Simultaneous spectrophotometric determination of caffeine and theobromine in Iranian tea by artificial neural networks and its comparison with PLS

Tea (Camellia sinensis) cultivated in three agro-ecological regions of Bangladesh: Unveiling the variability of methylxanthine, bioactive phenolic compound, and antioxidant activity

Tea (Camellia sinensis) is a widely consumed beverage known for its numerous health benefits, largely attributed to its rich content of quality determining secondary metabolites such as methylxanthine compounds and bioactive phenolic compounds. The goal of this study was to find out variations of the levels of methylxanthines, bioactive phenolic compounds, and antioxidant activity in methanolic and hot water extracts of 129 tea samples grown in three different ecological regions of Bangladesh named Panchagar, Sylhet, and Chattogram. Methylxanthine and bioactive phenolic compounds were determined by using HPLC-DAD, and the antioxidant profile was analysed by UV-vis spectrophotometric methods for methanol and hot water extracts of tea leaves. The IC50 values showed the trend as Panchagar > Sylhet > Chattogram and Sylhet > Chattogram > Panchagar for water and methanol extract, respectively. The results revealed significant (p < 0.05) variations in the levels of methylxanthines content: Panchagar > Chattogram > Sylhet. Caffeine was significantly higher (103.02 ± 5.55 mg/g of dry extract) in the methanolic extract of tea leaves of Panchagar district and lower (53.33 ± 4.30 mg/g of dry extract) in the hot water extract of Sylhet district. Panchagar and Chattogram possessed significantly (p < 0.05) higher catechin content for methanol (57.01 ± 5.50 mg/g dry extract) and hot water (55.23 ± 4.11 mg/g dry extract) extracts, respectively. The utilization of canonical discriminant functions yielded highly favorable outcomes in the classification of tea from three distinct cultivation origins in Bangladesh, relying on their inherent features. This study demonstrated the potential effects of geographical variations on the bioactive compounds and antioxidant properties of tea, emphasizing the importance of regional differences in tea cultivation for optimizing its health benefits as well as dispersing tea cultivation across the country.

The effectiveness of multivariate and univariate spectrophotometric techniques for the concurrent estimation of ornidazole and ciprofloxacin HCl in tablet formulation and spiked serum: estimating greenness and whiteness profile

In this manuscript, the effectiveness of multivariate and univariate tools in conjunction with spectrophotometric techniques was evaluated for the concurrent analysis of ciprofloxacin (CI) and ornidazole (OR) in prepared mixtures, tablets, and human serum. The artificial neural network was chosen as the multivariate Technique. Bayesian regularization (trainbr) and Levenberg-Marquardt algorithms (trainlm), were constructed and trained using feed-forward back-propagation learning. The optimal logarithm was determined based on mean recovery, mean square error of prediction (MSEP), relative root mean square error of prediction (RRMSEP), and bias-corrected MSEP (BCMSEP) scores. Trainbr outperformed trainlm, yielding a mean recovery of 100.05% for CI and 99.84% for OR, making it the preferred algorithm. Fourier self-deconvolution and mean-centering transforms were chosen as the univariate Techniques. Fourier self-deconvolution was applied to the zero-order spectra of ciprofloxacin and ornidazole by electing an appropriate full width at half maximum, enhancing peak resolution at 380.1 nm and 314.2 nm for CI and OR, respectively. Mean centering transform was applied to CI and OR ratio spectra to eliminate constant signals, enabling accurate quantification of CI and OR at 272.0 nm and 306.2 nm, respectively. The introduced approaches were optimized and validated for precise CI and OR analysis, with statistical comparison against the HPLC method revealing no notable differences. The sustainability of these approaches was confirmed through the green certificate (modified eco-scale), AGP, and whiteness-evaluation tool, corroborating their ecological viability.

Alkaloids in food: a review of toxicity, analytical methods, occurrence and risk assessments

Alkaloids have been utilized by humans for years. They have diverse applications in pharmaceuticals. They have been proven to be effective in treating a number of diseases. They also form an important part of regular human diets, as they are present in food items, food supplements, diet ingredients and food contaminants. Despite their obvious importance, these alkaloids are toxic to humans. Their toxicity is dependent on a range of factors, such as specific dosage, exposure time and individual properties. Mild toxic effects include nausea, itching and vomiting while chronic effects include paralysis, teratogenicity and death. This review summarizes the published studies on the toxicity, analytical methods, occurrence and risk assessments of six major alkaloid groups that are present in food, namely, ergot, glycoalkaloids, purine, pyrrolizidine, quinolizidine and tropane alkaloids.

Quality assessment of African herbal medicine: A systematic review and the way forward

BACKGROUND

In Africa, herbalism supplements allopathic medicine's efforts to ensure Universal Health Coverage attainment. This review was conducted to identify and to summarise current literature on methodological approaches used for quality control of herbal medicines in Africa, to evaluate the gaps associated with existing strategies within context of best practices, and make recommendations for future improvements.

METHODS

A systematic search was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Articles were screened and assessed for eligibility.

RESULTS

118 articles were included into the study. There was a high preference for impurity profiling tests (77%) indicating the prioritization for tests that guarantee safety despite the limited analytical resources available. Other classes of tests reported included identification tests (29%), physicochemical tests (18%), and content assays (12%). Although standard methods exist in preparing samples for impurity tests, different techniques were observed in different studies, and this could lead to differences in analytical outcomes. Content assays focused on single marker assessments, which may be inadequate to comprehensively assess the quality of products.

CONCLUSION

This review provides knowledge of existing strengths and challenges for herbal medicine quality assessments in Africa. For future it is recommended to implement more studies on contaminants (e.g. mycotoxins) and pharmaceutical adulterants. The use of chemometrics to develop analytical methods should be promoted. Also, stakeholders in the medicine quality industry in Africa need to effectively collaborate to establish a well co-ordinated and harmonized system to provide a sustainable framework for the GACP and GMP guided production and quality assurance of herbal medicines.

Investigation of some univariate and multivariate spectrophotometric methods for concurrent estimation of Vancomycin and Ciprofloxacin in their laboratory prepared mixture and application to biological fluids

Four simple, rapid, accurate and precise spectrophotometric methods were established and validated in accordance with ICH Q2 (R1) guidelines for the simultaneous determination of Vancomycin (VNC) and Ciprofloxacin (CPR) in their raw materials, laboratory prepared mixtures and pharmaceutics. Method A depends on using first derivative spectrophotometry (D¹) where VNC and CPR were resolved at 243.6 and 262.0 nm, respectively. Concerning method B, it is based on utilizing first derivative of ratio spectra (DD¹) where determination was performed at the peak maxima at 244.0 nm and 258.0 nm for VNC and CPR, respectively. Two chemometric models were applied for the quantitative analysis of both drugs in their laboratory prepared mixtures, namely, partial least squares (PLS) (method C) and artificial neural network (ANN) (method D). For univariate methods linearity range for both drugs was in the range of 3-30 and 1-10 μg/mL for VNC and CPR, respectively. Multivariate calibration methods using five level, two factor calibration model for the development of 25 mixtures were also applied for the simultaneous estimation of the two drugs in their laboratory prepared mixture using spectral region from 200.0 to 300.0 nm using interval 1 nm. The suggested methods have been successfully extended to the assay of the two studied drugs in laboratory-prepared mixtures and pharmaceuticals with excellent recovery. First derivative spectrophotometry (D¹) was also applied for the assay of both analytes in spiked human plasma with good recovery. No interaction with common pharmaceutical additives has been observed which indicate the selectivity of the method. The results obtained were favourably compared with those obtained applying the reported methods. The methods are validated in compliance with the ICH Q2 (R1) guidelines and the measured accuracy and precision are assessed to be within the accepted limits.

Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form

Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted.

A comparative study of two analytical techniques for the simultaneous determination of amprolium HCl and ethopabate from combined dosage form and in presence of their alkaline degradation

Chemometric and separation-based techniques (HPLC) are the most applicable and versatile analytical techniques for the analysis of multicomponent mixtures, in the present contribution, a comparison was highlighted between the two analytical techniques of utmost importance as stability indicating assays: UV-spectrophotometry and HPLC-UV focusing on the greenness of each for the simultaneous determination of amprolium HCl (AMP) and ethopabate (ETHOP) in the presence of their alkaline degradation products. The first method was chemometric methods applied were PLS-1, GA-PLS and GA-ANN. To compare the prediction ability of the models, a 4-factor 5-level experimental design was used to establish a calibration set of 25 mixtures containing different ratios of the drugs and their degradation products. The validity of the proposed methods was assessed using an independent validation set of 5 mixtures. The comparison between the different models showed the superiority of ANN model in solving the highly overlapped spectra of the quaternary mixture, yet using inexpensive and easy to handle instruments like the UV-VIS spectrophotometer. The ANN method was used for the quantitative analysis of the drugs in pharmaceutical dosage form via handling the UV spectral data. The second method was based on liquid chromatographic HPLC determination of AMP and ETHOP using C18 column (250 × 4.6 mm²)-PRONTOSIL 5 μm, a mobile phase consisting of methanol: Hexane sulphonic acid sodium salt at (pH = 3.4 ± 0.2) adjusted by orthophosphoric acid (55: 45 v/v). Quantitation was achieved with UV detection at 270 nm at temperature 24 °C. Linearity, accuracy and precision were found to be acceptable over the concentration range of 10.0-70.0 and 1.0-25.0 μg·mL^-1 for AMP and ETHOP, respectively. The proposed methods could be successfully applied for the routine analysis of the studied drugs either in their pure bulk powders or in their pharmaceutical preparations without any preliminary separation step. The results obtained were statistically compared with those obtained by applying the reported method.

The use of green spectroscopy with multivariate calibration models for simultaneous analysis of ternary drug mixture in combined oral antidiabetic pill

In this work, different chemometric calibration models were developed and validated for the purpose of determining of ternary mixture of oral antidiabetic drugs; vildagliptin (VDG), saxagliptin (SAX) and sitagliptin phosphate (STG). The used models were Partial least squares (PLS) and Artificial Neural Networks (ANN). However, on these various models the impact of genetic algorithm (GA) as a form of variable selection was also investigated. The UV spectral data was used as basis in the quantitative study of the drugs analyzed in bulk and product formulations. The concentration range of the calibration curves of VDG, SAX and STG were 10-22 μg mL^-1, 24-40 μg mL^-1 and 82-130 μg mL^-1, respectively. The calibration set included nineteen mixtures and the others six were used as a validation set to test the predictability of the developed multivariate models. The validation parameters of the evaluated methods were statistically determined. For the analysis of drugs studied in laboratory-prepared mixtures and their dosage forms, PLS-1, GA-PLS-1, ANN, and GA-ENN were successfully employed. The results obtained by the developed methods were compared to those given by a reported method and there were no statistically significant differences regarding accuracy and precision.

Development of innovative artificial neural networks for simultaneous determination of lapatinib and foretinib in human urine by micellar enhanced synchronous spectrofluorimetry

A highly selective and simple micellar synchronous spectrofluorimetric method was described for simultaneous analysis of two tyrosine kinase inhibitors (TKIs); namely lapatinib (LPB) and foretinib (FTB) in human urine. The method depended on measuring synchronous fluorescence of the two drugs in micellar media composed of cremophor RH 40 (Cr RH 40) surfactant using feed-forward and cascade-forward neural networks preceded by genetic algorithm for data manipulation. Different experimental conditions that affect fluorescence of the cited drugs are optimized including pH, diluting solvent, surfactant's type and concentration. A training set of nine mixtures containing different concentrations of both drugs was prepared for models' construction. Extra validation set composed of other nine mixtures was prepared to validate prediction performance for the constructed models. Root mean square error of prediction (RMSEP) was used as a tool to compare prediction power of each model. The method was extended for quantification of LPB and FTB in spiked human urine.

FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples

Daily consumption of caffeine in coffee, tea, chocolate, cocoa, and soft drinks has gained wide and plentiful public and scientific attention over the past few decades. The concentration of caffeine in vivo is a crucial indicator of some disorders—for example, kidney malfunction, heart disease, increase of blood pressure and alertness—and can cause some severe diseases including type 2 diabetes mellitus (DM), stroke risk, liver disease, and some cancers. In the present study, near-infrared spectroscopy (NIRS) coupled with partial least-squares regression (PLSR) was proposed as an alternative method for the quantification of caffeine in 25 commercially available tea samples consumed in Oman. This method is a fast, complementary technique to wet chemistry procedures as well as to high-performance liquid chromatography (HPLC) methods for the quantitative analysis of caffeine in tea samples because it is reagent-less and needs little or no pre-treatment of samples. In the current study, the partial least-squares (PLS) algorithm was built by using the near-infrared NIR spectra of caffeine standards prepared in tea samples scanned by a Frontier NIR spectrophotometer (L1280034) by PerkinElmer. Spectra were collected in the absorption mode in the wavenumber range of 10,000–4000 cm⁻¹, using a 0.2 mm path length and CaF₂ sealed cells with a resolution of 2 cm⁻¹. The NIR results for the contents of caffeine in tea samples were also compared with results obtained by HPLC analysis. Both techniques provided good results for predicting the caffeine contents in commercially available tea samples. The results of the proposed study show that the suggested FT-NIRS coupled with PLS regression algorithun has a high potential to be routinely used for the quick and reproducible analysis of caffeine contents in tea samples. For the NIR method, the limit of quantification (LOQ) was estimated as 10 times the error of calibration (root mean square error of calibration (RMSECV)) of the model; thus, RMSEC was calculated as 0.03 ppm and the LOQ as 0.3 ppm.

A novel electrochemical analysis of the legal psychoactive drug caffeine using a zeolite/MWCNT modified carbon paste sensor

Caffeine (Caf) is a natural central nervous system stimulant categorized by the US Food and Drug Administration as a safe drug and its maximal amount in soft drinks has been approximately determined to be lower than 200 mg L⁻¹.

Simultaneous determination of ascorbic acid and caffeine in commercial soft drinks using reversed-phase ultraperformance liquid chromatography

A new reversed-phase ultraperformance liquid chromatography method with a photodiode array detector was developed for the quantification of ascorbic acid (AA) and caffeine (CAF) in 11 different commercial drinks consisting of one energy drink and 10 ice tea drinks. Separation of the analyzed AA and CAF with an internal standard, caffeic acid, was performed on a Waters BEH C₁₈ column (100 mm × 2.1 mm, 1.7 μm i.d.), using a mobile phase consisting of acetonitrile and 0.2M H₃PO₄ (11:89, v/v) with a flow rate of 0.25 mL/min and an injection volume of 1.0 μL. Calibration graphs for AA and CAF were computed from the peak area ratio of AA/internal standard and CAF/internal standard detected at 244.0 nm and 273.6 nm, respectively. The developed reversed-phase ultraperformance liquid chromatography method was validated by analyzing standard addition samples. The proposed reversed-phase ultraperformance liquid chromatography method gave us successful results for the quantitative analysis of commercial drinks containing AA and CAF substances.

Comparison between Two Linear Supervised Learning Machines' Methods with Principle Component Based Methods for the Spectrofluorimetric Determination of Agomelatine and Its Degradants

Four accurate, sensitive and reliable stability indicating chemometric methods were developed for the quantitative determination of Agomelatine (AGM) whether in pure form or in pharmaceutical formulations. Two supervised learning machines' methods; linear artificial neural networks (PC-linANN) preceded by principle component analysis and linear support vector regression (linSVR), were compared with two principle component based methods; principle component regression (PCR) as well as partial least squares (PLS) for the spectrofluorimetric determination of AGM and its degradants. The results showed the benefits behind using linear learning machines' methods and the inherent merits of their algorithms in handling overlapped noisy spectral data especially during the challenging determination of AGM alkaline and acidic degradants (DG1 and DG2). Relative mean squared error of prediction (RMSEP) for the proposed models in the determination of AGM were 1.68, 1.72, 0.68 and 0.22 for PCR, PLS, SVR and PC-linANN; respectively. The results showed the superiority of supervised learning machines' methods over principle component based methods. Besides, the results suggested that linANN is the method of choice for determination of components in low amounts with similar overlapped spectra and narrow linearity range. Comparison between the proposed chemometric models and a reported HPLC method revealed the comparable performance and quantification power of the proposed models.

Resolution and quantification challenge of modern chemometric models in the determination of anti-migraine tablets containing ergotamine, caffeine, acetaminophen, and metoclopramide

Unique mixture of ergotamine (ERG), metoclopramide (MET), caffeine (CAF) and the study presents a comparison between five multivariate models in the determination of the paracetamol (PAR) in laboratory mixtures and in pharmaceutical formulations.

Electrochemical Determination of Caffeine Content in Ethiopian Coffee Samples Using Lignin Modified Glassy Carbon Electrode

Lignin film was deposited at the surface of glassy carbon electrode potentiostatically. In contrast to the unmodified glassy carbon electrode, an oxidative peak with an improved current and overpotential for caffeine at modified electrode showed catalytic activity of the modifier towards oxidation of caffeine. Linear dependence of peak current on caffeine concentration in the range 6 × 10^-6 to 100 × 10^-6 mol L^-1 with determination coefficient and method detection limit (LoD = 3 s/slope) of 0.99925 and 8.37 × 10^-7 mol L^-1, respectively, supplemented by recovery results of 93.79-102.17% validated the developed method. An attempt was made to determine the caffeine content of aqueous coffee extracts of Ethiopian coffees grown in four coffee cultivating localities (Wonbera, Wolega, Finoteselam, and Zegie) and hence to evaluate the correlation between users preference and caffeine content. In agreement with reported works, caffeine contents (w/w%) of 0.164 in Wonbera coffee; 0.134 in Wolega coffee; 0.097 in Finoteselam coffee; and 0.089 in Zegie coffee were detected confirming the applicability of the developed method for determination of caffeine in a complex matrix environment. The result indicated that users' highest preference for Wonbera and least preference for Zegie cultivated coffees are in agreement with the caffeine content.

Full spectrum and selected spectrum based multivariate calibration methods for simultaneous determination of betamethasone dipropionate, clotrimazole and benzyl alcohol: Development, validation and application on commercial dosage form

Five different chemometric methods were developed for the simultaneous determination of betamethasone dipropionate (BMD), clotrimazole (CT) and benzyl alcohol (BA) in their combined dosage form (Lotriderm® cream). The applied methods included three full spectrum based chemometric techniques; namely principal component regression (PCR), Partial Least Squares (PLS) and Artificial Neural Networks (ANN), while the other two methods were PLS and ANN preceded by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. A multilevel multifactor experimental design was adopted for proper construction of the models. A validation set composed of 12 mixtures containing different ratios of the three analytes was used to evaluate the predictive power of the suggested models. All the proposed methods except ANN, were successfully applied for the analysis of their pharmaceutical formulation (Lotriderm® cream). Results demonstrated the efficiency of the four methods as quantitative tool for analysis of the three analytes without prior separation procedures and without any interference from the co-formulated excipient. Additionally, the work highlighted the effect of GA on increasing the predictive power of PLS and ANN models.

Structure-Bioactivity Relationships of Methylxanthines: Trying to Make Sense of All the Promises and the Drawbacks

Methylxanthines are a group of phytochemicals derived from the purine base xanthine and obtained from plant secondary metabolism. They are unobtrusively included in daily diet in common products as coffee, tea, energetic drinks, or chocolate. Caffeine is by far the most studied methylxanthine either in animal or epidemiologic studies. Theophylline and theobromine are other relevant methylxanthines also commonly available in the aforementioned sources. There are many disseminated myths about methylxanthines but there is increased scientific knowledge to discuss all the controversy and promise shown by these intriguing phytochemicals. In fact, many beneficial physiologic outcomes have been suggested for methylxanthines in areas as important and diverse as neurodegenerative and respiratory diseases, diabetes or cancer. However, there have always been toxicity concerns with methylxanthine (over)consumption and pharmacologic applications. Herein, we explore the structure-bioactivity relationships to bring light those enumerated effects. The potential shown by methylxanthines in such a wide range of conditions should substantiate many other scientific endeavors that may highlight their adequacy as adjuvant therapy agents and may contribute to the advent of functional foods. Newly designed targeted molecules based on methylxanthine structure may originate more specific and effective outcomes.

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

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.

Advanced stability indicating chemometric methods for quantitation of amlodipine and atorvastatin in their quinary mixture with acidic degradation products

Two advanced, accurate and precise chemometric methods are developed for the simultaneous determination of amlodipine besylate (AML) and atorvastatin calcium (ATV) in the presence of their acidic degradation products in tablet dosage forms. The first method was Partial Least Squares (PLS-1) and the second was Artificial Neural Networks (ANN). PLS was compared to ANN models with and without variable selection procedure (genetic algorithm (GA)). For proper analysis, a 5-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the interfering species. Fifteen mixtures were used as calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested models. The proposed methods were successfully applied to the analysis of pharmaceutical tablets containing AML and ATV. The methods indicated the ability of the mentioned models to solve the highly overlapped spectra of the quinary mixture, yet using inexpensive and easy to handle instruments like the UV-VIS spectrophotometer.

Evaluation of ice-tea quality by DART-TOF/MS

DART (Direct Analysis in Real Time) coupled with Time-of-Flight Mass Spectrometry (TOF/MS) has been used for analyses of ice-teas. The article focuses on quality and authenticity of ice-teas as one of the most important tea-based products on the market. Twenty-one samples of ice-teas (black and green) were analysed. Selected compounds of ice-teas were determined: theobromine, caffeine, total phenolic compounds, total soluble solids, total amino acid concentration, preservatives and saccharides were determined. Fingerprints of DART-TOF/MS spectra were used for comprehensive assessment of the ice-tea samples. The DART-TOF/MS method was used for monitoring the following compounds: citric acid, caffeine, saccharides, artificial sweeteners (saccharin, acesulphame K), and preservatives (sorbic and benzoic acid), phosphoric acid and phenolic compounds. The measured data were subjected to a principal components analysis. The HPLC and DART-TOF/MS methods were compared in terms of determination of selected compounds (caffeine, benzoic acid, sorbic acid and saccharides) in the ice-teas. The DART-TOF/MS technique seems to be a suitable method for fast screening, testing quality and authenticity of tea-based products.

Spectrophotometric Analysis of Caffeine

The nature of caffeine reveals that it is a bitter white crystalline alkaloid. It is a common ingredient in a variety of drinks (soft and energy drinks) and is also used in combination with various medicines. In order to maintain the optimum level of caffeine, various spectrophotometric methods have been developed. The monitoring of caffeine is very important aspect because of its consumption in higher doses that can lead to various physiological disorders. This paper incorporates various spectrophotometric methods used in the analysis of caffeine in various environmental samples such as pharmaceuticals, soft and energy drinks, tea, and coffee. A range of spectrophotometric methodologies including chemometric techniques and derivatization of spectra have been used to analyse the caffeine.

Comparative artificial neural network and partial least squares models for analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in pharmaceutical preparations

Metronidazole (MNZ) is a widely used antibacterial and amoebicide drug. Therefore, it is important to develop a rapid and specific analytical method for the determination of MNZ in mixture with Spiramycin (SPY), Diloxanide (DIX) and Cliquinol (CLQ) in pharmaceutical preparations. This work describes simple, sensitive and reliable six multivariate calibration methods, namely linear and nonlinear artificial neural networks preceded by genetic algorithm (GA-ANN) and principle component analysis (PCA-ANN) as well as partial least squares (PLS) either alone or preceded by genetic algorithm (GA-PLS) for UV spectrophotometric determination of MNZ, SPY, DIX and CLQ in pharmaceutical preparations with no interference of pharmaceutical additives. The results manifest the problem of nonlinearity and how models like ANN can handle it. Analytical performance of these methods was statistically validated with respect to linearity, accuracy, precision and specificity. The developed methods indicate the ability of the previously mentioned multivariate calibration models to handle and solve UV spectra of the four components' mixtures using easy and widely used UV spectrophotometer.

Different approaches in Partial Least Squares and Artificial Neural Network models applied for the analysis of a ternary mixture of Amlodipine, Valsartan and Hydrochlorothiazide

Different chemometric models were applied for the quantitative analysis of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in ternary mixture, namely, Partial Least Squares (PLS) as traditional chemometric model and Artificial Neural Networks (ANN) as advanced model. PLS and ANN were applied with and without variable selection procedure (Genetic Algorithm GA) and data compression procedure (Principal Component Analysis PCA). The chemometric methods applied are PLS-1, GA-PLS, ANN, GA-ANN and PCA-ANN. The methods were used for the quantitative analysis of the drugs in raw materials and pharmaceutical dosage form via handling the UV spectral data. A 3-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the drugs. Fifteen mixtures were used as a calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested methods. The validity of the proposed methods was assessed using the standard addition technique.

Comparison of capillary electrophoresis and high performance liquid chromatography methods for caffeine determination in decaffeinated coffee

Decaffeinated coffee accounts for 10 percent of coffee sales in the world; it is preferred by consumers that do not wish or are sensitive to caffeine effects. This article presents an analytical comparison of capillary electrophoresis (CE) and high performance liquid chromatography (HPLC) methods for residual caffeine quantification in decaffeinated coffee in terms of validation parameters, costs, analysis time, composition and treatment of the residues generated, and caffeine quantification in 20 commercial samples. Both methods showed suitable validation parameters. Caffeine content did not differ statistically in the two different methods of analysis. The main advantage of the high performance liquid chromatography (HPLC) method was the 42-fold lower detection limit. Nevertheless, the capillary electrophoresis (CE) detection limit was 115-fold lower than the allowable limit by the Brazilian law. The capillary electrophoresis (CE) analyses were 30% faster, the reagent costs were 76.5-fold, and the volume of the residues generated was 33-fold lower. Therefore, the capillary electrophoresis (CE) method proved to be a valuable analytical tool for this type of analysis.

Application of smart spectrophotometric methods and artificial neural network for the simultaneous quantitation of olmesartan medoxamil, amlodipine besylate and hydrochlorothiazide in their combined pharmaceutical dosage form

Background

New, simple and specific spectrophotometric methods and artificial neural network (ANN) were developed and validated in accordance with ICH guidelines for the simultaneous estimation of Olmesartan (OLM), Amlodipine (AML), and Hydrochlorothiazide (HCT) in commercial tablets.

Results

For spectrophotometric methods: First, Amlodipine (AML) was determined by direct spectrophotometry at 359 nm and by application of the ratio subtraction, the AML spectrum was removed from the mixture spectra. Then Hydrochlorothiazide (HCT) was determined directly at 315 nm without interference from Olmesartan medoxamil (OLM) which could be determined using the isoabsorptive method. The calibration curve is linear over the concentration range of 5–40, 2.5-40 and 2–40 μg mL^-1 for AML, OLM and HCT, respectively. ANN (as a multivariate calibration method) was also applied for the simultaneous determination of the three analytes in their combined pharmaceutical dosage form using spectral region from 230–340 nm.

Conclusions

The proposed methods were successfully applied for the assay of the three analytes in laboratory prepared mixtures and combined pharmaceutical tablets with excellent recoveries. No interference was observed from common pharmaceutical additives. The results were favorably compared with those obtained by a reference spectrophotometric method. The methods are validated according to the ICH guidelines and accuracy, precision and repeatability are found to be within the acceptable limit.

Application of chemometrics in authentication of herbal medicines: a review

INTRODUCTION

Herbal medicines (HM) and their preparations have been widely used for hundreds of years all over the world. However, they have not been officially recognised due to a lack of adequate or accepted research methodology for their evaluation.

OBJECTIVE

To present a concise overview of the recent applications of chemometrics in solving the ambiguity of herbal medicine authentication during the last two decades.

METHODOLOGY

Studies involving the applications of chemometric analysis in combination with different analytical methods were classified according to the method of analysis used including chromatographic (HPLC, GC and CE), spectroscopic (NMR, IR, UV and ICP) and genetic analysis (RAPD). The purpose of each of these studies was classified into one of three main categories: taxonomic discrimination, quality assessment or classification between plants of different geographic origins.

RESULTS

This review comprises over 150 studies, covering the past two decades, emphasising the significance of chemometric methods in the discrimination of many herbs from closely related species and from adulterants, based on the principal bioactive components and phytochemical diversity. Furthermore, the differentiation between varieties and hybrids was achieved in addition to the prediction of the active components by quantitative methods of analysis. Discrimination according to geographical origin and localities, processing methods, DNA profiling and metabolomics were also efficiently investigated.

CONCLUSION

Chemometric methods have provided an efficient and powerful tool for the quality control and authentication of different herbs.

Functional analysis of the relative growth rate, chemical composition, construction and maintenance costs, and the payback time of Coffea arabica L. leaves in response to light and water availability

In this study, the combined effects of light and water availability on the functional relationships of the relative growth rate (RGR), leaf chemical composition, construction and maintenance costs, and benefits in terms of payback time for Coffea arabica are presented. Coffee plants were grown for 8 months in 100% or 15% full sunlight and then a four-month water shortage was implemented. Plants grown under full sunlight were also transferred to shade and vice versa. Overall, most of the traits assessed were much more responsive to the availability of light than to the water supply. Larger construction costs (12%), primarily associated with elevated phenol and alkaloid pools, were found under full sunlight. There was a positive correlation between these compounds and the RGR, the mass-based net carbon assimilation rate and the carbon isotope composition ratio, which, in turn, correlated negatively with the specific leaf area. The payback time was remarkably lower in the sun than in shade leaves and increased greatly in water-deprived plants. The differences in maintenance costs among the treatments were narrow, with no significant impact on the RGR, and there was no apparent trade-off in resource allocation between growth and defence. The current irradiance during leaf bud formation affected both the specific leaf area and leaf physiology upon transferring the plants from low to high light and vice versa. In summary, sun-grown plants fixed more carbon for growth and secondary metabolism, with the net effect of an increased RGR.

Chemometric characterization of fruit juices from Spanish cultivars according to their phenolic compound contents: I. Citrus fruits

The data set composed by phenolic compound profiles of 83 Citrus juices (determined by HPLC-DAD-MS/MS) was evaluated by chemometrics to differentiate them according to Citrus species (sweet orange, tangerine, lemon, and grapefruit). Cluster analysis (CA) and principal component analysis (PCA) showed natural sample grouping among Citrus species and even the Citrus subclass. Most of the information contained in the full data set can be captured if only 15 phenolic compounds (concentration ≥10 mg/L), which can be quantified with fast and accurate methods in real samples, are introduced in the models; a good classification which allows the confirmation of the authenticity of juices is achieved by linear discriminant analysis. Using this reduced data set, fast and routine methods have been developed for predicting the percentage of grapefruit in adulterated sweet orange juices using principal component regression (PCR) and partial least-squares regression (PLS). The PLS model has provided suitable estimation errors.

Support vector regression and artificial neural network models for stability indicating analysis of mebeverine hydrochloride and sulpiride mixtures in pharmaceutical preparation: a comparative study

A comparison between support vector regression (SVR) and Artificial Neural Networks (ANNs) multivariate regression methods is established showing the underlying algorithm for each and making a comparison between them to indicate the inherent advantages and limitations. In this paper we compare SVR to ANN with and without variable selection procedure (genetic algorithm (GA)). To project the comparison in a sensible way, the methods are used for the stability indicating quantitative analysis of mixtures of mebeverine hydrochloride and sulpiride in binary mixtures as a case study in presence of their reported impurities and degradation products (summing up to 6 components) in raw materials and pharmaceutical dosage form via handling the UV spectral data. For proper analysis, a 6 factor 5 level experimental design was established resulting in a training set of 25 mixtures containing different ratios of the interfering species. An independent test set consisting of 5 mixtures was used to validate the prediction ability of the suggested models. The proposed methods (linear SVR (without GA) and linear GA-ANN) were successfully applied to the analysis of pharmaceutical tablets containing mebeverine hydrochloride and sulpiride mixtures. The results manifest the problem of nonlinearity and how models like the SVR and ANN can handle it. The methods indicate the ability of the mentioned multivariate calibration models to deconvolute the highly overlapped UV spectra of the 6 components' mixtures, yet using cheap and easy to handle instruments like the UV spectrophotometer.

Antioxidant activity prediction and classification of some teas using artificial neural networks

In order to characterise and to classify some teas a simple, rapid and economical method based on composition, antioxidant activity and artificial neural networks (ANNs) is proposed. For these purpose two types of ANN based applications have been developed: one for predicting the antioxidant activity and a second one for establishing the class of the teas. The complex relationship between the total antioxidant activity (AA) depending on the total flavonoids content (F), total catechins content (C) and total methyl-xanthines content (MX) of commercial teas was revealed by the first designed feed-forward ANN. Secondly, using a probabilistic ANN, successful tea classification in various classes (green tea, black tea and express black tea) was also performed.