Simultaneous and accurate determination of water- and fat-soluble vitamins in multivitamin tablets by using an RP-HPLC method

In the present study, a reversed-phase high-performance liquid chromatographic (RP-HPLC) procedure was developed and validated for the simultaneous determination of seven water-soluble vitamins (thiamine, riboflavin, niacin, cyanocobalamin, ascorbic acid, folic acid, and p-aminobenzoic acid) and four fat-soluble vitamins (retinol acetate, cholecalciferol, α-tocopherol, and phytonadione) in multivitamin tablets. The linearity of the method was excellent (R² > 0.999) over the concentration range of 10 - 500 ng mL-1. The statistical evaluation of the method was carried out by performing the intra- and inter-day precision. The accuracy of the method was tested by measuring the average recovery; values ranged between 87.4% and 98.5% and were acceptable quantitative results that corresponded with the label claims.

Simultaneous analysis of saturated and unsaturated fatty acids present in pequi fruits by capillary electrophoresis

In the current study, an alternative method has been proposed for simultaneous analysis of palmitic, stearic, oleic, linoleic, and linolenic acids by capillary zone electrophoresis (CZE) using indirect detection. The background electrolyte (BGE) used for the analysis of these fatty acids (FAs) consisted of 15.0 mmol L−1 NaH2PO4/Na2HPO4 at pH 6.86, 4.0 mmol L−1 SDBS, 8.3 mmol L−1 Brij 35, 45% v/v acetonitrile (can), and 2.1% n-octanol. The FAs quantification of FAs was performed using a response factor approach, which provided a high analytical throughput for the real sample. The CZE method, which was applied successfully for the analysis of pequi pulp, has advantages such as short analysis time, absence of lipid fraction extraction and derivatization steps, and no significant difference in the 95% confidence intervals for FA quantification results, compared to the gas chromatography official method (AOCS Ce 1h-05).

Stability-indicating RP-HPLC method for simultaneous determination of gatifloxacin and flurbiprofen in binary combination

A stability-indicating RP-HPLC method is presented for determination of gatifloxacin and flurbiprofen in binary combination. Gatifloxacin, flurbiprofen and their degradation products were detected at 254 nm using a BDS Hypersil C8 (250 X 4.6 mm, 5 µm) column and mixture of 20 mM phosphate buffer (pH 3.0) and methanol 30:70 v/v as mobile phase. Response was linear over the range of 15-105 mg mL-1 for gatifloxacin (r² > 0.998) and of 1.5-10.5 mg mL-1 for flurbiprofen (r² > 0.999). The developed method efficiently separated the analytical peaks from degradation products (peak purity index > 0.9999). The method developed can be applied successfully for determination of gatifloxacin and flurbiprofen in human serum, urine, pharmaceutical formulations, and their stability studies.

Simultaneous determination of four phenolic compounds in extracts of aerial parts of Ipomoea pes-caprae (L.) R. Br. (Convolvulaceae) by HPLC-UV

Among other applications, Ipomoea pes-caprae is popularly used to treat jellyfish stings, supporting the development of a product for dermatological use. Hydroethanolic spray-dried extract was chosen for the further development of phytomedicines, and a stability-indicative HPLC-UV method was developed and validated for the determination of isoquercitrin and isochlorogenic acids A, B and C. The method was developed using a C18 column (250 x 4.6 mm, 5 µm) with an acetonitrile:water mobile phase at pH 3.0 in a gradient run. The four constituents and other unidentified components of the extract were appropriately resolved without interference of degradation products after stress tests (acid, alkali, neutral, oxidant, photolysis). The method showed linearity in the isoquercitrin concentration range from 5.0-50.0 µg mL-1, with adequate precision (RSD% < 2.5% for the intra- and inter-day studies), accuracy (recovery of 100.0 ± 2.0%), and robustness. Both the herbal drug and spray-dried extract of I. pes-caprae were subjected to stability studies in accelerated and long-term conditions over four months. The samples maintained their characteristics and marker contents (< 10% of variation).

SIMULTANEOUS DETERMINATION OF PROTOCATECHUIC ALDEHYDE AND PROTOCATECHUIC ACID USING THE LOCALIZED SURFACE PLASMON RESONANCE PEAK OF SILVER NANOPARTICLES AND CHEMOMETRIC METHODS

A simple and sensitive spectrophotometric method is proposed for the simultaneous determination of protocatechuic acid and protocatechuic aldehyde. The method is based on the difference in the kinetic rates of the reactions of analytes with [Ag(NH3)2]+ in the presence of polyvinylpyrrolidone to produce silver nanoparticles. The data obtained were processed by chemometric methods using principal component analysis artificial neural network and partial least squares. Excellent linearity was obtained in the concentration ranges of 1.23-58.56 µg mL-1 and 0.08-30.39 µg mL-1 for PAC and PAH, respectively. The limits of detection for PAC and PAH were 0.039 and 0.025 µg mL-1, respectively.

Estimation of phenotypic diversity in field populations of Magnaporthe grisea from two upland rice cultivars

The phenotypic diversity of Magnaporthe grisea was evaluated based on leaf samples with blast lesions collected from eight commercial fields of the upland rice cultivars 'BRS Primavera' and 'BRS Bonança', during the growing seasons of 2001/2002 and 2002/2003, in Goias State. The number of M. grisea isolates from each field utilized for virulence testing varied from 28 to 47. Three different indices were used based on reaction type in the eight standard international differentials and eight Brazilian differentials. The M. grisea subpopulations of ´Primavera' and 'Bonança', as measured by Simpson, Shannon and Gleason indices, showed similar phenotypic diversities. The Simpson index was more sensitive relation than those of Shannon and Gleason for pathotype number and standard deviation utilizing Brazilian differentials. However, the Gleason index was sensitive to standard deviation for international differentials. The sample size did not significantly influence the diversity index. The two sets of differential cultivars used in this study distinguished phenotypic diversity in different ways in all of the eight subpopulations analyzed. The phenotypic diversity determined based on eight differential Brazilian cultivars was lower in commercial rice fields of 'Primavera' than in the fields of 'Bonança,' independent of the diversity index utilized, year and location. Considering the Brazilian differentials, the four subpopulations of 'BRS Primavera' did not show evenness in distribution and only one pathotype dominated in the populations. The even distribution of pathotype was observed in three subpopulations of 'BRS Bonança'. The pathotype diversity of M. grisea was determined with more precision using Brazilian differentials and Simpson index.

Mobile system for pump working point estimation

In the current economy situation companies try to reduce their expenses. One of the solutions is to improve the energy efficiency of the processes. It is known that the energy consumption of pumping applications range from 20 up to 50% of the energy usage in the certain industrial plants operations. Some studies have shown that 30% to 50% of energy consumed by pump systems could be saved by changing the pump or the flow control method. The aim of this thesis is to create a mobile measurement system that can calculate a working point position of a pump drive. This information can be used to determine the efficiency of the pump drive operation and to develop a solution to bring pump’s efficiency to a maximum possible value. This can allow a great reduction in the pump drive’s life cycle cost. In the first part of the thesis, a brief introduction in the details of pump drive operation is given. Methods that can be used in the project are presented. Later, the review of available platforms for the project implementation is given. In the second part of the thesis, components of the project are presented. Detailed description for each created component is given. Finally, results of laboratory tests are presented. Acquired results are compared and analyzed. In addition, the operation of created system is analyzed and suggestions for the future development are given.

Estimation of existing cable systems in generator-busbar unit and choice the optimal solution for its modernization

The aim of this master’s thesis is to develop an algorithm to calculate the cable network for heat and power station CHGRES. This algorithm includes important aspect which has an influence on the cable network reliability. Moreover, according to developed algorithm, the optimal solution for modernization cable system from economical and technical point of view was obtained. The conditions of existing cable lines show that replacement is necessary. Otherwise, the fault situation would happen. In this case company would loss not only money but also its prestige. As a solution, XLPE single core cables are more profitable than other types of cable considered in this work. Moreover, it is presented the dependence of value of short circuit current on number of 10/110 kV transformers connected in parallel between main grid and considered 10 kV busbar and how it affects on final decision. Furthermore, the losses of company in power (capacity) market due to fault situation are presented. These losses are commensurable with investment to replace existing cable system.

Simultaneous determination of Cd and Pb in antibiotics used in sugar-cane fermentation process by GFAAS

A method has been developed for the simultaneous determination of Cd and Pb in antibiotics used in sugar-cane fermentation by GFAAS. The integrated platform of transversely heated graphite atomizer was treated with tungsten to form a coating of tungsten carbide. Six samples of commercial solid antibiotics were analyzed by injecting 20 µL of digested samples into the pretreated graphite platform with co-injection of 5 µL of 1000 mg L-1 Pd as chemical modifier. Samples were mineralized in a closed-vessel microwave-assisted acid-digestion system using nitric acid plus hydrogen peroxide. The pyrolysis and atomization temperatures of the heating program of the atomizer were selected as 600°C and 2200°C, respectively. The calculated characteristic mass for Cd and Pb was 1.6 pg and 42 pg, respectively. Limits of detection (LOD) based on integrated absorbance were 0.02 µg L-1 Cd and 0.7 µg L-1 Pb and the relative standard deviations (n = 10) for Cd and Pb were 5.7% and 8.0%, respectively. The recoveries of Cd and Pb added to the digested samples varied from 91% to 125% (Cd) and 80% to 112% (Pb).

Direct and simultaneous spectrophotometric determination of Ni (II) and Co (II) using diethanoldithiocarbamate as complexing agent

A direct spectrophotometric method for simultaneous determination of Co(II) and Ni(II), with diethanoldithiocarbamate (DEDC) as complexing agent, is proposed using the maximum absorption at 360 and 638 nm (Co(II)/DEDC) and 390 nm (Ni/DEDC). Adjusting the best metal/ligand ratio, supporting eletrolite, pH, and time of analysis, linear analytical curves from 1.0 10-6-4.0 10-4 for Co(II) in the presence of Ni 1.0 10-6-1.0 10-4 mol L-1 were observed. No further treatment or calculation processes have been necessary. Recoveries in different mixing ratios were of 99%. Interference of Fe(III), Cu(II), Zn(II) and Cd(II), and anions as NO3-, Cl-, ClO4-, citrate and phosphate has been evaluated. The method was applied to natural waters spiked with the cations.

Flow-batch analyser for preparation of calibration standard mixtures in simultaneous multicomponent spectrometric analysis

The application of multivariate calibration techniques to multicomponent analysis by UV-VIS molecular absorption spectrometry is a powerful tool for simultaneous determination of several chemical species. However, when this methodology is accomplished manually, it is slow and laborious, consumes high amounts of reagents and samples, is susceptible to contaminations and presents a high operational cost. To overcome these drawbacks, a flow-batch analyser is proposed in this work. This analyser was developed for automatic preparation of standard calibration and test (or validation) mixtures. It was applied to the simultaneous determination of Cu2+, Mn2+ and Zn2+ in polyvitaminic and polymineral pharmaceutical formulations, using 4-(2-piridilazo) resorcinol as reagent and a UV-VIS spectrophotometer with a photodiode array detector. The results obtained with the proposed system are in good agreement with those obtained by flame atomic absorption spectrometry, which was employed as reference method. With the proposed analyser, the preparation of calibration and test mixtures can be accomplished about four hours, while the manual procedure requires at least two days. Moreover, it consumes smaller amounts of reagents and samples than the manual procedure. After the preparation of calibration and test mixtures, 60 samples h-1 can be carried out with the proposed flow-batch analyser.

Estimation of standard enthalpy of formation of alkanes in gaseous state by calculating size, structural and electronic parameters in the molecules

A quantitative analysis is made on the correlation ship of thermodynamic property, i.e., standard enthalpy of formation (ΔH fº) with Kier's molecular connectivity index(¹Xv),vander waal's volume (Vw) electrotopological state index (E) and refractotopological state index (R) in gaseous state of alkanes. The regression analysis reveals a significant linear correlation of standard enthalpy of formation (ΔH fº) with ¹Xv, Vw, E and R. The equations obtained by regression analysis may be used to estimate standard enthalpy of formation (ΔH fº) of alkanes in gaseous state.

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.