Abstract

Model selection for optimization of performance of the juice extractor has been carried out using +juice extraction efficiency and extraction capacity for a newly developed juice machine. The quadratic model was chosen to predict the EE and EC. It was ascertained that extraction processing parameters influence the EE and EC. The study revealed that the highest juice extraction efficiency of 83.66% was obtained at the machine operating speed of 400 rpm, feed rate of 1.5 kg/min, blanching temperature of 80oC and blanching time of 6 min, while the lowest juice extraction efficiency of 31.63% was obtained at the machine operating speed of 300 rpm, feed rate of 1.0 kg/min, blanching temperature of 20oC and blanching time of 3 min. Also, the highest extraction capacity of 3.89 L/min was obtained at the machine operating speed of 500 rpm, feed rate of 1.0 kg/min, blanching temperature of 60oC and blanching time of 9 min, while the lowest extraction capacity of 0.26 L/min was obtained at the machine operating speed of 200 rpm, feed rate of 1.5 kg/min, blanching temperature of 40oC and blanching time of 6 min. Considering the model with the highest R2 value and lower standard deviation, quadratic model was selected to predict the percentage juice extraction efficiency and juice extraction capacity using the developed fruit juice extractor. It was observed that all the juice extraction process parameters have direct relationship with EE and EC. This implies that both EE and EC exhibited an increase with increase in the extraction process parameters. Feed rate was found to be the most significant parameter which affects EE and EC. The Model F-values of 3.60 and 3.41, implies that the model is significant. This model can be used to navigate the design space. The model was significant with a very low probability value (0.0095) and a satisfactory coefficient of determination (R2 = 0.77). The high coefficient of determination showed excellent correlations between the independent variables (dehydration parameters). This value indicates that the response (extraction efficiency) model can explain 77% of the total variability in the response. Predicted optimum EE and EC of 81.32% and 3.89 L/min respectively at operating speed of 525.23 rpm, feed rate of 2.13 kg/min, blanching temperature of 54.20oC and blanching time of 8.73 min was obtained with a desirability of 0.977. Under these optimal juice extraction process conditions, the experimental values of 81.56% and 3.57 L/min were obtained for EE and EC respectively. The deviations between experimental and predicted values were low and statistically insignificant. The coefficient of determination (R2) of 0.77 and 0.76 for EE and EC respectively, show that there is an excellent correlation between the juice extraction parameters (independent variables). Consequently, in view of the range of variables investigated, the chosen models have the adequacy to predict the extraction efficiency and extraction capacity for fruit juice using the developed machine. The developed fruit juice extraction machine serves as a viable option for the small-scale fruit juice processors.