# Differentiation of Fresh and Processed Fruit Juices Using Volatile Composition
## 2 Results and Discussion
### Multivariate Data Analysis  
The data matrix (variables versus fresh and processed fruit juices) was normalized using cubic root and data scaling by the mean-center and was submitted to principal component analysis (PCA) to explore the main sources of similarity and variability as well as to characterize and differentiate the target types of fruit juices, fresh versus processed, based on the volatomic signature (Figure 3).  
For all of the analyzed fruit juices, the PCA score scatter plot of the two first principal components (PC1 (32.2%) and PC2 (19.3%)), which explain 51.5% of the total variability of the GC-qMS dataset, is shown in Figure 3a, and the corresponding loading weight plot, establishing the magnitude of each VOC (variable), is illustrated in Figure 3b. Analysis of the loading plot revealed the VOCs responsible for the discrimination of fruit juices based on fruit species/type. Fresh and processed red fruit juices, projected in PC1 negative and PC2 positive, were mainly characterized by methyl hexanoate (34), whereas apple, pear, and grape juices (PC1 and PC2 negative) were mainly characterized by ethyl acetate (3) and 3-methylbutanol acetate (22). Finally, orange juices projected in PC1 and PC2 positive were mainly associated with limonene (35).  
Additionally, the partial least squares-discriminant analysis (PLS-DA) (Figure 4a,b) was used as a supervised cluster, and 30 differently expressed VOCs were found with variable importance in projection (VIP) scores higher than 1: 2-furfurral (77), ethyl dodecanoate (146), valencene (123), ethyl 3-hydrohexanoate (112), ethyl 3-methylbutanoate (18), methyl hexanoate (34), hexyl acetate (44), 2,4-decadienal (127), linalool (90), and propyl butanoate (40) were the most significant. Figure S3 shows the resulting dendrogram associated with the heat map constructed using [PERSON]'s correlation, providing an intuitive visualization of the dataset, which is often applied to identify samples or features that are unusually high or low. Five major clusters were responsible for grouping the juice samples by fruit variety (horizontal axis), and not by the type of juice (fresh or processed). An analogous color tone in the heat map indicated the area, a group of samples, considering that the concentration of the analyzed VOCs was similar.  
Figure 5 shows the heat map constructed using [PERSON]'s correlation for the VOCs with VIP scores higher than 1. It is possible to observe that 2-furral (77), ethyl dodecanoate (146), ethyl 3-hydrohexanoate (112), ethyl 3-methylbutanoate (18), 4-ethyl phenol (167), butyl octanoate (104), 2-heptanone (32), and \(\gamma\)-declactone (164) had a higher chromatographic area in processed juice than in fresh fruit juice, whereas valencene (123) had a higher chromatographic area in fresh than in processed fruit juice.  
Figure 3: Principal component analysis (PCA) of the volatile signature of fresh and processed fruit juices. (**a**) PC1 \(\times\) PC2 score scatter plot and (**b**) loading weight plot (attribution of the peak number is shown in Table S1).  
Figure 4: Partial least squares-discriminant analysis (PLS-DA) of the volatile signature of fresh and processed fruit juices. **(a)** Score scatter plot, and **(b)** Variable importance in projection (VIP) scores (attribution of the peak number is shown in Table 1).  
Figure 5: Hierarchical cluster analysis (HCA). The heat maps of the 30 VOCs (VIP values > 1), identified in fresh and processed fruit juices were generated by an average algorithm and Pearson’s distance analysis (attribution of the peak number is shown in Table 1). Abbreviation: OP—orange juice processed, OF—orange juice fresh, GP—grape juice processed, GF—grape juice fresh, PP—pear juice processed, PF—pear juice fresh, ApP—apple juice processed, ApF—apple juice fresh.