Statistical analysis Table 2 summarizes the experimental results of capacity, energy demand and FFA content under various treatment conditions. Statistical analysis showed that the proposed model was valid with acceptable R
2 values for all the responses and non-significant lack of fit. The R
2 values for capacity, energy demand and FFA content were 0.989, 0.917 and 0.916, respectively. The empirical model more accurately represents the real data when the R
2 value is higher. The lower the R
2 value, the less relevant the dependent variables in the model must explain variation in behavior (
Little and Hills, 1982;
Mendenhall, 1975). All regression models had probability (p) values less than 0.000, indicating that there was no lack of fit.
Effect of exposure time and bed thickness on rice bran stabilizer capacity
The capacity increased as the thickness of the rice bran bed increased at constant power density and moisture content, as seen predicted response surface plot (Fig 2). The capacity was minimum (6.7 kg/h) at 0.5 cm thickness and 5 min exposer time, whereas it was maximum (40.2 kg/h) at 1 cm bed thickness and 3 min exposer time. The quantity of material carried on the conveying belt at every turn of the belt increases as the thickness of the rice bran bed rises, resulting in an increase in the capacity of the stabilizer. It has been indicated that under constant power density and moisture, capacity declined with infrared exposure time. Infrared exposure time is proportional to the speed of the conveyor belt,
i.e., as belt speed increases, infrared exposure time reduces. Since the amount of material released per unit time increases, the speed of the conveying system increases and the capacity of the stabilizer increases. As a result, reducing the exposure time enhances the continuous infrared stabilizer’s capacity.
The experimental data can be adequately fitted using quadratic model (p-0.001). F-value (201.14) revealed that the capacity had been significantly impacted by the linear terms of independent variables (thickness, time) and their interaction terms. The second-order nonlinear regression model has been developed based on the actual values of the independent variables moisture content (X
1), thickness (X
2), power density (X
3) and time(X
4) for the dependent variable capacity. Equation-(1) provides the derived correlation with real values (after the non-significant components have been eliminated).
Capacity = 24.33+0.002 X1+0.191 X2+57.977 X3-13.39 X4-7.15 X3X4-0.0006360 X22-0.9158X32+1.685X4 ..........(1)
Predicted R
2 of 0.9695 and adjusted R
2 of 0.9898 are reasonably in agreement; that is, the difference between the two values is less than 0.2, indicating that the derived model is quite well-fitted. The values of the CV (4.07) and APR (52.749) indicate that the experiment and model had appropriate accuracy and consistency.
Energy requirement of stabiliser as a function of exposure time and power density
For the interaction of independent variables on energy demand, the model-predicted response surfaces are shown in Fig 3. It has been demonstrated that the energy requirement increased as power density and exposure duration increased at constant bed thickness and moisture content. While power density and exposure duration increased, energy consumption increased as well, peaking at higher power densities and longer exposure times. The energy demand varied between 0.006-0.015 kWhkg
-1. The most often used method of stabilizing rice bran in the literature, extrusion at 130°C for a short period, followed by holding the bran for three minutes at 97-99°C before cooling, was estimated to consume 0.076 kWhkg
-1 of energy. Additionally, it was claimed that extrusion processing for stabilizing rice bran requires a significant capital investment as well as high operational and equipment maintenance expenses, rendering the method unprofitable
(Malekian et al., 2000). Therefore, it was revealed that the energy consumption of continuous IR stabilization was identical to that of extrusion. It can be inferred that IR stabilization of rice bran is appropriate for industrial use in terms of energy efficiency, even if the energy consumption of IR stabilization relies on the type and quantity of IR emitters, the bran feeding capacity and the dimensions of the belt.
The experimental values can be effectively fitted by the quadratic model (p-0.001). The capacity had been significantly impacted by the linear terms of the independent variables, power density, thickness and time, as shown by the F-value (23.86) and the interaction terms between the squares of the variables are significant (p-0.001).
Energy demand = -0.056+0.0001X1+0.031 X3+0.006 X4+0.000012 X1X2-0.00075 X2X3+0.000063 X2X4+0.0022 X3X4-0.000117 X12-0.0188 X32-0.00092 X42 ..........(2)
The adjusted R
2 of 0.9169 agrees well with the predicted R
2 of 0.7890. The APR (17.551) confirmed that the model has sufficient accuracy and reliability.
Free fatty acid response to independent variables
Fig 4 and 5 depict the model-predicted response surfaces for independent variables on FFA. The FFA decreased while rising power density and time at the constant thickness and moisture content, however, the trend of the graph is nonlinear as seen in Fig 4. Better lipase inactivation resulted through rising power density and infrared exposure time, but treated bran exhibits undesirable visual and sensory changes. To prevent undesirable changes in rice bran, exposure time can be increased at low radiation intensities or decreased at elevated radiation intensities.
Yilmaz et al., (2014) reported that stabilization between 200-400 W infrared radiation for 10 min is not enough to inhibit hydrolytic rancidity, stabilization at these powers levels may take longer time to achieve better results. Short process time of 1 min was not sufficient to inactivate lipases even at high radiation intensities, considering stabilization between 800 and 900 W is an unacceptable strategy. Moreover, process durations longer than 1 min generated unpleasant sensory and visual changes in the bran.
At constant power density and time, FFA increased with increase in thickness and moisture content as shown in Fig 5. The FFA content was low at lower bed thickness and moisture content and found to increase relatively at a slower rate as thickness and moisture content rises to 0.8 cm and 15% respectively, further rise in bed thickness and moisture content caused rapid increase in the rate of FFA content. Since infrared radiation has a lower penetration depth, increasing the thickness of the bed causes uneven exposure of the rice bran along the bed thickness, resulting variation in the rice bran-free fatty content.
Sandu (1986) reported that, even with short wavelength infrared radiation, the depth of penetration reported is relatively low, with depths seldom exceeding a few thousandths of an inch.
According to the model (F-value: 23.56), the FFA content had been substantially influenced by power density, moisture content, thickness and time and the interaction terms of the squares of the variables are significant (p 0.001). The nonlinear second-order regression equation is illustrated below:
FFA = 123.32-0.108 X1-6.473 X2-38.45 X3-16.94 X4+0.000065 X12+0.194 X22+23.31 X32+1.900 X42 .........(3)
The adjusted R
2 of 0.9159 and the predicted R2 of 0.7496 are reasonably compatible. The APR (14.625) greater than 4 indicates the experiment’s and model’s sufficient accuracy and reliability.
Optimum conditions
To ensure maximum capacity, efficiency and minimum energy demand and FFA, optimal conditions for continuous infrared rice bran stabilizer were established. The second-order polynomial regression equations were solved in Design Expert 11 using sequential quadratic programming. The optimum values obtained by substituting the respective coded values are 600W, 12% moisture content, 0.5cm thickness and 3 min exposure time. At these optimum conditions, capacity, efficiency, energy demand and FFA were 17.85 kg/h, 20.12%, 0.006kW-h/kg and 5.01% respectively.
Changes in FFA of infrared stabilized rice bran during the storage
The best sample obtained in optimization (600W, 12% moisture content, 0.5 cm thickness and 3 min time) was kept for storage in zip lock polyethylene packs at 4°C. During storage, the FFA content of rice bran was studied at 10 days intervals for 30 days.
The FFA content of raw rice bran increased from 3.32% initiallyto 22.08% at the end of the month during storage at 4°C. The FFA content of rice bran IR stabilized at different treatment conditions was below 6% after treatment (Table 4). However, the FFA level of rice bran stabilized at 450 Wm
-2 for 4 min and 700 W/m
2, 15% mc, 0.75 cm was above 6% after a month of storage. Also, the FFA content of rice bran stabilized at 600 W/m
2, 12% mc, 0.5 cm thickness for 3 min was 5.56% after 30 days of storage. Considering the initial FFA level of 3.32%, it can be stated that IR stabilization is effective in terms of preventing hydrolytic rancidity and that, by optimizing the operational parameters of stabilization; the shelf life of rice bran can be extended. Literature data on the FFA content of rice bran are highly variable. In raw bran, FFA increased rapidly throughout the course of 4 weeks of storage at 25°C, according to (
Ramezanzadeh, 1999).
Malekian et al., (2000) reported that the raw bran held in zip-lock bags for eight weeks had a rise in FFA content from 3.7 to 22.2%. In raw rice bran, FFA content was found to be 9.5% initially, raised to 96.8% over 345 days of storage (
Mujahid, 2005).