Evaluation of the acidic strengths on electronic ground and excited states of proton transfer dye using Excitation-Emission fluorescence matrix (EEM) and Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS).

Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS) was for the first time successfully used to evaluate an intricate photophysical behavior, where deprotonation on the electronic ground state (S0), intra and intermolecular proton transfer processes (ESPT and ESIPT) on the electronic excited state (S1) can simultaneously be presented. In this sense, the organic dye 2-(2'-hydroxyphenyl)benzothiazole (HBT) was used as a proof-of-concept model, where MCR-ALS showed to be a powerful tool for discriminate chemical reactions that occur concomitantly on different potential energy surfaces, which include photochemical reactions. As a result, the chemometric method showed to be a straightforward approach for the determination of the acidic strengths of those equilibria were estimated as 8.61 and 1.11 to hydroxyl deprotonation on electronic ground and excited states, respectively.

Authentication and identification of adulterants in virgin coconut oil using ATR/FTIR in tandem with DD-SIMCA one class modeling.

This study evaluates the use of Fourier transform infrared spectroscopy with attenuated total reflectance (ATR/FTIR) in tandem with data driven soft independent modeling of class analogy (DD-SIMCA) to check authenticity and monitor virgin coconut oil adulteration. By using infrared spectra of pure samples and samples adulterated with canola, corn, sunflower and soybean, one class models were developed to evaluate the authenticity and adulteration of virgin coconut oil. The proposed methodology was able to confirm the authenticity and to detect the adulteration with all tested oils in a concentration range of 10-40%. Also, it was possible to identify the four adulterants oils studied with 88-100% of sensitivity and 96-100% of specificity. The results indicated that ATR/FTIR spectroscopy in conjunction with a one-class strategy based on DD-SIMCA is a clean and fast methodology that can be easily implemented for virgin coconut oil purity control.

Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters.

This study evaluates the use of visible and near infrared spectroscopy (Vis-NIRS) combined with multivariate regression based on random forest to quantify some quality soil parameters. The parameters analyzed were soil cation exchange capacity (CEC), sum of exchange bases (SB), organic matter (OM), clay and sand present in the soils of several regions of Brazil. Current methods for evaluating these parameters are laborious, timely and require various wet analytical methods that are not adequate for use in precision agriculture, where faster and automatic responses are required. The random forest regression models were statistically better than PLS regression models for CEC, OM, clay and sand, demonstrating resistance to overfitting, attenuating the effect of outlier samples and indicating the most important variables for the model. The methodology demonstrates the potential of the Vis-NIR as an alternative for determination of CEC, SB, OM, sand and clay, making possible to develop a fast and automatic analytical procedure.

Raman hyperspectral imaging in conjunction with independent component analysis as a forensic tool for explosive analysis: The case of an ATM explosion.

In this work, Raman hyperspectral imaging, in conjunction with independent component analysis, was employed as an analytical methodology to detect an ammonium nitrate fuel oil (ANFO) explosive in banknotes after an ATM explosion experiment. The proposed methodology allows for the identification of the ANFO explosive without sample preparation or destroying the sample, at quantities as small as 70µgcm-2. The explosive was identified following ICA data decomposition by the characteristic nitrate band at 1044cm-1. The use of Raman hyperspectral imaging and independent component analysis shows great potential for identifying forensic samples by providing chemical and spatial information.

Chemotaxonomic study of Chrysobalanus icaco Linnaeus (Chrysobalanaceae) using ultra-high performance liquid chromatography coupled with diode array detection fingerprint in combination with multivariate analysis.

We investigated a strategy for the chemotaxonomy study of Chrysobalanus icaco Linnaeus (Chrysobalanaceae) based on ultra-high performance liquid chromatography coupled with diode array detection fingerprint in combination with multivariate analysis. Two models using principal component analysis and partial least squares discriminant analysis were developed, and the samples could be successfully classified into two classes: Class 1 (red morphotype) and Class 2 (white and black morphotypes). Furthermore, ultra-high performance liquid chromatography coupled with diode array and electrospray ionization tandem mass spectrometry was used to identify the main compounds responsible for class separation. The partial least squares discriminant analysis model accurately classified the C. icaco samples using an external validation subset with prediction ability of 100% and revealed the existence of two chemotypes. The most important finding obtained in this study is that the three morphotypes distinguished by the mature fruit color (white, red, and black) are not all phytoequivalent to each other.

Assessment of nucleosides as putative tumor biomarkers in prostate cancer screening by CE-UV.

Cancer is responsible for millions of deaths worldwide, but most base diseases may be cured if detected early. Screening tests may be used to identify early-stage malignant neoplasms. However, the major screening tool for prostate cancer, the prostate-specific antigen test, has unsuitable sensitivity. Since cancer cells may affect the pattern of consumption and excretion of nucleosides, such biomolecules are putative biomarkers that can be used for diagnosis and treatment evaluation. Using a previously validated method for the analysis of nucleosides in blood serum by capillary electrophoresis with UV-vis spectroscopy detection, we investigated 60 samples from healthy individuals and 42 samples from prostate cancer patients. The concentrations of nucleosides in both groups were compared and a multivariate partial least squares-discriminant analysis classification model was optimized for prediction of prostate cancer. The validation of the model with an independent sample set resulted in the correct classification of 82.4% of the samples, with sensitivity of 90.5% and specificity of 76.7%. A significant downregulation of 5-methyluridine and inosine was observed, which can be indicative of the carcinogenic process. Therefore, such analytes are potential candidates for prostate cancer screening. Graphical Abstract Separation of the studied nucleosides and the internal standard 8-Bromoguanosine by CE-UV (a); classification of the external validation samples (30 from healthy volunteers and 21 from prostate cancer patients) by the developed Partial Least Square - Discriminant Analysis (PLS-DA) model with accuracy of 82.4% (b); Receiver Operating Characteristics (ROC) curve (c); and Variable Importance in the Projection (VIP) values for the studied nucleosides (d). A significant down-regulation of 5- methyluridine (5mU) and inosine (I) was observed, which can be indicative of the presence of prostate tumors.

Metabolomics and lipidomics analyses by 1H nuclear magnetic resonance of schizophrenia patient serum reveal potential peripheral biomarkers for diagnosis.

Using 1H NMR-based metabolomics in association to chemometrics analysis, we analyzed here the metabolic differences between schizophrenia patients (SCZ) compared to healthy controls (HCs). HCs and SCZ patients underwent clinical interview using the Structured Clinical Interview for DSM Disorders (SCID). SCZ patients were further assessed by Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale, Global Assessment of Functioning Scale (GAF), and Clinical Global Impressions Scale (CGI). Using the principal component analysis (PCA) and supervised partial least-squares discriminate analysis (PLS-DA) in obtained NMR data, a clear group separation between HCs and SCZ patients was achieved. Interestingly, all metabolite compounds identified as exclusively present in the SCZ group, except for the gamma-aminobutyric acid (GABA), were never previously associated with mental disorders. Although the initial perception of an absence of obvious biological link among the different key molecules exclusively observed in each group, and no identification of any specific pathway yet, the present work represents an important contribution for the identification of potential biomarkers to inform diagnosis, as it was possible to completely separate the affected SCZ patients from HCs, with no outliers or exceptions. In addition, the data presented here reinforced the role of the modulation of glycolysis pathway and the loss of GABA interneuron/hyperglutamate hypothesis in SCZ.

Heart fossilization is possible and informs the evolution of cardiac outflow tract in vertebrates.

Elucidating cardiac evolution has been frustrated by lack of fossils. One celebrated enigma in cardiac evolution involves the transition from a cardiac outflow tract dominated by a multi-valved conus arteriosus in basal actinopterygians, to an outflow tract commanded by the non-valved, elastic, bulbus arteriosus in higher actinopterygians. We demonstrate that cardiac preservation is possible in the extinct fish Rhacolepis buccalis from the Brazilian Cretaceous. Using X-ray synchrotron microtomography, we show that Rhacolepis fossils display hearts with a conus arteriosus containing at least five valve rows. This represents a transitional morphology between the primitive, multivalvar, conal condition and the derived, monovalvar, bulbar state of the outflow tract in modern actinopterygians. Our data rescue a long-lost cardiac phenotype (119-113 Ma) and suggest that outflow tract simplification in actinopterygians is compatible with a gradual, rather than a drastic saltation event. Overall, our results demonstrate the feasibility of studying cardiac evolution in fossils.

Quantification of animal fat biodiesel in soybean biodiesel and B20 diesel blends using near infrared spectroscopy and synergy interval support vector regression.

In this work, multivariate calibration based on partial least squares (PLS) and support vector regression (SVR) using the whole spectrum and variable selection by synergy interval (siPLS and siSVR) were applied to NIR spectra for the determination of animal fat biodiesel content in soybean biodiesel and B20 diesel blends. For all models, prediction errors, bias test for systematic errors and permutation test for trends in the residuals were calculated. The siSVR produced significantly lower prediction errors compared to the full spectrum methods and siPLS, with a root mean squares error (RMSEP) of 0.18%(w/w) (concentration range: 0.00%-69.00%(w/w)) in the soybean biodiesel blend and 0.10%(w/w) in the B20 diesel (concentration range: 0.00%-13.80%(w/w)). Additionally, in the models for the determination of animal fat biodiesel in blends with soybean diesel, PLS and SVR showed evidence of systematic errors, and PLS/siPLS presented trends in residuals based on the permutation test. For the B20 diesel, PLS presented evidence of systematic errors, and siPLS presented trends in the residuals.

Infrared imaging spectroscopy and chemometric tools for in situ analysis of an imiquimod pharmaceutical preparation presented as cream.

In the present work the homogeneity of a pharmaceutical formulation presented as a cream was studied using infrared imaging spectroscopy and chemometric methodologies such as principal component analysis (PCA) and multivariate curve resolution with alternating least squares (MCR-ALS). A cream formulation, presented as an emulsion, was prepared using imiquimod as the active pharmaceutical ingredient (API) and the excipients: water, vaseline, an emulsifier and a carboxylic acid in order to dissolve the API. After exposure at 45°C during 3 months to perform accelerated stability test, the presence of some crystals was observed, indicating homogeneity problems in the formulation. PCA exploratory analysis showed that the crystal composition was different from the composition of the emulsion, since the score maps presented crystal structures in the emulsion. MCR-ALS estimated the spectra of the crystals and the emulsion. The crystals presented amine and C-H bands, suggesting that the precipitate was a salt formed by carboxylic acid and imiquimod. These results indicate the potential of infrared imaging spectroscopy in conjunction with chemometric methodologies as an analytical tool to ensure the quality of cream formulations in the pharmaceutical industry.

Quantitative analysis of biodiesel in blends of biodiesel and conventional diesel by comprehensive two-dimensional gas chromatography and multivariate curve resolution.

In this paper, a method to determine the composition of blends of biodiesel with mineral diesel (BXX) by multivariate curve resolution with Alternating Least Squares (MRC-ALS) combined to comprehensive two-dimensional gas chromatography with Flame Ionization Detection (GC×GC-FID) is presented. Chromatographic profiles of BXX blends produced with biodiesels from different sources were used as input data. An initial evaluation carried out after multiway principal component analysis (MPCA) was used to reveal regions of the chromatograms were the signal was likely to be dependent on the concentration of biodiesel, regardless its vegetable source. After this preliminary step MCR-ALS modeling was carried out only using relevant parts of the chromatograms. The resulting procedure was able to predict accurately the concentration of biodiesel in the BXX samples regardless of its origin.

Simultaneous determination of hydrocarbon renewable diesel, biodiesel and petroleum diesel contents in diesel fuel blends using near infrared (NIR) spectroscopy and chemometrics.

Highly polluting fuels based on non-renewable resources such as fossil fuels need to be replaced with potentially less polluting renewable fuels derived from vegetable or animal biomass, these so-called biofuels, are a reality nowadays and many countries have started the challenge of increasing the use of different types of biofuels, such as ethanol and biodiesel (fatty acid alkyl esters), often mixed with petroleum derivatives, such as gasoline and diesel, respectively. The quantitative determination of these fuel blends using simple, fast and low cost methods based on near infrared (NIR) spectroscopy combined with chemometric methods has been reported. However, advanced biofuels based on a mixture of hydrocarbons or a single hydrocarbon molecule, such as farnesane (2,6,10-trimethyldodecane), a hydrocarbon renewable diesel, can also be used in mixtures with biodiesel and petroleum diesel fuel and the use of NIR spectroscopy for the quantitative determination of a ternary fuel blend of these two hydrocarbon-based fuels and biodiesel can be a useful tool for quality control. This work presents a development of an analytical method for the quantitative determination of hydrocarbon renewable diesel (farnesane), biodiesel and petroleum diesel fuel blends using NIR spectroscopy combined with chemometric methods, such as partial least squares (PLS) and support vector machines (SVM). This development leads to a more accurate, simpler, faster and cheaper method when compared to the standard reference method ASTM D6866 and with the main advantage of providing the individual quantification of two different biofuels in a mixture with petroleum diesel fuel. Using the developed PLS model the three fuel blend components were determined simultaneously with values of root mean square error of prediction (RMSEP) of 0.25%, 0.19% and 0.38% for hydrocarbon renewable diesel, biodiesel and petroleum diesel, respectively, the values obtained were in agreement with those suggested by reference methods for the determination of renewable fuels.

Determination of disease biomarkers in Eucalyptus by comprehensive two-dimensional gas chromatography and multivariate data analysis.

In this paper is reported the use of the chromatographic profiles of volatiles to determine disease markers in plants - in this case, leaves of Eucalyptus globulus contaminated by the necrotroph fungus Teratosphaeria nubilosa. The volatile fraction was isolated by headspace solid phase microextraction (HS-SPME) and analyzed by comprehensive two-dimensional gas chromatography-fast quadrupole mass spectrometry (GC×GC-qMS). For the correlation between the metabolic profile described by the chromatograms and the presence of the infection, unfolded-partial least squares discriminant analysis (U-PLS-DA) with orthogonal signal correction (OSC) were employed. The proposed method was checked to be independent of factors such as the age of the harvested plants. The manipulation of the mathematical model obtained also resulted in graphic representations similar to real chromatograms, which allowed the tentative identification of more than 40 compounds potentially useful as disease biomarkers for this plant/pathogen pair. The proposed methodology can be considered as highly reliable, since the diagnosis is based on the whole chromatographic profile rather than in the detection of a single analyte.

Raman imaging spectroscopic characterization of modified poly(dimethylsiloxane) for micro total analysis systems applications.

Methacryloxypropyl-modified poly(dimethylsiloxane) rubbers were obtained from poly(dimethylsiloxane), PDMS, and methacryloxypropyltrimethoxysilane, MPTMS, by polycondensation reactions. The modified rubbers, prepared with 20 and 30% (v/v) of MPTMS, were used as substrates for microchannel fabrication by the CO(2) laser ablation technique. Raman imaging spectroscopy was used for the surface characterization, showing the homogeneity of the rubbery material, with uniform distribution of the crosslinking centers. Under the experimental conditions used, damage to the rubber from the CO(2) laser radiation used for the channel engraving was not observed. Correlation maps of the surface were obtained in order to spatially evaluate the modification inside and outside the channels. The correlations between the methacryloxypropyl-modified poly(dimethylsiloxane) rubbers and MPTMS (spectral range of 1800-1550 cm(-1)) and PDMS (spectral range of 820-670 cm(-1)) precursors were higher than 0.95 and 0.99, respectively. In addition, Raman imaging spectroscopy allows monitoring the topography of the fabricated microchannel.

Multivariate curve resolution combined with gas chromatography to enhance analytical separation in complex samples: a review.

This review describes the major advantages and pitfalls of iterative and non-iterative multivariate curve resolution (MCR) methods combined with gas chromatography (GC) data using literature published since 2000 and highlighting the most important combinations of GC coupled to mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) and coupled to mass spectrometry (GC×GC-MS). In addition, a brief summary of some pre-processing strategies will be discussed to correct common issues in GC, such as retention time shifts and baseline/background contributions. Additionally, algorithms such as evolving factor analysis (EFA), heuristic evolving latent projection (HELP), subwindow factor analysis (SFA), multivariate curve resolution-alternating least squares (MCR-ALS), positive matrix factorization (PMF), iterative target transformation factor analysis (ITTFA) and orthogonal projection resolution (OPR) will be described in this paper. Even more, examples of applications to food chemistry, lipidomics and medicinal chemistry, as well as in essential oil research, will be shown. Lastly, a brief illustration of the MCR method hierarchy will also be presented.

Homogeneity study of ointment dosage forms by infrared imaging spectroscopy.

Ointment dosage forms are semi-solid preparations intended for local or transdermal delivery of active substances usually for application to the skin and it is important that they present a homogeneous appearance. In this work, a study of the homogeneity of a tacrolimus ointment dosage form was performed using infrared imaging spectroscopy coupled with principal component analysis (PCA) and multivariate curve resolution with alternating least squares (MCR-ALS) to interpret the imaging data. Optical visible microscopy images indicated possible phase separation in the ointment and, based on the results presented by distribution concentration maps from infrared imaging, it was possible to conclude that, in fact, there was phase separation incorporated in the ointment. Thus, infrared imaging spectroscopy associated to PCA and MCR-ALS is demonstrated to be a powerful tool for the development process of ointment dosage forms.

Multivariate control charts based on net analyte signal (NAS) and Raman spectroscopy for quality control of carbamazepine.

Raman spectroscopy and control charts based on the net analyte signal (NAS) were applied to polymorphic characterization of carbamazepine. Carbamazepine presents four polymorphic forms: I-IV (dihydrate). X-ray powder diffraction was used as a reference technique. The control charts were built generating three charts: the NAS chart that corresponds to the analyte of interest (form III in this case), the interference chart that corresponds to the contribution of other compounds in the sample and the residual chart that corresponds to nonsystematic variations. For each chart, statistical limits were developed using samples within the quality specifications. It was possible to identify the different polymorphic forms of carbamazepine present in pharmaceutical formulations. Thus, an alternative method for the quality monitoring of the carbamazepine polymorphic forms after the crystallization process is presented.

Quantitative analysis of essential oils in perfume using multivariate curve resolution combined with comprehensive two-dimensional gas chromatography.

The use of multivariate curve resolution (MCR) to build multivariate quantitative models using data obtained from comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) is presented and evaluated. The MCR algorithm presents some important features, such as second order advantage and the recovery of the instrumental response for each pure component after optimization by an alternating least squares (ALS) procedure. A model to quantify the essential oil of rosemary was built using a calibration set containing only known concentrations of the essential oil and cereal alcohol as solvent. A calibration curve correlating the concentration of the essential oil of rosemary and the instrumental response obtained from the MCR-ALS algorithm was obtained, and this calibration model was applied to predict the concentration of the oil in complex samples (mixtures of the essential oil, pineapple essence and commercial perfume). The values of the root mean square error of prediction (RMSEP) and of the root mean square error of the percentage deviation (RMSPD) obtained were 0.4% (v/v) and 7.2%, respectively. Additionally, a second model was built and used to evaluate the accuracy of the method. A model to quantify the essential oil of lemon grass was built and its concentration was predicted in the validation set and real perfume samples. The RMSEP and RMSPD obtained were 0.5% (v/v) and 6.9%, respectively, and the concentration of the essential oil of lemon grass in perfume agreed to the value informed by the manufacturer. The result indicates that the MCR algorithm is adequate to resolve the target chromatogram from the complex sample and to build multivariate models of GC×GC-FID data.

Prediction of the physicochemical properties of gasoline by comprehensive two-dimensional gas chromatography and multivariate data processing.

The estimation of physicochemical parameters such as distillation points and relative densities still plays an important role in the quality control of gasoline and similar fuels. Their measurements according to standard ASTM procedures demands specific equipments and are time and work consuming. An alternative method to predict distillation points and relativity density by multivariate analysis of comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) data is presented here. Gasoline samples, previously tested according to standard methods, were used to build regression models, which were evaluated by external validation. The models for distillation points were built using variable selection methods, while the model for relativity density was built using the whole chromatograms. The root mean square prediction differences (RMSPD) obtained were 0.85%, 0.48%, 1.07% and 1.71% for 10, 50 and 90% v/v of distillation and for the final point of distillation, respectively. For relative density, the RMSPD was 0.24%. These results suggest that GC×GC-FID combined with multivariate analysis can be used to predict these physicochemical properties of gasoline.

Quantitative analysis by comprehensive two-dimensional gas chromatography using interval Multi-way Partial Least Squares calibration.

A new approach for target quantitative analysis for comprehensive two-dimensional gas chromatography (GC × GC), interval Multi-way Partial Least Square (iNPLS) is presented and evaluated in this paper. In iNPLS, the two-dimensional chromatogram is split in small sections; each of these pieces is treated as an independent new chromatogram. Separated conventional NPLS calibration models for the concentration of the target analyte are built for each of the pieces of the whole chromatogram, and the best model is selected for quantitative analysis. An algorithm for iNPLS running on MatLab platform was written, preliminarily evaluated with using solutions of model compounds with different chemical properties and subsequently applied to quantify some allergens in perfume samples. The results were found to be adequate, and good precision and accuracy was obtained even for poorly resolved peaks.