Edvan Cirino da Silva

A method for calibration and validation subset partitioning

This paper proposes a new method to divide a pool of samples into calibration and validation subsets for multivariate modelling. The proposed method is of value for analytical applications involving complex matrices, in which the composition variability of real samples cannot be easily reproduced by optimized experimental designs. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. The proposed technique is illustrated in a case study involving the prediction of three quality parameters (specific mass and distillation temperatures at which 10 and 90% of the sample has evaporated) of diesel by NIR spectrometry and PLS modelling. For comparison, PLS models are also constructed by full cross-validation, as well as by using the Kennard-Stone and random sampling methods for calibration and validation subset partitioning. The obtained models are compared in terms of prediction performance by employing an independent set of samples not used for calibration or validation. The results of F-tests at 95% confidence level reveal that the proposed technique may be an advantageous alternative to the other three strategies.

Digital image-based flame emission spectrometry

A digital image-based flame emission spectrometric (DIB-FES) method for the quantitative chemical analysis is proposed here for the first time. The DIB-FES method employs a webcam to capture the digital images which are associated to a radiation emitted by the analyte into an air-butane flame. Since the detection by webcam is based on the RGB (red-green-blue) colour system, a novel mathematical model was developed in order to build DIB-FES analytical curves and estimate figures of merit for the proposed method. In this approach, each image is retrieved in the three R, G and B individual components and their values were used to define a position vector in RGB three-dimensional space. The norm of this vector is then adopted as the RGB-based value (analytical response) and it has revealed to be linearly related to the analyte concentration. The feasibility of the DIB-FES method is illustrated in three applications involving the determination of lithium, sodium and calcium in anti-depressive drug, physiological serum and water, respectively. In comparison with the traditional flame emission spectrometry (trad-FES), no statistic difference has been observed between the results by applying the paired t-test at the 95% confidence level. However, the DIB-FES method has offered the largest sensitivities and precision, as well as the smallest limits of detection and quantification for the three analytes. These advantageous characteristics are attributed to the trivariate nature of the detection by webcam.

A digital image-based method for determining of total acidity in red wines using acid–base titration without indicator

This work proposes the use of digital image-based method for determination of total acidity in red wines by means of acid-base titration without using an external indicator or any pre-treatment of the sample. Digital images present the colour of the emergent radiation which is complementary to the radiation absorbed by anthocyanines present in wines. Anthocyanines change colour depending on the pH of the medium, and from the variation of colour in the images obtained during titration, the end point can be localized with accuracy and precision. RGB-based values were employed to build titration curves, and end points were localized by second derivative curves. The official method recommends potentiometric titration with a NaOH standard solution, and sample dilution until the pH reaches 8.2-8.4. In order to illustrate the feasibility of the proposed method, titrations of ten red wines were carried out. Results were compared with the reference method, and no statistically significant difference was observed between the results by applying the paired t-test at the 95% confidence level. The proposed method yielded more precise results than the official method. This is due to the trivariate nature of the measurements (RGB), associated with digital images.

Cross-validation for the selection of spectral variables using the successive projections algorithm

This work compares the use of a separate validation set and leave-one-out cross-validation to guide the selection of variables in the Successive Projections Algorithm (SPA) for multivariate calibration. Two case studies involving diesel and corn analysis by NIR spectrometry are presented. A graphical interface for SPA is available at www.ele.ita.br/~kawakami/spa/

Implementation of an automatic standard addition method in a flow–batch system: application to copper determination in an alcoholic beverage by atomic absorption spectrometry

A novel strategy for implementing the automatic standard addition method (SAM) is described. By using a flow–batch system that presents the intrinsic favourable characteristics of the flow and batch techniques, the proposed strategy performs fast standard additions with sufficient flexibility and versatility and employs only one standard solution per analyte. To calculate the analyte concentration, a mathematical model based on a classical SAM and flow variables of the system was developed. The proposed flow–batch SAM was applied to copper determination by flame atomic absorption spectrometry (AAS) in sugar cane-made alcoholic beverages, known as “Cachaca”, available in Brazil. A SAM has been recommended for these analyses because “Cachacas” presents a significantly different composition causing matrix effects and copper determination by calibration using matrix-matching standards can yield inaccurate results. The results show good agreement between the obtained values with the proposed flow–batch SAM and a manual SAM. The mean relative errors and overall standard deviations were always <1.0% (n = 6) and 0.2 mg l −1 , respectively, for 1.0–7.0 mg l −1 Cu. By using five standard addition levels, the sample throughput was 70 h −1 and the consumption of sample and standard solution were 1.5 and 0.5 ml per

A solution to the wavelet transform optimization problem in multicomponent analysis

The wavelet transform has been shown to be an efficient tool for data treatment in multivariate calibration. However, previous works had the limitation of using fixed wavelets, which must be chosen a priori, because adjusting the wavelets to the data set involves a complex constrained optimization problem. This difficulty is overcome here and the mathematical background involved is described in detail. The proposed approach maximizes the compression performance of the quadrature-mirror filter bank used to process the spectra. After the optimization phase, the recently proposed successive projections algorithm is used to select subsets of wavelet coefficients in order to minimize collinearity problems in the regression. To demonstrate the efficiency of the entire strategy, a low-resolution ICP-AES was deliberately chosen to tackle a hard multivariate calibration problem involving the simultaneous multicomponent determination of Mn, Mo, Cr, Ni and Fe in steel samples. This analysis is intrinsically complex, due to strong collinearity and severe spectral overlapping, problems that are aggravated by the use of low-resolution optics. Moreover, there are also several regions in the spectra where the signal-to-noise ratio is poor. The results demonstrate that the optimization leads to models with better parsimony and prediction ability when compared to the fixed-wavelet approach adopted in previous papers.

Improvement of prediction ability of PLS models employing the wavelet packet transform: A case study concerning FT-IR determination of gasoline parameters

The wavelet packet transform (WPT) is a variant of the standard wavelet transform that offers greater flexibility in the decomposition of instrumental signals. Although encouraging results have been published concerning the use of WPT for signal compression and denoising, its application in multivariate calibration problems has received comparatively little attention, with very few contributions reported in the literature. This paper presents an investigation concerning the use of WPT as a feature extraction tool to improve the prediction ability of PLS models. The optimization of the wavelet packet tree is accomplished by using the classic dynamic programming algorithm and an entropy cost function modified to take into account the variance explained by the WPT coefficients. The selection of WPT coefficients for inclusion in the PLS model is carried out on the basis of correlation with the dependent variable, in order to exploit the joint statistics of the instrumental response and the parameter of interest. This WPT-PLS strategy is applied in a case study involving FT-IR spectrometric determination of four gasoline parameters, namely specific mass (SM) and the distillation temperatures at which 10%, 50%, 90% of the sample has evaporated. The dataset comprises 103 gasoline samples collected from gas stations and 6144 wavelengths in the range 2500-15000nm. By applying WPT to the FT-IR spectra, considerable compression with respect to the original wavelength domain is achieved. The effect of varying the wavelet and the threshold level on the prediction ability of the resulting models is investigated. The results show that WPT-PLS outperforms standard PLS in most wavelet-threshold combinations for all determined parameters.

Standard additions in flow injection analysis based on merging zones and gradient exploitation: application to copper determination in spirits

A novel strategy for implementing the standard addition method in flow analysis is described. It requires only one standard solution and provides several addition levels using a single injection. Data acquisition and treatment are performed by means of a microcomputer running a software in C-language. A mathematical model to account for the changes in sample matrix due to differences in dispersion associated with the different addition levels is derived. It corrects also deviations related to the asymmetry of the merging zones process. Model systems are critically examined for potassium determination in synthetic samples by flame photometry, to which ethanol or glycerol are added to promote different matrix effects. As application, the determination of copper in spirits by flame atomic absorption spectrometry was selected. The proposed system handles about 60 samples per hour and yields precise results (R.S.D. usually < 5%) regardless of the ethanol content of the assayed samples. Comparison with an alternative procedure involving sample evaporation and manual water dilution revealed systematic deviations usually < 3%.

Indirect determination of sodium diclofenac, sodium dipyrone and calcium gluconate in injection drugs using digital image-based (webcam) flame emission spectrometric method

This paper proposes a digital image-based flame emission spectrometric (DIB-FES) method for indirect determination of sodium diclofenac, sodium dipyrone and calcium gluconate in injectable forms. The proposed DIB-FES method uses digital images obtained from a webcam, based on the RGB (Red-Green-Blue) system. It offers a simple and inexpensive way to quantify these organic substances using the radiation emitted by the alkaline and earth-alkaline metals present in their formulae. Analytical curves were constructed on the basis of the relationship between RGB values and calibration solution concentrations. The results showed no statistical difference between the proposed and reference methods when applying the paired t-test at a 95% confidence level. The proposed DIB-FES method also performed well in terms of the figures of merit LOD, LOQ, linear range, precision, and the accuracy as revealed by recovery tests.

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.