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Commercial cigarettes are made from a blend of different tobacco varieties, which in turn are the results of different agronomic practices and post-harvest curing processes. The highly complex mixture of smoke compounds reflects each tobacco variety and the levels of sensory-relevant markers. Therefore, the aim of this work was to identify potential relevant chemosensory markers in the mainstream smoke of four main types of commercial tobaccos and establish any possible relationship between them and the tobacco growing/curing practices. The tobacco samples were segregated into four segments: (1) three curing stages of flue-cured Virginia, (2) three curing stages of air-cured Burley, (3) three geo-regions of sun-cured Oriental and (4) three different process applied to tobacco. One hundred and twenty cigarettes (10 batches per flavour category) were produced and smoked under standard machine-smoking protocols. The mainstream smoke samples collected were extracted and analysed by GC × GC TOFMS. The processed data was analysed by partial least square discriminant analysis (PLS-DA) and the selectivity ratio was used to identify key chemosensory markers responsible for the four segments. All models had sensitivity and specificity equal to unity. Flue-cured Virginia (193 markers) and air-cured Burley (184 markers) showed a similar trend for O-heterocycles markers in the lighter leaf colours and N-heterocycles in the darker leaf colours post-processing, but they had compounds of different flavour descriptions, e. g. sweet and nutty. The three geo-regions of sun-cured Oriental (290 markers) also presented O-heterocycles markers in correlation with leaf sugar contents in addition of sucrose esters markers. The three unusually processed tobacco generated many chemical markers (436 markers), some derived from the so-called Cavendish fermentation process with sweet, spicy and peppery notes, whereas the dark fermented air-cured tobacco presented similar descriptors as air-cured Burley. In addition, some polycyclic aromatic hydrocarbons (PAH) were detected as markers from the fire-curing process. The PLS-DA with selectivity ratio evidenced total of 1098 chemosensory markers in cigarette smoke, in which 173 were tentatively identified.

Classification systems are frequently used in tobacco Green Leaf Threshing (GLT) facilities to assess the chemical characteristics and quality of tobacco leaves. This classification is usually performed by a trained specialist who has to analyse and classify the leaf, in a short time, based on experience and visual information. However, this approach is not robust and may be biased due to its highly subjective nature. This work proposes the use of infrared hyperspectral imaging and chemometric tools for discriminant analysis. A fast and real-time method to classify flue-cured Virginia and air-cured Burley tobacco was developed by classifying the leaf based on three criteria: (a) stalk position, (b) leaf colour, and (c) leaf quality; this occurs within 5 seconds. The applicability of the method was evaluated by analysing standard tobacco leaf bundles using near-infrared imaging and Support Vector Machine-Discriminant Analysis (SVM-DA). The models classified the stalk position with a global prediction accuracy of 80.4% for flue-cured Virginia and 88.1% for air-cured Burley. The models for targeting leaf classification by colour presented a global prediction accuracy of 95.9% for flue-cured Virginia and 96.5% for air-cured Burley. For leaf quality, the prediction accuracy ranged from 61.5% to 100.0% for flue-cured Virginia and 78.8% to 100.0% for air-cured Burley. The system was implemented in a tobacco GLT facility for one year to cover an entire crop season and extended to include tobacco bales and leaf bundles. For the most complex classification parameter, leaf quality, flue-cured Virginia models presented a global accuracy of 50.2% to 71.6%. Considering the high complexity of the system and short period of analysis, the obtained accuracy was accepted as suitable when compared to traditional human classification.

This study presents a fast method to estimate sensory parameters of commercial capsules of roasted coffee using flow injection analysis coupled to high–resolution mass spectrometry (FIA–HRMS) as an alternative to traditional sensory analysis, which is a laborious and subjective method. Over 25 types of coffee capsules were studied. The samples were partitioned into an aqueous and organic extract, which were analyzed by FIA–HRMS in the positive and negative ionization modes. Data fusion of such mass spectra was performed to explore the complementary information of sample preparation and ionization conditions. Orthogonalized partial least square discriminant analysis (OPLS–DA) models were built and trained to determine the type of capsule and to estimate important coffee parameters (e.g., acidity, bitterness, body, intensity, and roasting level), achieving accuracy values higher than 91.1%. In addition, variable importance in projection (VIP) scores enabled assignment of the elemental composition and, in some cases, putative identification of compounds in coffee (e.g., caffeine, caffearine, and quinides) that exhibited an important role in class discrimination.

Tobacco-specific nitrosamines (TSNAs), mainly the 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), are known carcinogens. Part of the NNK found in smoke is provided from matrix-bound NNK, and its determination is extremely relevant. However, the reference extraction procedure of matrix-bound NNK is time-consuming and labor-intensive and has a limited analytical capacity. Three different methodologies were proposed to predict matrix-bound NNK: simple linear regression (LR) with soluble NNK; multiple linear regression (MLR) considering soluble NNK and characteristic parameters of the samples; and orthogonal partial least-squares (O-PLS) regression using high-throughput screening by flow injection analysis coupled to high-resolution mass spectrometry (HTS-FIA-HRMS) data. Simple linear regression showed a high influence of matrix and leaf origin. Although an existing linearity trend has been observed (R2 = 0.62) for the global model, higher correlation values were achieved for matrix and country segregat...

The high-throughput screening by flow injection coupled to high-resolution mass spectrometry (HTS-FIA-HRMS) is a powerful technique that enables the identification of several types of samples in a short period of time, either with qualitative or quantitative purposes. Sensory attributes of tobacco are affected by its chemical composition, and it is very important to quantify multi-analytes in a high-throughput methodology. HTS-FIA-HRMS coupled to multivariate analysis was used to create calibration models for 27 analytes, or group of compounds, of tobacco sensory interest. The models were validated by different approaches, including permutation test to avoid overfitting, evaluation of the equipment repeatability by control samples, reproducibility comparison of results from two different equipment and analysts, and with a blind test analysis. All tests demonstrated a good response to the proposed method. No statistical difference between the errors of both equipment was observed, with less than 7% error from the control samples, and a blind test error between 5.96% and 20.10%. The partial least squares (O-PLS) regression models were applied to 815 samples, and a principal component analysis (PCA) was performed from the predicted concentration values, aiming at the non-supervised classification based on tobacco type. We expect that this proposed methodology shows not only the applicability in tobacco samples, but also demonstrates a guideline to an efficient performance of multi-analytes target analysis using the flow injection mass spectrometry with reliable and robust validation steps.

This study presents a comparison of partial least squares (PLS) and multivariate curve resolution (MCR-ALS) in the quantification of polymorphic forms I and II of fexofenadine. HCl in pharmaceutical tablets using near infrared chemical imaging (NIR-CI). The PLS model built using a standard normal variate (SNV) pre-processing method resulted in satisfactory fits between the reference and predicted values, with a root mean square error of prediction ( RMSEP) for both polymorphic forms below 1.5% (w/w). The MCR-ALS results were obtained using an augmented matrix and SNV pre-processing method. The lack of fit value for decomposition was 0.13%, the correlation coefficient between the pure spectra and the obtained spectral profiles was 99.94% and the RMSEP was below 6% (w/w). The MCR-ALS model efficiently quantified the polymorphic forms and generated distribution maps; however, the PLS model exhibited better recovery of the concentrations.

A methodology based on Raman image spectroscopy and chemometrics for homogeneity evaluation of formulations containing atorvastatin calcium in Gelucire® 44/14 is presented. In the first part of the work, formulations with high amounts of Gelucire® 44/14 (80%) and solvents of different polarities (diethylene glycol monoethyl ether, propyleneglycol, propylene glycol monocaprylate and glyceryl mono/dicaprylate/caprate) were prepared for miscibility screening evaluation by classical least squares (CLS). It was observed that Gelucire® 44/14 presented higher affinity for the lipophilic solvents glyceryl mono/dicaprylate/caprate and propylene glycol monocaprylate, whose samples were observed to be homogeneous, and lower affinity for the hydrophilic solvents diethylene glycol monoethyl ether and propyleneglycol, whose samples were heterogeneous. In the second part of the work, the ratio of glyceryl mono/dicaprylate/caprate and Gelucire® 44/14 was determined based on studies in water and allowed the selection of the proportions of these two excipients in the preconcentrate that provided supersaturation of atorvastatin upon dilution. The preconcentrate was then evaluated for homogeneity by partial least squares (PLS) and an excellent miscibility was observed in this proportion as well. Therefore, it was possible to select a formulation that presented simultaneously homogeneous preconcentrate and solubility enhancement in water by Raman image spectroscopy and chemometrics.

The development and optimization of a novel UV spectrophotometric methodology was proposed for simultaneous analysis of ethambutol (ETB), isoniazid (ISO), rifampicin (RIF) and pyrazinamide (PYR), using multivariate calibration based on the partial least squares method (PLS). The methodology was successfully applied for analysis of four-drug fixed dose combination (4-FDC) tablets used for tuberculosis treatment. A 34 Box-Behnken design, with triplicate in central point, was used for sample preparation in the calibration step. In the present case, nine latent variables were chosen for the model development that presented the smallest RMSECV and explain 98.76% of data variance in Y block (concentrations of ETB ISO, RIF and PYR) and 99.93% of data variance in X block (spectral data). PLS models for ETB, ISO, RIF and PYR presented RMSEP and R2 values of 0.23 mg L−1 and 0.971; 0.14 mg L−1 and 0.731; 0.11 mg L−1 and 0.990 and 0.57 mg L−1 and 0.972, respectively. A validation step was performed based on the comparison between the UV spectrophotometric proposed methodology and capillary zone electrophoresis (CZE) in 4-FDC real samples and no significant difference was found between two methodologies at 95% of confidence level.

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.

Nowadays, near-infrared spectroscopy chemical imaging (NIR-CI) has been widely used in pharmaceutical analysis since it provides important surface information about the samples. In this work the information of NIR-CI at the pixel level was compared through calculation of the similarity between distribution maps of concentration obtained by different multivariate calibration approaches. The comparison was performed by using four different multivariate methods (MCR, MLR, CLS and PLS) in analysis of carbamazepine pharmaceutical formulations. For global determination, all models developed showed RMSEP below 1.9% (w/w) for active principal ingredient (API) and better than 4.6% (w/w) for excipients. Also, the distribution maps obtained by PLS, CLS and MCR showed great similarity for all compounds of the formulation as well with concentrations in the tablets. However, comparing the distribution maps obtained by MLR with those from the other chemometric tools, a lower similarity was observed. Thus, this fitted model does not ensure, by itself, that the images obtained are reliable or accurate. The paper also compares the distribution maps of concentrations obtained from all constituents present in the pharmaceutical formulation with their respective micrographs.

It has been recognized that crystal polymorphism is an important factor related to the physicochemical and biological properties of drug substances and formulations. In this work, the piroxicam polymorphic forms 1 and 2 were studied using near-infrared chemical imaging (NIR-CI) technology to map the distribution of both species in pharmaceutical formulations. In this direction, the partial least squares (PLS) method was used to construct calibration models of concentrations per pixel of the sample. The RMSEP results for both models of the polymorphic forms remained below 4% (w/w). It was also possible to distinguish local and global information of the constituents through this method. These results seem to be a suitable tool for quality process control and final product quality assurance.

The chemical imaging technique by near infrared spectroscopy was applied for characterization of formulations in tablets of sildenafil citrate of six different sources. Five formulations were provided by Brazilian Federal Police and correspond to several trademarks of prohibited marketing and one was an authentic sample of Viagra. In a first step of the study, multivariate curve resolution was properly chosen for the estimation of the distribution map of concentration of the active ingredient in tablets of different sources, where the chemical composition of all excipients constituents was not truly known. In such cases, it is very difficult to establish an appropriate calibration technique, so that only the information of sildenafil is considered independently of the excipients. This determination was possible only by reaching the second-order advantage, where the analyte quantification can be performed in the presence of unknown interferences. In a second step, the normalized histograms of images from active ingredient were grouped according to their similarities by hierarchical cluster analysis. Finally it was possible to recognize the patterns of distribution maps of concentration of sildenafil citrate, distinguishing the true formulation of Viagra. This concept can be used to improve the knowledge of industrial products and processes, as well as, for characterization of counterfeit drugs. Fil: Sabin, Guilherme P.. Universidade Estadual de Campinas; Brasil Fil: Lozano, Valeria Antonella. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Instituto de Química Rosario; Argentina Fil: Rocha, Werickson F. C.. National Institute of Metrology, Quality and Technology. Directorate of Industrial and Scientific Metrology. Chemical Metrology Division; Brasil Fil: Romão, Wanderson. Instituto Federal de Educação, Ciência e Tecnologia do Spirito Santo; Brasil Fil: Ortiz, Rafael S.. Policia Federal Brasileira; Brasil Fil: Poppi, Ronei J.. Universidade Estadual de Campinas; Brasil

Raman imaging spectroscopy is a highly useful analytical tool that provides spatial and spectral information on a sample. However, CCD detectors used in dispersive instruments present the drawback of being sensitive to cosmic rays, giving rise to spikes in Raman spectra. Spikes influence variance structures and must be removed prior to the use of multivariate techniques. A new algorithm for correction of spikes in Raman imaging was developed using an approach based on comparison of nearest neighbor pixels. The algorithm showed characteristics including simplicity, rapidity, selectivity and high quality in spike removal from hyperspectral images.

The production of counterfeited drugs is a criminal problem that carries serious risks to public health in the worldwide. In Brazil, Viagra and Cialis are the most counterfeit medicines, being used to inhibit the phosphodiesterase type 5 (PDE-5), treating thus, problems related to erectile dysfunction. X-ray fluorescence (XRF) is a suitable technique to control the quality of new pharmaceutical formulations and distinguish between authentic and counterfeit tablets. XRF has advantageous features like multielemental capability, good detectivity, high precision, short analysis times, and is nondestructive, which makes it suitable to be extended to a great variety of samples. In this work, the inorganic fingerprinting chemical of forty-one commercial samples (Viagra, Cialis, Lazar, Libiden, Maxfil, Plenovit, Potent 75, Rigix, V-50, Vimax and Pramil) and fifty-six counterfeit samples (Viagra and Cialis) were obtained from XRF data. XRF presented an excellent analytical methodology for semi-quantitative determination of active ingredient (in case of sildenafil citrate that presents S in its structure) and excipients such as calcium phosphate, titanium oxide and iron oxide (P, Ca, Ti and Fe). The matrix data were allied to chemometric methods (Principal Component Analysis and Hierarchical Cluster Analysis) to classify the tablets investigated between authentic and counterfeit, grouping the samples into of seven groups: A, B, C, D and E (counterfeit group) and F and G (authentic group).

Pharmaceutical pellets are spherical or nearly spherical multi-unit dosage forms designed to optimize pharmacokinetics and pharmacodynamics features of drug release. The distribution of the pharmaceutical ingredients in the layers and core is a very important parameter for appropriate drug release, especially for pellets manufactured by the process of layer gain. Physical aspects of the sample are normally evaluated by Scanning Electron Microscopy (SEM), but it is in many cases unsuitable to provide conclusive chemical information about the distribution of the pharmaceutical ingredients in both layers and core. On the other hand, methods based on spectroscopic imaging can be very promising for this purpose. In this work, a Near-Infrared Chemical Imaging (NIR-CI) method was developed and applied to the analysis of diclophenac sodium pellets. Since all the compounds present in the sample were known in advance, Classical Least Squares (CLS) was used for calculations. The results have shown that the method was capable of providing chemical information about the distribution of the active ingredient and excipients in the core and coating layers and therefore can be complementary to SEM for the pharmaceutical development of pellets.

This paper reports the development of an easy, fast and effective procedure for the verification of the ideal gas law in splitless injection systems in order to improve the response. Results of a group of pesticides were used to demonstrate the suitability of the approach. The procedure helps establish experimental parameters through theoretical aspects. The improved instrumental response allowed extraction with lower sample volumes, the minimization of time and costs and the simplification of sample preparation.

In this work, a method using solid-phase extraction and gas chromatography coupled mass spectrometry in the selective ion monitoring (SIM) mode was developed and validated for the multi-class determination of 20 pesticides regulated by the Brazilian legislation for drinking water. Because these pesticides must be determined at low concentrations, a high preconcentration factor associated to the high sensitivity of chromatographic analysis was necessary. The method presented quantification limits between 0.003 and 0.093 µg L-1. Most of the compounds presented mean recoveries between 51 and 116%. Although the differing chemical nature of the pesticides analyzed difficult the attainment of good recovery for all of the compounds evaluated, the precision of the results was excellent. The selectivity of the method was evaluated through the relative intensity of quantification and qualification ions and was considered adequate. Analysis in real samples met criteria for instrumental qualification and the system suitability evaluation. Neste trabalho, um método usando extração em fase sólida e cromatografia a gás acoplada à espectrometria de massas no modo de monitoramento seletivo de íons, foi desenvolvido e validado para a determinação multiclasse de 20 pesticidas regulados pela legislação brasileira para água potável. Como estes pesticidas devem ser determinados em baixas concentrações, um elevado fator da pré-concentração associado à sensibilidade elevada da análise cromatográfica foi necessário. O método apresentou limites de quantificação entre 0,003 e 0,093 µg L-1. A maioria dos compostos apresentou recuperações médias entre 51 e 116%. Embora a natureza química distinta dos pesticidas analisados dificulte a obtenção de boa recuperação para todos os compostos avaliados, a precisão dos resultados foi excelente. A seletividade do método foi avaliada através da intensidade relativa dos íons de quantificação e de qualificação, sendo considerada adequada. A análise em amostras reais cumpriu os critérios para a qualificação instrumental e a avaliação da conformidade do sistema.