3. Results & Discussion 3.1 Moisture change 3.1.1 Effect of water contents in starch doughs The effect of water contents in the starch dough on moisture changes during conditioning times were shown in figure 1.Figure 1. The water content effect on the moisture change (%) of the wheat starch foam fabricated at 2000C temperature. The results were shown that the moisture change of a 45% water content in the starch dough was increased with storage times and reached to optimum at 3 days storage times. After 3 days, the moisture change was gradually declined.
40 sets of data are collected in anthropometry workshop. Linear regression-derived coefficient of determination (R2) show the correlation of body fat ratio with BMI, WHR, and skin-fold thickness. From the calculation of coefficient of determination. It found that body fat ratio had the stronger link (R2 =0.87) with skin-fold thickness
Effect of bed height on breakthrough curve for phenol adsorption. (Amount of CFA= 5, 10 and 20 grams, Bed Height = 7.5, 13.5 and 27.5 cm, Influent flow rate = 0.375 ml/ min, Initial concentration (C0) = 1039.9 mg/lit) It is observed that as the bed height increases, breakpoint time also increases. This shows that at the smaller bed height the effluent adsorbate concentration ratio increases more rapidly than for a higher bed height. Additionally the bed is saturated in less time for smaller bed height than the bigger
The velocity distribution in the boundary layer of natural convection represents different values of thermal Grashof numbers or mass Grashof number are shown graphically in Figure. 6. This shows an effective increase in velocity due to buoyancy with increasing thermal Grashof numbers or mass Grashof number. The dimensionless Schmidt number is the ratio of momentum diffusivity to the convection mass diffusivity. The concentration profiles for a different Schmidt number and chemical reaction parameter, , , are shown in Figure .7.
It determines a fat’s spread capability. It is major characteristic to consider when selecting a fat to be used in the preparation of cookies and other baked products. Although, most fats seem solid at room temperature, but actually comprised of liquid oil with a network of solid fat crystals keeping it in place. This combination of solid and liquid proportion allows the fat to be molded into different shapes. Temperature influences plasticity, hard fats such as butter becoming soft and more spreadable when warmed while chilled butter has very little plasticity.
Surface roughness value increases with increase in feed rate from 0.2mm/min to 0.4mm/min and then decreases with increase in value of feed rate from 0.4-0.6mm/min. In case of concentration surface roughness decreases with increase in value of concentration from 20-30 g/l and then increases with increase in concentration from 30- 40 g/l.so most effective factor looks to be tool feed rate and then concentration. The analysis of variances for the factors is shown in Table 5.5 which is clearly indicates that the on one factor is not important for influencing MRR and V and
In this work it was used an Ø 0.8 mm tool, and for simplicity this effect was not assumed. Second, the effect of the cutting edge radius is not negligible: it affects the chip forming mechanism. Minimum chip thickness is a function of this parameter, and determines the transition between two cutting conditions; where chips are produced and where ploughing takes place (Dhanorker, A. 2008). Figure 2.1 shows the inputs and influences in micro
The independent variable having the odds ratio equal or greater than 1 and the p-value equal or less 0.05 would have a significant effect. The Odds ratio of the contaminated feed, the workers working in many farms, unhygienic wet litter, visiting veterinarian and distance between different farms, was 4.61, 3.23, 2.89, 1.04 and 1.02 respectively. The maximum Odds ratio was attained by the contaminated feed followed by the workers working in many farms, unhygienic wet litter, visiting veterinarian and distance between different farms respectively. The p-value of the contaminated feed, the workers working in many farms, unhygienic wet litter, visiting veterinarian and distance between different farms, was 0.006, 0.012, 0.027, 0.034 and 0.049 respectively. The lowest p-value was accomplished by the contaminated feed followed by the workers working in many farms, unhygienic wet litter, visiting veterinarian and distance between different farms respectively.
(1983) The adequacy of the model for the best fitting of experimental data was evaluated by obtaining the coefficient of determination R2 and least RMSE and percent mean relative deviation of modulus (E%) using following equations: E(%)=100/N ∑_(i=1)^N▒[(Experimental value-predicted value)/(Experimental value)] Chi Square=χ^2=∑_(i=1)^N▒[〖(Experimental value-predicted value)〗^2/((N-n))] Where, n = no. of unknown and N= number of data points RMSE=Root mean square error=∑_(i=1)^N▒[√(〖(Experimental value-predicted value)〗^2/N) ] The nonlinear regression analysis was performed by using Statsoft Statistica (22.214.171.124 EN). RESULT AND DISCUSSION The effect of all process variables including solution concentration, solution temperature and immersion time on mass transfer kinetics namely water loss and solute gain was investigated. The detailed description of effect of process variables on mass transfer kinetics and its modeling has been discussed as
+ 0.6649x! the model is significant,but the p value of x3’s coefficient is greater than 0.05, thus it is not significant,the total socre is just related to the socre of 6 months ago. Whats more ,VIF values are less than 2, it shows that there is no colinearity of these parameters, they independently contribute to the total score. We use the same measure mentioned to test the heteroscedasticity, we find that they have homogeneity of variance. 6.Explainations for the both models From the results we can see that the rating model for them are very different: