25 anos de quimiometria no Brasil

Chemometric activities in Brazil are described according to three phases: before the existence of microcomputers in the 1970s, through the initial stages of microcomputer use in the 1980s and during the years of extensive microcomputer applications of the ´90s and into this century. Pioneering activities in both the university and industry are emphasized. Active research areas in chemometrics are cited including experimental design, pattern recognition and classification, curve resolution for complex systems and multivariate calibration. New trends in chemometrics, especially higher order methods for treating data, are emphasized.

Quantificação de antimônio em garrafas de politereftalato de etileno (PET) brasileiras por fluorescência de raios-X e avaliação quimiométrica para verificar a presença de pet reciclado através do teor de ferro

Antimony is a common catalyst in the synthesis of polyethylene terephthalate used for food-grade bottles manufacturing. However, antimony residues in final products are transferred to juices, soft drinks or water. The literature reports mentions of toxicity associated to antimony. In this work, a green, fast and direct method to quantify antimony, sulfur, iron and copper, in PET bottles by X-ray fluorescence spectrometry is presented. 2.4 to 11 mg Sb kg-1 were found in 20 samples analyzed. The coupling of the multielemental technique to chemometric treatment provided also the possibility to classify PET samples between bottle-grade PET/recycled PET blends by Fe content.

Multivariate analysis of the effects of soil parameters and environmental factors on the flavonoid content of leaves of Passiflora incarnata L., Passifloraceae

The aim of the present study was to evaluate the effect of soil characteristics (pH, macro- and micro-nutrients), environmental factors (temperature, humidity, period of the year and time of day of collection) and meteorological conditions (rain, sun, cloud and cloud/rain) on the flavonoid content of leaves of Passiflora incarnata L., Passifloraceae. The total flavonoid contents of leaf samples harvested from plants cultivated or collected under different conditions were quantified by high-performance liquid chromatography with ultraviolet detection (HPLC-UV/PAD). Chemometric treatment of the data by principal component (PCA) and hierarchic cluster analyses (HCA) showed that the samples did not present a specific classification in relation to the environmental and soil variables studied, and that the environmental variables were not significant in describing the data set. However, the levels of the elements Fe, B and Cu present in the soil showed an inverse correlation with the total flavonoid contents of the leaves of P. incarnata.

Laser-induced breakdown spectroscopy and chemometrics for classification of toys relying on toxic elements

Quality control of toys for avoiding children exposure to potentially toxic elements is of utmost relevance and it is a common requirement in national and/or international norms for health and safety reasons. Laser-induced breakdown spectroscopy (LIBS) was recently evaluated at authors` laboratory for direct analysis of plastic toys and one of the main difficulties for the determination of Cd. Cr and Pb was the variety of mixtures and types of polymers. As most norms rely on migration (lixiviation) protocols, chemometric classification models from LIBS spectra were tested for sampling toys that present potential risk of Cd, Cr and Pb contamination. The classification models were generated from the emission spectra of 51 polymeric toys and by using Partial Least Squares - Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA) and K-Nearest Neighbor (KNN). The classification models and validations were carried out with 40 and 11 test samples, respectively. Best results were obtained when KNN was used, with corrected predictions varying from 95% for Cd to 100% for Cr and Pb. (C) 2011 Elsevier B.V. All rights reserved.

Optimization and validation of a LIBS method for the determination of macro and micronutrients in sugar cane leaves

Laser induced breakdown spectrometry (LIBS) was applied for the determination of macro (P, K, Ca, Mg) and micronutrients (B, Cu, Fe, Mn and Zn) in sugar cane leaves, which is one of the most economically important crops in Brazil. Operational conditions were previously optimized by a neuro-genetic approach, by using a laser Nd:YAG at 1064 nm with 110 mJ per pulse focused on a pellet surface prepared with ground plant samples. Emission intensities were measured after 2.0 mu s delay time, with 4.5 mu s integration time gate and 25 accumulated laser pulses. Measurements of LIBS spectra were based on triplicate and each replicate consisted of an average of ten spectra collected in different sites (craters) of the pellet. Quantitative determinations were carried out by using univariate calibration and chemometric methods, such as PLSR and iPLS. The calibration models were obtained by using 26 laboratory samples and the validation was carried out by using 15 test samples. For comparative purpose, these samples were also microwave-assisted digested and further analyzed by ICP OES. In general, most results obtained by LIBS did not differ significantly from ICP OES data by applying a t-test at 95% confidence level. Both LIBS multivariate and univariate calibration methods produced similar results, except for Fe where better results were achieved by the multivariate approach. Repeatability precision varied from 0.7 to 15% and 1.3 to 20% from measurements obtained by multivariate and univariate calibration, respectively. It is demonstrated that LIBS is a powerful tool for analysis of pellets of plant materials for determination of macro and micronutrients by choosing calibration and validation samples with similar matrix composition.

Exploring the emission intensities of ICP OES aided by chemometrics in the geographical discrimination of mineral waters

A rapid method for classification of mineral waters is proposed. The discrimination power was evaluated by a novel combination of chemometric data analysis and qualitative multi-elemental fingerprints of mineral water samples acquired from different regions of the Brazilian territory. The classification of mineral waters was assessed using only the wavelength emission intensities obtained by inductively coupled plasma optical emission spectrometry (ICP OES), monitoring different lines of Al, B, Ba, Ca, Cl, Cu, Co, Cr, Fe, K, Mg, Mn, Na, Ni, P, Pb, S, Sb, Si, Sr, Ti, V, and Zn, and Be, Dy, Gd, In, La, Sc and Y as internal standards. Data acquisition was done under robust (RC) and non-robust (NRC) conditions. Also, the combination of signal intensities of two or more emission lines for each element were evaluated instead of the individual lines. The performance of two classification-k-nearest neighbor (kNN) and soft independent modeling of class analogy (SIMCA)-and preprocessing algorithms, autoscaling and Pareto scaling, were evaluated for the ability to differentiate between the various samples in each approach tested (combination of robust or non-robust conditions with use of individual lines or sum of the intensities of emission lines). It was shown that qualitative ICP OES fingerprinting in combination with multivariate analysis is a promising analytical tool that has potential to become a recognized procedure for rapid authenticity and adulteration testing of mineral water samples or other material whose physicochemical properties (or origin) are directly related to mineral content.

Assessing the association between phenolic compounds and the antioxidant activity of Brazilian red wines using chemometrics

The objective of this study was to evaluate the association among chemical parameters, the commercial value, and the antioxidant activity of Brazilian red wines using chemometric techniques. Twenty-nine samples from five different varieties were assessed. Samples were separated into three groups using hierarchical cluster analysis: cluster 1 presented the highest antioxidant activity towards DPPH (68.51% of inhibition) and ORAC (30,918.64 mu mol Trolox Equivalents/L), followed by cluster 3 (DPPH = 59.36% of inhibition: ORAC = 25,255.02 mu mol Trolox Equivalents/L) and then cluster 2 (DPPH = 46.67% of inhibition; ORAC = 19,395.74 gmol Trolox Equivalents/L). Although the correlation between the commercial value and the antioxidant activity on DPPH and ORAC was not statistically significant (P = 0.13 and P = 0.06, respectively), cluster 1 grouped the samples with higher commercial values. Cluster analysis applied to the variables suggested that non-anthocyanin flavonoids were the main phenolic class exerting antioxidant activity on Brazilian red wines. (C) 2010 Elsevier Ltd. All rights reserved.

Synthesis and evaluation of sesquiterpene lactone inhibitors of phospholipase A(2) from Bothrops jararacussu

Several sesquiterpene lactone were synthesized and their inhibitive activities on phospholipase A(2) (PLA(2)) from Bothrops jararacussu venom were evaluated. Compounds Lac01 and Lac02 were efficient against PLA(2) edema-inducing, enzymatic and myotoxic activities and it reduces around 85% of myotoxicity and around 70% of edema-inducing activity. Lac05-Lac08 presented lower efficiency in inhibiting the biological activities studied and reduce the myotoxic and edema-inducing activities around only 15%. The enzymatic activity was significantly reduced. The values of inhibition constants (K(1)) for Lac01 and Lac02 were approximately 740 mu M, and for compounds Lac05-Lac08 the inhibition constants were approximately 7.622-9.240 mu M. The enzymatic kinetic studies show that the sesquiterpene lactones inhibit PLA(2) in a non-competitive manner. Some aspects of the structure-activity relationships (topologic, molecular and electronic parameters) were obtained using ab initio quantum calculations and analyzed by chemometric methods (HCA and PCA). The quantum chemistry calculations show that compounds with a higher capacity of inhibiting PLA(2) (Lac01-Lac04) present lower values of highest occupied molecular orbital (HOMO) energy and molecular volume (VOL) and bigger values of hydrophobicity (LogP). These results indicate some topologic aspects of the binding site of sesquiterpene lactone derivatives and PLA(2). (C) 2010 Elsevier Ltd. All rights reserved.

Commercial squids: Characterization, assessment of potential health benefits/risks and discrimination based on mineral, lipid and vitamin E concentrations

The most consumed squid species worldwide were characterized regarding their concentrations of minerals, fatty acids, cholesterol and vitamin E. Interspecific comparisons were assessed among species and geographical origin. The health benefits derived from squid consumption were assessed based on daily minerals intake and on nutritional lipid quality indexes. Squids contribute significantly to daily intake of several macro (Na, K, Mg and P) and micronutrients (Cu, Zn and Ni). Despite their low fat concentration, they are rich in long-chain omega-3 fatty acids, particularly docosahexaenoic (DHA) and eicosapentanoic (EPA) acids, with highly favorable ω-3/ω-6 ratios (from 5.7 to 17.7), reducing the significance of their high cholesterol concentration (140–549 mg/100 g ww). Assessment of potential health risks based on minerals intake, non-carcinogenic and carcinogenic risks indicated that Loligo gahi (from Atlantic Ocean), Loligo opalescens (from Pacific Ocean) and Loligo duvaucelii (from Indic Ocean) should be eaten with moderation due to the high concentrations of Cu and/or Cd. Canonical discriminant analysis identified the major fatty acids (C14:0, C18:0, C18:1, C18:3ω-3, C20:4ω-6 and C22:5ω-6), P, K, Cu and vitamin E as chemical discriminators for the selected species. These elements and compounds exhibited the potential to prove authenticity of the commercially relevant squid species.

Octopus Lipid and Vitamin E Composition: Interspecies, Interorigin, and Nutritional Variability

Octopus vulgaris, Octopus maya, and Eledone cirrhosa from distinct marine environments [Northeast Atlantic (NEA), Northwest Atlantic (NWA), Eastern Central Atlantic, Western Central Atlantic (WCA), Pacific Ocean, and Mediterranean Sea] were characterized regarding their lipid and vitamin E composition. These species are those commercially more relevant worldwide. Significant interspecies and interorigin differences were observed. Unsaturated fatty acids account for more than 65% of total fatty acids, mostly ω-3 PUFA due to docosahexaenoic (18.4−29.3%) and eicosapentanoic acid (11.4− 23.9%) contributions. The highest ω-3 PUFA amounts and ω-3/ω-6 ratios were quantified in the heaviest specimens, O. vulgaris from NWA, with high market price, and simultaneously in the lowest graded samples, E. cirrhosa from NEA, of reduced dimensions. Although having the highest cholesterol contents, E. cirrhosa from NEA and O. maya from WCA have also higher protective fatty acid indexes. Chemometric discrimination allowed clustering the selected species and several origins based on lipid and vitamin E profiles.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

The application of chemometrics on Infrared and Raman spectra as a tool for the forensic analysis of paints

The aim of this work is to evaluate the capabilities and limitations of chemometric methods and other mathematical treatments applied on spectroscopic data and more specifically on paint samples. The uniqueness of the spectroscopic data comes from the fact that they are multivariate - a few thousands variables - and highly correlated. Statistical methods are used to study and discriminate samples. A collection of 34 red paint samples was measured by Infrared and Raman spectroscopy. Data pretreatment and variable selection demonstrated that the use of Standard Normal Variate (SNV), together with removal of the noisy variables by a selection of the wavelengths from 650 to 1830 cm−1 and 2730-3600 cm−1, provided the optimal results for infrared analysis. Principal component analysis (PCA) and hierarchical clusters analysis (HCA) were then used as exploratory techniques to provide evidence of structure in the data, cluster, or detect outliers. With the FTIR spectra, the Principal Components (PCs) correspond to binder types and the presence/absence of calcium carbonate. 83% of the total variance is explained by the four first PCs. As for the Raman spectra, we observe six different clusters corresponding to the different pigment compositions when plotting the first two PCs, which account for 37% and 20% respectively of the total variance. In conclusion, the use of chemometrics for the forensic analysis of paints provides a valuable tool for objective decision-making, a reduction of the possible classification errors, and a better efficiency, having robust results with time saving data treatments.

Detection, Determination of Chemical Composition and Chemical Profiling of Counterfeit Medicines by Raman Spectroscopy.

Raman spectroscopy has become a widespread technique for the analysis ofpharmaceutical solid forms. The application proposed here is the investigationof counterfeit medicines. This serious global issue requires quick and accurateidentification methods to fight against this phenomenon. Thanks to its chemicalselectivity, rapidity of analysis and potential of generating repeatable spectralprofiles, Raman spectroscopy presents distinct advantages for the analysis ofcounterfeits. Combined with chemometric tools, the technique enablesthe detection, the determination of chemical composition and the profiling ofmedicine counterfeits.

The application of NMR and MS methods for detection of adulteration of wine, fruit juices, and olive oil. A review

This review covers two important techniques, high resolution nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), used to characterize food products and detect possible adulteration of wine, fruit juices, and olive oil, all important products of the Mediterranean Basin. Emphasis is placed on the complementary use of SNIF-NMR (site-specific natural isotopic fractionation nuclear magnetic resonance) and IRMS (isotope-ratio mass spectrometry) in association with chemometric methods for detecting the adulteration.

Profiling of counterfeit medicines by vibrational spectroscopy

Counterfeit pharmaceutical products have become a widespread problem in the last decade. Various analytical techniques have been applied to discriminate between genuine and counterfeit products. Among these, Near-infrared (NIR) and Raman spectroscopy provided promising results.The present study offers a methodology allowing to provide more valuable information fororganisations engaged in the fight against counterfeiting of medicines.A database was established by analyzing counterfeits of a particular pharmaceutical product using Near-infrared (NIR) and Raman spectroscopy. Unsupervised chemometric techniques (i.e. principal component analysis - PCA and hierarchical cluster analysis - HCA) were implemented to identify the classes within the datasets. Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to determine the number of different chemical profiles within the counterfeits. A comparison with the classes established by NIR and Raman spectroscopy allowed to evaluate the discriminating power provided by these techniques. Supervised classifiers (i.e. k-Nearest Neighbors, Partial Least Squares Discriminant Analysis, Probabilistic Neural Networks and Counterpropagation Artificial Neural Networks) were applied on the acquired NIR and Raman spectra and the results were compared to the ones provided by the unsupervised classifiers.The retained strategy for routine applications, founded on the classes identified by NIR and Raman spectroscopy, uses a classification algorithm based on distance measures and Receiver Operating Characteristics (ROC) curves. The model is able to compare the spectrum of a new counterfeit with that of previously analyzed products and to determine if a new specimen belongs to one of the existing classes, consequently allowing to establish a link with other counterfeits of the database.