Title of article :
Study on Predictive Models Relating Physico-chemical Properties of Iranian Royal Jelly and it’s Sensory Evaluation
Author/Authors :
kamyab ، soheila Department of Food Science and Technology - Islamic Azad University, Science and Research Branch , Gharachorloo ، Maryam Department of Food Science and Technology - Islamic Azad University, Science and Research Branch , Honarvar ، Masoud Department of Food Science and Technology - Islamic Azad University, Science and Research Branch , Ghavami ، Mehrdad Department of Food Science and Technology - Islamic Azad University, Science and Research Branch
Abstract :
Royal jelly is a substance of complex physico-chemical structure that have been used as a nutritional supplement and functional food for many years. In this paper, the physico-chemical analysis and organoleptic characteristics of royal jelly obtained from different climatic regions of Iran including Ardebil (cold and dry), Amol (wet and moderate), and Mashhad (hot and dry) have been evaluated. The average of Iranian royal jelly composition consisted of moisture content 64.03±3.67 %; proteins 14.13±2.36 %; carbohydrates 13.92±1.67 %; reducing sugars 8.78±1.55 %; fats 6.17±1.45 %; ash 2.08±0.85 %, pH 3.94±0.34 and acidity 31.34±4.90 mL/100g. The one-way analysis of variance (ANOVA) has illustrated that environmental factors had a significant influence on physico-chemical characteristics of Iranian royal jelly (P 0.05). Regarding the influence of temperature and relative humidity on the composition of royal jelly some valid prediction models have been provided. Test panel group evaluated the samples by using 5 points Hedonic and descriptive scales. In sensory evaluation, Ardebil royal jelly with a 4.64±0.230 score evaluated as a good quality royal jelly.
Keywords :
Environmental factors , Iranian Royal Jelly , Physico , Chemical Characteristics , Predictive Modelling , Sensory Evaluation
Journal title :
Journal of Food Biosciences and Technology
Journal title :
Journal of Food Biosciences and Technology