Application of chemometric tools in the development and sensory evaluation of gluten-free cracknel biscuits with the addition of chia seeds and turmeric powder.

Gluten-free biscuits were developed with the addition of chia seeds (Salvia hispanica L.) and turmeric powder. An experimental design 22 were employed in the formulation optimization that promotes better sensory acceptance through acceptance test with a hedonic scale of 9 points. For characterization purposes, the centesimal composition from chia seeds and the turmeric powder were determined. The biscuits were produced from an optimized formulation with and without chia seeds and turmeric powder with microbiological results safe for consumption by evaluation Salmonella sp., positive coagulase Staphylococcus, and Coliforms to 45 °C. The Principal Components Analysis (PCA) was used in the investigation of sensory results (color, flavor, texture, smell, appearance, overall impression). It was also considered the habits of consuming food with/without gluten, purchase intentions, including age and gender. The results show that there is no distinction between the biscuits with the addition of chia seeds and turmeric powder. A statistical test using the confidence ellipse confirms that there no significant difference, at a 95% confidence level, among the sensory results for the biscuits with and without chia seeds and turmeric powder.

Combinatorial projection pursuit analysis for exploring multivariate chemical data.

Kurtosis-based projection pursuit analysis (kPPA) has demonstrated the ability to visualize multivariate data in a way that complements other exploratory data analysis tools, such as principal components analysis (PCA). It is especially useful for partitioning binary data sets (2k classes) with a balanced design. Since kPPA is not a variance-based method, it can often provide unsupervised class separation where other methods fail. However, when multiple classifications are possible (e.g. by gender, age, disease state, etc.), the projection provided by kPPA (corresponding to the global minimum kurtosis) will not necessarily be the one of greatest interest to the researcher. Fortunately, the optimization algorithm for kPPA allows for interrogation of projections obtained from numerous local minima. This strategy provides the basis of a new method described here, referred to as combinatorial projection pursuit analysis (CombPPA) because it presents alternative combinations of class separation. The method is truly exploratory in that it allows the landscape of interesting projections to be more fully probed. The approach uses Procrustes rotation to map local minima among the kPPA solutions, whereupon the researcher can visualize different projections. To demonstrate the new method, the clustering of grape juice samples using visible spectroscopy is presented as a model problem. This problem is well-suited to this type of study because there are eight classes of samples symmetrically partitioned into two classes by type (organic/non-organic) or four classes by brand. Results presented show the different combinations of projections that can be obtained, including the desired partitions. In addition, this work describes new enhancements to the kPPA algorithm that improve the orthogonality of solutions obtained.