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A hard cap espresso extraction procedure has been developed to recover airborne pesticides in particulate matter trapped in filters. This extraction step was made for 20 s at 72 degrees C and 19 bar using 50 mL of 20% (v/v) acetonitrile in water. After that, based on NaCl salting out, extracts were concentrated 22 times and analysed by ultra-high performance liquid chromatography high resolution mass spectrometry. 35 pesticides were evaluated, as a proof of concept, being validated the whole methodology and compared the extraction method with that based on microwave assisted extraction for 20 min. In short, the method avoids cross-contamination of samples, it is relatively fast and consumes only 10 mL acetonitrile and 8 g NaCl per sample; thus, offering a low cost and green alternatively to available methods based on pressurized solvent extraction or microwave-assisted treatment. (C) 2017 Elsevier B.V. All rights reserved.

Metabolic profiling is increasingly being used for understanding biological processes but there is no single analytical technique that provides a complete quantitative or qualitative profiling of the metabolome. Data fusion (i.e. joint analysis of data from multiple sources) has the potential to circumvent this issue facilitating knowledge discovery and reliable biomarker identification. Another field of application of data fusion is the simultaneous analysis of metabolomic changes through several biofluids or tissues. However, metabolomics typically deals with large datasets, with hundreds to thousands of variables and the identification of shared and individual factors or structures across multiple sources is challenging due to the high variable to sample ratios and differences in intensity and noise range. In this work we apply a recent method, Joint and Individual Variation Explained (JIVE), for the integrated unsupervised analysis of metabolomic profiles from multiple data sources. This method separates the shared patterns among data sources (i.e. the joint structure) from the individual structure of each data source that is unrelated to the joint structure. Two examples are described to show the applicability of JIVE for the simultaneous analysis of multi-source data using: (i) plasma samples subjected to different analytical techniques, sample treatment and measurement conditions; and (ii) plasma and urine samples subjected to liquid chromatography-mass spectrometry measured using two ionization conditions.

Liquid chromatography-mass spectrometry (LC-MS) has been increasingly used in biomedicine to study the dynamic metabolomic responses of biological systems under different physiological or pathological conditions. To obtain an integrated snapshot of the system, metabolomic methods in biomedicine typically analyze biofluids (e.g. plasma) that require clean-up before being injected into LC-MS systems. However, high resolution LC-MS is costly in terms of resources required for sample and data analysis and care must be taken to prevent chemical (e.g. ion suppression) or statistical artifacts. Because of that, the effect of sample preparation on the metabolomic profile during metabolomic method development is often overlooked. This work combines an Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) and a multivariate exploratory data analysis for a cost-effective qualitative evaluation of major changes in sample composition during sample preparation. ATR-FTIR and LC-time of flight mass spectrometry (TOFMS) data from the analysis of a set of plasma samples precipitated using acetonitrile, methanol and acetone performed in parallel were used as a model example. Biochemical information obtained from the analysis of the ATR-FTIR and LC-TOFMS data was thoroughly compared to evaluate the strengths and shortcomings of FTIR biospectroscopy for assessing sample preparation in metabolomics studies. Results obtained show the feasibility of ATR-FTIR for the evaluation of major trends in the plasma composition changes among different sample pretreatments, providing information in terms of e.g., amino acids, proteins, lipids and carbohydrates overall contents comparable to those found by LC-TOFMS. (C) 2014 Elsevier B.V. All rights reserved.

An electrothermal atomic absorption (ETAAS) method has been developedfor the direct determination of Si in natural waters. Measurements were carried out on previously alkalinized samples and using a mixture of Pd and Mg(NO3)2 as matrix modifier, with an ashing temperature of 1400 oC and an atomization temperature of 2400 oC. Transversally heated pyrolitically graphite tubes, equipped with Lfvov platform and Zeeman effect correction, was employed in this study. A limit of detection of 2.6 ƒÊg/L Si was obtainedand accuracy was evaluated by analyzing two Standard Reference Material at different concentration levels, a good agreement (

Variable subset selection is often mandatory in high throughput metabolomics and proteomics. However, depending on the variable to sample ratio there is a significant susceptibility of variable selection towards chance correlations. The evaluation of the predictive capabilities of PLSDA models estimated by cross-validation after feature selection provides overly optimistic results if the selection is performed on the entire set and no external validation set is available. In this work, a simulation of the statistical null hypothesis is proposed to test whether the discrimination capability of a PLSDA model after variable selection estimated by cross-validation is statistically higher than that attributed to the presence of chance correlations in the original data set. Statistical significance of PLSDA CV-figures of merit obtained after variable selection is expressed by means of p-values calculated by using a permutation test that included the variable selection step. The reliability of the approach is evaluated using two variable selection methods on experimental and simulated data sets with and without induced class differences. The proposed approach can be considered as a useful tool when no external validation set is available and provides a straightforward way to evaluate differences between variable selection methods.

The use of chemometrics in order to improve the molecular selectivity of infrared (IR) spectra has been evaluated using classic least squares (CLS), partial least squares (PLS), science-based calibration (SBC), and multivariate curve resolution-alternate least squares (MCR-ALS) techniques for improving the discriminatory and quantitative performance of infrared hollow waveguide gas sensors. Spectra of mixtures of isobutylene, methane, carbon dioxide, butane, and cyclopropane were recorded, analyzed, and validated for optimizing the prediction of associated concentrations. PLS, CLS, and SBC provided equivalent results in the absence of interferences. After addition of the spectral characteristics of water by humidifying the sample mixtures, CLS and SBC results were similar to those obtained by PLS only if the water spectrum was included in the calibration model. In the presence of an unknown interferant, CLS revealed errors up to six times higher than those obtained by PLS. However, SBC provided similar results compared to PLS by adding a measured noise matrix to the model. Using MCR-ALS provided an excellent estimation of the spectra of the unknown interference. Furthermore, this method also provided a qualitative and quantitative estimation of the components of an unknown set of samples. In summary, using the most suitable chemometrics approach could improve the selectivity and quality of the calibration model derived for a sensor system, and may avoid the need to analyze expensive calibration data sets. The results obtained in the present study demonstrated that (1) if all sample components of the system are known, CLS provides a sufficiently accurate solution; (2) the selection between PLS and SBC methods depends on whether it is easier to measure a calibration data set or a noise matrix; and (3) MCR-ALS appears to be the most suitable method for detecting interferences within a sample. However, the latter approach requires the most extensive calculations and may thus result in limited temporal resolution, if the concentration of a component should be continuously monitored.

A new method is proposed for the elimination of the spectral contribution of two atmospheric gases (CO2 and H2O) in Fourier transform infrared (FT-IR) spectra of clinical samples. The algorithm is based on the measurement of reference spectra of H2O followed by an automatic calculation of the spectral contribution of the above-mentioned gases to the sample spectra. Then this contribution is compensated by spectral subtraction. Attenuated total reflectance FT-IR spectra of serum and urine samples in the presence of atmospheric gases were corrected and compared with spectra obtained with an N2 purge. Visual inspection of the spectra as well as calculated noise levels confirmed that the method compensated for the contribution of CO2 and H2O to serum and urine spectra to a great extent. As compared with the correction performed by a commercial software package, the proposed method clearly improved the correction of CO2 and provided similar results for H2O compensation. Correlation values between corrected spectra and those obtained under N2-purged conditions in the fingerprint region were higher than 0.995, and noise values in regions where samples did not absorb confirmed the suitability of the correction. The proposed method is fast, easy, and extensible to other gaseous interferences, devices, and acquisition conditions.

A fast, reliable and economical methodology has been developed to control the production process of sugar-based depilatories. The method is based on the use of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy in combination with multivariate data analysis. A very simple sample preparation process involving the dissolution of samples in water was applied. Employing a multivariate calibration model established from data of 15 well characterized samples, prediction errors equal or below 3.04 mg mL(-1) for the quantitative determination of fructose, glucose, sucrose, maltose and maltotriose were obtained. Results found in this preliminary study indicate a great potential for the development of at-line ATR-FTIR-PLS methods based on a careful selection of variables from IR spectra, delivering fast and reliable results. As a reference method, a liquid chromatography (LC)-IR method was adapted for sample characterization.

A green analytical method was developed for the analysis of sugar-based depilatories. Three independent partial least squares (PLS) regression models were built for the direct determination of glucose, fructose and maltose without any sample pretreatment based on their attenuated total reflectance - Fourier transform infrared (ATR-FTIR) spectra. The models showed adequate prediction capabilities with root-mean-square-errors of prediction ranging from 7.04 to 12.55 mg sugar g(-1) sample. As a reference procedure, gradient liquid chromatography with on-line infrared detection, employing background correction based on cubic smoothing splines, was used. The analysis revealed changes in the sugar concentration due to the formulation process as compared to information on the ingredients provided by the manufacturers. Although fructose, glucose and sucrose were declared to be used for the production of depilatories, in the final products only fructose, glucose and maltose were determined. This fact was attributed to pH and temperature conditions employed during the production process as well as to the use of glucose syrup instead of crystalline glucose. The present ATR-FTIR-PLS method enables an accurate, cheap and fast determination without solvent consumption or toxic waste generation and offers therefore a green screening alternative to methods employing chromatographic techniques.

The use of multivariate curve resolution-alternating least-squares (MCR-ALS) in liquid chromatography-infrared detection (LC-IR) is troublesome due to the intense background absorption changes during gradient elution. Its use has been facilitated by previous removal of a significant part of the solvent background IR contributions due to common mobile phase systems employed during reversed phase gradient applications. Two straightforward background correction approaches based on simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and principal component analysis (PCA) are proposed and evaluated on reversed phase gradient LC-IR data sets obtained during the analysis of carbohydrate and nitrophenol mixtures. After subtraction of the calculated background signal, MCR-ALS provided improved signal-to-noise ratios, removed remaining mobile phase and background signal contributions, and resolved overlapping chromatographic peaks. The present approach tends to enable easy-to-use background correction to facilitate the use of MCR-ALS in online LC-IR, even in challenging situations when gradient conditions are employed and only poor chromatographic resolution is achieved. It, therefore, shows great potential to facilitate the full exploitation of the advantages of simultaneous quantification and identification of a vast amount of analytes employing online IR detection, making new exciting applications more accessible.

The selection of an appropriate calibration set is a critical step in multivariate method development. In this work, the effect of using different calibration sets, based on a previous classification of unknown samples, on the partial least squares (PLS) regression model performance has been discussed. As an example, attenuated total reflection (ATR) mid-infrared spectra of deep-fried vegetable oil samples from three botanical origins (olive, sunflower, and corn oil), with increasing polymerized triacylglyceride (PTG) content induced by a deep-frying process were employed. The use of a one-class-classifier partial least squares-discriminant analysis (PLS-DA) and a rooted binary directed acyclic graph tree provided accurate oil classification. Oil samples fried without foodstuff could be classified correctly, independent of their PTG content. However, class separation of oil samples fried with foodstuff, was less evident. The combined use of double-cross model validation with permutation testing was used to validate the obtained PLS-DA classification models, confirming the results. To discuss the usefulness of the selection of an appropriate PLS calibration set, the PTG content was determined by calculating a PLS model based on the previously selected classes. In comparison to a PLS model calculated using a pooled calibration set containing samples from all classes, the root mean square error of prediction could be improved significantly using PLS models based on the selected calibration sets using PLS-DA, ranging between 1.06 and 2.91% (w/w).

A partial least squares (PLS) regression model based on attenuated total reflectance-Fourier transform infrared spectra of heated olive oil samples has been developed for the determination of polymerized triacylglycerides (PTGs) generated during thermal treatment of oil. Three different approaches for selection of the spectral regions used to build the PLS model were tested and compared: (1) variable selection based on expert knowledge, (2) uninformative variable elimination PLS, and (3) interval PLS. Each of the three variable selection methods provided PLS models from heated olive oil samples with excellent performance for the prediction of PTGs in fried olive oils with comparable model statistics. However, besides a high coefficient of determination (R (2) of 0.991) and low calibration, validation, and prediction errors of 1.14%, 1.21%, and 1.40% w/w, respectively, variable selection based on expert knowledge gave additionally almost identical low calibration (-0.0017% w/w) and prediction (-0.0023% w/w) bias. Furthermore, it was verified that the determination of PTGs was not influenced by the type of foodstuff fried in the olive oil.

Literature published in the last decade concerning the use of mid-infrared spectrometry as a detection system in separation techniques employing a liquid mobile phase is reviewed. In addition to the continued use of isocratic liquid chromatographic (LC) techniques, advances in chemometric data evaluation techniques now allow the use of gradient techniques on a routine basis, thus significantly broadening the range of possible applications of LC-IR. The general trend towards miniaturized separation systems was also followed for mid-IR detection where two key developments are of special importance. Firstly, concerning on-line detection the advent of micro-fabricated flow-cells with inner volumes of only a few nL for transmission as well as attenuated total reflection measurements enabled on-line mid-IR detection in capillary LC and opened the path for the first successful realization of on-line mid-IR detection in capillary zone electrophoresis as well as micellar electrokinetic chromatography. Secondly, concerning off-line detection the use of micro-flow through dispensers now enables to concentrate eluting analytes on dried spots sized a few tens of micrometers, thus matching the dimensions for sensitive detection by mid-IR microscopy. Finally in an attempt to increase detection sensitivity of on-line mid-IR detection, mid-IR quantum cascade lasers have been used. Applications cover the field of food analysis, environmental analysis and the characterization of explosives among others. Best detection sensitivities for on-line and off-line detection have been achieved in miniaturized systems and are in the order of 50 ng and 2 ng on column, respectively.

A background correction method for the on-line coupling of gradient liquid chromatography and Fourier transform infrared spectrometry (LC-FTIR) is proposed. The developed approach applies univariate background correction to each variable (i.e. each wave number) individually. Spectra measured in the region before and after each peak cluster are used as knots to model the variation of the eluent absorption intensity with time using cubic smoothing splines (CSS) functions. The new approach has been successfully tested on simulated as well as on real data sets obtained from injections of standard mixtures of polyethylene glycols with four different molecular weights in methanol:water, 2-propanol:water and ethanol:water gradients ranging from 30 to 90, 10 to 25 and from 10 to 40% (v/v) of organic modifier, respectively. Calibration lines showed high linearity with coefficients of determination higher than 0.98 and limits of detection between 0.4 and 1.4, 0.9 and 1.8, and 1.1 and 2.7 mgmL?¹ in methanol:water, 2-propanol:water and ethanol:water, respectively. Furthermore the method performance has been compared with a univariate background correction approach based on the use of a reference spectra matrix (UBC-RSM) to discuss the potential as well as pitfalls and drawbacks of the proposed approach. This method works without previous variable selection and provides minimal user-interaction, thus increasing drastically the feasibility of on-line coupling of gradient LC-FTIR.

This work exploits the possibilities offered by the recently developed multivariate method named Science-Based Calibration (SBC), for the extraction of 'analyte-specific' chromatograms in on-line gradient reversed phase LC-infrared spectrometry (IR) in the presence of a high spectral and chromatographic overlapping between the analyte of interest, co-eluting sample matrix constituents and the mobile phase components. The SBC method uses an experimentally measured single response spectrum of the analyte of interest and representative noise to calculate an optimum regression vector (b(opt(1))). Then, the b(opt(1)) vector is used to predict the concentration of the analyte of interest in the spectra of the LC-IR sample chromatograms. To evaluate the advantages and pitfalls of the proposed approach, two different situations were analysed on real LC-IR data sets obtained from the injection of a series of standard solutions of four nitrophenols (p-nitrophenol, 3-methyl-4-nitrophenol, 2,4-dinitrophenol and 4-nitrophenol) in a reversed phase system under gradient conditions. In the first situation, the extraction of the 'analyte-specific' chromatogram was carried out without previous knowledge of the spectral features of other interferents present in the sample matrix. In a second situation evaluated, data obtained from the LC injection of a sample blank is available. Results show the potential applicability of this technique in a variety of situations and evidenced that the proposed chemometric approach improves the selectivity and sensitivity of the LC-IR hyphenation.

In the present study a new approach for the chemometric background correction in on-line gradient LC-FTIR is introduced. For this purpose, the spectral changes of the elution mixture during gradient elution were analyzed applying 2D correlation spectroscopy. The fundamentals of the new background correction algorithm, based on polynomial fits calculated from a reference spectra matrix (Polyfit-RSM method) are explained. The Polyfit-RSM approach was applied on blank gradient runs as well as on LC-FTIR data obtained from the injection of a soft drink sample using acetonitrile:water as eluent. Results found were critically assessed and compared to those obtained by two previous background correction methods which are likewise based on the use of a reference spectra matrix (RSM). The Polyfit-RSM method provided lower noise levels throughout the whole spectral range than other alternative background correction methods, an excellent recovery of analyte spectra as well as chromatograms with a low noise level and also free from baseline shifts. A significant finding, which implies a major advantage for the practical applicability of the algorithm, is that the size of the RSMs can be reduced without affecting the accuracy of the correction method.

Developments in the sampling and determination of pesticides in ambient air have been discussed and data on the occurrence of pesticides in atmosphere have been presented. Developments in active sampling methods were reviewed and the different materials used for trapping pesticides from gas and particulate phases were discussed. Likewise, the use and developments of passive air samplers were reviewed. This article pays special attention to the analysis of pesticides trapped from ambient air, and recapitulate the procedures for extraction, clean-up and determination of these substances. Improvements in sampling procedures. analytical methods and monitoring activities are necessary to advance the knowledge of occurrence of currently used pesticides in atmosphere and their impact over environment and humans. (C) 2009 Elsevier B.V. All rights reserved.

A new cut-off criterion has been proposed for the selection of uninformative variables prior to chemometric partial least squares (PLS) modelling. After variable elimination, PLS regressions were made and assessed comparing the results with those obtained by PLS models based on the full spectral range. To assess the prediction capabilities, uninformative variable elimination (UVE)-PLS and PLS were applied to diffuse reflectance near-infrared spectra of heroin samples. The application of the proposed new cut-off criterion, based on the t-Students distribution, provided similar predictive capabilities of the PLS models than those obtained using the original criteria based on quantile value. However, the repeatability of the number of selected variables was improved significantly.