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Maria Fernanda Pimentel

Publications and source records attributed to Maria Fernanda Pimentel.

2 recordsLinked to original sources

Analytical curve or standard addition method: how to elect and design--a strategy applied to copper determination in sugarcane spirits using AAS.

In most instrumental analysis, the analyte concentration is usually obtained the by Analytical Curve Method (ACM) or Standard Addition Method (SAM). Thus, it is important for the analyst to select the most appropriate method, to seek the best conditions of analysis, and to provide parameters of analytical performance. A strategy to do so is proposed in this paper in conjunction with MATLAB software to implement it. The proposed strategy was applied to copper determination by atomic absorption spectrometry in Brazilian sugarcane spirits termed 'Cachaça' and SAM was chosen as the most appropriate method. To select the best experimental design for SAM, the influence of some factors, such as the number of standard additions and concentration levels, the location of the levels and the average concentration of the standard additions were demonstrated. The design with six standard additions, four concentration levels located near the inferior and superior levels and the average concentration of the standard additions closer to zero yielded SAM with an adequate compromise between precision, cost and time of analysis. The uniform distribution of concentration levels, usually used in routine analysis, is not a good design regarding precision. On the other hand, it is adequate when the linear range is unknown. Generally, the proposed strategy can be applied to different instrumental techniques and samples, which aim to improve their analytical performance.

Alcoholic Beverages↗

A linear semi-infinite programming strategy for constructing optimal wavelet transforms in multivariate calibration problems.

A novel strategy for the optimization of wavelet transforms with respect to the statistics of the data set in multivariate calibration problems is proposed. The optimization follows a linear semi-infinite programming formulation, which does not display local maxima problems and can be reproducibly solved with modest computational effort. After the optimization, a variable selection algorithm is employed to choose a subset of wavelet coefficients with minimal collinearity. The selection allows the building of a calibration model by direct multiple linear regression on the wavelet coefficients. In an illustrative application involving the simultaneous determination of Mn, Mo, Cr, Ni, and Fe in steel samples by ICP-AES, the proposed strategy yielded more accurate predictions than PCR, PLS, and nonoptimized wavelet regression.

Journal Article↗