Márcio José Coelho Pontes

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.

Classification of Brazilian soils by using LIBS and variable selection in the wavelet domain

This paper proposes a novel analytical methodology for soil classification based on the use of laser-induced breakdown spectroscopy (LIBS) and chemometric techniques. In the proposed methodology, linear discriminant analysis (LDA) is employed to build a classification model on the basis of a reduced subset of spectral variables. For the purpose of variable selection, three techniques are considered, namely the successive projection algorithm (SPA), the genetic algorithm (GA), and a stepwise formulation (SW). The use of a data compression procedure in the wavelet domain is also proposed to reduce the computational workload involved in the variable selection process. The methodology is validated in a case study involving the classification of 149 Brazilian soil samples into three different orders (Argissolo, Latossolo and Nitossolo). For means of comparison, soft independent modelling of class analogy (SIMCA) models are also employed. The best discrimination of soil types was attained by SPA-LDA, which achieved an average classification rate of 90% in the validation set and 72% in cross-validation. Moreover, the proposed wavelet compression procedure was found to be of value by providing a 100-fold reduction in computational workload without significantly compromising the classification accuracy of the resulting models.

Near infrared reflectance spectrometry classification of cigarettes using the successive projections algorithm for variable selection

This paper proposes a methodology for cigarette classification employing Near Infrared Reflectance spectrometry and variable selection. For this purpose, the Successive Projections Algorithm (SPA) is employed to choose an appropriate subset of wavenumbers for a Linear Discriminant Analysis (LDA) model. The proposed methodology is applied to a set of 210 cigarettes of four different brands. For comparison, Soft Independent Modelling of Class Analogy (SIMCA) is also employed for full-spectrum classification. The resulting SPA-LDA model successfully classified all test samples with respect to their brands using only two wavenumbers (5058 and 4903 cm(-1)). In contrast, the SIMCA models were not able to achieve 100% of classification accuracy, regardless of the significance level adopted for the F-test. The results obtained in this investigation suggest that the proposed methodology is a promising alternative for assessment of cigarette authenticity.

Detection of adulteration in hydrated ethyl alcohol fuel using infrared spectroscopy and supervised pattern recognition methods

This paper proposes an analytical method to detect adulteration of hydrated ethyl alcohol fuel based on near infrared (NIR) and middle infrared (MIR) spectroscopies associated with supervised pattern recognition methods. For this purpose, linear discriminant analysis (LDA) was employed to build a classification model on the basis of a reduced subset of wavenumbers. For variable selection, three techniques are considered, namely the successive projection algorithm (SPA), the genetic algorithm (GA) and a stepwise formulation (SW). For comparison, models based on partial least squares discriminant analysis (PLS-DA) were also employed using full-spectrum. The method was validated in a case study involving the classification of 181 hydrated ethyl alcohol fuel samples, which were divided into three different classes: (1) authentic samples; (2) samples adulterated with water and (3) samples contaminated with methanol. LDA/GA and PLS-DA models were found to be the best methods for classifying the spectral data obtained in NIR region, which achieved a correct prediction rate of 100% in the test set, while the LDA/SPA and LDA/SW were correctly classified at 84.4% and 97.8%, respectively. For MIR data, all models (PLS-DA and LDA coupled with the SW, SPA and GA) employed in this study correctly classified all samples in the test set.

Classification of edible vegetable oils using square wave voltammetry with multivariate data analysis.

This paper proposes a simple and non-expensive electroanalytical methodology for classification of edible vegetable oils with respect to type (canola, sunflower, corn and soybean) and conservation state (expired and non-expired shelf life). The proposed methodology employs an alcoholic extraction procedure followed by square wave voltammetry (SWV). Two chemometric methods were compared for classification of the resulting voltammograms, namely Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) with variable selection by the Successive Projections Algorithm (SPA). The results were evaluated in terms of errors in a set of samples not included in the modelling process. The best results were obtained with the SPA-LDA method, which correctly classified all samples in terms of type and conservation state.

Screening analysis to detect adulteration in diesel/biodiesel blends using near infrared spectrometry and multivariate classification

This paper proposes an analytical method to detect adulteration of diesel/biodiesel blends based on near infrared (NIR) spectrometry and supervised pattern recognition methods. For this purpose, partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) coupled with the successive projections algorithm (SPA) have been employed to build screening models using three different optical paths and the following spectra ranges: 1.0mm (8814-3799 cm(-1)), 10mm (11,329-5944 cm(-1) and 5531-4490 cm(-1)) and 20mm (11,688-5952 cm(-1) and 5381-4679 cm(-1)). The method is validated in a case study involving the classification of 140 diesel/biodiesel blend samples, which were divided into four different classes, namely: diesel free of biodiesel and raw vegetal oil (D), blends containing diesel, biodiesel and raw oils (OBD), blends of diesel and raw oils (OD), and blends containing a fraction of 5% (v/v) of biodiesel in diesel (B5). LDA-SPA models were found to be the best method to classify the spectral data obtained with optical paths of 1.0 and 20mm. Otherwise, PLS-DA shows the best results for classification of 10mm cell data, which achieved a correct prediction rate of 100% in the test set.

Using near-infrared overtone regions to determine biodiesel content and adulteration of diesel/biodiesel blends with vegetable oils.

This work evaluates the use of near-infrared (NIR) overtone regions to determine biodiesel content, as well potential adulteration with vegetable oil, in diesel/biodiesel blends. For this purpose, NIR spectra (12,000-6300 cm(-1)) were obtained using three different optical path lengths: 10 mm, 20 mm and 50 mm. Two strategies of regression with variable selection were evaluated: partial least squares (PLS) with significant regression coefficients selected by Jack-Knife algorithm (PLS/JK) and multiple linear regression (MLR) with wavenumber selection by successive projections algorithm (MLR/SPA). For comparison, the results obtained by using PLS full-spectrum models are also presented. In addition, the performance of models using NIR (1.0 mm optical path length, 9000-4000 cm(-1)) and MIR (UATR - universal attenuated total reflectance, 4000-650 cm(-1)) spectral regions was also investigated. The results demonstrated the potential of overtone regions with MLR/SPA regression strategy to determine biodiesel content in diesel/biodiesel blends, considering the possible presence of raw oil as a contaminant. This strategy is simple, fast and uses a fewer number of spectral variables. Considering this, the overtone regions can be useful to develop low cost instruments for quality control of diesel/biodiesel blends, considering the lower cost of optical components for this spectral region.

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.

A new validation criterion for guiding the selection of variables by the successive projections algorithm in classification problems

A simplification in SPA-LDA is proposed to circumvent the need for separate training and validation sets. The number of degrees of freedom is employed in the cost function to avoid model overfitting. Three examples are presented: classification of coffee, diesel and vegetable oils by using UV-Vis spectrometry, NIR spectrometry and voltammetry, respectively.

Near-infrared spectrometric determination of dipyrone in closed ampoules

The present paper proposes an analytical method for fast near-infrared (NIR) determination of dipyrone in injectable formulations with a nominal content of 50.0%mv(-1) without violation of the ampoule. For this purpose, two multivariate calibration methods are evaluated, namely Partial-Least-Squares (PLS) and Multiple Linear Regression (MLR) with variable selection by the Successive Projections Algorithm (SPA). The resulting models comprised four latent variables (PLS) and five spectral variables (MLR-SPA). Appropriate predictions were obtained in both cases, with RMSEP values of 0.39 (PLS) and 0.35%mv(-1) (MLR-SPA) and correlation coefficients of 0.9970 (PLS) and 0.9975 (MLR-SPA) for a calibration range of 40-60%mv(-1). No systematic error was observed and no significant differences were found between the predicted and reference values, according to a paired t-test at 95% confidence level.