Um software para análise multivariada foi desenvolvido com o objetivo de oferecer uma ferramenta computacional livre com interface gráfica amigável para pesquisadores, professores e estudantes com interesse em quimiometria. O Chemoface possui módulos capazes de resolver problemas relacionados com planejamento experimental, reconhecimento de padrões, classificação e calibração multivariada. É possível obter uma variedade de gráficos e tabelas para explorar os resultados. Neste trabalho, as principais funcionalidades do Chemoface são exploradas usando estudos de caso reportados na literatura, tais como otimização de adsorção de corante índigo em quitosana usando planejamento fatorial completo, análise exploratória de amostras de própolis caracterizadas por ESI-MS (espectrometria de massas com ionização electrospray) usando PCA (análise de componentes principais) e HCA (análise hierárquica de agrupamentos), modelagem MIA-QSAR (análise multivariada de imagem aplicada à relações quantitativas estrutura-atividade) para predição de parâmetro cinético relacionado à atividade de peptídeos contra dengue usando PLS (método de quadrados mínimos parciais), e classificação de amostras de vinho de diferentes variedades usando PLS-DA (PLS para análise discriminante). Todos os exemplos são ilustrados com gráficos e tabelas obtidos no Chemoface. A software for multivariate analysis was developed in order to provide a free computational tool with user-friendly graphical interface for researchers, professors and students with interest in chemometrics. Chemoface comprises modules that can solve problems related to experimental design, pattern recognition, classification and multivariate calibration. It allows obtaining a variety of high quality graphics and tables to explore results. In this work, the main features of Chemoface are explored using case studies reported in the literature, such as optimization of adsorption of indigo dye on chitosan using full factorial design, exploratory analysis of propolis samples characterized by ESI-MS (electrospray ionization-mass spectrometry) using PCA (principal component analysis) and HCA (hierarchical cluster analysis), MIA-QSAR (multivariate image analysis applied to quantitative structure activity relationship) modeling for the prediction of kinetic parameter related to activities of peptides against dengue using PLS (partial least squares), and classification of wine samples from different varieties using PLS-DA (PLS discriminant analysis). All examples are illustrated with graphs and tables obtained by means of Chemoface.

In this work, it is demonstrated that consumer acceptance analysis can be evaluated by simultaneously considering several attributes using a three-way internal preference map obtained by parallel factor analysis (PARAFAC). Considerations regarding the building of this three-way map by PARAFAC are reported. Pilot case studies with real data sets from herb cakes and beef burgers are also carried out, and comparisons with results from regular internal preference maps are obtained by principal component analysis. Three-way internal preference maps enable the simultaneous analysis of interactions among consumer preferences, products and different evaluated attributes, which facilitate the selection of favorite samples. This method highlights the efficiency of the three-way analysis of consumer acceptance data with different sources of data variability, allowing the extraction of relevant information and the graphic display of this information with improved interpretability. Three-way internal preference mapping is a useful tool for the analysis of consumer acceptance tests, which can provide a more evidence-based and general interpretation of data. PRACTICAL APPLICATIONSThree-way internal preference mapping is another useful tool for the analysis of consumer acceptance tests, allowing the extraction of more relevant information and the graphic display of this information with improved interpretability. This tool makes it possible to simultaneously analyze the interactions among consumer preferences, products and different evaluated attributes, which can facilitate the selection of favorite samples. Furthermore, it enables a comparison of the overall performance of the samples in consumer acceptance tests, simultaneously taking into account the influence of all analyzed attributes. This method is useful in new product development and product improvement studies in research institutions and industries.

Strict requirements of scientific journals allied to the need to prove the experimental data are (in)significant from the statistical standpoint have led to a steep increase in the use and development of statistical software. In this aspect, it is observed that the increasing number of software tools and packages and their wide usage has created a generation of 'click and go' users, who are eagerly destined to obtain the p-values and multivariate graphs (projection of samples and variables on the factor plane), but have no idea on how the statistical parameters are calculated and the theoretical and practical reasons he/she performed such tests. However, in this paper, some published examples are listed and discussed in detail to provide a holistic insight (positive points and limitations) about the uses and misuses of some statistical methods using different available statistical software. Additionally, a brief description of several commercial and free statistical software is made highlighting their advantages and limitations.

SensoMaker is a free software for data analysis from sensory studies, which has modules with user-friendly interface. Data acquisition can be performed using different methods, such as category scale, linear scale, temporal dominance of sensations (TDS), and time-intensity (TI). Results can be analyzed by a variety of methods, such as conventional internal and external preference mapping, three-way internal and external preference mapping, principal component analysis, hierarchical cluster analysis, TDS and TI curves, in addition to Tukey and Dunnett tests. High quality graphics are easily obtained and exported to several formats. The software is useful during the development or improvement of products, when it is important to carefully note consumer preferences and to relate it to descriptive characteristics in order to ensure good product acceptance. Index terms:Consumer, sensory analysis, software. RESUMOSensoMaker é um software livre para análise de dados de estudos sensoriais que tem módulos com uma interface gráfica amigável. A aquisição de dados pode ser realizada por meio de diferentes métodos, tais como escala de categoria, escala linear, dominância temporal de sensações (TDS) e tempo-intensidade (TI). Os resultados podem ser analisados por uma variedade de métodos, tais como mapas de preferência interno e externo (convencional e de três vias), análise de componentes principais, análise de agrupamento hierárquico, curvas de TDS e TI, além de testes de Tukey e Dunnett. Gráficos de alta qualidade são facilmente obtidos e exportados para vários formatos. O software é útil durante o desenvolvimento ou a melhoria de produtos, quando é importante observar cuidadosamente as preferências dos consumidores e relacioná-la com características descritivas, a fim de garantir sua boa aceitação.Termos para indexação: Consumidor, análise sensorial, programa de computador.

The use of univariate, bivariate, and multivariate statistical techniques, such as analysis of variance, multiple comparisons of means, and linear correlations, has spread widely in the area of Food Science and Technology. However, the use of supervised and unsupervised statistical techniques (chemometrics) in order to analyze and model experimental data from physicochemical, sensory, metabolomics, quality control, nutritional, microbiological, and chemical assays in food research has gained more space. Therefore, we present here a manuscript with theoretical details, a critical analysis of published work, and a guideline for the reader to check and propose mathematical models of experimental results using the most promising supervised and unsupervised multivariate statistical techniques, namely: principal component analysis, hierarchical cluster analysis, linear discriminant analysis, partial least square regression, k-nearest neighbors, and soft independent modeling of class analogy. In addition, the overall features, advantages, and limitations of such statistical methods are presented and discussed. Published examples are focused on sensory, chemical, and antioxidant activity of a wide range of fruit juices consumed worldwide.

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Data of methylene blue number and iodine number of activated carbons samples were calibrated against the respective surface area, micropore volume and total pore volume using multiple regression. The models obtained from the calibrations were used in predicting these physical properties of a test group of activated carbon samples produced from several raw materials. In all cases, the predicted values were in good agreement with the expected values. The method allows extracting more information from the methylene blue and iodine adsorption studies than normally obtained with this type of material.

The syringyl/guaiacyl ratio was determined for six different Eucalyptus spp. wood clones cultivated in four regions in Brazil. The determinants were made by pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) and the results were compared with those obtained by alkaline nitrobenzene oxidation method. The S/G ratios were obtained considering all the identified lignin derivatives in the pyrograms and also using two groups of markers. The first group of markers consisted of guaiacol, 4-methylguaiacol, 4-vinylguaiacol, trans-isoeugenol, syringol, 4-methylsyringol, 4-vinylsyringol and trans-4-propenylsyringol compounds as markers. The second group included guaiacol, 4-methylguaiacol, 4-vinylguaiacol, vanillin, 4-ethylsyringol, 4-vinylsyringol, syringaldehyde, syringylacetone and trans-4-propenylsyringol. It was observed from the statistical analysis that the values of S/G obtained by Py-GC-MS using the two groups of markers did not differ significantly from those obtained by nitrobenzene oxidation method.