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Effect of genetic algorithm as a variable selection method on different chemometric models applied for the analysis of binary mixture of amoxicillin and flucloxacillin: A comparative study
Abstract
Different chemometric models were applied for the quantitative analysis of amoxicillin (AMX), and flucloxacillin (FLX) in their binary mixtures, namely, partial least squares (PLS), spectral residual augmented classical least squares (SRACLS), concentration residual augmented classical least squares (CRACLS) and artificial neural networks (ANNs). All methods were applied with and without variable selection procedure (genetic algorithm GA). The methods were used for the quantitative analysis of the drugs in laboratory prepared mixtures and real market sample via handling the UV spectral data. Robust and simpler models were obtained by applying GA. The proposed methods were found to be rapid, simple and required no preliminary separation steps.
... While offering advantages in terms of simplicity, speed, and environmental impact, conventional UV spectroscopic methods face limitations in resolving complex mixtures due to spectral overlap, highlighting the need for advanced data analysis approaches 26 . Machine learning algorithms, such as Artificial neural networks (ANN), have emerged as powerful tools for the analysis of complex spectroscopic data, demonstrating the ability to model both linear and non-linear relations and extract relevant chemical information from UV fingerprints 27,28 . For example, ANN has been successfully applied to the quantification of amoxicillin and flucloxacillin in their pharmaceutical formulations using UV spectroscopy 27 . ...
... Machine learning algorithms, such as Artificial neural networks (ANN), have emerged as powerful tools for the analysis of complex spectroscopic data, demonstrating the ability to model both linear and non-linear relations and extract relevant chemical information from UV fingerprints 27,28 . For example, ANN has been successfully applied to the quantification of amoxicillin and flucloxacillin in their pharmaceutical formulations using UV spectroscopy 27 . Moreover, approaches like the use of variable selection techniques such as genetic and firefly algorithms have been employed to further enhance the performance of these ANN-based UV spectroscopic methods 29 . ...
In the present study, a simple, rapid and cost-effective analytical method was developed for the simultaneous determination of three commonly prescribed cardiovascular drugs: propranolol, rosuvastatin and valsartan. The method employed artificial neural networks (ANN) to model the relation between the UV absorption spectra of the drugs and their concentrations. An experimental design of 25 samples was employed as a calibration set, and a central composite design of 20 samples was used as a validation set. The firefly algorithm (FA) was evaluated as a variable selection procedure to optimize the developed ANN models resulting in simpler models with improved predictive performance as evident by lower relative root mean square error of prediction (RRMSEP) values compared to the full spectrum ANN models. Validation of the developed FA-ANN models demonstrated excellent accuracy, precision and selectivity for the quantification of the target analytes as per international conference on harmonisation (ICH) guidelines. Additionally, the greenness, analytical practicality and sustainability of the developed models were assessed using the analytical greenness (AGREE), blue applicability grade index (BAGI) and the red-green-blue (RGB) tools, confirming their environmentally friendly, practical and sustainable nature. This research shed the light on the potential of ANN coupled with UV fingerprinting for the rapid and simultaneous determination of critical cardiovascular drugs posing a significant impact on pharmaceutical quality control and patient monitoring.
... PLS uses spectral data matrix and substance concentrations to obtain the latent variables, in parallel, maximizing the covariance between spectral and concentration data [9]. There is extensive literature on using PLS to quantify one or more than one API in a test sample [5,7,[9][10][11] or API's with their degradation products [4,6,8,[12][13][14]. ...
... The statistical test to evaluate whether the average recoveries (r i ) for each concentration level are significantly different from 1 (100%) is performed as described in Eq. (11) [19,28,44,46]: ...
Multivariate spectrophotometric procedures are widely used in drug analysis to quantify one or more substances in a sample. Even if the analytical procedure is validated, there is always some degree of uncertainty related to the analytical results. No studies were found in the literature that evaluated the measurement uncertainty for multivariate spectrophotometric procedures. Thus, this paper aims to estimate the measurement uncertainty of the quantification of acetylsalicylic acid (ASA) and salicylic acid (SA), in an ASA samples, by a stability-indicating multivariate spectrophotometric procedure. The measurement uncertainties of ASA and SA were estimated by the top-down approach, using the Nordtest/ISO 11352 and VAM Project methodologies, and by the bottom-up approach, using the Monte Carlo method (MCM). The calculation of measurement uncertainty considered uncertainty sources related to the preparation of sample and standard solutions and the impact of these sources on the model obtained by partial least squares (PLS). The uncertainty of the spectra was estimated by bootstrap resampling method. The expanded uncertainties estimated for ASA were 3.7%, 4.1% and 3.7% calculated by MCM, Nordtest/ISO 11352 and VAM Project, respectively. The expanded uncertainties estimated for SA considering the concentration range from 2.67 to 6.67 μg/mL were 0.25, 0.36 and 0.35 μg/mL and for the concentration range from 6.67 to 18.67 μg/mL were 5.3%, 5.3% and 6.0% calculated by MCM, Nordtest/ISO 11352 respectively. It was concluded that the developed methodology allows to estimate the measurement uncertainty of a multivariate spectrophotometric analytical procedure by bottom-up and top-down approaches.
... For the first time, firefly as a variable selection algorithm [5][6][7][8] in UV spectral data was introduced in combination with three different multivariate models namely, concentration residual augmented classical least squares (CRACLS) [9,10], artificial neural network [11][12][13] (ANN) and support vector regression (SVR) [14][15][16][17][18]. Also, a comparative study was developed between this algorithm and the well-known genetic algorithm [19][20][21] on the same multivariate models. This study was applied for the determination of ciprofloxacin (CIP) Fig. 1 (a) in the presence of metronidazole (MET) as interferent Fig. 1 (b) in laboratory prepared mixtures and in their pharmaceutical dosage form. ...
... ACCEPTED MANUSCRIPT 10 The above discussion revealed that FA and GA are similar in some points as: ...
... Its robustness against spectral interferences and strong predictive capabilities, particularly when combined with variable selection techniques such as the genetic algorithm, position PLS as an optimal choice for developing a reliable and accurate quantification method. Specifically the use of variable selection techniques, such as genetic algorithm-based variable selection or other swarm intelligence algorithms, can help in identifying the optimal combination of emission wavelengths for each analyte, thereby improving the sensitivity, selectivity as well as predictability of the method [20][21][22]. Another key advantage of coupling of coupling synchronous spectrofluorimetry with multivariate calibration is its environmental friendliness and cost-effectiveness, as it requires minimal sample preparation and avoids the use of hazardous organic solvents typically associated with traditional chromatographic techniques, an important consideration given for the emphasis on more environmentally friendly analytical techniques [23,24]. ...
This study introduces a novel synchronous spectrofluorimetry coupled with chemometric tools for the determination of tenofovir and dolutegravir antiretroviral drugs. Utilizing partial least squares regression (PLS) fine‐tuned by genetic algorithm as variable selection tool, the developed models demonstrate greater sensitivity, cost‐effectiveness, and reduced environmental impact compared to traditional HPLC methods. The model's validation was further confirmed using external validation in addition to QC samples as per ICH M10 guidelines, which yielded high accuracy ranged between 94.78% and 102.48% for tenofovir and 94.56% and 103.31% for dolutegravir, respectively. Additionally, the %CV values for within‐run precision were below 4%, while those for between‐run precision stayed under 6%, for tenofovir and dolutegravir, respectively. Hence, the developed models were adeptly applied to pharmacokinetic studies in a rat model, revealing slight changes in the tenofovir and dolutegravir plasma concentration profiles and half‐lives when administered in combination, highlighting possible drug interactions. Greenness analysis of this spectrofluorimetric approach using the AGREE tool further substantiates its advantages over conventional chromatographic techniques. Hence, this simple, rapid, and environmentally friendly analytical methodology presents a promising alternative for therapeutic drug monitoring and pharmacokinetic studies of tenofovir and dolutegravir antiretroviral drugs.
... Genetic algorithm (GA) is a variable selection algorithm that imitates the biological evolution mechanisms to attain the most substantial data [51]. It proved success in improving the prediction power and reducing dimensionality of the data [52][53][54]. ...
Two accurate, precise and robust multivariate chemometric methods were developed for the simultaneous determination of montelukast sodium (MON), rupatadine fumarate (RUP) and desloratadine (DES). These methods provide a cost-effective alternative to chromatographic techniques by utilizing spectrophotometry in pharmaceutical quality control. The proposed approaches, partial least squares-1 (PLS-1) and artificial neural network (ANN), were optimized using genetic algorithm (GA) to select the most influential wavelengths, enhancing model performance. A five-level, three-factor design was employed to construct a calibration set with 25 mixtures, utilizing concentration ranges of 3–19, 5–25, and 4–20 µg.mL⁻¹ for MON, RUP, and DES, respectively. An independent validation set was employed to assess the performance of the models. GA significantly improved the PLS-1 and ANN models for RUP and DES, though minimal enhancement was observed for MON. These methods were successfully applied to the simultaneous quantification of the compounds in pharmaceutical formulations and proved useful as stability-indicating assays for RUP, given that DES is a known degradation product. The developed methods offer a valuable tool for impurity profiling and quality control in pharmaceutical analysis.
... In this case, the least absolute shrinkage and selection operator are used (Lasso ) 15 and least angle regression (LAR) 16 are considered a viable option. The regression based algorithm are used for nondestructive pharmaceutical preparation discrimination 17 , for modeling the anitbactrial activity of ionic liquids 18,19 , in a semi-solid matrix for multivariate analysis of nystatin and Metronidazole 20 , in tablet formulations for simultaneous quantitative assessment of paracetamol and tramadol 21 , for determining the active pharmaceutical ingredient in transdermal gel formulations on a quantitative basis 22 , for predicting effective dose as a cytotoxicity biomarker 23 , for antibiotic discovery using metabolic fingerprinting and a screening assay 24 , for determining the concentrations of active ingredients in semi-solid pharmaceutical formulations 25 , for orally disintegrating tablet template formulation and non-destructive methods of assessment 26 , for anti-Helicobacter pylori drug screening 27 and for the analysis of a binary amoxicillin-flucloxacillin mixture 28 . ...
Imidazole has anti-inflammatory, antituberculotic, antimicrobial, antimycotic, antiviral, and antitumor properties in the human body, to name a few. Metronidazole [1-(2-Hydroxyethyl)-2-methyl-5-nitroimidazole] is a widely used antiprotozoan and antibacterial medication. Using fourier transform infrared spectroscopy, the current study models the antibacterial activity of already synthesised Metronidazole (MTZ) complexes (MTZ-Benz, MTZ-Benz-Cu, MTZ-Benz-Cu-Cl2CHCOOH, MTZ, MTZ-Cu, MTZ-Cu-Cl2CHCOOH, MTZ-Benz-Ag, MTZ-Benz-Ag-Cl2CHCOOH, MTZ-Ag and MTZ-Ag-Cl2CHCOOH) against E. coli, B. bronceptica, S. epidermidis, B. pumilus and S. aureus. To characterise the Metronidazole complexes for antibacterial activity against 05 microbes, the least angular regression and least absolute shrinkage selection operators were used. Asymmetric Least Squares was used to correct the spectrum baseline. Least angular regression outperforms cross-validated root mean square error in the fitted models. Using Least angular regression, influential wavelengths that explain the variation in antibacterial activity of Metronidazole complexes were identified and mapped against functional groups.
... Although spectral overlaps are usually attained when employing this technique for mixture analysis, this can be resolved by employing post mathematical processing algorithms. These include those based on univariate approaches via the use of a divisor to obtain a ratio spectra and further processing of these ratio spectra using signal processing methods such as derivatives, ratio difference, and mean centering methods (19,20) or those based on multivariate approaches such as principal component regression (PCR) and partial least squares (PLS) (21,22). ...
Background
A recent combination of aspirin (ASP) and omeprazole (OMP) has been presented in a fixed dosage form for treatment of many CVD, particularly in patients with gastric diseases. However, ASP is very sensitive to degradation into salicylic acid (SAL) as its main degradation product. Hence, it is very important to develop methods for the determination of ASP and OMP in the presence of SAL.
Objective
In this study, UV spectrophotometry assisted by different univariate/multivariate post processing algorithms have been presented for quantitative determination of ASP, OMP and SAL without any prior separation.
Methods
The univariate/multivariate algorithms include double divisor ratio difference and double divisor mean centering as the univariate approaches while the multivariate methods include principal component regression (PCR) and partial least squares (PLS) models. Validation of the univariate methods was done according to the ICH guidelines while the multivariate models were validated using external validation set.
Results
The univariate algorithms displayed excellent regression and validation capabilities in terms of linearity, accuracy, precision, and selectivity. Regarding PCR and PLS, the number of latent variables were carefully optimized, and the model’s validation criteria displayed excellent recoveries and lower errors of prediction.
Conclusion
Our findings indicate that the developed methods were comparable to the only reported chromatographic methods but with much shorter analysis time and simplicity.
Highlights
Overall, this report presents the first spectrophotometric methods applied for determination of possible combination of ASP, OMP, and SAL, and poses theses methods as valuable analytical tools in in-process testing and quality control analysis.
... This is due to their structures and mechanisms, because both of them are comparable to evolutionary processes in nature, namely, equivalents of genes and chromosomes in GA [25] or the biological (human or animal) central nervous system (including neurons) in ANN [26]. They can be successfully used separately [27] or often as a combined tool [28,29]. It is worth noting that these are not all of the techniques that may be used for this purpose. ...
In this review, we present the applications of chemometric techniques for green and sustainable chemistry. The techniques, such as cluster analysis, principal component analysis, artificial neural networks, and multivariate ranking techniques, are applied for dealing with missing data, grouping or classification purposes, selection of green material, or processes. The areas of application are mainly finding sustainable solutions in terms of solvents, reagents, processes, or conditions of processes. Another important area is filling the data gaps in datasets to more fully characterize sustainable options. It is significant as many experiments are avoided, and the results are obtained with good approximation. Multivariate statistics are tools that support the application of quantitative structure–property relationships, a widely applied technique in green chemistry.
... The drawbacks of the electrochemical methods were overcome by UV spectrophotometric methods that have the ability to resolve sophisticated mixures [36][37][38][39][40][41]. Although the analysis of such complex mixtures by UV spectrophotometry requires some post mathematical processing mainly via chemometric analysis [42][43][44], however, the proposed UV spectrophotometric methods in this work had the ability to resolve the investigated drugs in mixtures without sophistication of the chemometric approaches. These methods only depend on simple manipulations of the zeroorder spectrum saving the time and effort for analysis [45]. ...
Two newly introduced pharmaceutical mixtures of amlodipine/celecoxib and amlodipine/ramipril were developed to manage hypertension and the associated osteoarthritis. the current work presents three newly developed UV spectrophotometric methods depending on minimal mathematical manipulations on the zero-order spectrum namely: absorption correction, induced dual-wavelength, and Fourier self deconvoluted method; for the simultaneous determination of celecoxib and ramipril in their pharmaceutical combined dosage forms with amlodipine. In absorption correction and induced dual-wavelength method, celecoxib and ramipril were determined at 253 and 222 nm for absorption correction and (251-270 nm) and (222-230 nm) for induced dual-wavelength method, respectively from the zero-order spectrum after calculating the absorption correction and equality factors for amlodipine. Amlodipine itself was determined at 361 nm from the zero-order spectrum in both methods. In Fourier self deconvoluted method, celecoxib and amlodipine zero-order spectra were deconvoluted, using the spectrophotometer software built-in Fourier wavelet function, and then was determined at 360 and 269 nm, respectively. The proposed methods were simple, accurate, and sensitive requiring minimal mathematical manipulations saving the time needed for analysis. The methods were linear over the range of (5-60 μg/ml), (5-30 μg/ml), and (5-110 μg/ml) for each of amlodipine, celecoxib, and ramipril, respectively. The limit of detection was in the range of (0.5781 - 0.7132 μg/ml) for amlodipine, (0.6497 - 1.0450 μg/ml) for celecoxib, and (0.0001 - 0.0003 μg/ml) for ramipril that indicated the sensitivity of these suggested methods. All methods were validated as per ICH recommendations regarding linearity, range, accuracy, precision, and selectivity. A statistical comparative study executed for the proposed methods with each other and with the reported methods showed no significant difference between the proposed methods and the reported methods.
... Of all chemometrics methods, partial least square (PLS) has been employed extensively for the analysis of several pharmaceutical compounds owing to its capability to capture the maximum variance and provides maximum correlation between the spectral and concentration variables [28,29]. Moreover, integrating of variable selection procedure such as genetic algorithm (GA) warrants improving of the PLS model performance attributing to its ability to eliminate the uninformative or irrelevant variables; resulting in more robust models [30]. ...
Herein, UV spectrophotometry assisted by multivariate chemometric analysis has been presented for quantitative determination of complex quinary therapy containing atenolol, ramipril, hydrochlorothiazide, simvastatin and aspirin without any prior separation. Such combination is very useful for treating various cardiovascular diseases (CVD) including high blood pressure, hypercholesterolemia in addition to its antiplatelet aggregating activity. Calibration (15 samples) and validation (10 samples) sets were prepared of different concentrations for these drugs via implementing partial factorial experimental design. The zero order UV spectra of these sets were recorded and then subjected for further chemometric analysis. Partial least square (PLS) with/without variable selection procedure i.e. genetic algorithm (GA) were employed to untangle the UV spectral overlapping of these mixtures. The performance of these chemometric techniques are compared in terms of accuracy and predictive abilities using cross-validation and external validation methods. It was found that PLS provides good recoveries with prompt predictive ability albeit GA-PLS exhibits better analytical performance owing to its capability to remove redundant variables i.e. the number of absorbance variables has been reduced to about 19–28%. The developed methods have allowed for reliable determination of such complex therapy in its laboratory prepared mixtures and pharmaceutical preparation within comparable results to those reported by HPLC method, posing these chemometric methods as valuable and indispensable analytical tools in in-process testing and quality control analysis of many pharmaceutical compounds targeting CVD.
... [25] Partial least squares One of the most popular chemometrics tools is PLS, where the information of responses and concentrations is simultaneously taken into account. [26][27][28][29] In our study, the cross-validation method (leaving out one sample at a time) was used to select the optimum number of factors (Fig. 2). The least significant prediction error was provided by the use of seven LVs. ...
This study was concerned with the assay of ascorbic acid (ASC), rutin, and hesperidin (HES) in their combined formulation using a multivariate approach. Three chemometric-assisted spectrophotometric models namely: partial least squares, multivariate curve resolution-alternating least squares, and artificial neural networks were developed and validated. The quantitative analyses of all the proposed models were assessed by percentage recoveries, root mean square error of prediction, and standard error of prediction. The proposed models were used in the range of 10.0–70.0, 2.0–10.0, and 2.0–10.0 µg mL⁻¹ for ASC, rutin, and HES, respectively. In addition, correlation coefficients between the pure and estimated spectral profiles were used for the qualitative analysis of a multivariate curve resolution-alternating least squares model. Artificial neural networks showed higher speed and methodological simplicity over the other two models. These models presented powerful multivariate statistical tools that were applied to the analysis of the combined dosage form in the Australian market. They have the ability to overcome difficulties such as colinearity and spectral overlaps. Statistical comparison between the proposed and reported methods showed no significant difference. The proposed methods can be used for the routine analysis of the studied drugs in quality control laboratories.
... GA; act by exploring all regions of the potential solutions and exponentially exploiting promising area through mutation, crossover, and selection operation applied to individuals in the populations. When GA coupled with PLS or ANNs as a preceding step, it improves the quality of the calibration by improving the predictability of these models through eliminating uninformative variables and enhancing the interpretability this by selecting the most informative ones [33][34][35]. The adjusted GA parameters were listed in Table 2, in which, the GA was run for 201 variables. ...
This work, introduces different powerful chemometric methods for determination of cefoxitin-sodium in presence of its alkali-induced degradation product without prior separation steps, dubbed; Principal component regression, Partial least squares with and without variable selection (Genetic Algorithm), Artificial neural network with and without variable selection (Genetic Algorithm), and Classical least square. The predictive abilities of the models were tested and the results proved that proposed methods were successfully applied for the determination of cefoxitin-sodium in its pure form and in its powder for injection.
... GA; act by exploring all regions of the potential solutions and exponentially exploiting promising area through mutation, crossover, and selection operation applied to individuals in the populations. When GA coupled with PLS or ANNs as a preceding step, it improves the quality of the calibration by improving the predictability of these models through eliminating uninformative variables and enhancing the interpretability this by selecting the most informative ones [33][34][35]. The adjusted GA parameters were listed in Table 2, in which, the GA was run for 201 variables. ...
This work, introduces different powerful chemometric methods for determination of cefoxitin-sodium in presence of its alkali-induced degradation product without prior separation steps, dubbed; Principal component regression, Partial least squares with and without variable selection (Genetic Algorithm), Artificial neural network with and without variable selection (Genetic Algorithm), and Classical least square. The predictive abilities of the models were tested and the results proved that proposed methods were successfully applied for the determination of cefoxitin-sodium in its pure form and in its powder for injection. Keywords: Cefoxitin-sodium, Principal component regression, Partial least squares, Genetic algorithm, Artificial neural network, Classical least square
... Different univariate spectrophotometric and multivariate chemometric methods were reported for determination of flucloxacillin-sodium in combination with amoxicillin by Khalid et al, 17,18 . ...
Objectives: The aim of this study was to develop four simple and accurate spectrophotometric methods for determination of flucloxacillin-sodium in binary mixture with ampicillin-trihydrate without separation. Methods: One of them was a univariate constant center method and the other three methods were multivariate chemometric methods named; Savitsky-Golay filters, continuous wavelet transform of ratio spectra and wavelet transform of first derivative of the ratio spectra. Results: The proposed methods adopted for selective determination of flucloxacillin-sodium and obey Beer’s law in the range (2-20 µg mL-1). Conclusion: The proposed methods were simple, rapid, economic, accurate and precise; they were successfully applied for the determination of flucloxacillin sodium in pure form and in pharmaceutical preparations.
... However, analyzing a large number of chemical shifts is a challenge. variable selection are regarded as a promising research field in chemometrics [14]. Genetic algorithm (GA) variable selection is a precise and accurate regression model for identifying a subset of measured variables [15]. ...
The public concern and increasing awareness of herbal medicines calls for methods available to assure quality and authenticity. In the present study, a novel strategy combing 1H nuclear magnetic resonance (1H NMR) spectral analyses coupled with chemometrics was developed and validated to allow comprehensive analysis and rapid authentication of herbal medicines. Polygoni Multiflori Radix, a widely used herbal medicine and a kind of popular functional food, was taken as an example. Characteristic profiling of 1H NMR fingerprints was achieved by genetic algorithm and a counter-propagation artificial neural network. Multivariate classification methods were employed to evaluate and validate the performance of the developed characteristic profiling of 1H NMR fingerprints. The results well showed that the proposed method improved the predictive ability of the PLS-DA, and presented equivalent performance to the original spectrum in other classification methods. In conclusion, the developed method is an accurate, sensitive and rapid authentication of Polygoni Multiflori Radix, which could be a useful tool for rapid authentication and quality control of herbal medicines.
... In order to avoid the risk of over fitting, a number of independent short runs was done and the results of all the runs were taken into consideration to obtain the final model. Doing this, a much more consistent (and less over fitted) solution can be obtained 11,12 . The adjusted GA parameters with the lowest mean square error were shown in Table 2. et al., 2017, 1 (4), 185-192 http://aprh.journals.ekb.eg/ ...
... From our point of view, the goal of this work is develop the first three UV spectrophotometric methods for simultaneous estimation of EBV and GRV in bulk powder and in their combined pharmaceutical dosage form. The described methods namely simultaneous equation [SE] [9,10] and partial least squares [PLS] with and without variable selection procedure, genetic algorithm [GA], [11,12] and a comparative study was done between both models. ...
The first three UV spectrophotometric methods have been developed of simultaneous determination of two new FDA approved drugs namely; elbasvir and grazoprevir in their combined pharmaceutical dosage form. These methods include simultaneous equation, partial least squares with and without variable selection procedure (genetic algorithm). For simultaneous equation method, the absorbance values at 369 (λmax of elbasvir) and 253nm (λmax of grazoprevir) have been selected for the formation of two simultaneous equations required for the mathematical processing and quantitative analysis of the studied drugs. Alternatively, the partial least squares with and without variable selection procedure (genetic algorithm) have been applied in the spectra analysis because the synchronous inclusion of many unreal wavelengths rather than by using a single or dual wavelength which greatly increases the precision and predictive ability of the methods. Successfully assay of the drugs in their pharmaceutical formulation has been done by the proposed methods. Statistically comparative analysis for the obtained results with the manufacturing methods has been performed. It is noteworthy to mention that there was no significant difference between the proposed methods and the manufacturing one with respect to the validation parameters.
... Few methods were reported for determination of both components including high-performance liquid chromatography [34][35][36][37], spectrophotometry [38] and chemometric assisted techniques [39]. ...
Three different spectrophotometric methods were applied for the quantitative analysis of flucloxacillin and amoxicillin in their binary mixture, namely, ratio subtraction, absorbance subtraction and amplitude modulation. A comparative study was done listing the advantages and the disadvantages of each method. All the methods were validated according to the ICH guidelines and the obtained accuracy, precision and repeatability were found to be within the acceptable limits. The selectivity of the proposed methods was tested using laboratory prepared mixtures and assessed by applying the standard addition technique. So, they can be used for the routine analysis of flucloxacillin and amoxicillin in their binary mixtures.
This study aimed to develop a green and sustainable analytical method for the quantitative determination of three statins—rosuvastatin, pravastatin, and atorvastatin—using their UV spectral fingerprints. Partial Least Squares (PLS) regression...
During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.
Penjaminan mutu produk obat kombinasi salah satunya dilakukan melalui analisis kuantitatif. Teknik kemometrika menjadi salah satu alternatif pilihan pengenalan pola pada analisis kuantitatif senyawa obat kombinasi tanpa tahap pemisahan yang mudah diterapkan, sensitif, dan cukup terjangkau. Keunggulan teknik kemometrika seperti ini telah dibuktikan pada berbagai penelitian yang relevan. Tujuan artikel review ini adalah untuk mengetahui lebih lanjut pemanfaatan teknik kemometrika pengenalan pola pada analisis kuantitatif senyawa obat kombinasi tanpa tahap pemisahan. Penyusunan artikel review ini menggunakan metode penelusuran literatur ilmiah primer terbitan sepuluh tahun terakhir pada rentang 2011 hingga 2021. Hasilnya menunjukkan bahwa teknik kemometrika yang paling banyak diaplikasikan adalah PLS 78,95% dengan instrumen tanpa tahap pemisahan yang paling banyak digunakan sebagai kombinasinya adalah spektrofotometer UV-Vis 84,21%. Selain itu, campuran obat dari dua senyawa 71,05% pada berbagai golongan obat menjadi jumlah campuran yang paling banyak dianalisa dengan kombinasi teknik kemometrika dan instrumen analisis.
Herein, a simple spectrophotometric method coupled with chemometric techniques i.e. partial least square (PLS) and genetic algorithm (GA) were utilized for the simultaneous determination of the vital ternary antiretroviral therapy dolutegravir (DTG), lamivudine (LMV), and abacavir (ACV) in their combined dosage form. Calibration (25 samples) and validation (13 samples) sets were prepared for these drugs at different concentrations via implementing partial factorial experimental designs. The zero order UV spectra of calibration and validation sets were measured and then subjected for further chemometric analysis. Partial least squares with/without variable selection procedures i.e. genetic algorithm (GA) were utilized to untangle the UV spectral overlapping of these mixtures. Cross-validation and external validation methods were applied to compare the performance of these chemometric techniques in terms of accuracy and predictive abilities. It was found that six latent variables were optimum for modelling DTG, four latent variables for modelling LMV and three latent variables for modelling ACV. Although, good recoveries with prompt predictive ability were attained by these PLS, GA-PLS showed better analytical performance owing to its capability to remove redundant variables i.e. the number of absorbance variables have been reduced to about 21-29 %. The proposed chemometric methods can be reliably applied for simultaneous determination of DTG, LMV, and ACV in their laboratory prepared mixtures and pharmaceutical preparation posing these chemometric methods as worthy and substantial analytical tools in in-process testing and quality control analysis of many antiretroviral pharmaceutical preparations.
Background
In many real-world situations there are a large number of components in a mixture that produce enormous number of information.
Objective
The main task is to build up balanced models that convert these data into meaningful information to deal with. Hence, different chemometric models were applied for the analysis of data obtained from a mixture containing sofosbuvir, ledipasvir, velpatasvir, daclatasvir and valacyclovir, that recently used world wide for their antiviral activity.
Methods
Partial Least Squares (PLS), Spectral Residual Augmented Classical Least Squares (SRACLS) and Concentration Residual Augmented Classical Least Squares (CRACLS) designs were applied with and without variable selection procedure (Genetic Algorithm GA). The methods were used for the quantitative analysis of the drugs in laboratory prepared mixtures and real market sample through handling the UV spectral data.
Results
Robust models were obtained by applying GA. The proposed methods were found to be rapid, simple and required no preliminary separation steps.
Conclusion
These models can be used on routine basis in quality control laboratories or factories giving competitor results to that obtained by the reported methods.
Highlights
The proposed models offer a powerful analytical alternative for laboratories that consider economic strategies in their requirements.
A novel spectrophotometric method is described, including multivariate regression/model updating, for the analysis of a quaternary mixture of clopidogrel, atorvastatin, aspirin, and its degradation product salicylic acid. The multivariate algorithms adopted are partial least squares with and without using a "Genetic Algorithm" for selecting variables. Upon updating both models, they could be effectively applied to determine the studied drugs in their pharmaceutical formulations. Similarly, clopidogrel and aspirin in their combined pharmaceutical preparations could be readily determined. Moreover, the proposed method could be extended to the determination of spiked salicylic acid as a minor component in aspirin raw material and dosage forms. The accuracy and precision of the proposed methods were approved through statistical comparison with the reported methods.
A new sensitive, selective and accurate thin-layer chromatography (TLC) - densitometry method developed for simultaneous estimation of Amoxicillin (AMX) and Flucloxacillin (FLX) in their pure forms or their pharmaceutical formulations. Chromatographic separation was completed on aluminum plate pre-coated with silica gel 60 F254 plates using a mobile phase consist of chloroform: methanol: ammonia (5: 2: 0.5 by volume) on silica gel, followed by densitometry detection of spots at 254 nm . Calibration curves were developed in the range of 4-40 μg/spot and 5-30 μg/spot with good correlation for AMX and FLX, respectively. The adopted method was validated according to ICH guidelines and showed good accuracy and precision. The acquired results were statistically compared with the reported method, where no significant difference has been showed. The developed method can be applied for routine analysis of AMX and FLX in their pure forms and capsule dosage form.
The present work represents a simple, accurate, selective and sensitive UV-spectro-photometric methods for simultaneous estimation of xipamide and triamterene in pure and combined dosage form. The proposed methods are based on presence of iso-absorptive point. These methods include absorbance subtraction, amplitude modulation, Q analysis and absorbance ratio method. The methods have been validated for linearity, accuracy and precision and found to be rapid, precise and economical.
We here for the first time demonstrate an analytical approach for the highly selective and sensitive detection of amoxicillin (Amox) in aqueous medium based on the fluorescence quenching of quantum dots (QDs). The change in fluorescence intensity of mercaptopropionic acid-capped cadmium sulphide (MPA-CdS) QDs is attributed to the increasing concentration of Amox. The results show that the fluorescence quenching of QDs by Amox takes place through both static and dynamic types of quenching mechanism. The fluorescence quenching of QDs with increase in concentration of Amox shows the linear range between 5 μg ml(-1) and 30 μg ml(-1) and the limit of detection (LOD) is 5.19 μg ml(-1) . There is no interference of excipients, which are commonly present in pharmaceutical formulation and urine samples. For the practical application approach, the developed method has been successfully applied for the determination of Amox in pharmaceutical formulations and urine samples with acceptable results.
Herein, this paper aimed to preparation, molecular structure characterizations, spectroscopic and thermal studies of Cu(II), Co(II), Ni(II) and Fe(III) chelates of amoxicillin (AMX) antibiotic drug. The stoichiometry of 1:2 (Metal : AMX) complexes were explained by micro-analytical analyses and spectroscopic tools (e.g., FT-IR, ESR and UV-vis.). The values of molar conductance for the AMX complexes dissolved in dimethylsulfoxide led to electrolytic behavior. These outcome data prove that AMX acts with mentioned metal chlorides as a tri-dentate chelate through –NH2, –NH, and oxygen of carbonyl β-lactam groups. The general formula of AMX complexes can be summarized as [M(AMX-Na)2].xCl.yH2O (where M = Cu2 + (x = 2, y = 4), Co2 + (x = 2, y = 2), Ni2 + (x = 2, y = 1) and Fe3 + (x = 3, y = 0). The activation thermodynamic parameters E*, ΔS*, ΔH* and ΔG* were estimated using two official methods as Coats–Redfern and Horowitz–Metzger dependent on thermogravimetric curves. The molecular structure of AMX drug was optimized by HF method with 3-21G basis. The molecular docking analysis was carried out using the receptor of prostate cancer mutant 2Q7K-hormone.
A simple, precise, fast and accurate HPLC method has been developed for the simultaneous estimation Amoxicillin and Flucloxacillin in capsules. The analytes were resolved, by using a mobile phase mixture of buffer (prepared from 0.001m Diammonium hydrogen orthophosphate and 0.04m tetra butyl ammonium bromide pH adjusted to 7.0±0.1 with ortho phosphoric acid) and acetonitrile in the ratio (99:10v/v), on a strong cation exchange column, (LUNA SCX, 250mm x 4.6mm I.D. 5 μm particles).The retention times for amoxicillin and flucloxacillin were found to be 3.828 and 5.89, respectively. The validation of the method was performed, and specificity, precision, accuracy, linearity, range, robustness were confirmed.
A new and rapid potentiometric method for determination of flucloxacillin is developed. The method involves development of a flucloxacillin sensor with a membrane consisting of Aliquat 336S-flucloxacillin as an electroactive material in poly vinyl chloride matrix membrane plasticized with orthonitrophenyl-octylether or dioctylphthalate. The sensor shows fast, stable and reproducible response over the concentration range of 1.0 × 10−5–1.0 × 10−2 M flucloxacillin with anionic slopes of 60.7 ± 0.3 and 61.2 ± 0.2 and pH ranges of 6–11 and 7–11 for o-nitrophenyloctylether (o-NPOE) and dioctylphthalate (DOP) plasticized based membrane sensors, respectively. The response time of the sensor is stable and fast (7 s). The sensor exhibits high selectivity towards flucloxacillin in presence of amoxicillin, ampicillin, dicluxacillin, pencillin, many anions and drug excipients and diluents. Validation of the method according to the quality assurance standards shows suitability of the proposed sensors for use in the quality control assessment of the drug. Results with average recoveries of 99.6% and 99.7% and mean standard deviations of ±1.2% and ±1.5% for o-NPOE and DOP plasticized based membrane sensors, respectively, of the nominal are obtained which compare fairly well with data obtained using the British Pharmacopoeia method.
A reverse phase high performance liquid chromatographic method was developed for simultaneous determination of amoxicillin trihydrate and flucloxacillin sodium in bulk and pharmaceutical formulation. The separation w as made by a Kromasil C18 column ( 250 cm × 4.6 mm, 5µm) using 0.020 M potassium dihydrogen orthophosphate - acetonitrile ( 75:25) as mobile phase. The validation of the method was performed, and specificity, reproducibility, precision and accuracy were confirmed. The limits of quantification were approximately 0.16 µg/ml for amoxicillin trihydrate and 0.25 µg/ml for flucloxacillin sodium. Due to simplicity and accuracy the method particularly suitable for routine pharmaceutical quality control.
This paper provides a discussion of the development and application of Artificial Neural Networks (ANNs) to flow forecasting in mio flood-prone UK catchments using real hydrometric data. Given relatively brief calibration data sets it was possible to construct robust models of 15-min flows with six hour lead times for the Rivers Amber and Mole. Comparisons were made between the performance of the ANN and those of conventional flood forecasting systems. The results obtained for validation forecasts were of comparable quality to those obtained from operational systems for the River Amber. The ability of the ANN to cope with missing data and to "learn" from the event currently being forecast in real time makes it an appealing alternative to conventional lumped or semi-distributed flood forecasting models. However, further research is required to determine the optimum ANN training period for a given catchment, season and hydrological contexts.
Multivariate and derivative spectrophotometric techniques (first derivative and derivative ratio) were developed for the determination of four β-lactam antibiotic binary mixtures; ampicillin with flucloxacillin (mix I), ampicillin with dicloxacillin (mix II), amoxicillin with flucloxacillin (mix III) and amoxicillin with dicloxacillin (mix IV) in pharmaceutical combinations containing these compounds. The simultaneous determination of these compounds was accomplished by first derivative (dA/dλ) spectrophotometric technique, applying zero-crossing technique and first derivative of the ratio spectrum. The influence of ∆λ for obtaining the first derivative of the ratio spectra and the effect of the divisor concentration on the calibration graphs were studied. Lastly by multivariate methods; (classical least squares (CLS) and principle component regression (PCR)). Absorption spectra of compounds were used to optimize the spectral data set performs the calibration by CLS and PCR. These calibration models were evaluated by internal validation (prediction of compounds in its own designed training set of calibration), by cross-validation (obtaining statistical parameters that show the efficiency for a calibration fit model) and by external validation over synthetic and pharmaceutical mixtures. The four described procedures were successfully applied to the determination of these compounds in synthetic mixtures and in pharmaceutical preparations with high percentage of recovery, accuracy and precision. The procedures do not require any separation step.
Chemometrics has been used for some 30 years but there is still need for disseminating the potential benefits to a wider audience. In this paper, we claim that proper analytical chemistry (1) must in fact incorporate a chemometric approach and (2) that there are several significant advantages of doing so. In order to explain this, an indirect route will be taken, where the most important benefits of chemometric methods are discussed using small illustrative examples. Emphasis will be on multivariate data analysis (for example calibration), whereas other parts of chemometrics such as experimental design will not be treated here. Four distinct aspects are treated in detail: noise reduction; handling of interferents; the exploratory aspect and the possible outlier control. Additionally, some new developments in chemometrics are described.
A generalized form of the cross‐validation criterion is applied to the choice and assessment of prediction using the data‐analytic concept of a prescription. The examples used to illustrate the application are drawn from the problem areas of univariate estimation, linear regression and analysis of variance.
Two simple, rapid and selective spectrophotometric procedures were developed for the determination of amoxicillin and cefadroxil. The methods are based on the selective oxidation of the drugs with N-bromosuccinimide or N-chlorosuccinimide in an alkaline medium to give an intense yellow product (λmax= 395 nm). The reaction conditions were studied and optimized. The reactions obey Beer's law over the range 1–20 µg ml–1 for the two drugs with the two reagents. Interferences from other antibiotics, additives and common degradation products were investigated. The proposed methods were applied to the analysis of pharmaceutical formulations containing amoxicillin, either alone or in combination with potassium clavulanate or flucloxacillin. They were also applied to the analysis of some cefadroxil dosage forms. The results obtained compared favourably with those obtained with other reported methods.
A significant extension to the classical least-squares (CLS) algorithm called concentration residual augmented CLS (CRACLS) has been developed. Previously, unmodeled sources of spectral variation have rendered CLS models ineffective for most types of problems, but with the new CRACLS algorithm, CLS-type models can be applied to a significantly wider range of applications. This new quantitative multivariate spectral analysis algorithm iteratively augments the calibration matrix of reference concentrations with concentration residuals estimated during CLS prediction. Because these residuals represent linear combinations of the unmodeled spectrally active component concentrations, the effects of these components are removed from the calibration of the analytes of interest. This iterative process allows the development of a CLS-type calibration model comparable in prediction ability to implicit multivariate calibration methods such as partial least squares (PLS) even when unmodeled spectrally active components are present in the calibration sample spectra. In addition, CRACLS retains the improved qualitative spectral information of the CLS algorithm relative to PLS. More importantly, CRACLS provides a model compatible with the recently presented prediction-augmented CLS (PACLS) method. The CRACLS/PACLS combination generates an adaptable model that can achieve excellent prediction ability for samples of unknown composition that contain unmodeled sources of spectral variation. The CRACLS algorithm is demonstrated with both simulated and real data derived from a system of dilute aqueous solutions containing glucose, ethanol, and urea. The simulated data demonstrate the effectiveness of the new algorithm and help elucidate the principles behind the method. Using experimental data, we compare the prediction abilities of CRACLS and PLS during cross-validated calibration. In combination with PACLS, the CRACLS predictions are comparable to PLS for the prediction of the glucose, ethanol, and urea components for validation samples collected when significant instrument drift was present. However, the PLS predictions required recalibration using nonstandard cross-validated rotations while CRACLS/PACLS was rapidly updated during prediction without the need for time-consuming cross-validated recalibration. The CRACLS/PACLS algorithm provides a more general approach to removing the detrimental effects of unmodeled components.
Multivariate standardization techniques [slope/bias (S/B) correction, single wavelength standardization (SWS) and piecewise direct standardization (PDS)] were used to attempt to correct changes over time in multivariate calibration models for potassium and calcium. These models were constructed with ion-selective electrode (ISE) arrays. Multivariate PDS local models which included the correlation between the sensors of the array were better than the other simple techniques. We considered the relationship between the variables (sensors) and, in the PDS treatment, we have indicated their arrangement which is taken from the loadings plot. We used the Kennard–Stone algorithm to select the standardization samples from the original responses of the samples and the partial least squares (PLS) scores of each model. These scores include information about the concentrations. The models and standardizations were validated by predictions on real samples such as natural waters. The best standardization conditions provided unbiased predictions with no loss of precision.
Multilevel experiments are common in chemistry, especially in calibration and mixture problems. This paper presents designs for l = 2–5 different concentration levels and l2 corresponding experiments. The importance of orthogonality between successive factors is discussed. It is shown that up to 12 mutually orthogonal factors can be generated for a five-level design. Methods of generating designs are generalised. The designs are restricted to first-order (linear) models, typical of most instrumental calibration experiments.
A partial least-squares treatment of multivariate data related through a complex model allows simultaneous evaluation of the interactions between large numbers of features. Results are given for a model in which water sources flow together; each source is represented by water quality data to allow the influence of the various sources to be evaluated with respect to their importance on the resulting flow downstream.
By means of factor analysis (FA) or principal components analysis (PCA) a matrix Y with the elements yik is approximated by the modelHere the parameters α, β and θ express the systematic part of the data yik, “signal,” and the residuals ∊ik express the “random” part, “noise.”When applying FA or PCA to a matrix of real data obtained, for example, by characterizing N chemical mixtures by M measured variables, one major problem is the estimation of the rank A of the matrix Y, i.e. the estimation of how much of the data yik is “signal” and how much is “noise.”Cross validation can be used to approach this problem. The matrix Y is partitioned and the rank A is determined so as to maximize the predictive properties of model (I) when the parameters are estimated on one part of the matrix Y and the prediction tested on another part of the matrix Y.
Genetic algorithms constitute a set of powerful search heuristics. A modified genetic algorithm was used to optimize calibration data sets. In order to construct an ideal genetic procedure, the diversity in a population is crucial. The idea proposed is to estimate the diversities along two directions, namely the diversity between the chromosomes in a population and the diversity between the alleles in all chromosomes. The newly defined diversity functions are able to describe the procedure of a genetic algorithm in detail and can be used as a feedback for dynamic control of the process in an almost ideal way. The optimization results show that for both short and long runs the dynamic genetic algorithm is superior to the “classical” genetic algorithms and that after optimization not only can the data sets be compacted and refined but also the predictive ability of the calibration model can be improved.
A review is presented of multivariate calibration in analytical chemistry. Calibration methods discussed include: univariate calibration, multiple linear regression, principal components regression, partial least-squares, and multiway methods. Model validation is discussed with reference to: autoprediction, cross-validation, independent test sets and experimental design. Two case studies are presented. Appendices covering the following are included: vectors and matrices, notation and definitions, matrix operations, algorithms, principal components analysis, PLS1, PLS2 and trilinear PLS1. (39 references).
For partial least-squares regression with one response (PLS1), many variable-reduction methods have been developed. However, only a few address the case of multiple-response partial-least-squares (PLS2) modeling. The calibration performance of PLS1 can be improved by elimination of uninformative variables. Many variable-reduction methods are based on various PLS-model-related parameters, called predictor-variable properties. Recently, an important adaptation, in which the model complexity is optimized, was introduced in these methods. This method was called Predictive-Property-Ranked Variable Reduction with Final Complexity Adapted Models, denoted as PPRVR-FCAM or simply FCAM. In this study, variable reduction for PLS2 models, using an adapted FCAM method, FCAM-PLS2, is investigated. The utility and effectiveness of four new predictor-variable properties, derived from the multiple response PLS2 regression coefficients, are studied for six data sets consisting of ultraviolet-visible (UV-vis) spectra, near-infrared (NIR) spectra, NMR spectra, and two simulated sets, one with correlated and one with uncorrelated responses. The four properties include the mean of the absolute values as well as the norm of the PLS2 regression coefficients and their significances. The four properties were found to be applicable by the FCAM-PLS2 method for variable reduction. The predictive abilities of models resulting from the four properties are similar. The norm of the PLS2 regression coefficients has the best selective abilities, low numbers of variables with an informative meaning to the responses are retained. The significance of the mean of the PLS2 regression coefficients is found to be the least-selective property.
A significant improvement to the classical least-squares (CLS) multivariate analysis method has been developed. The new method, called prediction-augmented classical least-squares (PACLS), removes the restriction for CLS that all interfering spectral species must be known and their concentrations included during the calibration. We demonstrate that PACLS can correct inadequate CLS models if spectral components left out of the calibration can be identified and if their "spectral shapes" can be derived and added during a PACLS prediction step. The new PACLS method is demonstrated for a system of dilute aqueous solutions containing urea, creatinine, and NaCl analytes with and without temperature variations. We demonstrate that if CLS calibrations are performed with only a single analyte's concentrations, then there is little, if any, prediction ability. However, if pure-component spectra of analytes left out of the calibration are independently obtained and added during PACLS prediction, then the CLS prediction ability is corrected and predictions become comparable to that of a CLS calibration that contains all analyte concentrations. It is also demonstrated that constant-temperature CLS models can be used to predict variable-temperature data by employing the PACLS method augmented by the spectral shape of a temperature change of the water solvent. In this case, PACLS can also be used to predict sample temperature with a standard error of prediction of 0.07°C even though the calibration data did not contain temperature variations. The PACLS method is also shown to be capable of modeling system drift to maintain a calibration in the presence of spectrometer drift.
A simple and sensitive spectrophotometric method to the simultaneous determination of Mn(2+) and Fe(3+) in foods, vegetable and water sample with the aid of artificial neural networks (ANNs) is described. It relies on the complexation of analytes with recently synthesised bis pyrazol base ligand as 4,4'[(4-cholorophenyl)methylene] bis(3-methyl-1-phenyl-1H-pyrazol-5-ol)(CMBPP). The analytical data show that the ratio of ligand to metal in metal complexes is 1:1 and 1:2 for Fe(3+) and Mn(2+), respectively. It was found that the complexation reactions are completed at pH 6.7 and 5min after mixing. The results showed that Mn(2+) and Fe(3+) could be determined simultaneously in the range of 0.20-7.5 and 0.30-9.0mgl(-1), respectively. The analytical characteristics of the method such as the detection limit and the relative standard error predictions were calculated. The data obtained from synthetic mixtures of the metal ions were processed by radial basis function networks (RBFNs) and feed forward neural networks (FFNNs). The optimal conditions of the neural networks were obtained by adjusting various parameters by trial-and-error. Under the working conditions, the proposed methods were successfully applied to the simultaneous determination of elements in different water, tablet, rice, tea leaves, tomato, cabbage and lettuce samples.
A simple and sensitive spectrophotometric method is described for determination of amoxicillin. The method is based on a nucleophilic substitution reaction to measure the pink compound produced by the reaction of amoxicillin with sodium 1,2‐naphthoquinone‐4‐sulfonate in pH 9.00 buffer solution. The stoichiometric ratio of the compound is 1:1, and its maximum absorption wavelength is at 468 nm, ϵ=3.91×10 L · mol · cm. The Beer's law is obeyed in the range of 0.8–120 µg · mL of amoxicillin. The linear regression equation is A=0.041239+0.22128 C, with 0.9994 of a linear regression correlation coefficient. The detection limit is 2.0 µg · mL, and average recovery is over 98.5%. This paper further optimizes the determination of amoxcillin compared to the previous methods, and the kinetic property and reaction mechanism are studied intensively. This proposed method has been successfully applied to the determination of amoxicillin in tablets and capsules. The results obtained by this method agreed well with those by the official method high pressure liquid chromatography (HPLC).
The study of electrochemical behavior of amoxicillin (AMX), a β-lactam antibiotic, is described on a multiwalled carbon nanotubes (MWCNTs) modified electrode by electrochemical impedance spectroscopy (EIS) and adsorptive stripping voltammetry for sensitive determination of AMX in pharmaceutical and human urine samples within a wide pH range from 2.0 to 10.0. Also, studies by Fe2O3 nanoparticles modified carbon paste electrode show that iron oxide impurities in the MWCNTs are not active sites for sensing of amoxicillin. Under optimized conditions, the oxidation peak has two linear dynamic ranges of 0.6–8.0 and 10.0–80.0 μM with a detection limit of 0.2 μM and a precision of <4%.
Rofecoxib (I) has been determined in presence of its photodegradate (II) and alkaline degradation products (III) and (IV) by liquid chromatography method using a Shimpak ODS C18 column (150 mm × 4.5 mm i.d.) and acetonitrile/0.05% phosphoric acid (35:65) at pH 2.6. The method could determine (I) in the range of 0.3–28 μg ml−1 with mean percentage recovery of 100.29 ± 0.979%. Also, two chemometric methods were developed using partial least square (PLS) and concentration residual augmented classical least square method (CRACLS). The methods could determine (I) in presence of (II), (III) and (IV) with linearity range of 2–36 μg ml−1 and with mean percentage recovery of 99.53 ± 1.613 and 99.31 ± 1.42% for PLS and CRACLS, respectively. The PLS method was used to carry out a kinetic study of the alkaline degradation. The reaction rate constant was found to be k = 0.051 min−1 and the t1/2 =13.5 min using 0.051 M sodium hydroxide at 25 °C, other kinetic data were also presented. The three methods were applied to the analysis of pharmaceutical dosage forms and bulk powder.
A batch and flow injection analysis (FIA) spectrophotometric methods have been developed for the determination of amoxicillin (AMX) in aqueous solution and in pharmaceutical preparations. The methods are based on the reaction of AMX with N,N-dimethyl-p-phenylenediamine in the presence of potassium hexacyanoferrate(III) in alkaline medium. The water soluble blue colour product was measured at λmax 660 nm. Linearity was observed from 20 to 400 and 100 to 700 μg AMX in a final volume of 10 ml (i.e. 2–40 and 10–700 μg ml−1 AMX) with detection limits of 0.637 and 4.90 μg ml−1 AMX by batch and FIA procedure respectively. The effect of chemical and physical parameters have been carefully considered and the proposed procedures were successfully applied to the determination of AMX in pharmaceutical formulations.
In this article we provide an introduction to data structures and algorithms. We consider some basic data structures and deal with implementations of a dictionary and a priority queue. Algorithms for such basic problems as matrix multiplication, binary search, sorting, and selection are given. The concepts of randomized computing and parallel computing are also visited. 1 Preliminaries By an algorithm we mean any technique that can be used to solve a given problem. The problem under concern could be that of rearranging a given sequence of numbers, solving a system of linear equations, finding the shortest path between two nodes in a graph, etc. An algorithm consists of a sequence of basic operations such as addition, multiplication, comparison, and so on and is typically described in a machine independent manner. When an algorithm gets coded in a specified programming language such as C, C++, or Java, it becomes a program that can be executed on a computer. For any given problem, there could possibly be many different techniques that solve it. Thus it becomes necessary to define performance measures that can be used to judge different algorithms. Two popular measures are the time complexity and the space complexity. The time complexity or the run time of an algorithm refers to the total number of basic operations performed in the algorithm. As an example, consider the problem of finding the minimum of n given numbers. This can be accomplished using n − 1 comparisons. Of the two measures perhaps time complexity is more important. This measure is useful for the following reasons. 1) We can use the time complexity of an algorithm to predict its actual run time when it is coded in a programming language and run on a specific machine. 2) Given several different algorithms for solving the same problem we can use their run times to identify the best one.
A rapid, simple, accurate, sensitive and reproducible high performance liquid chromatographic (HPLC) method for the quantitation
of amoxicillin in human plasma using cefadroxil as an internal standard (IS) has been developed and validated. The procedure
involves an ultrafiltration step prior to a reversed-phase liquid chromatography. The drug and the IS were eluted from Symmetry® C18 stainless steel column (5μm, 150×4.6mm I.D.) at room temperature with a mobile phase consisting of methanol: 75mMpotassium
dihydrogen phosphate buffer solution (10:90,v/v) (pH adjusted to 3.0 with phosphoric acid), at a flow rate of 1.5mLmin−1. The effluent was monitored using a UV detector set at 228nm. Each analysis required no longer than 10min. Quantitation
was achieved by measurement of the peak area ratio of the drug to the internal standard, and the limit of quantification of
amoxicillin in plasma was 0.5μgmL−1 (RSD% and DEVs% were <15%). The method showed good precision: the intraday RSD% values for amoxicillin were in the range
of 1.41–6.86% whereas the values for interday were in the range of 1.13–8.16% at four different concentrations. The method
is rapid (total run time <12min) and accurate (DEVs,% <10%). The mean relative recovery was 99.67% and the mean absolute
recovery was 86.68%. Stability testing shows that amoxicillin is stable in plasma for at least 4weeks when stored at −70°C.
The method was successfully applied in a pharmacokinetic study involving healthy rabbits.
A simple and fast liquid chromatographic method is described, applicable to the routine analysis of isoxazolylpenicillins
(cloxacillin, dicloxacillin, flucloxacillin) in biological fluids (plasma, urine). The method is based on a simple dilution
step employed to destroy the protein binding, which is over 95%, and allows the detection of concentrations down to 10µg/ml. In order to analyze concentrations of less than 10µg/ml, a liquid-liquid extraction with dichloromethane must be executed prior to the reversed-phase analysis with absorbance
detection at 206nm. The minimum detectable amounts of the isoxazolylpenicillins with this procedure are between 2.5 and 5.1
ng in 100µl plasma samples. The stability of the penicillin samples in aqueous solutions (stock solutions, eluents) was investigated
and no significant degradation was observed during the storage and analysis of the samples. Furthermore, the degree of protein
binding was established by using a suitable ultrafiltration technique, and the usefulness of the developed procedures in pharmacokinetic
studies was demonstrated.
A simple and sensitive spectrophotometric method is described for the determination of some phenolic antibiotics namely:
cefadroxil, amoxicillin and vancomycin. The method is based on the measurement of the orange yellow species produced when
the drugs are coupled with diazotized benzocaine in triethylamine medium. The method is applicable over the range of 0.8–12 μg/ml
for cefadroxil, 2–16 μg/ml for amoxicillin and 2–18 μg/ml for vancomycin. The formed compounds absorb at 455 nm for both cefadroxil
and amoxicillin and at 442 nm for vancomycin. The proposed method has detection limits of 0.018 μg for cefadroxil, 0.0034 μg
for amoxicillin and 0.0156 μg for vancomycin.
The stoichiometric ratio for the studied compounds was found to be 1:1 and a proposal of the reaction pathway was made. The
proposed method was applied for the analysis of the cited drugs in their pharmaceutical preparations. The results are in good
agreement with those obtained by the official methods.
Four simple and selective spectrophotometric methods were developed for the quantitative determination of some phenolic β-lactam antibiotics (amoxicillin trihydrate, cefoperazone sodium, cefadroxil monohydrate, and cefprozil anhydrous) in pure forms as well as in their pharmaceutical formulations through their nitration and subsequent complexation with an nucleophilic reagent (method I), nitrosation and subsequent metal chelation (method II), coupling with diazo reagent (method III), and reaction with copper and extraction of the resulting chelate into chloroform (method IV). The reaction conditions were studied and optimized. Beer’s plots were obeyed in a general concentration range of 5–30 ug ml−1 with correlation coefficients not less than 0.9997 for the four drugs. The methods are successfully applied to the analysis of pharmaceutical formulations containing amoxicillin, either alone or in combination with potassium clavulanate. They were also applied to the analysis of the other three studied drugs in vials, capsules, tablets, and suspensions with good recovery; percentage ranged from 99.0 (±1.42) to 100.2 (±1.25) in method I, 99.0 (±0.82) to 100.5 (±0.92) in method II, 99.5 (±0.09) to 100.8 (±0.98) in method III, and 99.3 (±0.01) to 100.2 (±0.05) in method IV. Interferences from other antibiotics and additives were investigated.
A rapid analytical procedure for the routine identification and quantification of two penicillins, amoxicillin (AMOX) and ampicillin (AMPI), in feeds by liquid chromatography (LC) was developed and tested. AMOX and AMPI are normally the only penicillins added to medicated feeds because of their good resistance to gastric juice. The ground feed samples were extracted using water–acetonitrile (75:25, v/v) and derivatized, without any clean-up, with a formaldehyde solution in acidic medium at 100 °C for 30 min. The fluorescent derivatives were analysed by reversed-phase on an ODS column ( mm; 5 μm) with a gradient between acetonitrile, methanol and phosphate buffer containing thiosulfate as mobile phase. Fluorescence detection was carried out at excitation and emission wavelengths of 358 and 440 nm, respectively. The recoveries of both penicillins from spiked samples were, in a concentration range of 200–500 mg kg−1, >80% with repeatabilities below 15% R.S.D. The limits of detection of AMOX and AMPI in feed, based on a detector signal-to-noise ratio of 3, were 5 mg kg−1.
How successful has chemometrics been? The answer depends, of course, on how success is defined and measured. A brief discussion of this subject is given, with reference to academic and industrial research and other application areas, notably industrial development and production. The areas where chemometrics has been most successful according to all measures are the following: (1) multivariate calibration, (2) structure—(re)activity modelling, (3) pattern recognition, classification, and discriminant analysis, and (4) multivariate process modelling and monitoring. Possible reasons are ventilated for these seemingly disparate success stories, together with some reflections of what remains to be done in these areas, and why success is slower in other areas. To continue the successful development of chemometrics, the most important is, in our opinion, that we continue to see ourselves primarily as chemical problem solvers—and only when needed, as developers of new methodology. To illustrate the chemistry driven development of chemometrics, we shall describe some recent work in multivariate modelling and analysis, applied in the areas of structure–activity relationships (peptides, proteins, RNA, DNA, hemes), modelling of batch processes and complicated kinetics, wavelet data compression, and orthogonal preprocessing of spectral data (NIR) for multivariate calibration.
A voltammetric sensor for determination of amoxicillin (AMX) was developed based on a glutaraldehyde cross-linked polyglutamic acid modified glassy carbon electrode. The proposed method is based on pre-concentration of AMX by cathodic accumulation as its oxidative product before being determined indirectly at potential as low as +0.23 V by square wave voltammetry. Linear response range, sensitivity and limit of detection were 2.0–25.0, 1.06 and 0.9.2 μmol L−1, respectively, for AMX in 0.1 mol L−1 acetate buffer pH 5.2, pre-accumulation time of 60 s and accumulation potential of +1.0 V. It was demonstrated that the glassy carbon electrode modified with PGA/GLU could be used for AMX determination in human urine without any separation step.
In this study, we report a method for quantifying amoxicillin in pharmaceuticals in the presence of interferents using sequential injection analysis (SIA) with a diode-array spectrophotometric detector and multivariate curve resolution with alternating least squares (MCR–ALS). With a suitable analytical sequence, we can use SIA to generate a pH gradient and, for each sample, obtain a data matrix. We used augmented matrices to resolve the system and obtain the spectra and concentration profiles of the components in the sample.We studied what are the effects of imposing trilinearity at the resolution stage, how to choose the species that will be used for quantification (acid, basic or the sum of the two), and which is the most suitable concentration of the reference standard. Once the optimum conditions were established, we performed the quantification in three amoxicillin-containing pharmaceuticals (flubiotic, augmentine, and clamoxyl). With this method, determination is quick, the reactants and instrumentation are inexpensive, and pretreatment of the sample is unnecessary.
A simple and rapid method for the joint polarographic determination of rifampicin (RIF), isoniazid (INH) and pyrazinamide (PZA) by differential pulse polarography (DPP) is described. In this paper, a partial least square (PLS) regression was used for the resolution of the overlapped polarographic signals from mixtures of the three drugs. A genetic algorithm (GA) was used to select some of the predictor variables (potentials of the polarogram). The PLS model constructed with these selected potentials also leads to satisfactory results. The procedure was successfully applied to pharmaceutical preparations and biological fluids.
In order to determine the amount of caffeine and theobromine, spectrophotometry was used as a simple, rapid and economical method. Because of severe overlapping between these components, artificial neural network was used. The 230–300 nm spectral window with 1 nm interval was used for data acquisition. An artificial neural network (5-5-3) with linear transfer function between input-hidden and hidden-output layers was trained and applied for prediction of concentration of these methylxanthines in four Iranian tea samples. The model was compared with PLS modeling method. HPLC technique was used as a standard method.
Fourier transform (FT) Raman spectra have been obtained for a range of synthetic and semisynthetic samples to evaluate the utility of the technique for the characterization of compounds of pharmaceutical interest. These spectra are compared with the IR KBr disc spectra. Examples of the additional, important information which may be extracted from Raman data in comparison with IR spectroscopy are reported for the range of chemical structures studied. The superior ability of Raman to characterize the stretching modes of the CH bond compared with IR spectroscopy is demonstrated for several compounds.The characterization of highly symmetric vibrational modes is one of the most prominent and useful advantages of Raman spectroscopy and several examples of this primary application are also cited. These include the assignment of the ring breathing frequency of monosubstituted benzene rings, the CCC symmetric skeletal mode of a tricyclic fused ring system, the NO symmetric stretch of aromatic nitro groups and the stretching vibrations of various double bonds, several of which possess a high degree of local symmetry.
A tutorial on the partial least-squares (PLS) regression method is provided. Weak points in some other regression methods are outlined and PLS is developed as a remedy for those weaknesses. An algorithm for a predictive PLS and some practical hints for its use are given.
The electrocatalytic oxidation of amoxicillin was investigated on a nickel-based (Ni(II)-curcumin) chemically modified electrode. This modified electrode was prepared by electropolymerization of complex (curcumin = 1,7-bis[4-hydroxyl-3-methoxyphenyl]-1,6-heptadiene-3,5-dione) in alkaline solution. For the first time, the catalytic oxidation of amoxicillin was demonstrated by cyclic voltammetry, chronoamperometry, chronocoulometry and amperometry methods at the surface of this modified carbon paste electrode. The obtained results showed that NiOOH acts as an electrocatalyst for oxidation of amoxicillin. This electrocatalytic oxidation exhibited a good linear response for amoxicillin concentration over the range of 8 × 10⁻⁶-1×10⁻⁴ M with a detection limit of 5 × 10⁻⁶ M. Therefore, this electrocatalytic method was used as a simple, selective and rapid method able to determine amoxicillin in pharmaceutical preparations and biological media.
Partial least-squares (PLS) methods for spectral analyses are related to other multivariate calibration methods such as classical least-squares (CLS), inverse least-squares (ILS), and principal component regression (PCR) methods which have been used often in quantitative spectral analyses. The PLS method which analyzes one chemical component at a time is presented, and the basis of each step in the algorithm is explained. PLS calibration is shown to be composed of a series of simplified CLS and ILS steps. This detailed understanding of the PLS algorithm has helped to identify how chemically interpretable qualitative spectral information can be obtained from the intermediate steps of the PLS algorithm. These methods for extracting qualitative information are demonstrated by use of simulated spectral data. The qualitative information directly available from the PLS analysis is superior to that obtained from PCR but is not complete as that which can be generated during CLS analyses. Methods are presented for selecting optimal numbers of loading vectors for both the PLS and PCR models in order to optimize the model while simultaneously reducing the potential for overfitting the calibration data. Outlier detection and methods to evaluate the statistical significance of results obtained from the different calibration methods applied to the same spectral data are also discussed.
A novel method has been developed for the simultaneous determination of flucloxacillin and ampicillin in human plasma by ultra performance liquid chromatography combined with tandem mass spectrometry (UPLC-MS/MS). The plasma was treated by single step of protein precipitation (PPT) with acetonitrile. The chromatographic separation was performed with a mobile phase consisting of 10mM ammonium formate and acetonitrile (68:32, v/v). The analyses were carried out by multiple reaction monitoring (MRM) using the precursor-to-product combinations of m/z 454.1→160.3 (flucloxacillin), m/z 350.1→106.4 (ampicillin) and m/z 436.1→277.3 (IS). Validation results indicated that the lower limit of quantification (LLOQ) were both 0.2 μg/mL and both assay exhibited a linear range of 0.2-500 μg/mL. The intra-batch precision (R.S.D.) was less than 10.6% and inter-batch R.S.D. was less than 11.2%, while accuracy was with ±8% and ±9.9%, determined from QC samples for flucloxacillin and ampicillin. A rapid, sensitive and specific method for simultaneous quantifying flucloxacillin and ampicillin in human plasma have been devised and successfully applied to a clinic pharmacokinetic study.
The mathematical basis of improved calibration through selection of informative variables for partial least-squares calibration has been identified. A theoretical investigation of calibration slopes indicates that including uninformative wavelengths negatively affect calibrations by producing both large relative bias toward zero and small additive bias away from the origin. These theoretical results are found regardless of the noise distribution in the data. Studies are performed to confirm this result using a previously used selection method compared to a new method, which is designed to perform more appropriately when dealing with data having large outlying points by including estimates of spectral residuals. Three different data sets are tested with varying noise distributions. In the first data set, Gaussian and log-normal noise was added to simulated data which included a single peak. Second, near-infrared spectra of glucose in cell culture media taken with an FT-IR spectrometer were analyzed. Finally, dispersive Raman Stokes spectra of glucose dissolved in water were assessed. In every case considered here, improved prediction is produced through selection, but data with different noise characteristics showed varying degrees of improvement depending on the selection method used. The practical results showed that, indeed, including residuals into ranking criteria improves selection for data with noise distributions resulting in large outliers. It was concluded that careful design of a selection algorithm should include consideration of spectral noise distributions in the input data to increase the likelihood of successful and appropriate selection.
This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems. The first objective is tackled by the editor, Lawrence Davis. The remainder of the book is turned over to a series of short review articles by a collection of authors, each explaining how genetic algorithms have been applied to problems in their own specific area of interest. The first part of the book introduces the fundamental genetic algorithm (GA), explains how it has traditionally been designed and implemented and shows how the basic technique may be applied to a very simple numerical optimisation problem. The basic technique is then altered and refined in a number of ways, with the effects of each change being measured by comparison against the performance of the original. In this way, the reader is provided with an uncluttered introduction to the technique and learns to appreciate why certain variants of GA have become more popular than others in the scientific community. Davis stresses that the choice of a suitable representation for the problem in hand is a key step in applying the GA, as is the selection of suitable techniques for generating new solutions from old. He is refreshingly open in admitting that much of the business of adapting the GA to specific problems owes more to art than to science. It is nice to see the terminology associated with this subject explained, with the author stressing that much of the field is still an active area of research. Few assumptions are made about the reader's mathematical background. The second part of the book contains thirteen cameo descriptions of how genetic algorithmic techniques have been, or are being, applied to a diverse range of problems. Thus, one group of authors explains how the technique has been used for modelling arms races between neighbouring countries (a non- linear, dynamical system), while another group describes its use in deciding design trade-offs for military aircraft. My own favourite is a rather charming account of how the GA was applied to a series of scheduling problems. Having attempted something of this sort with Simulated Annealing, I found it refreshing to see the authors highlighting some of the problems that they had encountered, rather than sweeping them under the carpet as is so often done in the scientific literature. The editor points out that there are standard GA tools available for either play or serious development work. Two of these (GENESIS and OOGA) are described in a short, third part of the book. As is so often the case nowadays, it is possible to obtain a diskette containing both systems by sending your Visa card details (or $60) to an address in the USA.
An easy to use and low time consuming capillary electrophoresis (CE) method was developed and applied to the simultaneous determination of six antibiotics (ampicillin, amoxicillin, cloxacillin, penicillin, tetracycline and chloramphenicol) in spiked milk samples. Samples of milk were cleaned up by solid-phase extraction (with a C(18) cartridge) after protein precipitation. Analysis was performed by CE and results compared with the obtained via HPLC, both coupled to a UV-vis detector (210nm). CE employed a 58.5cm long fused-silica capillary (50cm to detector), 75microm i.d., a 2.7x10(-2)M KH(2)PO(4), 4.3x10(-2)M Na(2)B(4)O(7) separation buffer, pH 8; an applied voltage of 18kV; a hydrostatic injection of 0.5psi during 3s; and a run temperature of 25 degrees C. Under the described conditions, amoxicillin was not separated by HPLC, while CE was able to separate, and, therefore, allow detection. Regardless of amoxicillin, comparable results were obtained by HPLC and CE. The average recoveries of antibiotics, from milk fortified at 2.5 and 5microg/mL, was over 72% with R.S.D.s within 5%. Recovery levels were essentially dictated by the used SPE cartridge.
New complexes of Co(2+), Ni(2+), Cu(2+) and Zn(2+) with a recently synthesized Schiff base derived from 3,6-bis((aminoethyl)thio)pyridazine were applied for their simultaneous determination with artificial neural networks. The analytical data show the ratio of metal to ligand in all metal complexes is 1:1. The absorption spectra were evaluated with respect to Schiff base concentration, pH and time of the color formation reactions. It was found that at pH 10.0 and 60min after mixing, the complexation reactions are completed and the colored complexes exhibited absorption bands in the wavelength range 300-500nm. Spectral data was reduced using principal component analysis and subjected to artificial neural networks. The data obtained from synthetic mixtures of four metal ions were processed by principal component-feed forward neural networks (PCFFNNs) and principal component-radial basis function networks (PCRBFNs). Performances of the proposed methods were tested with regard to root mean square errors of prediction (RMSEP%), using synthetic solutions. Under the working conditions, the proposed methods were successfully applied to simultaneous determination of Co(2+), Ni(2+), Cu(2+) and Zn(2+) in different vegetable, foodstuff and pharmaceutical product samples.
A simple and selective method for the determination of amoxycillin in pure form and in pharmaceutical preparations is described. The procedure is based on the reaction of amoxycillin with 4-nitrophenol (I), 2,4-dinitrophenol (II), 3,5-dinitrobenzoic acid (III) or 3,5-dinitrosalicylic acid (IV) in alkaline medium. The method has been used for the determination of 1-24 mug/ml of amoxycillin trihydrate in solution. The method is selective for the determination of amoxycillin in the presence of its degradation products, other antibiotics and different amines that are normally encountered in dosage forms.
The hydrolysis of flucloxacillin at pH 4.9 yields a degradation product which is polarographically oxidizable. This derivative has not been identified, but would seem to contain a thiol group. It gives a diffusion-controlled anodic polarographic wave with a half-wave potential at -0.24 V vs. SCE. The method developed has been applied to the analysis of flucloxacillin capsules, and a recovery of 99% has been obtained.
A 3 X 3 factorial design has been used to study the effects of pH and acetonitrile concentration of the eluents on the retention and resolution of cloxacillin, flucloxacillin and dicloxacillin on a C18 column. The logarithm of the capacity factors of these solutes have been found to vary linearly with the pH and quadratically with the acetonitrile content. The equations generated have been employed to predict experimental conditions necessary for an optimum separation. The chromatographic condition selected has been applied to the quantitation of flucloxacillin in human plasma using dicloxacillin as the interval standard. Sample preparation consists of protein precipitation and solid-phase extraction. The detection limit of the assay at 220 nm for flucloxacillin is in the region of 0.1 microgram/ml. This assay has been employed in a study of the relative bioavailability of two commercial flucloxacillin sodium capsules in ten healthy volunteers.