Lívia R Brito | Universidade de Pernambuco - UFPE (Brasil) (original) (raw)
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Papers by Lívia R Brito
Analytica Chimica Acta, 2014
A method using the ring-oven technique for pre-concentration in filter paper discs and near infra... more A method using the ring-oven technique for pre-concentration in filter paper discs and near infrared hyperspectral imaging is proposed to identify four detergent and dispersant additives, and to determine their concentration in gasoline. Different approaches were used to select the best image data processing in order to gather the relevant spectral information. This was attained by selecting the pixels of the region of interest (ROI), using a pre-calculated threshold value of the PCA scores arranged as histograms, to select the spectra set; summing up the selected spectra to achieve representativeness; and compensating for the superimposed filter paper spectral information, also supported by scores histograms for each individual sample. The best classification model was achieved using linear discriminant analysis and genetic algorithm (LDA/GA), whose correct classification rate in the external validation set was 92%. Previous classification of the type of additive present in the gasoline is necessary to define the PLS model required for its quantitative determination. Considering that two of the additives studied present high spectral similarity, a PLS regression model was constructed to predict their content in gasoline, while two additional models were used for the remaining additives. The results for the external validation of these regression models showed a mean percentage error of prediction varying from 5 to 15%.
Food Research International, 2013
This work proposes an analytical method for cereal bar classification based on the use of near in... more This work proposes an analytical method for cereal bar classification based on the use of near infrared spectroscopy (NIRS) and supervised pattern recognition techniques. Linear discriminant analysis (LDA) is employed to build a classification model on the basis of a reduced subset of variables (wavenumbers). For the purpose of variable selection, three techniques are considered, namely successive projection algorithm (SPA), Genetic Algorithm (GA), and stepwise (SW) formulation. The methodology is validated in a case study involving the classification of 121 cereal bar samples into three different types (conventional, diet and light). The results show that the LDA/GA model is superior to the LDA/SPA and LDA/SW models with respect to classification accuracy in an independent prediction set. Some advantages of the proposed method are speed, that the analytical measurement is performed quickly (one minute or less per sample), no reagents, low sample consumption and minimum sample preparation demands. In view of the results obtained in this study the proposed method may be considered valid for use in cereal bar classification.
Analytica Chimica Acta, 2014
A method using the ring-oven technique for pre-concentration in filter paper discs and near infra... more A method using the ring-oven technique for pre-concentration in filter paper discs and near infrared hyperspectral imaging is proposed to identify four detergent and dispersant additives, and to determine their concentration in gasoline. Different approaches were used to select the best image data processing in order to gather the relevant spectral information. This was attained by selecting the pixels of the region of interest (ROI), using a pre-calculated threshold value of the PCA scores arranged as histograms, to select the spectra set; summing up the selected spectra to achieve representativeness; and compensating for the superimposed filter paper spectral information, also supported by scores histograms for each individual sample. The best classification model was achieved using linear discriminant analysis and genetic algorithm (LDA/GA), whose correct classification rate in the external validation set was 92%. Previous classification of the type of additive present in the gasoline is necessary to define the PLS model required for its quantitative determination. Considering that two of the additives studied present high spectral similarity, a PLS regression model was constructed to predict their content in gasoline, while two additional models were used for the remaining additives. The results for the external validation of these regression models showed a mean percentage error of prediction varying from 5 to 15%.
Food Research International, 2013
This work proposes an analytical method for cereal bar classification based on the use of near in... more This work proposes an analytical method for cereal bar classification based on the use of near infrared spectroscopy (NIRS) and supervised pattern recognition techniques. Linear discriminant analysis (LDA) is employed to build a classification model on the basis of a reduced subset of variables (wavenumbers). For the purpose of variable selection, three techniques are considered, namely successive projection algorithm (SPA), Genetic Algorithm (GA), and stepwise (SW) formulation. The methodology is validated in a case study involving the classification of 121 cereal bar samples into three different types (conventional, diet and light). The results show that the LDA/GA model is superior to the LDA/SPA and LDA/SW models with respect to classification accuracy in an independent prediction set. Some advantages of the proposed method are speed, that the analytical measurement is performed quickly (one minute or less per sample), no reagents, low sample consumption and minimum sample preparation demands. In view of the results obtained in this study the proposed method may be considered valid for use in cereal bar classification.