Jmp pro 10

Genotypic relatedness based on pedigree and biochemical profiles were compared using hierarchical cluster analyses. Two-way ward cluster analysis of genotypic relatedness based on pedigree information was performed in R (R, Vienna, Austria). Two-way ward cluster analysis of genotypic relatedness based on biochemical profiles was constructed in JMP® Pro 10 (SAS Institute Inc., Cary, NC).
Pair-wise correlations and significance between sensory measurements and primary biochemical components were calculated, with panelist as a random effect. Least Square Means (LSMeans) of the sensory data for each sample were obtained to account of the random effect of panelist. A partial least squares (PLS) analysis was constructed in JMP® Pro 10 (SAS Institute Inc., Cary, NC) to reveal effectors of sensory ratings [33 –37 ]. The response variables (Y) considered corresponded to LSMeans of overall liking, sweetness, sourness, and blueberry flavor intensity. Glucose, fructose, sucrose, TA, pH, and 52 volatile compounds (S2 Table) were considered as explanatory variables (X). The inclusion of texture liking was evaluated independently given that no explanatory physical measure was made for sample texture. Variables were centered and scaled (hence, correlation matrices were used), and a 20-fold cross-validation method was used. The model was created without texture for analysis.

Descriptive statistics for data were reported as mean ± Standard Deviation (SD). Differences were considered significant at p<0.05. A multivariate data analysis was performed to classify the subjects on the pre-existing different groups based on ocular variables collected using Quadratic discriminant analysis (QDA). The QDA classifier produced a group of five discriminative functions and each subject was classified according to the cut-off point. The assumptions required to apply QDA were tested as described by Hair [39 ]. In order to detect potential problems with multicollinearity, the pooled within-group correlation was tested between all variables. As recommended, all the correlation coefficient were lower than 0.8 [39 ].
Additionally, a receiver operating characteristic curve (ROC) was built to determine the area under the curve (AUC). The AUC was used to evaluate the classification performance of individuals in different diseases [40 (link)]. All analyses were performed using JMP Pro 10.0 (SAS Institute, Cary, North Carolina).