**3. Conclusion**

A tie can be observed if verbal analysis is considered, but neural analysis showed preference for Sample B. For one more test, the average between the verbal/neural scores of each sample was made in order to be able to compare them to each other, giving Sample B the winner again. Considering previous experiences and those scheduled for the coming years, the multicriteria algorithm is developed according to the number of linguistic terms, areas to be analyzed and the number of participants. The linguistic terms vary according to the segment (food, perfumes, etc.) and experts must always be used so that the terminology reproduces the objectives of the experiment. In this case, the verbal scale was discussed and approved by professionals from different areas related to food and beverages, such as restaurant chefs, sommeliers, food and beverage entrepreneurs, as well as empirical specialists (people who cook for home or small businesses) and professional of a supermarket chain in Brazil. The neural matrix was built based on models and studies by CARNELL S. and Emotiv's EEG software.

### **3.1 Future reviews**

This same data can be used to analyze peaks and falls individually and to understand what factors can be determined by these effects. Analysis of each element of a layout, product or service such as colors, images, typography, packaging, internal and external experience, etc. Thus, the same experience can be analyzed at satisfactory and unsatisfactory peak times, identifying them and using the information both to avoid unsatisfactory, and to praise and build their offers and experiences based on the observer's satisfaction, both for a product/service and for an environment/experience.

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