## 2 Materials and Methods
### Statistical Analysis  
The product profile was based on the assessment of the chemical (moisture, ash, fat, protein), textural (hardness, adhesiveness, chewiness, springiness) and sensory objective properties (hardness, cohesiveness, elasticity, juiciness, brown color), using triplicate samples for the first two sets and a particular balanced incomplete block design for the latter set including the sensory parameters: t = number of treatments (10 points from the mixture design), k = 4 restricted number of treatments presented to each panelist (4 servings), b = 15 panelists, r = 6 replicates per treatment and \(\lambda\) = 2 similar pairs of treatments in the design (plan 11.16 from [PERSON], 1957) [PERSON] (1957). The sensory experiment was conducted twice; thus, two adjusted variable means per treatment were calculated. Individual values were dot-plotted to detect and remove the potential outliers between each treatment and panelist combination.  
For each variable Y, the special cubic mixture equation was performed, which includes the main three components and their first and second order interactions:  
\[\hat{Y}=\text{b}_{1}X_{1}+\text{b}_{2}X_{2}+\text{b}_{3}X_{3}+\text{b}_{12}X_{ 1}X_{2}+\text{b}_{13}X_{1}X_{3}+\text{b}_{23}X_{2}X_{3}+\text{b}_{123}X_{1}X_{ 2}X_{3}\]  
where \(\hat{Y}=\) the fitted variable response under question, \(X_{1}=\) buffalo meat, \(X_{2}=\) pork fat, and \(X_{3}=\) buffalo fat.  
The statistical significance of each term in the polynomial equation was tested applying the forward selection of variables (p-entrance \(\leq\) 0.05), so selecting only the parsimonious model. The main components were always forced to retain in the final equation, no matter the statistical significance, paying also attention to the desirable non-significant effect of the lack-of-fit test for detecting curvature. The results were expressed as tables with regression coefficients plus the determined and predicted R\({}^{2}\) coefficients of fit reliability and predictability. Trace plots were also constructed to detect particular trends of the variable responses as affected by the blending components. In these plots, the interest is focused on how each variable responds when one ingredient increases in proportion (right part of graph) and the other two always decrease. On the three sets of attributes, a two-way cluster analysis was conducted using the Euclidean distance for samples similarity and the Ward technique for samples linkage along with the clustering of attributes. The forthcoming groups are expected each to include samples with common effects on specific variables from different sets. Finally, to detect potential relationships among the three variable sets with reference on samples, a principal component analysis (PCA) was performed--a method which is known to extract as much information as possible from the whole set of variables into the two first major components. When samples and variables of different sets are found in a proximate position in the PCA graph, they signal unique information.  
The statistical information on the regression equations and the trace plots were obtained using the MINITAB 19.0 (Minitab LLC, State College, PA, USA) software, and the cluster and PCA analysis was performed via the JMP 17.0 statistical software (Statistical Discovery LLC, Cary, NC, USA).