The aim of this manuscript was to show the concepts involving data treatment of RGB based image analysis by univariate and multivariate approaches - the latter using Partial Least Squares (PLS) - using as a practical example application the determination of extravirgin olive oil adulteration. Digital images were collected using a device made of LEDs, batteries and a smartphone and results are shown in a tutorial format using Matlab computing environment. The experiment can serve as an example for teaching the subject for undergraduate and graduate students.

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 aim of this manuscript is to present a didactic experiment based on the combination of Near Infrared chemical imaging and Multivariate Curve Resolution for beginners, undergraduate and graduate students. In this tutorial, using MATLAB environment, the instrumental signals will be separated into two matrices, of spectral profiles and relative concentrations in the samples. Then, chemical images were generated to identify and verify the distribution of the components in the pellet samples. Chemical imaging was obtained from the mapping of predetermined area and the results for relative concentrations obtained in the ALS optimization for the analyzed area. This approach was successfully tested in chemistry undergraduate course at the Chemistry Institute of Campinas State University.

The objective of this manuscript is to describe a practical experiment that can be employed for teaching concepts related to design of experiments using Matlab or Octave computing environment to beginners, undergraduate and graduate students. The classical experiment for determination of Fe (II) using o-phenanthroline was selected because it is easy to understand, and all the required materials are readily available in most analytical laboratories. The approach used in this tutorial is divided in two steps: first, the students are introduced to the concept of multivariate effects, how to calculate and interpret them, and the construction and evaluation of a linear model to describe the experimental domain by using a 2³ factorial design. Second, an extension of the factorial design by adding axial points is described, thereby, providing a central composite design. The quadratic model is then introduced and used to build the response surface.

Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS) is a resolution method that has been efficiently applied in many different fields, such as process analysis, environmental data and, more recently, hyperspectral image analysis. When applied to second order data (or to three-way data) arrays, recovery of the underlying basis vectors in both measurement orders (i.e. signal and concentration orders) from the data matrix can be achieved without ambiguities if the trilinear model constraint is considered during the ALS optimization. This work summarizes different protocols of MCR-ALS application, presenting a case study: near-infrared image spectroscopy.

The aim of this manuscript was to show the basic concepts and practical application of Partial Least Squares (PLS) as a tutorial, using the Matlab computing environment for beginners, undergraduate and graduate students. As a practical example, the determination of the drug paracetamol in commercial tablets using Near-Infrared (NIR) spectroscopy and Partial Least Squares (PLS) regression was shown, an experiment that has been successfully carried out at the Chemical Institute of Campinas State University for chemistry undergraduate course students to introduce the basic concepts of multivariate calibration in a practical way.

This study describes the use of Principal Component Analysis to evaluate the chemical composition of water produced from eight oil wells in three different production areas. A total of 609 samples of produced water, and a reference sample of seawater, were characterized according to their levels of salinity, calcium, magnesium, strontium, barium and sulphate (mg L-1) contents, and analyzed by using PCA with autoscaled data. The method allowed the identification of variables salinity, calcium and strontium as tracers for formation water, and variables magnesium and sulphate as tracers for seawater.

This manuscript aims to show the basic concepts and practical application of Principal Component Analysis (PCA) as a tutorial, using Matlab or Octave computing environment for beginners, undergraduate and graduate students. As a practical example it is shown the exploratory analysis of edible vegetable oils by mid infrared spectroscopy.

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.

A determinação de nimesulida em formulações farmacêuticas na faixa de 10,38 a 39,47% m/m foi estudada e validada empregando figuras de mérito, espectroscopia no infravermelho próximo e calibração multivariada baseada no método dos mínimos quadrados parciais (PLS) e no sinal analítico líquido (NAS). Resultados satisfatórios foram obtidos com limite de detecção de 0,61%, limite de quantificação de 2,03%, erro médio quadrático de predição (RMSEP) de 1,05%, seletividade média 0,006, sensibilidade de 0,004%, inverso da sensibilidade analítica de (0,20%)-1 e relação sinal-ruído de 181. Além disso, obtiveram-se erros relativos menores que 10%, que são valores aceitáveis para o controle de qualidade desse fármaco. Esses resultados indicam que o método proposto pode ser utilizado na indústria farmacêutica como uma alternativa ao procedimento padrão de análise.

The determination of nimesulide in pharmaceutical formulation in the range of 10.38 to 39.47% m/m using near infrared spectroscopy and multivariate calibration based on partial least squares (PLS) and net analyte signal (NAS) was studied and validated by establishing its figures of merit. Suitable results were obtained, with limit of detection of 0.61%, limit quantification of 2.03%, accuracy estimated as root mean square error of prediction (RMSEP) of 1.05%, mean selectivity of 0.006, sensitivity of 0.004%, inverse analytical sensitivity of (0.20%)-1 and signal-to-noise ratio of 181. In addition, it was obtained relative errors lower than 10% that are acceptable values for quality control of this drug. These results indicate that the proposed method can be used in pharmaceutical industries as an alternative to standard analytical procedures.

This paper presents the fundamental principles, instrumentation and selected applications of comprehensive two-dimensional gas chromatography (GC × GC). In this technique, introduced in 1991, two capillary columns are coupled and proper modulating interfaces continuously collect the eluate from the first column, transferring it to the second column. The result is a geometric increment in the chromatographic resolution, ensuring separation of extremely complex mixtures in time periods shorter or comparable to those of analysis using conventional gas chromatography and with better detectabilities and sensitivities.

Anthocyanins are among the most important plant pigments. Due to their potential benefits for human health, there is considerable interest in these natural pigments. Nonetheless, there is great difficulty in finding a technique that could provide the identification of structurally similar compounds and estimate the number and concentration of the species present. A lot of techniques have been tried to find the best methodology to extract information from these systems. In this paper, a review of the most important procedures is given, from the extraction to the identification of anthocyanins in natural extracts.

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.

Inductively Coupled Plasma Optical Emission Spectrometry was used to determine Ca, Mg, Mn, Fe, Zn and Cu in samples of processed and natural coconut water. The sample preparation consisted in a filtration step followed by a dilution. The analysis was made employing optimized instrumental parameters and the results were evaluated using methods of Pattern Recognition. The data showed common concentration values for the analytes present in processed and natural samples. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) indicated that the samples of different kinds were statistically different when the concentrations of all the analytes were considered simultaneously.

A diferenciação de bactérias causadoras de gastrenterites através de métodos microbiológicos clássicos é muito eficiente, na maior parte dos casos. Todavia, o elevado custo dos reagentes e o tempo necessário para tais determinações, cerca de 4 dias, podem causar sérias conseqüências, quando os pacientes são crianças, idosos ou adultos com baixa resistência imunológica. Assim, a pesquisa por métodos espectroscópicos de baixo custo, que permitam tais determinações com pouco uso de reagentes e em curtos intervalos de tempo é extremamente relevante. Neste trabalho os principais microrganismos causadores de gastrenterites, E. coli, S. chroleraesuis, S. flexneri foram avaliados. Foram preparadas sessenta dispersões para cada um dos microrganismos, usando solução fisiológica como solvente, e seus espectros adquiridos. Os espectros obtidos foram muito similares, tornando muito difícil a diferenciação dos microrganismos. Entretanto, aplicando a teoria de correlação generalizada em 2D, foi possível diferenciar os microrganismos avaliados usando o espectro síncrono como "impressão digital do organismo" em um curto intervalo de tempo (~10h).

The discrimination of the bacteria that cause gastroenteritis through classical microbiological methods is very efficient in the great majority of the cases. However, the high cost of chemicals and the time spent for such identifications, about four days, could generate serious consequences for the patients. Thus, the search for low cost spectroscopic methods which would allow a fast and reagentless discrimination of these microorganisms is extremely relevant. In this work the main microorganisms that cause gastroenteritis: E. coli, S. chroleraesuis, S. flexneri were studied. For each of the microorganisms sixty different dispersions were prepared using physiological solution as solvent and its Raman spectra recorded. The 1D spectra obtained were similar, making it very difficult to differentiate the microorganisms. However, applying the 2D correlation method, it was possible to identify the microorganisms evaluated using the synchronous spectrum as "whole-organism fingerprinting" in a reduced time interval (~10 h).