-
[show abstract]
[hide abstract]
ABSTRACT: 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.
Talanta 04/2009; 77(5):1584-9. · 3.79 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: A portable, microcontrolled and low-cost spectrophotometer (MLCS) is proposed. The instrument combines the use of a compact disc (CD) media as diffraction grid and white light-emitting diode (LED) as radiation source. Moreover, it employs a phototransistor with spectral sensitivity in visible region as phototransductor, as well as a programmable interrupt controller (PIC) microcontroller as control unit. The proposed instrument was successfully applied to determination of food colorants (tartrazine, sunset yellow, brilliant blue and allura red) in five synthetics samples and Fe2+ in six samples of restorative oral solutions. For comparison purpose, two commercial spectrophotometers (HP and Micronal) were employed. The application of the t-paired test at the 95% confidence level revealed that there are not significant differences between the concentration values estimated by the three instruments. Furthermore, a good precision in the analyte concentrations was obtained by using MLCS. The overall relative standard deviation (R.S.D.) of each analyte was smaller than 1.0%. Therefore, the proposed instrument offers an economically viable alternative for spectrophotometric chemical analysis in small routine, research and/or teaching laboratories, because its components are inexpensive and of easy acquisition.
Talanta 02/2009; 77(3):1155-1159. · 3.79 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: 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.
Talanta 11/2005; 67(4):736-40. · 3.79 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The term bagging refers to a class of techniques in which an ensemble model is obtained by combining different member models generated by resampling the available data set. It has been shown that bagging can lead to substantial gains in accuracy for both classification and regression models, specially when alterations in the training set cause significant changes in the outcome of the modelling procedure. However, in the context of chemometrics, the use of bagging for quantitative multicomponent analysis is still incipient. More recently, an alternative aggregation scheme termed subagging, which is based on subsampling without replacement, has been shown to provide performance improvements similar to bagging at a smaller computational cost. The present paper proposes a strategy for using subagging in conjunction with three multivariate calibration methods, namely Partial Least Squares (PLS) and Multiple Linear Regression with variable selection by using either the Successive Projections Algorithm (MLR-SPA) or a Genetic Algorithm (MLR-GA). The subagging member models are generated by subsampling the pool of samples available for modelling and then forming new calibration sets. Such a strategy is of value in analytical problems involving complex matrices, in which reproducing the composition variability of real samples by means of optimized experimental designs may be a difficult task. The efficiency of the proposed strategy is illustrated in a problem involving the NIR spectrometric determination of four diesel quality parameters (specific mass, sulphur content, and the distillation temperatures T10% and T90% at which 10% and 90% of the sample has evaporated, respectively). In this case study, the use of 30 subsampling iterations provides relative improvements of up to 16%, 33%, and 35% in the prediction accuracy of PLS, MLR-SPA, and MLR-GA models, respectively, with respect to the expected results of individual (non-ensemble) models.
Chemometrics and Intelligent Laboratory Systems.
-
[show abstract]
[hide abstract]
ABSTRACT: UV–Vis spectrometry and chemometric techniques were used to classify aqueous extracts of Brazilian ground roast coffee with respect to type (caffeinated/decaffeinated) and conservation state (expired and non-expired shelf-life). Two classification methods were compared: soft independent modelling of class analogy (SIMCA) and linear discriminant analysis (LDA) with wavelength selection by the successive projections algorithm (SPA). The best results were obtained by SPA–LDA, which correctly classified all test samples. The classification accuracy of this model remained high (96%) even after the introduction of artificial spectral noise. These results suggest that UV–Vis spectrometry and SPA–LDA modelling provide a promising alternative for assessment of conservation state and decaffeination condition of coffee samples.
Food Chemistry. 119(1):368-371.
-
[show abstract]
[hide abstract]
ABSTRACT: The successive projections algorithm (SPA) is a variable selection technique designed to minimize collinearity problems in multiple linear regression (MLR). This paper proposes a modification to the basic SPA formulation aimed at further improving the parsimony of the resulting MLR model. For this purpose, an elimination procedure is incorporated to the algorithm in order to remove variables that do not effectively contribute towards the prediction ability of the model as indicated by an F-test. The utility of the proposed modification is illustrated in a simulation study, as well as in two application examples involving the analysis of diesel and corn samples by near-infrared (NIR) spectroscopy. The results demonstrate that the number of variables selected by SPA can be reduced without significantly compromising prediction performance. In addition, SPA is favourably compared with classic Stepwise Regression and full-spectrum PLS. A graphical user interface for SPA is available at www.ele.ita.br/∼kawakami/spa/.
Chemometrics and Intelligent Laboratory Systems 92(1):83-91. · 1.92 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: 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 “Cachaça”, available in Brazil. A SAM has been recommended for these analyses because “Cachaças” 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 analysis, respectively.
Analytica Chimica Acta. 486(1):143-148.
