## 3 Results and Discussion
### Fingerprinting for Saffron Authenticity
#### 3.1.3 Statistical Analysis to Reveal Adulterated Saffron Organic Extracts  
To reveal the adulteration through the study of the less hydrophilic metabolites, a spectroscopic non-targeted method was applied also to the safron samples obtained by extraction with DMSO-\(d_{\delta}\) (see Section 2.3.3 for further details). The determination of the metabolic composition of authentic safron was achieved by NMR analysis (1D \({}^{1}\)H NOESY, COSY, and TOCSY), and the results were compared to spectral data reported in the literature [27; 32]. Less hydrophilic metabolites could be detected, like safranal, glycosylated forms of kaempfer, linoleic acid, and further fatty acids (see Figure S9 and Table S4 in Supplementary Materials for further details). A data matrix (57 samples by 245 spectra bins) was obtained from the processing (normalization to the sum and auto scaling) of the recorded \({}^{1}\)H NMR spectra (see Figure S10 in Supplementary Materials for a visual inspection of \({}^{1}\)H NMR spectra of extracts in DMSO-\(d_{6}\) of authentic and adulterated safron samples) and was, next, subjected to univariate analysis (_t_-test) and unsupervised multivariate analysis (PCA) in analogy to the statistical elaboration performed on the corresponding aqueous extracts. As a result, a less noticeable separation between the authentic samples and the adulterated ones could be extrapolated. Indeed, discrimination between these groups of samples could be detected only along the PC5 with a relatively low (4.5%) explained variance (see Figure S11 in Supplementary Materials for further details). A more noticeable clustering of these two classes of samples could be detected through supervised multivariate data analysis, namely OPLS-DA, as depicted in the corresponding scores plot (Figure 5a). Such a model was characterized by one predictive component and four orthogonal ones, and it was successfully validated upon passing the permutation test (see Figures S12 and S13 in Supplementary Materials for further details). As illustrated in Figure 5b, the top ten variables with VIP[t] > 1.70 on Component 1 (_t_ score) included buckets related to the compounds belonging to the class of curcuminoids (indicated in Figure 5b as Cur), namely curcumin, demethylox Curcumin, and bisdemethoxys Curcumin. These compounds are natural phenols and they are often employed to color foods and medicines, because of the typical yellow color derived from them [51]. They were previously isolated from turmeric and characterized via NMR [52]. The adulterated samples presented a relatively higher content of curcuminoids (Cur), along with linoleic acid (Lin. Ac), picrocroccin (Picr), and safranal (Safr). The analysis of the hierarchical clustering heat map performed on the normalized data (distance measurement, Euclidean; clustering method, Ward) helped to identify the spectral regions most susceptible to changes in the metabolic composition induced by the two different types of adulteration, namely by addition of turmeric vs. safflower (Figure 6). It is interesting to note that the adulteration through turmeric could be spotted even at a very low concentration, i.e., 5 wt%. Indeed, diagnostic buckets were those related to croccin (1.97 ppm), picrocroccin (1.76 ppm), curcuminoids (7.07, 7.01, 7.33, and 6.05 ppm),  
Figure 3: Results of PLS-DA performed on the spectral data obtained from the aqueous extracts of authentic (PS, represented as circles) and adulterated safron (AS, represented as triangles): (**a**) Scores plot between the selected PCs. The explained variances are shown in brackets; (**b**) Analysis of the Variable Importance in Projection (VIP) identified by PLS-DA. The colored boxes on the right indicate the relative concentrations of the corresponding metabolite in each group under study. and safranal (5.97 ppm). On the other hand, the samples adulterated with safflower could be detected only at higher levels of concentration, like 20 wt%.