**2.4 Results**

The attributes selected for the option selection:


*Multicriteria Algorithm for Multisensory Food Analysis DOI: http://dx.doi.org/10.5772/intechopen.96135*


## *2.4.1 Sensorial scale of verbal attributes*

Before interpreting the data, a classification vector was constructed using the sets below (Dispensable: 1 to 3; Unsatisfactory: 3 to 4; Important: 4 to 5; Satisfactory: 5 to 7 and Crucial: 7 to 9). Where the attributes for grades 1 to 9 of the verbal scale (SAATY) are developed.

Set 1: Dispensable if 1 < or = x, then u (x) = 1 if 2 < ou = x < ou = 3, then u (x) = x+3 Set 2: Unsatisfactory if 2 < ou = x < ou = 3, then u (x) = x - 2 if 3 < ou = x < ou = 4, then u (x) = x+4 Set 3: Important if 3 < ou = x < ou = 4, then u (x) = x - 3 if 4 < ou = x < ou = 5, then u (x) = x+5 Set 4: Satisfactory if 4 < ou = x < ou = 5, then u (x) = x - 4 if 5 < ou = x < ou = 7, then u (x) = x+7 Set 5: Crucial if 5 < ou = x < ou = 7, then u (x) = x - 7 if 7 < ou = x < ou = 9, then u (x) = 1

At the same time that the participant responds verbally, his brain is analyzed in fMRI, checking if the areas responsible for the feeding process, such as salivation, digestion and etc. are stimulated, and for how long the stimulus occurs in each area. To measure whether the participant's interest, involvement, excitement, focus, relaxation and stress, EEG equipment was used. Some of these states are linked to the production of hormones such as serotonin, endorphins and dopamines, among others, which are related to pleasure. These data generate an interference model to establish the criteria of the Fuzzy set of pertinence and can be crossed to generate a score for each experience with each coffee, which can be measured and compared.

In the table below, we can identify the sensation (column sensations) according to the linguistic terms (2.4 Results). The grades for each sample A, B and C are presented in the respective columns and the importance for the objective is applied by the researcher according to the sets above (which represent each grade).

The notes in **Table 1** are represented in the graph below (**Figure 13**). The graph in **Figure 14** represents the description of the Fuzzy Matrix, between the "linguistic terms" and the importance for the objective.


#### **Table 1.**

*Verbal scale.*

#### *2.4.2 Sensorial scale of neural attributes*

To interpret the neural matrix data, a new classification "Vector" is required, this time between 0 and 100, which was represented by the sets below (Dispensable: 10 to 30; Unsatisfactory: 30 to 40; Important: 40 to 50; Satisfactory: 50 to 70 and Crucial: 70 to 90 or greater). Where the attributes for the notes analyzed through the fMRI are developed (where we check 30 images per second to define how long each stimulus takes place, as well as making mathematical adjustments for quantification (each sample was tasted for 1 min, therefore, having the same time interval of each sample and the time in which each area was stimulated, we can interpret this data as a number between 0 and 100 that will fit perfectly in the neural matrix and in the Vector for interpretation of the algorithm (**Table 2**).

```
Set 1: Dispensable (g) = 10
c (g) = x
Set 2: Dispensable (g) = 20
10 < n < or = 20, n (g) = x + 10
Set 3: Dispensable (g) = 30
20 < r < or = 30, n (g) = x + 20
Set 4: Unsatisfactory (g) = 40
30 < i < ou = 40, n (g) = x + 30
Set 5: Important (g) = 50
40 < b < ou = 50, n (g) = x + 40
Set 6: Satisfactory (g) = 70
50 < o < ou = 70, n (g) = x + 50
Set 7: Crucial (g) = 90
```

$$70 < \text{d} < \text{ou} = \bar{\text{90}}, \text{ n (g)} = \text{x} + \text{7}$$


### *Multicriteria Algorithm for Multisensory Food Analysis DOI: http://dx.doi.org/10.5772/intechopen.96135*


**Table 2.**

*Neural stimuli.*

The notes for stress, excitement, engagement, focus, interest and relaxation stimuli are interpreted and sent by the software. Below in **Figures 15** and **16**, we can see the fMRI images and the areas stimulated with different frequencies (**Figure 16**). In **Figure 17**, we can analyze the EEG data during the process of the experiment with one of the coffee samples, thus being able to understand peaks and falls of the stimuli, as well as identify what happened in the experiment that may have been responsible for the changes in the graph. **Figure 18** shows the results of the EEG software, it can be seen that the colors of each attribute are represented in the graph of **Figure 17**.

In **Figure 19**, we can see the graph of the Neural response and its interpretation is very similar to the graph in **Figure 13**, where the terms of importance for the objective are on the vertical axis and the stimulated areas, as well as the EEG attributes are positioned to give us a visual representation of the difference between samples for one participant.
