Fernanda Araújo Honorato

A comparative study of calibration transfer methods for determination of gasoline quality parameters in three different near infrared spectrometers

This work presents a comparative study of calibration transfer among three near infrared spectrometers for determination of naphthenes and RON (Research Octane Number) in gasoline. Seven transfer methods are compared: direct standardization (DS), piecewise direct standardization (PDS), orthogonal signal correction (OSC), reverse standardization (RS), piecewise reverse standardization (PRS), slope and bias correction (SBC) and model updating (MU). Two pre-treatment procedures, namely standard normal variate (SNV) and multiplicative scatter correction (MSC), are also investigated. The choice of an appropriate number of transfer samples for each technique, as well as the effect of window size in PDS/PRS and OSC components, are discussed. A broad set of gasoline samples representative of the Northeastern states of Brazil is employed in the investigation. The results show that the use of calibration transfer yields prediction errors comparable to those obtained with complete recalibration of the secondary instrument. Overall, the results point to RS as the best method for the analytical problem under consideration. When storage and/or physical transportation of transfer samples are impractical, MU is more appropriate. The comprehensive investigation carried out in the present work will be of value for practitioners involved in networks of fuel monitoring.

Transferência de calibração em métodos multivariados

Calibration transfer has received considerable attention in the recent literature. Several standardization methods have been proposed for transferring calibration models between equipments. The goal of this paper is to present a general revision of calibration transfer techniques. Basic concepts will be reviewed, as well as the main advantages and drawbacks of each technique. A case study based on a set of 80 NIR spectra of maize samples recorded on two different instruments is used to illustrate the main calibration transfer techniques (direct standardization, piecewise direct standardization, orthogonal signal correction and robust variable selection).

Classification of Brazilian and foreign gasolines adulterated with alcohol using infrared spectroscopy

The smuggling of products across the border regions of many countries is a practice to be fought. Brazilian authorities are increasingly worried about the illicit trade of fuels along the frontiers of the country. In order to confirm this as a crime, the Federal Police must have a means of identifying the origin of the fuel. This work describes the development of a rapid and nondestructive methodology to classify gasoline as to its origin (Brazil, Venezuela and Peru), using infrared spectroscopy and multivariate classification. Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling Class Analogy (SIMCA) models were built. Direct standardization (DS) was employed aiming to standardize the spectra obtained in different laboratories of the border units of the Federal Police. Two approaches were considered in this work: (1) local and (2) global classification models. When using Approach 1, the PLS-DA achieved 100% correct classification, and the deviation of the predicted values for the secondary instrument considerably decreased after performing DS. In this case, SIMCA models were not efficient in the classification, even after standardization. Using a global model (Approach 2), both PLS-DA and SIMCA techniques were effective after performing DS. Considering that real situations may involve questioned samples from other nations (such as Peru), the SIMCA method developed according to Approach 2 is a more adequate, since the sample will be classified neither as Brazil nor Venezuelan. This methodology could be applied to other forensic problems involving the chemical classification of a product, provided that a specific modeling is performed.

Determination of detergent and dispensant additives in gasoline by ring-oven and near infrared hypespectral imaging

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%.

Standardization from a benchtop to a handheld NIR spectrometer using mathematically mixed NIR spectra to determine fuel quality parameters

The interest in performing in field measures using portable instruments is growing increasingly. Calibration transfer techniques can be used to enable models, predicted values or spectra obtained in a benchtop instrument be used in portable instrument, saving money and time required for a complete recalibration. Most of the calibration transfer methods require a set of transfer samples which spectra have to be acquired in both spectrometers. The present work evaluates the use of virtual standards as transfer samples in the reverse standardization (RS) method in order to standardize very dissimilar spectral responses of fuel samples (gasoline and biodiesel blends) from a high-resolution benchtop Frontier FT-NIR (PerkinElmer) spectrometer and a handheld MicroNIR™1700 (JDSU). These virtual standards can be created by mathematically mixing spectra from the pure solvents present in gasoline or diesel/biodiesel (D/B) blends, to avoid volatilization and changes in the composition of the compounds during storage and/or transportation of the real transfer fuel samples. Virtual standards were created using ten and five pure solvents for gasoline and D/B blends, respectively. Partial least squares regression (PLS) models were built for five quality parameters of gasoline (distillation temperatures at 10%, 50%, 90% and final boiling point (FBP) volume recovered and density) and one of D/B blends (biodiesel content). The RMSEP values obtained after the standardization approaches were equivalent to the reproducibility of the reference methods, except for density and biodiesel content parameters obtained for the virtual samples standardization approach. RS procedure provided promising results showing that it is possible to transfer gasoline or D/B blend spectra acquired with a high-resolution benchtop instrument to the handheld MicroNIR using virtual standards as transfer samples.

Authenticity assessment of banknotes using portable near infrared spectrometer and chemometrics

Spectra recorded using a portable near infrared (NIR) spectrometer, Soft Independent Modeling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) associated to Successive Projections Algorithm (SPA) models were applied to identify counterfeit and authentic Brazilian Real (R

Robust modeling for multivariate calibration transfer by the successive projections algorithm

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Using principal component analysis to find the best calibration settings for simultaneous spectroscopic determination of several gasoline properties

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Classification of gasoline as with or without dispersant and detergent additives using infrared spectroscopy and multivariate classification

100) banknotes, enabling a simple field analysis. NIR spectra (950-1650nm) were recorded from seven different areas of the banknotes (two with fluorescent ink, one over watermark, three with intaglio printing process and one over the serial numbers with typography printing). SIMCA and SPA-LDA models were built using 1st derivative preprocessed spectral data from one of the intaglio areas. For the SIMCA models, all authentic (300) banknotes were correctly classified and the counterfeits (227) were not classified. For the two classes SPA-LDA models (authentic and counterfeit currencies), all the test samples were correctly classified into their respective class. The number of selected variables by SPA varied from two to nineteen for R

Classification of cereal bars using near infrared spectroscopy and linear discriminant analysis

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