# Black Truffle Aroma Evaluation: SPME-GC-MS vs. Sensory Experts
## 3 Results and Discussion
### Truffle sensory Evaluation by the Trained Panel  
#### 3.2.1 Training Truffle Aroma Analysis  
Since aroma is one of the most difficult attributes to evaluate due to subjectivity issues, some training was necessary before starting the experiment. Each expert tested, three times, each truffle (and two truffles per session) to check the agreement among experts in the scores of the attributes. The Tucker-1 plots (Figure S1) display how the experts were related to each other in the attributes evaluation. In these plots, the more structured information an attribute contains, the closer it appears to the outer circle (100% explained variance for that attribute). The attributes sulfur, black olives, mushroom, and leather plots showed all the expert variance in the circles except for expert 9 and 13 in mushroom and black olives, respectively. In general, these results indicate a good evaluation of these attributes. However, in those in which some experts were outside the circle, more focused training with those experts was carried out to ensure a more objective evaluation. Figure 1: PCA results for the VOCs profile of fresh black truffles (n = 14) sampled at three different harvesting times: (A) score plot of the analyzed asocorarp samples and (B) loading plot for the VOCs detected via SPME-GC-MS. In (A), sample color indicates the quality of representation for the sample (cos2). In the sample names, the first number (1, 2, 3) refers to the month harvested: 1—December, 2—January, 3—February; S1 or S2 refer to the session number (two per month), T1–T4 correspond with the number of truffles in the session. In (B), compounds are identified with numbers corresponding to those in Table S1, and arrow color indicates the contribution of a compound to the PCA components (contrib). The 95% confidence ellipses colors correspond to the harvesting time: December (red), January (blue), February (green). In order to assess the relevance of the odor descriptors and their relationships, an in-depth analysis of the aromatic attributes was conducted (Figure 2). Despite the between-truffles variability in the scores of the olfactive attributes, in general, sulfur, black olives, leather, and butter showed the highest scores. Fermentation, nuts, and straw attributes showed a more constant and lower score evaluation throughout samples. Also, equilibrium, intensity, and complexity score were high in all the samples, indicating that fresh truffles usually have a strong and complex aroma (Figure 2A). Figure 2: Sensory panel training data: (**A**) sensory attribute scores of six truffles (named from 1 to 6 and shown in different colors); (**B**) heatmap of sensory profile of each truffle sample characterized by the trained panel; (**C**) correlation among sensory attributes evaluated by the trained panel during the training. Data from correlation and \(p\)-value are shown in Table S2. Correlation values higher than 0.116 in absolute value are significant at alpha = 0.05 level (n = 288). A heatmap was used to visualize the aromatic profile of each truffle (Figure 2B). Truffles 1, 2, and 4 showed a similar profile with high intensities in sulfur, black olives, and butter attributes, whereas truffles 3, 5, and 6 reported higher intensities in mushroom and leather attributes. Truffles 1 and 5 seemed to have more intense and complex aromas, and this could be correlated with the black olives aroma content or with the combination of several aromatic attributes. The possible correlation among aromatic attributes was analyzed (Figure 2C; Table S2). A high correlation among sulfur and black olives attributes (r = 0.583, \(p\) < 0.001, n = 288) was observed, as well as among the mushroom and leather combination (r = 0.545, \(p\) < 0.001, n = 288). The aromatic attribute butter showed a good correlation with sulfur (r = 0.370, \(p\) < 0.001, n = 288) and black olives (r = 0.195, \(p\) < 0.001, n = 288) but negative with mushroom (r = \(-\)0.254, \(p\) < 0.001, n = 288) and leather (r = \(-\)0.361, \(p\) < 0.001, n = 288).