The development of crust thickness of bread during baking is an important aspect of bread quality and shelf-life. Computer vision system was used for measuring the crust thickness via colorimetric properties of bread surface during baking process. Crust thickness had a negative and positive relationship with Lightness (L (*) ) and total color change (E (*) ) of bread surface, respectively. A linear negative trend was found between crust thickness and moisture ratio of bread samples. A simple mathematical model was proposed to predict the development of crust thickness of bread during baking, where the crust thickness was depended on moisture ratio that was described by the Page moisture losing model. The independent variables of the model were baking conditions, i.e. oven temperature and air velocity, and baking time. Consequently, the proposed model had well prediction ability, as the mean absolute estimation error of the model was 7.93 %.
A b s t r a c t. This study presents mathematical modelling of bread moisture loss or drying during baking in a conventional bread baking process. In order to estimate and select the appropriate moisture loss curve equation, 11 different models, semi-theoretical and empirical, were applied to the experimental data and compared according to their correlation coefficients, chi-squared test and root mean square error which were predicted by nonlinear regression analysis. Consequently, of all the drying models, a Page model was selected as the best one, according to the correlation coefficients, chi-squared test, and root mean square error values and its simplicity. Mean absolute estimation error of the proposed model by linear regression analysis for natural and forced convection modes was 2.43, 4.74%, respectively.K e y w o r d s: moisture losing, bread, mathematical modelling INTRODUCTIONAlthough people have practiced baking for a long time, the understanding of the whole process is not very clear. One of the possible reasons for this is that several fundamental complex physical processes are coupled during baking, like evaporation of water, volume expansion, gelatinization of starch, denaturation of protein, crust formation, etc. Several experimental and mathematical models have been developed for clear understanding of baking (Mondal and Datta, 2008).As soon as the product is placed in the oven, water evaporates from the warmer region, absorbing latent heat of vaporization, and the surface layers start drying. Beneath this drying region, water vapour diffuses through the interconnected pores towards the surface, under the influence of the water vapour concentration gradient. A concomitant liquid water gradient is formed and ensures the diffusive transfer of water from the core to the surface. As the diffusive flow of liquid water from the core is less rapid than evaporation flow at the surface, a drying zone is developed which slowly increases in thickness and forms the crust.Whereas bread crust is known to be closely related with moisture loss of bread during baking, the crust formation affects the amount of moisture evaporating from wet dough during the baking process as a thicker crust is produced with a higher moisture loss in bread (Mohd Jusoh et al., 2009;Wiggins, 1999). Besides that, moisture content or water activity of bread surface has an important effect on the beginning of the browning reactions. Purlis and Salvadori (2009) expressed that minimum requirements for the initiation of colour formation are temperature greater than 120°C and water activity less than 0.6. Also, in relating bread crust properties in terms of its colour with moisture loss, Purlis and Salvadori (2007) presented a strong correlation between the moisture loss and the crust colour formation in their study on browning kinetics of bread. From those reports, it seems viable to control moisture loss from bread during baking and to propose moisture loss models in order to predict bread moisture loss behaviour during baking.Mathematic...
In sugarcane production, the mean condition for high productivity depends on billets located in the furrow with uniform distribution. The focus of this paper was on development and evaluation three types of a new prototype of sugarcane metering device to plant the sugarcane billets with desired spacing. Accordingly, a prototype of the precision metering device for sugarcane was developed. The model of the metering device was designed with CATIA R21. The performance indices of the device, including quality of feed index (QFI), multiple index, miss index and precision have been taken as a set of criteria for billet spacing accuracy and were investigated under laboratory conditions using a test stand with camera system. There was significant difference of miss index, multiple index and quality of feed index among different tooth length while precision affected by speed. Four row cylindrical metering device showed a better performance compare to other types of metering device. Analytical hierarchy process (AHP) was used and 1.5 cm teeth length with forward speed of 15 m min -1 was best found for selected sugarcane billet metering device with 91.67% for quality of feed index with 5.03% precision.
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