## 3 Results and Discussion
### Chemometric Comparison and Classification  
The PCA model was applied to vitamin C, TPC and AC data for unsterilized and sterilized purees to determine the most important variables that explain the relationships between the ten selected genotypes of apples used and to identify any group patterns (Figure 3). The results from the current study showed that using the chemometric technique PCA, the physico-chemical quality attributes can be more comprehensively understood. The results showed that with a relative distance of 10, all the genotypes were grouped into four main clusters.  
Principal component analysis (PCA) uses the maximum variance of the data in order to transform the original axes (parameters) into new axes named principal components (PCs) or factors. The PCs are orthogonal, and the intercorrelations are removed. The graph representation of the translation coordinates provides the scores plot and reveals the similarity/dissimilarity between cases (the grouping of the samples). On the other hand, the graph representation of the cosines of the rotational angles provides the loadings plot, which reveals the importance of the original parameters to the classification.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Unsterilized Product Texture**} & \multicolumn{3}{c}{**Stellized Product Texture**} \\ \hline
**Puree** & **Cubesiveness** & **Firmness (N)** & **Deformation at** & **Adhesiveness** & **Cubesiveness** & **Firmness (N)** & **Deformation at** & **Adhesiveness** \\
**Sample** & **(n.a)** & **Firmness (mm)** & **Hardness (mm)** & **(m)** & **(n.a)** & **Hardness (mm)** & **(m)** \\ \hline
1 & 0.615 \(\pm\) 0.049 * & 2.46 \(\pm\) 0.39 * & 7.79 \(\pm\) 0.03 * & 0.55 \(\pm\) 0.07 * & 0.32 \(\pm\) 0.06 * & 3.49 \(\pm\) 0.12 * & _7.97_ \(\pm\) 0.03 * & 8.1 \(\pm\) 0.7 * \\
2 & 0.619 \(\pm\) 0.084 * & 2.08 \(\pm\) 0.09 * & 7.985 \(\pm\) 0.007 * & 0.35 \(\pm\) 0.35 * & 0.355 \(\pm\) 0.205 * & 1.49 \(\pm\) 0.31 * & _7.97_ \(\pm\) 0.03 * & 4.1 \(\pm\) 0.9 * \\
3 & 0.425 \(\pm\) 0.163 * & 1.055 \(\pm\) 0.007 * & 7.68 \(\pm\) 0.27 * & 1.85 \(\pm\) 0.92 * & 0.455 \(\pm\) 0.106 * & 2.455 \(\pm\) 0.332 * & _7.4_ \(\pm\) 0.8 * & 3.4 \(\pm\) 0.7 * \\
4 & 0.585 \(\pm\) 0.049 * & 1.88 \(\pm\) 0.08 * & 7.945 \(\pm\) 0.007 * & 0.55 \(\pm\) 0.54 * & 0.53 \(\pm\) 0.27 * & 1.44 \(\pm\) 0.12 * & _7.97_ \(\pm\) 0.03 * & 2.8 \(\pm\) 0.4 * \\
5 & 0.55 \(\pm\) 0.01 * & 1.99 \(\pm\) 0.41 * & 7.97 \(\pm\) 0.02 * & 0.7 \(\pm\) 0.14 * & 0.32 \(\pm\) 0.01 * & 2.09 \(\pm\) 0.29 * & _7.97_ \(\pm\) 0.03 * & 3.55 \(\pm\) 0.49 * \\
6 & 0.38 \(\pm\) 0.05 * & 1.49 \(\pm\) 0.15 * & 7.65 \(\pm\) 0.431 * & 1.35 \(\pm\) 0.49 * & 0.475 \(\pm\) 0.106 * & 1.635 \(\pm\) 0.091 * & 7.99 \(\pm\) 0.01 * & 3.8 \(\pm\) 0.3 * \\
7 & 0.528 \(\pm\) 0.007 * & 1.5 \(\pm\) 0.22 * & 7.99 \(\pm\) 0.01 * & 0.45 \(\pm\) 0.21 * & 0.42 \(\pm\) 0.01 * & 1.7 \(\pm\) 0.28 & 7.995 \(\pm\) 0.007 * & 1.95 \(\pm\) 0.63 * \\
8 & 0.59 \(\pm\) 0.05 * & 2.795 \(\pm\) 1.393 * & 7.92 \(\pm\) 0.01 * & 1.2 \(\pm\) 0.57 * & 0.41 \(\pm\) 0.07 * & 2.07 \(\pm\) 0.23 * & 7.975 \(\pm\) 0.035 * & 0.9 \(\pm\) 0.1 * \\
9 & 0.355 \(\pm\) 0.106 * & 1.17 \(\pm\) 0.21 * & 7.87 \(\pm\) 0.11 * & 1.15 \(\pm\) 0.07 * & 0.405 \(\pm\) 0.148 * & 1.375 \(\pm\) 0.233 * & 7.862 \(\pm\) 0.049 * & 2.85 \(\pm\) 0.35 * \\
10 & 0.411 \(\pm\) 0.099 * & 2.2 \(\pm\) 0.2* & 7.795 \(\pm\) 0.028 * & 2.3 \(\pm\) 0.6 * & 0.585 \(\pm\) 0.049 * & 1.74 \(\pm\) 0.41 * & _7.97_ \(\pm\) 0.03 * & 5.1 \(\pm\) 0.1 * \\ \hline \hline \end{tabular}  
*–mean values in columns indicated with various letters are significantly different. Pure sample column represents the apple variety used in order to obtain the puree: 1–Champion, 2–Elstar, 3–Baajade, 4–[PERSON], 5–[PERSON], 6—Pattul, 7—[PERSON], 8—Juliana, 9—Dominec, 10—[PERSON].  
\end{table}
Table 7: Texture determination.