# Assessment of Milk Quality in Skopelos Goats Under Low- and High-Input Farming Systems
## 3 Results  
### Principal Component Analysis (PCA)  
Principal Component Analysis (PCA) was performed to investigate the relationships among the physicochemical, SCC, and TBC parameters of goat milk and to evaluate the differences between extensive and intensive farming systems. The first two principal components (Dim1 and Dim2) explained 31.6% and 18.5% of the total variance, respectively, effectively summarizing the main sources of variation in the dataset. The remaining principal components (Dim3 and beyond) explained a relatively small proportion of the total variability, each contributing to a minor extent to the overall variation in the dataset. The PCA biplot (Figure 1a) provides insights into the relationships between the milk quality parameters. The angles between these variables serve as indicators of correlation; specifically, a smaller angle signifies a strong positive correlation, while variables that point in opposite directions indicate a negative relationship. In this analysis, SCC_log10, TBC_log10, and EC are closely aligned, suggesting a strong positive correlation among these variables. Conversely, milk composition traits, including fat, protein, and total solids (TSs), show a different directional contribution, highlighting their influence on a separate component of variability. The cos2 values, represented by the color gradient, indicate how well each variable is explained by Dim1 and Dim2. The PCA individual plot (Figure 1b) visualizes the distribution of milk samples, with each point representing an individual sample, classified by the farming system (extensive vs. intensive). Each point represents a milk sample, and the grouping is indicated by color-coded markers (red for extensive and blue for intensive). The confidence ellipses (95% confidence interval) surrounding each group illustrate the variation within each system, showing how individual samples are distributed along the first two principal components. The overlap of extensive and intensive samples suggests no clear distinction between the extensive and intensive samples based on the first two principal components alone. However, the confidence ellipses indicate that intensive samples are slightly more clustered, while extensive samples show greater dispersion along Dim1; this implies that milk from extensive systems exhibits greater compositional variability, potentially due to dietary differences or seasonal variations in grazing conditions. Correlation coefficients were calculated and are shown in Figure 1c. Among the compositional traits, a particularly strong correlation was found between fat and TSs (r = 0.86), as well as between protein and caseins (r = 0.80), reflecting their shared biochemical basis and consistent contribution to overall milk solids. Similarly, the Brix value, which serves as a rapid estimation method for milk solids, showed a strong positive association with TSs (r = 0.86), fat (r = 0.50) and protein (r = 0.45), supporting its use as a proxy for compositional richness. Furthermore, lactose was moderately positively correlated with the Brix value (r = 0.39) and protein (r = 0.26) but negatively associated with SCC_log10 (r = \(-\)0.23), which may reflect compromised under health affecting sugar synthesis. The distribution of milk traits and farming systems by clusters are presented in Figure 2. Cluster 2 exhibited the highest concentrations of key milk components, including fat (5.09%), protein (3.95%), lactose (4.62%), and total solids (13.7%). However, this cluster was also characterized by elevated levels of SCC (SCClog = 6.20) and TBC (TBClog = 2.78), reflecting a trade-off between richer milk composition and increased indicators of udder health challenges. Cluster 3 showed intermediate values for most traits, while cluster 1 had  
Figure 1: Principal component analysis (PCA) of milk composition in relation to farming systems (extensive and intensive) (**a**) Represents the correlation between milk traits through a PCA plot.