SIMULTANEOUS ESTIMATION OF TELMISARTAN, HYDROCHLOROTHIAZIDE AND AMLODIPINE BESYLATE IN TABLET PREPARATION BY CHEMOMETRIC ASSISTED SPECTROPHOTOMETRIC ANALYSIS

INDIAN DRUGS ◽  
2018 ◽  
Vol 55 (07) ◽  
pp. 59-66
Author(s):  
A. S Ghule ◽  
T. Thomas ◽  
M. Joseph ◽  
K. S. Navya Sree ◽  
K. Bhat
Keyword(s):  
Spectrophotometric Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Data Matrix ◽  
Simultaneous Estimation ◽  
Amlodipine Besylate ◽  
Mixed Solutions ◽  
Validation Set ◽  
Tablet Preparation

A chemometric method was developed by application of Partial Least Square regression to for the simultaneous estimation of telmisartan, hydrochlorothiazide and amlodipine besylate in tablet preparations. Calibration set was prepared considering seven sets; each set with twenty-four mixed solutions and twenty-one ternary mixed solutions, were prepared as a validation set. The absorbance data matrix for training set was obtained by recording absorbance within wavelength range 220-320 nm at 2nm intervals. The developed method was validated according to ICH Q2 (R1) guidelines and results were reported. The developed and validated multivariate method was successfully tested for laboratory mixtures as well as commercial tablet formulation of telmisartan, hydrochlorothiazide and amlodipine besylate.

DEVELOPMENT AND VALIDATION OF A CHEMOMETRICS ASSISTED SPECTROSCOPIC METHOD FOR THE SIMULTANEOUS ESTIMATION OF GALLIC ACID, ELLAGIC ACID AND CURCUMIN IN POLYHERBAL ANTIDIABETIC FORMULATIONS

INDIAN DRUGS ◽  
2019 ◽  
Vol 56 (06) ◽  
pp. 67-73
Author(s):  
M. A Shah ◽  
◽  
H. U. Patel ◽  
H.A Raj
Keyword(s):  
Gallic Acid ◽  
Ellagic Acid ◽  
Spectroscopic Method ◽  
Least Square ◽  
Data Matrix ◽  
Simultaneous Estimation ◽  
Chemometric Methods ◽  
Mixed Solutions ◽  
Validation Set ◽  
Development And Validation

Two chemometric methods, Inverse Least Square (ILS) and Classical Least Square (CLS), were applied for the simultaneous estimation of gallic acid, ellagic acid and curcumin in polyherbal antidiabetic formulation. Twenty mixed solutions were prepared for the chemometric calibration as training set and 10 mixed solutions were prepared as validation set. The absorbance data matrix was obtained by measuring the absorbance at 20 different wavelengths, from 241 to 279 nm with the interval of 2 nm (Δλ= 2 nm). The developed calibrations were successfully tested for three antidiabetic polyherbal formulations for their gallic acid, ellagic acid and curcumin contents. Developed methods were validated and root mean square error of precision (RMSEP) was determined. Both chemometric methods in this study can be satisfactorily used for the quantitative analysis in polyherbal dosage forms. The chemometric calculations were performed by using the chemometrics toolbox with MATLAB R2015a software.

DEVELOPMENT AND VALIDATION OF CHEMOMETRIC-ASSISTED UV-SPECTROPHOTOMETRIC METHOD FOR EPIGALLOCATECHIN GALLATE AND CURCUMIN IN TABLET FORMULATION

INDIAN DRUGS ◽  
2020 ◽  
Vol 57 (04) ◽  
pp. 45-51
Author(s):  
N. C. Patel ◽  
A. P. Patel ◽  
J. K. Patel
Keyword(s):  
Epigallocatechin Gallate ◽  
Tablet Formulation ◽  
Partial Least Square ◽  
Least Square ◽  
Data Matrix ◽  
Simultaneous Estimation ◽  
Calibration Data ◽  
Mixed Solution ◽  
Validation Set ◽  
Development And Validation

A chemometric method, Partial Least Square, was applied for the simultaneous estimation of epigallocatechin gallate and curcumin in tablet formulation. Twenty five mixed sample solutions were prepared for chemometric calibration as training set and sixteen mixed solution for validation set using Full Factorial Design. The absorbance data matrix was obtained by measuring absorbance at 20 different wavelengths in the range of 220 to 410 nm (Δλ = 10 nm). The developed calibration data was used to test tablet formulation containing epigallocatechin gallate and curcumin. The developed methods were validated using RMSECV and RMSEP. The chemometric calculations were performed using Minitab 16.1.1 and Microsoft Excel 2010. The method is also more accurate and precise than conventional UV methods.

scholarly journals Development and validation of chemometric assisted analytical methods for simultaneous estimation of Atorvastatin calcium and Aspirin in capsule dosage form

2019 ◽  
Vol 10 (3) ◽  
pp. 1692-1697
Author(s):  
Keerthisikha Palur ◽  
BharathiKoganti ◽  
Sreenivasa Charan Archakam
Keyword(s):  
Analytical Methods ◽  
Dosage Form ◽  
Hplc Method ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Simultaneous Estimation ◽  
Uv Spectrophotometry ◽  
Atorvastatin Calcium ◽  
Rp Hplc

To develop two Chemometric-assisted analytical methods like UV spectrophotometry and RP-HPLC methods for the quantification of Atorvastatin calcium (ASC) and Aspirin (APN) in the capsule dosage form. Chemometric models used in UV spectrophotometry were Principal component regression model (PCRM) and Partial least-square regression (PLSR). Both the models were applied for the drugs in the calibration ranges of 4-20 and 30-150 μg/mL for ASC and APN respectively. Total of nineteen laboratory prepared mixtures were used for calibration and prediction set of the models. In addition, RP-HPLC method by using chemometric approach for was developed using C18 column at room temperature with a mobile phase of acetonitrile: methanol: triethylamine (53.1:11.9:35 v/v/v), pH- 3.0, with detection at 275 nm. PCRM and PLSR models were evaluated by statistical parameters and RP-HPLC method was optimized by using Response surface methodology. The developed methods like UV and RP-HPLC by using chemometrics showed almost similar results and both the methods can be used for their analysis.

CHEMOMETRICS ASSISTED SPECTROSCOPIC METHOD DEVELOPMENT AND VALIDATION FOR SIMULTANEOUS DETERMINATION OF SOFOSBUVIR AND DACLATASVIR DIHYDROCHLORIDE IN TABLET FORMULATION

INDIAN DRUGS ◽  
2020 ◽  
Vol 57 (03) ◽  
pp. 37-46
Author(s):  
Sapna M Rathod ◽  
Paresh U Patel
Keyword(s):  
Method Development ◽  
Spectroscopic Method ◽  
Principal Component ◽  
Tablet Formulation ◽  
Partial Least Square ◽  
Least Square ◽  
Simultaneous Estimation ◽  
Mixed Solution ◽  
Chemometric Methods ◽  
Validation Set

Four chemometric methods, namely Classical Least Square (CLS), Inverse Least Square (ILS), Partial Least Square (PLS) and Principal Component Regression (PCR), were developed for the simultaneous estimation of sofosbuvir and daclatasvir dihydrochloride in tablet formulation. Full factorial design was used to construct calibration set as well as validation set. Twenty five mixed solutions were prepared for calibration set and sixteen mixed solution of drugs were prepared for validation set. The absorbance of all prepared solutions was measured in the range of 230 nm to 335 nm at 16 wavelength points at an interval of 7 nm. Linearity was observed in the range of 10 – 90 µg/mL for sofosbuvir and 4 - 20 µg/mL for daclatasvir dihydrochloride. The developed chemometric methods were validated in terms of precision and accuracy as per ICH guidelines. The developed methods can be applied for the routine quantitative analysis of formulation.

SIMULTANEOUS ESTIMATION OF EMITRICITABINE, EFAVIRENZ, TENOFOVIR DISPROXIL FUMARATE BY CHEMOMETRY AND REVERSE PHASE-HIGH PERFORMANCE LIQUID CHROMATOGRAPHY

INDIAN DRUGS ◽  
2016 ◽  
Vol 53 (06) ◽  
pp. 62-69
Author(s):  
G. Ramya Kumari ◽  
◽  
N. C Deepika ◽  
Krishnamurthy Bhat
Keyword(s):  
High Performance ◽  
Ultra Violet ◽  
Hplc Method ◽  
Tablet Formulation ◽  
Partial Least Square ◽  
Least Square ◽  
Ternary Mixtures ◽  
Data Matrix ◽  
Simultaneous Estimation ◽  
Tenofovir Df

A chemometric ultra-violet spectrophotometric method of analysis, Partial Least Square (PLS) method was applied to simultaneous assay of emitricitrabine, efavirenz and tenofovir disoproxil fumarate (DF) in their combined dosage tablet formulation. For comparison of this chemometric method a HPLC method for simultaneous determination of emitricitabine, efavirenz and tenofovir DF in combination was developed. Twenty one mixed solutions were prepared for chemometric calibration set and twenty one ternary mixtures were prepared as validation sets. The absorbance data matrix for training set was obtained by recording absorbance within wavelength range 230-290 nm at 2nm intervals. The developed multivariate and HPLC methods were successfully tested for laboratory mixtures as well as commercial tablet formulation of emitricitrabine, efavirenz, tenofovir DF.

scholarly journals Kernel Partial Least Square Regression with High Resistance to Multiple Outliers and Bad Leverage Points on Near-Infrared Spectral Data Analysis

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 547
Author(s):  
Divo Dharma Silalahi ◽  
Habshah Midi ◽  
Jayanthi Arasan ◽  
Mohd Shafie Mustafa ◽  
Jean-Pierre Caliman
Keyword(s):  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Data Matrix ◽  
High Dimensional ◽  
Leverage Points ◽  
Multiple Outliers ◽  
Data Space ◽  
Least Square Regression

Multivariate statistical analysis such as partial least square regression (PLSR) is the common data processing technique used to handle high-dimensional data space on near-infrared (NIR) spectral datasets. The PLSR is useful to tackle the multicollinearity and heteroscedasticity problem that can be commonly found in such data space. With the problem of the nonlinear structure in the original input space, the use of the classical PLSR model might not be appropriate. In addition, the contamination of multiple outliers and high leverage points (HLPs) in the dataset could further damage the model. Generally, HLPs contain both good leverage points (GLPs) and bad leverage points (BLPs); therefore, in this case, removing the BLPs seems relevant since it has a significant impact on the parameter estimates and can slow down the convergence process. On the other hand, the GLPs provide a good efficiency in the model calibration process; thus, they should not be eliminated. In this study, robust alternatives to the existing kernel partial least square (KPLS) regression, which are called the kernel partial robust GM6-estimator (KPRGM6) regression and the kernel partial robust modified GM6-estimator (KPRMGM6) regression are introduced. The nonlinear solution on PLSR was handled through kernel-based learning by nonlinearly projecting the original input data matrix into a high-dimensional feature mapping that corresponded to the reproducing kernel Hilbert spaces (RKHS). To increase the robustness, the improvements on GM6 estimators are presented with the nonlinear PLSR. Based on the investigation using several artificial dataset scenarios from Monte Carlo simulations and two sets from the near-infrared (NIR) spectral dataset, the proposed robust KPRMGM6 is found to be superior to the robust KPRGM6 and non-robust KPLS.

Comparison of Chemometric assisted UV Spectrophotometric and RP-HPLC Method for the simultaneous determination of Ofloxacin and Tinidazole in their Combined dosage form

2021 ◽  
pp. 5713-5718
Author(s):  
Santosh V. Gandhi ◽  
Deepak Patil ◽  
Atul A. Baravkar
Keyword(s):  
Simultaneous Determination ◽  
Dosage Form ◽  
Hplc Method ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Simultaneous Estimation ◽  
Pharmaceutical Dosage Form ◽  
Rp Hplc

In present work, chemometric-assisted UV spectrophotometric methods as well as RP-HPLC method were developed for the simultaneous estimation of Ofloxacin and Tinidazole in their combined pharmaceutical dosage form. The two chemometric methods i.e. principle component regression (PCR) and partial least square regression (PLS) were successfully applied to quantify each drug in mixture using UV absorption spectra in range of 280 to 320nm at ∆λ of 0.5nm. Chemometric model development was done using 24 binary mixture solutions and 12 solutions were used for validation of model. The chemometric-assisted analysis does not require any prior separation step. In addition, RP-HPLC method was also developed using THERMOSIL C18 column with a mobile phase consisting ofAcetonitrile: Phosphate Buffer (85:15% v/v), flow rate of 1 ml/min and quantification was achieved using UV detector at 300 nm. The methods were successfully applied for the simultaneous determination of these drugs in synthetic mixture. The results obtained for analysis by PCR and PLS methods were compared with RP-HPLC method and a good agreement was found.

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

scholarly journals Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 618
Author(s):  
Tanvir Tazul Islam ◽  
Md Sajid Ahmed ◽  
Md Hassanuzzaman ◽  
Syed Athar Bin Amir ◽  
Tanzilur Rahman
Keyword(s):  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Quantitative Estimation ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Frequency Noise ◽  
Optical Noise

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

scholarly journals A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1546
Author(s):  
Ioanna Dagla ◽  
Anthony Tsarbopoulos ◽  
Evagelos Gikas
Keyword(s):  
Design Of Experiments ◽  
High Performance ◽  
Similarity Index ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Distance Measures ◽  
Linear Regression Models ◽  
Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

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