimination were used for feature selection, and partial least squares regression and support vector regression were applied for the prediction model. For tensile strength prediction, the pretreatments were multiplicative scatter correction or combination of Savitzky–Golay smoothing and multiplicative scatter correction, following which orthogonal signal correction and uninformative variable elimination were used for feature selection, and partial least squares regression and support vector regression were applied for the prediction model. From these processes, the r2 values were greater than 0.9, the bias values were among ±0.5, and the RMSEP values were lower than 5.

scholarly journals Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jordi Ortuño ◽  
Sokratis Stergiadis ◽  
Anastasios Koidis ◽  
Jo Smith ◽  
Chris Humphrey ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Multivariate Calibration ◽  
Plant Ecology ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Abstract Background The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT’s full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000–550 cm−1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization—mDP, procyanidins:prodelphidins ratio—PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. Results The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). Conclusions MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

scholarly journals Analysis of Methanol Gasoline by ATR-FT-IR Spectroscopy

2019 ◽  
Vol 9 (24) ◽  
pp. 5336 ◽  
Author(s):  
Qi XIA ◽  
Lei-ming YUAN ◽  
Xiaojing CHEN ◽  
Liuwei MENG ◽  
Guangzao HUANG
Keyword(s):  
Quantitative Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Internal Combustion Engines ◽  
Quantitative Model ◽  
Least Square ◽  
Support Vector ◽  
Least Squares Regression ◽  
Transportation Industry ◽  
Successive Projections Algorithm

Methanol gasoline blends are a more economical, and environmentally friendly fuels than gasoline alone, and are widely used in the transportation industry. The content of methanol in methanol gasoline plays an important role in ensuring the quality of gasoline. In some solutions, due to the shortage of energy and illegal profits, the problem of gasoline adulteration and its fineness, has received more and more attention, which would seriously affect the operating condition and service life of internal combustion engines. Therefore, it is very important to identify the correct level of gasoline. However, the traditional detection method is complex and time-consuming. To this end, the feasibility of using attenuated total reflectance Fourier transform infrared (ATR-FTIR) methods coupled with chemometrics methods were investigated to quantitatively and qualitatively analyze methanol gasoline. The qualitative analysis result of partial least squares discriminant analysis (PLS-DA) obtained 100% and 98.66% accuracy in the calibration set and the prediction set, respectively. As for quantitative analysis; two regression algorithms of partial least squares regression (PLSR) and the least square support vector machine (LS-SVM), as well as two variables selection methods of the successive projections algorithm (UVE) competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) were combined to establish the quantitative model. By comparing the performance of the optimal models; the UVE-PLSR model performed best with a residual predictive deviation (RPD) value of 6.420. The qualitative and quantitative analysis results demonstrate the feasibility of using ATR-FTIR spectra to detect the methanol in methanol gasoline. It is believed that the promising IR spectra will be widely used in gasoline energy quality control in the further.

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