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.

The growth of ochratoxigenic fungus and the presence of ochratoxin A (OTA) in grapes and their derivatives can be caused by a wide range of physical, chemical, and biological factors. The determination of interactions between these factors and fungal species from different climatic regions is important in designing models for minimizing the risk of OTA in wine and grape juice. This study evaluated the influence of temperature, water activity (aw), and pH on the development and production of OTA in a semisynthetic grape culture medium by Aspergillus carbonarius and Aspergillus niger strains. To analyze the growth conditions and production of OTA, an experimental design was conducted using response surface methodology as a tool to assess the effects of these abiotic variables on fungal behavior. A. carbonarius showed the highest growth at temperatures from 20 to 33°C, aw between 0.95 and 0.98, and pH levels between 5 and 6.5. Similarly, for A. niger, temperatures between 24 and 37°C, aw greater than 0.95, and pH levels between 4 and 6.5 were optimal. The greatest toxin concentrations for A. carbonarius and A. niger (10 μg/g and 7.0 μg/g, respectively) were found at 15°C, aw 0.99, and pH 5.35. The lowest pH was found to contribute to greater OTA production. These results show that the evaluated fungi are able to grow and produce OTA in a wide range of temperature, aw, and pH. However, the optimal conditions for toxin production are generally different from those optimal for fungal growth. The knowledge of optimal conditions for fungal growth and production of OTA, and of the stages of cultivation in which these conditions are optimal, allows a more precise assessment of the potential risk to health from consumption of products derived from grapes.

This study aimed to develop a type of hamburger meat product and evaluate the physical features and sensory formulations of oatmeal flour, flour of green banana pulp, flour of green banana peel, flour of apple peel and pulp of Green Banana as fat substitutes. Regarding color, the formulations containing fat substitutes based on green banana presented lower values for b* and L*. Hamburgers with added oatmeal and apple peel flour obtained high values of a* and low values of L*, producing the reddest burgers. Substitutes based on green banana differed from others, resulting in a higher yield of burgers and water-holding capacity during cooking, besides having lower toughness and less shrinkage. The sensory acceptance test for untrained consumers suggests that the flour of peel and pulp of green banana, and oatmeal flour are excellent choices for fat-substitution in beef burger. Although fat contributes to a series of physical and sensory attributes such as softness, juiciness and yield, it is possible to reduce the lipid content in beef burgers without depreciating the quality of food through the use of the following fat substitutes: oat flour, apple peel flour, green banana pulp flour, green banana peel flour and green banana pulp.

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