## 3 Results and Discussion
### Explorative Analysis of the Volatilome  
The relative concentrations of the VOCs identified across the different samples were used for the explorative analysis. The data were preprocessed using Pareto scaling and mean-centering to ensure that all variables contributed equally to the PCA model. Subsequently, a principal component analysis (PCA) [20,21] model was constructed using three principal components (PCs), capturing the underlying structure of the volatilome data set. The PCA model allows the generation of graphical representations of scores and loadings, providing insights into both sample clustering and the relationships between variables. The scores plot is displayed in Figure 2; it represents the projections of the samples on the principal components, highlighting their distribution and grouping. In this case, it includes red diamonds (textiles from Italy), green squares (textiles from China), and blue triangles (textiles from Egypt), allowing for a visual assessment of clustering based on sample origin. The PCA scores plot demonstrates distinct clustering based on geographic origin. The Italian samples form a tightly grouped cluster, suggesting consistency in their properties. The Chinese samples display a moderate spread, but they are still distinguishable as a cohesive group. The Egyptian samples exhibit slightly more dispersion but maintain a clear separation from the other origins. In the figure, the percentages of cotton in the different fabrics are displayed close to the projected individuals. Looking at the figure, it is possible to recognize that the percentages of cotton associated with each sample also correlate with their positions on the plot. It appears that Italian samples may have a higher or more uniform cotton content compared to the others. Samples with similar cotton percentages tend to cluster more closely within their respective groups, indicating a relationship between cotton content and PCs. The separation along the PCA axes suggests significant variation in chemical or physical characteristics among the textiles from different origins. Any outliers observed might reflect unique processing techniques or compositional differences specific to certain regions. Overall, the PCA plot validates its utility in distinguishing textiles based on their origin and highlights the importance of cotton percentage as a contributing factor to the observed variation. A further interpretation of the data can be obtained by investigating the loading plot (Figure 3). The loading vectors indicate how the original variables contribute to the principal components and can help explain the observed sample patterns. Figure 3: PCA model: loading plot. Figure 2: PCA model: projection of samples in the space spanned by the first two PCs. The PCA loading plot reveals a detailed chemical profile that explains the clustering of textiles from Italy, China, and Egypt in the scores plot. Italian textiles, which cluster prominently at positive PC1, are strongly correlated with compounds such as pentadecanal, alkane C17, alkane C19, and decanal. These compounds are commonly associated with surface finishing treatments, hydrophobic coatings, and preservatives, reflecting manufacturing processes that prioritize durability, water resistance, and esthetic qualities. The consistent presence of these chemicals indicates a uniform approach to textile finishing in Italian production. Chinese textiles, on the other hand, are dispersed across the negative PC1 side, with some variability in PC2, indicating a broader chemical diversity. This group correlates with compounds such as hexadecanoic acid (palmitic acid), octadecanoic acid (stearic acid), nonanal, and benzenamine, 2,5-dichloro. These chemicals suggest the use of fatty acids for softening or lubricating treatments and aldehydes from oxidized oils or fragrances, as well as variability in dyeing techniques or chemical treatments.