Box Behnken design to optimize parameter for vapor grafting of cinnamaldehyde essential oil onto polyvinyl alcohol
This paper discuss the use of Box Behnken design (BBD) to optimize parameters used in conducting experiment for radiation induced grafting (RIG) experiment of graft cinnamaldehyde (antimicrobial agent) to polyvinyl alcohol/sago starch (PVA/SS) film in order to develop antimicrobial film for food pac...
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Main Authors: | , , , , |
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Format: | Conference or Workshop Item |
Published: |
2022
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Subjects: | |
Online Access: | http://eprints.utm.my/103822/ http://dx.doi.org/10.4028/p-k1541w |
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Institution: | Universiti Teknologi Malaysia |
Summary: | This paper discuss the use of Box Behnken design (BBD) to optimize parameters used in conducting experiment for radiation induced grafting (RIG) experiment of graft cinnamaldehyde (antimicrobial agent) to polyvinyl alcohol/sago starch (PVA/SS) film in order to develop antimicrobial film for food packaging. BBD is having the maximum efficiency with objective to have maximum value of grafting yield (GY). This experiment involving three parameters which is absorbed dose (kGy), temperature (0 C), and reaction time (min), all in three levels. The proposed BBD requires 15 runs of experiment for data acquisition and modeling the response surface. Three regression models were developed, and their adequacies were verified to predict the output values at nearly all conditions. This work resulted in identifying the optimized set parameters values for RIG experiment, which is absorbed dose at 102.67 kGy, reaction time at 51.67 minutes and reaction temperature 44.680 C in order to achieve maximum value of grafting yield at 20.79%. Afterwards, the models were validated by performing actual experiments, taking three sets of random input values. The output parameters (actual value) measured through experiments are in good consistency with the predicted values, where the actual value of GY is 18.7% as compared to predicted value of GY of 20.79%. The deviation value 2.09% prove success of developed model in predicting grafting yield in RIG using limited number of experiments. |
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