A teaching experiment on supervised pattern recognition in chemometrics was proposed in this tutorial to introduce partial least squares discriminant analysis (PLS-DA). A new approach of the experiment published in the first tutorial of this series was revisited and employed to the classification of edible vegetable oils. The spectra of olive, canola, soybean and corn oils were obtained using an attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectrometer in the range of 600 to 4000 cm-1. The combination of ATR-FTIR and PLS-DA classification method was able to correctly classify 100% of the validation samples. The Matlab commands, routines and functions were presented, and a didactic explanation of the concepts and interpretation of the data was provided.

The resistance of Annona muricata roots to the nematode Meloidogyne javanica was investigated by nuclear magnetic resonance (NMR)-based metabolomics in combination with principal component analysis (PCA). Metabolic changes in roots exposed and not exposed to the nematode were evaluated and compared. In addition, the presence of nematicidal compounds in the root extracts was investigated through in vitro assay against Meloidogyne javanica second-stage juveniles. Plants exposed to nematodes showed significant changes in their metabolism after 24 h. Several resistance-related metabolites, including dopamine, xanthine and aromatic compounds, could be identified in the roots with the joint analysis of 1D/2D NMR and the loadings of PC3 (17.8%). A.muricata root chloroform extract, containing mainly acetogenins, has shown nematostatic activity against M. javanica, suggesting that a pre-formed defense mechanism can support the reported resistance. For the first time, metabolomic studies allowed to identify induced and pre-formed defense mechanisms and their related metabolites in Annona muricata.

<p>Methods based on multivariate calibration and diffuse reflectance infrared Fourier transform (DRIFT) and ultraviolet (UV) spectroscopies were developed for the simultaneous determination of two veterinary pharmaceutical drugs, pyrantel pamoate and praziquantel, in commercial tablets. The best UV model was obtained with the full spectra, 200-400 nm, and partial least squares (PLS). The best DRIFT model was optimized by selecting the most predictive spectral regions with synergy interval PLS, 3998-3636 cm^-1, 3274-1824 cm^-1 and 1100-735 cm^-1. Both methods were validated according to Brazilian and international guidelines through the estimate of figures of merit, such as trueness, precision, linearity, analytical sensitivity, bias and residual prediction deviation (RPD). These methods were applied to the determination of the drugs in three different veterinary formulations commercialized in the Brazilian market and the results were compared with high performance liquid chromatography (HPLC). DRIFT was considered more suitable for the quality control of these formulations, because it is faster, does not use solvents and does not generate chemical waste.</p>

O objetivo deste trabalho foi desenvolver uma metodologia quimiométrica para descrever o perfil químico de amostras de cocaína apreendidas no estado de Minas Gerais (Brasil). A identificação de adulterantes e a quantificação do teor de cocaína foram feitas por cromatografia gasosa acoplada à espectrometria de massa (GC-MS). Espectros de 91 amostras foram obtidos por espectroscopia no infravermelho usando refletância total atenuada (ATR-FTIR), e utilizados na construção de um modelo de análise de componentes principais (PCA). A primeira componente principal (PC1) discriminou as amostras de maior pureza das mais diluídas/adulteradas, nas quais foram identificadas lidocaína, cafeína e benzocaína. PC2 discriminou a forma química da cocaína, cloridrato ou base. Além disso, dois modelos supervisionados discriminantes (mínimos quadrados parciais para análise discriminante, PLS-DA) foram desenvolvidos para classificar as amostras em função de sua diluição (abaixo e acima de 15% m/m) e de sua forma química, apresentando taxas de acerto que variaram entre 83 e 97%. Os modelos de classificação constituem uma ferramenta simples, rápida e não destrutiva, de grande valor para peritos forenses e investigadores criminais.

The aim of this article was to develop a chemometric methodology for determining the chemical profile of cocaine samples seized in Minas Gerais State, Brazil. The adulterant detection and the cocaine determination were performed by gas chromatography-mass spectrometry (GC-MS). Spectra of 91 samples were obtained by attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) and used to build an exploratory principal component analysis (PCA) model. The first principal component (PC1) discriminated samples of more purity from the more diluted/adulterated ones, which were characterized by the presence of lidocaine, caffeine and benzocaine. PC2 discriminated the two chemical forms of cocaine, hydrochloride and base. In addition, two supervised discriminant partial least-squares models (partial least-squares discriminant analysis, PLS-DA) were developed for classifying the samples according to dilution (above and below 15% m/m) and chemical form, with a rate of success that varied between 83 and 97%. The classification models constitute a simple, rapid and non-destructive tool, of great value for both forensic experts and criminal investigators.

This study developed and validated a method for moisture determination in artisanal Minas cheese, using near-infrared spectroscopy and partial-least-squares. The model robustness was assured by broad sample diversity, real conditions of routine analysis, variable selection, outlier detection and analytical validation. The model was built from 28.5-55.5% w/w, with a root-mean-square-error-of-prediction of 1.6%. After its adoption, the method stability was confirmed over a period of two years through the development of a control chart. Besides this specific method, the present study sought to provide an example multivariate metrological methodology with potential for application in several areas, including new aspects, such as more stringent evaluation of the linearity of multivariate methods.

This work applied a 2² factorial design to the optimization of the extraction of seven elements (calcium, magnesium, potassium, iron, zinc, copper and manganese) in brachiaria leaves, determined by flame atomic absorption spectrometry. The factors sample mass and digestion type were evaluated at two levels: 200/500 mg, and dry/wet, respectively. Principal component analysis allowed simultaneous discrimination of all the significant effects in one biplot. Wet digestion and mass of 200 mg were considered the best conditions. The decrease of 60% in sample mass allowed to save costs and reagents. The method was validated through the estimation of figures of merit.

Este trabalho propôs um novo método para a determinação de dipirona (DIP) em soluções orais farmacêuticas, usando medidas de transflectância no infravermelho próximo (NIR) e calibração multivariada. A faixa de estudo foi de 300,0 a 569,2 mg mL-1 de DIP. O melhor modelo PLS foi obtido com 2 variáveis latentes e apresentou erros médios quadráticos de calibração e de previsão (RMSEC e RMSEP) iguais a 1,1 e 1,0 mg mL-1, respectivamente. O método foi validado de acordo com as resoluções da ANVISA e do ICH e foi considerado seletivo, linear, preciso, exato e robusto. Em comparação com as principais alternativas, titulação iodimétrica e CLAE, este método é mais simples, não-destrutivo, não gasta reagentes ou solventes nem gera resíduos. Além disso, a maior vantagem está na rapidez da análise, pois são gastos cerca de 50 s.

This work developed a new method for determination of dipyrone (DIP) in oral pharmaceutical formulations, through the use of near infrared (NIR) transflectance measurements and multivariate calibration. The studied range varied from 300.0 to 569.2 mg mL-1. The best PLS (partial least squares) model was obtained with two latent variables and the root mean square errors of calibration and prediction were 1.1 and 1.0 mg mL-1, respectively. The proposed method was validated in accordance with ANVISA, the Brazilian regulatory agency, and ICH, being considered selective, linear, precise, accurate and robust. By comparison with the main alternatives, iodimetric titration and HPLC, this method is simpler, non-destructive, does not use reagents or solvents and does not produce chemical waste. Besides, its rapidity is considered the major advantage over the other methods, since only about 50 s were spent per assay.

A cleaning validation method was developed and validated, based on swabbing sampling and simultaneous chromatographic determination of sulfamethoxazole (SMX) and trimethoprim (TMP) residues. The method presented limits of detection of 0.06 mg mL-1 for SMX and 0.09 mg mL-1 for TMP. It was considered selective, precise, accurate and robust according to the guidelines from ANVISA, the Brazilian regulatory agency, and International Conference on Harmonization. Mean swab recovery factors of 98.5% for SMX and 97.7% for TMP were obtained for spiked stainless steel plates. The method was successfully applied to the assay of actual swab samples collected from eleven points on an equipment surface.

The aim of this work was to develop and validate an analytical methodology for simultaneous determination of mebendazole and thiabendazole, two benzimidazoles used as anthelmintics. The method was based on high performance liquid chromatography, using a C18 column, a mobile phase composed of KH2PO4 0.05 mol L-1 and methanol 40:60 (v/v) and UV detection at 312 nm. The results showed that the method presented linearity from 60.0 to 140.0 µg mL-1 for mebendazole and from 99.6 to 232.4 g µL-1 for thiabendazole and it was considered selective, accurate, precise and robust according to the specific resolution from ANVISA, the Brazilian regulatory agency.

A análise integrada de indicadores edafobiológicos ligados ao manejo do solo constitui uma ferramenta importante para estimar níveis de sustentabilidade do agroecossistema, detectando-se pontos críticos para a devida correção de manejo. Essa ferramenta foi empregada na avaliação de sistemas de produção orgânica e convencional de tomate, em cultivo protegido e a campo aberto, no estado de São Paulo. Tomaram-se como referência solos de mata nativa e/ou pastagem natural, dependendo do local de estudo. Em Serra Negra, o solo sob sistema orgânico apresentou maior capacidade de campo e teor de argila dispersa mais baixo, indicativos da estabilidade dos agregados. No sistema convencional observou-se uma elevada condutividade elétrica, evidenciando a alta disponibilidade de sais solúveis. A análise de componentes principais (ACP) permitiu concluir que há maior grau de similaridade entre o solo sob sistema orgânico e aqueles das bases referenciais, com respeito aos indicadores químicos e biológicos. Constatou-se que C org, N total, polissacarídeos, FDA (hidrólise de diacetato de fluoresceína) e atividade enzimática de desidrogenase estão positivamente relacionados com o sistema orgânico, a mata nativa e a pastagem. Em contrapartida, a saturação por bases (V%), pH, teores de Mn, Mg e Ca, bem como a razão de dispersão estão inversamente relacionadas ao manejo orgânico. Já em Araraquara, os resultados da ACP distinguiram as áreas organicamente cultivadas das matas nativas, principalmente, com base nos indicadores biológicos.

The integrated analysis for physical, chemical and biological parameters in soil management systems is an important tool to check the sustainability of agroecosystems, allowing to identify the main critical constraints that impair the best management conditions. This approach has been used to evaluate production of tomato in organic and conventional systems in the state of São Paulo, Brazil, comparing greenhouse and field cultivation. In both cases, native forest or permanent pasture was taken as basal reference in each of the evaluated rural establishments. The results from organic systems in Serra Negra showed higher water holding capacity (CC) and lower clay dispersion (microaggregation increase) than the conventional system, indicating more stabilized soil aggregates. In conventional systems higher electrical conductivity was observed, which can be indicative of high soluble salt availability for the plants. The principal component analysis (PCA) for the data collected in Serra Negra allowed to conclude that there is greater similarity between organic systems and basal references, considering most of the biological and chemical indicators. It was also observed that organic C, total N, polysaccharides, fluorescein diacetate hydrolysis (FDA) and dehydrogenase activity were positively related to organic systems, natural forest (M1) and pasture (P1), whereas V%, pH, Mn, Mg, Ca and soil particle dispersion ratio were inversely related to those systems. The model of PCA for the data collected in Araraquara efficiently separated the cultivated areas from their respective native forests, based on biological indicators.

A simple method was proposed for determination of paracetamol and ibuprofen in tablets, based on UV measurements and partial least squares. The procedure was performed at pH 10.5, in the concentration ranges 3.00-15.00 µg ml-1 (paracetamol) and 2.40-12.00 µg ml-1 (ibuprofen). The model was able to predict paracetamol and ibuprofen in synthetic mixtures with root mean squares errors of prediction of 0.12 and 0.17 µg ml-1, respectively. Figures of merit (sensitivity, limit of detection and precision) were also estimated. The results achieved for the determination of these drugs in pharmaceutical formulations were in agreement with label claims and verified by HPLC.

Since the last decade, the combined use of chemometrics and molecular spectroscopic techniques has become a new alternative for direct drug determination, without the need of physical separation. Among the new methodologies developed, the application of PARAFAC in the decomposition of spectrofluorimetric data should be highlighted. The first objective of this article is to describe the theoretical basis of PARAFAC. For this purpose, a discussion about the order of chemometric methods used in multivariate calibration and the development of multi-dimensional methods is presented first. The other objective of this article is to divulge for the Brazilian chemical community the potential of the combination PARAFAC/spectrofluorimetry for the determination of drugs in complex biological matrices. For this purpose, two applications aiming at determining, respectively, doxorrubicine and salicylate in human plasma are presented.

This article shows the usefulness of a website to explain the concepts, operational events, vacuum system, applications and an experimental sequence of the Fourier Transform Ion Ciclotron Resonance Mass Spectrometry technique (<A HREF="http://143.107.46.113/icr/icrj.html">http://143.107.46.113/icr/icrj.html</A>).

The equilibria, the spectra and the identities of the species of Cr(VI) that are present in aqueous solution have long been an active subject of discussion in the literature. In this paper, three different chemometric methodologies are applied to sets of UV/Visible spectra of aqueous Cr(VI) solutions, in order to solve a chemical system where there is no available information concerning the composition of the samples nor spectral information about the pure species. Imbrie Q-mode factor analysis, followed by varimax rotation and Imbrie oblique projection, were used to estimate the composition of Cr(VI) equilibrium solutions and, by combining these results with the k-matrix method, to obtain the pure spectra of the species. Evolving factor analysis and self modeling curve resolution were used to confirm the number of the species and the resolution of the system, respectively. Sets of 3.3×10-4 and 3.3×10-5 mol L-1 Cr(VI) solutions, respectively, were analyzed in the pH range from 1 to 12. Two factors were identified, which were related to the chromate ion (CrO4(2-)) and bichromate ion (HCrO4-). The pK of the equilibrium was estimated as 5.8.

One of the major interests in soil analysis is the evaluation of its chemical, physical and biological parameters, which are indicators of soil quality (the most important is the organic matter). Besides there is a great interest in the study of humic substances and on the assessment of pollutants, such as pesticides and heavy metals, in soils. Chemometrics is a powerful tool to deal with these problems and can help soil researchers to extract much more information from their data. In spite of this, the presence of these kinds of strategies in the literature has obtained projection only recently. The utilization of chemometric methods in soil analysis is evaluated in this article. The applications will be divided in four parts (with emphasis in the first two): (i) descriptive and exploratory methods based on Principal Component Analysis (PCA); (ii) multivariate calibration methods (MLR, PCR and PLS); (iii) methods such as Evolving Factor Analysis and SIMPLISMA; and (iv) artificial intelligence methods, such as Artificial Neural Networks.