کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5768666 1628513 2017 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Physicochemical and rheological characterization of honey from Mozambique
ترجمه فارسی عنوان
مشخصات فیزیکوشیمیایی و ریاضی عسل از موزامبیک
کلمات کلیدی
عسل آفریقایی، رنگ، مدول الاستیسیته، ویسکوزیته پیچیده، شبکه های عصبی مصنوعی،
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک دانش تغذیه
چکیده انگلیسی


- Physicochemical and Newtonian behaviour of Mozambican honey are like other honeys.
- The majority of honey from Mozambique can be classed as honeydew honey.
- An artificial neural network was useful to predict the rheological parameters.
- A multilayer perceptron was the best model for loss modulus and complex viscosity.
- Moisture content influences rheological parameters more than sugars.

Obtaining information about honey from Mozambique is the first step towards the economic and nutritional exploitation of this natural resource. The aim of this study was to evaluate physicochemical (moisture, hydroxymethylfurfural “HMF”, electrical conductivity, Pfund colour, CIE L*a*b* colour and sugars) and rheological parameters elastic modulus G′, loss modulus G″ and complex viscosity η*) obtained at different temperatures (from 10 to 40 °C). All the physicochemical parameters were in agreement with the international regulations. Most of the honey samples were classed as honeydew honey since they were dark and had conductivity values above 0.800 mS/cm. The moduli G′, G″ and η* decreased with increasing temperature. G ′ and G″ were strongly influenced by the applied frequency, whereas η* did not depend on this parameter, demonstrating Newtonian behaviour. An artificial neural network (ANN) was applied to predict the rheological parameters as a function of temperature, frequency and chemical composition. A multilayer perceptron (MLP) was found to be the best model for G″ and η*(r2 > 0.950), while probabilistic neural network (PNN) was the best for G′(r2 = 0.758). Sensitivity testing showed that in the case of G″ and G′ frequency and moisture were the most important factors whereas for η* they were moisture and temperature.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: LWT - Food Science and Technology - Volume 86, December 2017, Pages 108-115
نویسندگان
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