Abstract
The objective of this work was to determine the sensory profile and the degree of acceptability of samples of commercial dehydrated orange juices (A-B-C) by quantitative descriptive analysis (QDA) and sensory acceptability testing. As samples B and C are marketed with the label “sweet orange”, in sensory tests it was also analyzed whether the assessors and consumers perceived them as any sweeter. A panel of 8 assessors was selected for the QDA test, and trained on evaluating of the dehydrated orange juices. The acceptance test was performed with 50 consumers of both genders, who were selected for their daily consumption of dehydrated juices. In addition, in this test, the influence of gender of consumers on evaluations of the samples was analyzed. In the descriptive test, B and C were characterized by a greater intensity in orange and acid aroma and orange and acid flavor, samples A and C by a larger body, and A and B by exhibiting a greater intensity of the sweet flavor descriptor. In the test with consumers, B and C were perceived as the sweetest and those that presented the greatest overall acceptability. Furthermore, no differences were found between the ratings provided by men and women.
DOI: https://doi.org/10.54167/tch.v17i3.1325
Publisher
Universidad Autonoma de Chihuahua
Reference26 articles.
1. Akasapu, K. & Ramagopal, V. S. (2023). Uppaluri Efficacy of score deviation method as a novel sensory evaluation technique for the identification of optimal mixed vegetable soup formulations. Int. J. Gastron. Food Sci. 33: 100761. https://doi.org/10.1016/j.ijgfs.2023.100761
2. Bécue-Bertaut, M. (2014). Tracking verbal-based methods beyond conventional descriptive analysis in food science bibliography. A statistical approach. Food Qual. Prefer. 32A: 2-15. https://doi.org/10.1016/j.foodqual.2013.08.010
3. Chegini, G. R., & Ghobadian, B. (2007). Spray Dryer Parameters for Fruit Juice Drying. World J. Agric. Sci. 3(2): 230-236. https://bitly.ws/ZZeB
4. Chegini, G. R., Khazaei, J., Ghobadian, B. & Goudarzi, A. M. (2008). Prediction of process and product parameters in an orange juice spray dryer using artificial neural networks. J. Food Eng., 84 (4): 534-543. https://doi.org/10.1016/j.jfoodeng.2007.06.007
5. Di Rienzo, J. A., Casanoves, F., Balzarini, M. G., Gonzalez, L., Tablada, M. & Robledo, C. W. (2014) InfoStat version 2014. InfoStat Group: FCA: National University of Córdoba, Argentina. https://www.infostat.com.ar/