Application of Mathematical Models and Thermodynamic Properties in the Drying of Jambu Leaves

Abstract

Jambu is a vegetable originally from the northern region of Brazil, has bioactive properties, being little explored by other regions, due to its high peresivity. And one of the methods to increase the shelf life of plant products is the removal of water. The objective of this work was to evaluate the drying kinetics of jambu leaf mass. Two treatments were carried out: The mass of fresh jambu leaves and the mass of fresh jambu leaves with the addition of drying foam, both submitted in an oven with forced air circulation at temperatures (50, 60 and 70 °C and thickness of 1.0 cm). The proximate composition of the materials was performed before and after drying. Twelve mathematical models were tested on drying kinetics data and thermodynamic properties were calculated. The parameters of the proximate composition for the mass of leaves and foam after drying were: Moisture content of (2 to 7%), ash content of (13 to 17%), protein content of (22 to 30%), lipids of (0.6 to 4%) and total titratable acidity (0.20 to 0.28%) of tartaric acid. The models that best fit the experimental data to describe the drying kinetics of jambu masses were: Wang & Singh. The use of foam mat presented higher values of effective diffusion coefficient and activation energy and lower values of enthalpy and entropy, reducing the drying time.

Drying kinetics, thermodynamic properties and physicochemical characteristics of Rue leaves

“…For complementary analysis purposes, only those conditions pre-fitted by the Gaussian Newton method were considered. This criterion has satisfactorily stood out in the selection of models of drying kinetics of various agricultural products 16 , 19 .…”

“…The selection of the models with the best fit was complemented using the Akaike Information Criterion (AIC) and the Shwarz’s Bayesian Information Criterion (BIC) (Eqs. 21 and 22), subjecting the pre-selected models to the Gauss–Newton criterion 16 , 19 . where p and n: number of model parameters and observations, L: maximum likelihood, considering the estimates of the parameters.…”

“…The selection of the models with the best fit was complemented using the Akaike Information Criterion (AIC) and the Shwarz's Bayesian Information Criterion (BIC) (Eqs. 21 and 22), subjecting the pre-selected models to the Gauss-Newton criterion 16,19 .…”

“…The drying process can be described by means of drying curves, depending on the drying conditions of each product. Mathematical modeling is an excellent tool for describing the drying process, with lots of application for estimating the drying time of the product and sizing equipment 16 , 17 . However, these curves are specific to each product and drying condition 18 , hence the need to study the behavior as a function of the product and associate it with quality parameters.…”

“…Several models have been proposed to satisfactorily describe the drying kinetics of various agricultural products 5 , 16 , 19 , 20 , 23 , 55 . Fitting helps to establish the best method and appropriate drying conditions, process description, drying time, and cost reduction, without significantly compromising physicochemical characteristics 12 .…”