Wallace D. Fragoso

Flow-batch technique for the simultaneous enzymatic determination of levodopa and carbidopa in pharmaceuticals using PLS and successive projections algorithm.

An enzymatic flow-batch system with spectrophotometric detection was developed for simultaneous determination of levodopa [(S)-2 amino-3-(3,4-dihydroxyphenyl)propionic acid] and carbidopa [(S)-3-(3,4-dihydroxyphenyl)-2-hydrazino-2-methylpropionic acid] in pharmaceutical preparations. The data were analysed by univariate method, partial least squares (PLS) and a novel variable selection for multiple lineal regression (MLR), the successive projections algorithm (SPA). The enzyme polyphenol oxidase (PPO; EC 1.14.18.1) obtained from Ipomoea batatas (L.) Lam. was used to oxidize both analytes to their respective dopaquinones, which presented a strong absorption between 295 and 540 nm. The statistical parameters (RMSE and correlation coefficient) calculated after the PLS in the spectral region between 295 and 540 nm and MLR-SPA application were appropriate for levodopa and carbidopa. A comparative study of univariate, PLS, in different ranges, and MLR-SPA chemometrics models, was carried out by applying the elliptical joint confidence region (EJCR) test. The results were satisfactory for PLS in the spectral region between 295 and 540 nm and for MLR-SPA. Tablets of commercial samples were analysed and the results obtained are in close agreement with both, spectrophotometric and HPLC pharmacopeia methods. The sample throughput was 18 h(-1).

Simultaneous determination of hydroquinone, resorcinol, phenol, m-cresol and p-cresol in untreated air samples using spectrofluorimetry and a custom multiple linear regression-successive projection algorithm

In this study, a novel, simple, and efficient spectrofluorimetric method to determine directly and simultaneously five phenolic compounds (hydroquinone, resorcinol, phenol, m-cresol and p-cresol) in air samples is presented. For this purpose, variable selection by the successive projections algorithm (SPA) is used in order to obtain simple multiple linear regression (MLR) models based on a small subset of wavelengths. For comparison, partial least square (PLS) regression is also employed in full-spectrum. The concentrations of the calibration matrix ranged from 0.02 to 0.2 mg L(-1) for hydroquinone, from 0.05 to 0.6 mg L(-1) for resorcinol, and from 0.05 to 0.4 mg L(-1) for phenol, m-cresol and p-cresol; incidentally, such ranges are in accordance with the Argentinean environmental legislation. To verify the accuracy of the proposed method a recovery study on real air samples of smoking environment was carried out with satisfactory results (94-104%). The advantage of the proposed method is that it requires only spectrofluorimetric measurements of samples and chemometric modeling for simultaneous determination of five phenols. With it, air is simply sampled and no pre-treatment sample is needed (i.e., separation steps and derivatization reagents are avoided) that means a great saving of time.

Classificação periódica: um exemplo didático para ensinar análise de componentes principais

A dataset of chemical properties of the elements is used herein to introduce principal components analysis (PCA). The focus in this article is to verify the classification of the elements within the periodic table. The reclassification of the semimetals as metals or nonmetals emerges naturally from PCA and agrees with the current SBQ/IUPAC periodic table. Dataset construction, basic preprocessing, loading and score plots, and interpretation have been emphasized. This activity can be carried out even when students with distinct levels of formation are together in the same learning environment.

A new and consistent parameter for measuring the quality of multivariate analytical methods: Generalized analytical sensitivity.

Generalized analytical sensitivity (γ) is proposed as a new figure of merit, which can be estimated from a multivariate calibration data set. It can be confidently applied to compare different calibration methodologies, and helps to solve literature inconsistencies on the relationship between classical sensitivity and prediction error. In contrast to the classical plain sensitivity, γ incorporates the noise properties in its definition, and its inverse is well correlated with root mean square errors of prediction in the presence of general noise structures. The proposal is supported by studying simulated and experimental first-order multivariate calibration systems with various models, namely multiple linear regression, principal component regression (PCR) and maximum likelihood PCR (MLPCR). The simulations included instrumental noise of different types: independently and identically distributed (iid), correlated (pink) and proportional noise, while the experimental data carried noise which is clearly non-iid.

Determination of the Mineral Composition of Watercress and Data Evaluation Using Multivariate Analysis

This paper reports the determination of the mineral composition of watercress (Nasturtium officinale Cruciferae, Brassicaceae), grown in soils from three cities in the Bahia State of Brazil, and in a fourth city where plants were grown in a hydroponic system. The sampling was carried out during the summer and winter. Analyses were performed with inductively coupled plasma optical emission spectrometry (ICP OES), and the accuracy was confirmed with a certified reference material of apple leaves. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed different mineral compositions of the samples collected in the summer and winter. Samples collected in the summer had a higher concentration of the nutrients iron, calcium, and magnesium, and the samples collected in the winter had a higher concentration of manganese and copper. The average contents of the samples (expressed as wet weight) were 2.50 and 3.03 mg g−1 for calcium, 0.37 and 0.86 mg g−1 for magnesium, 8.68 and 10.84 µg g−1 for iron, 8.42 and 8.47 µg g−1 for zinc, 0.61 and 0.47 µg g−1 for copper, and 7.78 and 5.03 µg g−1 for manganese for summer and winter, respectively. These results are in agreement with values previously reported in the literature. Samples collected from the hydroponic system in the winter had a lower concentration of all nutrients.

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 simul- taneous determination of several chemical species. However, when this methodol- ogy is accomplished manually, it is slow and laborious, consumes high amounts of reagents and samples, is susceptible to contaminations and presents a high opera- tional 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 Cu 2+ , Mn 2+ and Zn 2+ in polyvitaminic and polymineral pharmaceutical formula- tions, 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 prepara- tion 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.

Regression models based on new local strategies for near infrared spectroscopic data

In this work, a comparative study of two novel algorithms to perform sample selection in local regression based on Partial Least Squares Regression (PLS) is presented. These methodologies were applied for Near Infrared Spectroscopy (NIRS) quantification of five major constituents in corn seeds and are compared and contrasted with global PLS calibrations. Validation results show a significant improvement in the prediction quality when local models implemented by the proposed algorithms are applied to large data bases.

Determination of chemical oxygen demand in domestic wastewater by near infrared spectrometry of seston and partial least squares calibration

8 Introduction P ollution of water resources by domestic and/or industrial discharges has increased considerably in urban centres around the globe as a consequence of population growth. In view of this problem, appropriate monitoring of reservoirs has become increasingly important for environmental and public health reasons. In this context, our research group has been concerned with the development of practical, fast and low-cost analytical methods for the determination of water quality parameters. In particular, we have investigated the use of near infrared (NIR) refl ectance spectrometry in conjunction with multivariate analysis methods. Such a technique can be used in a fast, non-invasive and non-destructive manner, without the need for reagents, solvents or special sample pre-treatment procedures. Among the parameters currently used for assessing the quality of water resources, an important index is the chemical oxygen demand (COD), which refl ects a large part of the organic matter in the water. However, according to Zamorra et al., the APHA (American Public Health Association) reference method for determination of COD has a number of inconveniences: (i) risk of accidents during the oxidation of organic matter by potassium dicromate in sulphuric acid at high temperatures; (ii) need for expensive catalysts such as silver sulphate; (iii) generation of environmentally harmful residues such as chromium, silver and mercury; and (iv) low analytical speed, owing to the use of a digestion stage that lasts from two to three hours. In the present article, we report an alternative method for the determination of COD in domestic wastewater samples by using NIR refl ectance spectra of the particulate matter suspended in bodies of water (seston) and partial least squares (PLS) calibration. In order to reduce the level of noise (which is usually larger in NIR refl ectance measurements as compared to absorbance spectra), a wavelet decomposition procedure is also employed. The proposed method is applied to COD determination of samples from the Mangabeira municipal wastewater treatment plant in the city of João Pessoa (Paraíba, Brazil).

Assessment of semiempirical enthalpy of formation in solution as an effective energy function to discriminate native-like structures in protein decoy sets.

In this work, we tested the PM6, PM6‐DH+, PM6‐D3, and PM7 enthalpies of formation in aqueous solution as scoring functions across 33 decoy sets to discriminate native structures or good models in a decoy set. In each set these semiempirical quantum chemistry methods were compared according to enthalpic and geometric criteria. Enthalpically, we compared the methods according to how much lower was the enthalpy of each native, when compared with the mean enthalpy of its set. Geometrically, we compared the methods according to the fraction of native contacts (Q), which is a measure of geometric closeness between an arbitrary structure and the native. For each set and method, the Q of the best decoy was compared with the Q0, which is the Q of the decoy closest to the native in the set. It was shown that the PM7 method is able to assign larger energy differences between the native structure and the decoys in a set, arguably because of a better description of dispersion interactions, however PM6‐DH+ was slightly better than the rest at selecting geometrically good models in the absence of a native structure in the set.