Agnieszka Kaleta

Criteria of Determination of Safe Grain Storage Time – A Review

Therefore it can be stated that grain starts deteriorating from the time of harvest, due to in‐ teractions between the physical, chemical and biological variables within the environment (Mason et al., 1997). Cereal grains just after being harvested contain microbial contamination coming from several sources, such as dust, water, ill plants, insects, solid, fertilisers and ani‐ mal feces. Bacteria found in grains mainly belong to the families Pseudomonadaceae, Micrococ‐ caceae, Lactobacillaceae and Bacillaceae, and moulds are mostly Alternaria, Fusarium, Helminthosorium and Cladosporium, although other genus can also be present. The microbial composition of the cereals is of great importance for the storage of grains, since at high mois‐ ture levels the microorganisms could grow and alter the properties of product (Laca et al., 2006). Grain deterioration is also related to respiration of the grain itself and of the accompa‐ nying microorganisms. The evolution of carbon dioxide, water and heat is associated with this respiration or deterioration (Steele et al., 1969).

Some Remarks on Modelling of Mass Transfer Kinetics During Rehydration of Dried Fruits and Vegetables

Dehydration operations are important steps in the food processing industry. The basic objective in drying food products is the removal of water in the solids up to a certain level, at which microbial spoilage is minimized. The wide variety of dehydrated foods, which today are available to the consumer (dried fruits, dry mixes and soups, etc.) and the interesting concern for meeting quality specifications, emphasize the need for a thorough understanding of the operation [1].

Some Problems Related to Mathematical Modelling of Mass Transfer Exemplified of Convection Drying of Biological Materials

Drying is a widely used industrial process, consuming 7-15% of total industrial energy production in the industrialized world (Dincer & Dost, 1996). Drying is one of the most common and the oldest ways of biological materials preservation such as vegetables and fruits. The main objective in drying biological materials is the removal of water in the solids up to a certain level, at which microbial spoilage and deterioration chemical reactions are greatly minimalized. The most important aspect of drying technology is the mathematical modelling of the drying processes and the equipment. Its purpose is to allow design engineers to choose the most suitable operating conditions and then size the drying equipment and drying chamber accordingly to meet desired operating conditions. Full-scale experimentation for different products and systems configurations is sometimes costly or even not possible (Sacilik et al., 2006). Convection drying of biological products is a complex process that involves heat and mass transfer phenomena between the airflow and the product. Mathematical modelling of the drying process of vegetables, fruits and grass is especially difficult because of high initial moisture content (80-95% w.b.) and occurrence of shrinkage during drying. The course and time of drying process depend on drying conditions, on temperature and moisture profiles developed during the drying process, and above all, on moisture movement in the material. Moisture movement is governed by the properties, form and size of the product and the type of moisture bond in the material (Sander et al., 2003). The major factors affecting the moisture transport during solids drying can be classified as: i. external factors: these are the factors related to the properties of the surrounding air such as temperature, pressure, humidity, velocity and area of the exposed surface, ii. internal factors: these are the parameters related to the properties of the material such as moisture diffusivity, moisture transfer coefficient, water activity, structure and composition, etc. (Dincer & Hussain, 2004). The development of mathematical models to describe the drying process has been the topic of many research studies for several decades (Sander et al., 2003). Presently, more and more sophisticated drying models are becoming available, but a major question that still remains is the accuracy of predictions of drying processes using mathematical models. It is highly

Multi-objective optimization of convective drying of apple cubes

Abstract The effect of drying temperature and air velocity on apple quality parameters, such as color difference (CD), volume ratio (VR) and water absorption capacity (WAC) in convective drying was experimentally studied. Optimization of drying conditions was carried out in the range of air temperatures from 50 to 70 °C and air velocity from 0.01 to 6 m s−1. A novel algorithm of multi-objective optimization, based on artificial neural network (ANN), genetic algorithm (GA) and Pareto optimization was developed. Three optimization objectives included simultaneous minimization of CD, maximization of VR and maximization of WAC. Objective functions for CD, VR and WAC were developed by using ANN training on the experimental dataset of apple drying at 50, 60 and 70 °C. Pareto optimal set was developed with elitist non-dominated sorting genetic algorithm (NSGA II). Unique Pareto optimal solution within specified constraints was found at air temperature 65 °C and velocity 1 m s−1. This mode of apple drying resulted in CD = 5.24, VR = 49.66% and WAC = 0.488. Experimental verification showed that maximum error of modelling did not exceed 3.24%.

Modelling of rehydration kinetics of dried carrots using the Peleg model

Modelling of rehydration kinetics of dried carrots using the Peleg model. The objective of the study is to apply the Peleg model to the description of rehydration kinetics of dried carrots and to investigate the effect of the rehydration temperature and particle shape on the model constants. The changes of: mass, dry matter of solid, moisture content on dry basis and moisture content in percent wet basis were described using the Peleg model. Samples (slices of 10-millimeter thickness and 10 × 10 × 10 mm cubes) were dried at natural convection and the air temperature was kept at 60°C. The rehydration was carried out in the distilled water at the temperature of 20, 45 and 70°C. The accuracies of the model were measured using the coeffi cient of correlation (R), root mean square error (RMSE), and reduced chi-square (χ2). It was stated that the Peleg model may be assumed to represent the relative mass increment, relative dry matter of solid decrease, and relative moisture content in % w.b. increment during the rehydration of carrot slices and cubes. The model cannot be accepted for description of relative moisture content on d.b. increment. The rehydration temperature and particle shape signifi cantly infl uenced the Peleg rate constant (A1). Both parameters signifi cantly infl uenced the Peleg capacity constant (A2) in case of mass and moisture content in % w.b. only.

Thin-layer drying of sawdust mixture

Abstract Drying behaviour of sawdust mixture was investigated in a convective dryer at 0.01 m/s and 25, 60, and 150°C air temperature. Sawdust mixture (60% of spruce and 40% of the second ingredient: beech, willow, ash, alder) and sawdust of spruce, beech, willow, alder and ash was used in the drying experiments. The sawdust mixture drying was affected by the drying of its ingredients. The experimental drying data were fitted to the theoretical, semi–theoretical, and empirical thin-layer models. The accuracies of the models were measured using the correlation coefficient, root mean square error, and reduced chi–square. All semi-theoretical and empirical models described the drying characteristics of sawdust mixture satisfactorily. The theoretical model of a sphere predicts the drying of sawdust mixture better than the theoretical model of an infinite plane. The effect of the composition of the sawdust mixture on the drying models parameters were also taken into account.