RSM and ANN approach for optimization of ultrasonic assisted extraction of pumpkin seed oil and their quality assessment
The current study focuses on optimization of process parameters for ultrasonic assisted extraction (UAE) of pumpkin seed oil (PSO) using response surface methodology (RSM) and artificial neural network (ANN). Three parameters of UAE process, amplitude, time and solvent to seed (S:S) ratio were selec...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Food Chemistry Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772753X23003738 |
| _version_ | 1827576397949829120 |
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| author | Arunima Singh Vivek Kumar |
| author_facet | Arunima Singh Vivek Kumar |
| author_sort | Arunima Singh |
| collection | DOAJ |
| description | The current study focuses on optimization of process parameters for ultrasonic assisted extraction (UAE) of pumpkin seed oil (PSO) using response surface methodology (RSM) and artificial neural network (ANN). Three parameters of UAE process, amplitude, time and solvent to seed (S:S) ratio were selected in the range of 20–40 %, 15–45 min and 2–6 mL/g respectively and optimized on the basis of responses viz. yield, total phenolic compounds (TPC), squalene content and induction time of oil. The optimum process conditions of UAE were obtained as amplitude- 34.76 %, time- 34.37 min and the S:S ratio 6 mL/g. Under these optimum conditions, the oil showed good yield 39.05 %, TPC 45.02 mg GAE/g, squalene content 447.4 mg/100 g and induction time 5.27 h. The ANN model was developed using multilayer perceptron (MLP) and trained with back propagation algorithm utilizing training data set. The results indicate that the predicted values of responses by ANN model were in good agreement with the experimental values than the RSM model. The UAE-PSO showed better retention of bioactive compounds along with far better oil stability as compared to SE-PSO. The Scanning electron microscopy (SEM) analysis showed the random porous pores and rupture cell wall matrix in UAE treated seed powder. |
| first_indexed | 2024-03-08T21:10:58Z |
| format | Article |
| id | doaj.art-fa0ac226b4e242ff97b8442fe3cf4ffa |
| institution | Directory Open Access Journal |
| issn | 2772-753X |
| language | English |
| last_indexed | 2024-03-08T21:10:58Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Food Chemistry Advances |
| spelling | doaj.art-fa0ac226b4e242ff97b8442fe3cf4ffa2023-12-22T05:35:25ZengElsevierFood Chemistry Advances2772-753X2023-12-013100552RSM and ANN approach for optimization of ultrasonic assisted extraction of pumpkin seed oil and their quality assessmentArunima Singh0Vivek Kumar1Department of Food Technology, Harcourt Butler Technical University, Kanpur 208002 U.P., IndiaCorresponding author.; Department of Food Technology, Harcourt Butler Technical University, Kanpur 208002 U.P., IndiaThe current study focuses on optimization of process parameters for ultrasonic assisted extraction (UAE) of pumpkin seed oil (PSO) using response surface methodology (RSM) and artificial neural network (ANN). Three parameters of UAE process, amplitude, time and solvent to seed (S:S) ratio were selected in the range of 20–40 %, 15–45 min and 2–6 mL/g respectively and optimized on the basis of responses viz. yield, total phenolic compounds (TPC), squalene content and induction time of oil. The optimum process conditions of UAE were obtained as amplitude- 34.76 %, time- 34.37 min and the S:S ratio 6 mL/g. Under these optimum conditions, the oil showed good yield 39.05 %, TPC 45.02 mg GAE/g, squalene content 447.4 mg/100 g and induction time 5.27 h. The ANN model was developed using multilayer perceptron (MLP) and trained with back propagation algorithm utilizing training data set. The results indicate that the predicted values of responses by ANN model were in good agreement with the experimental values than the RSM model. The UAE-PSO showed better retention of bioactive compounds along with far better oil stability as compared to SE-PSO. The Scanning electron microscopy (SEM) analysis showed the random porous pores and rupture cell wall matrix in UAE treated seed powder.http://www.sciencedirect.com/science/article/pii/S2772753X23003738Pumpkin seed oilUltrasonic assisted extractionSqualene contentOil induction timeRSMANN |
| spellingShingle | Arunima Singh Vivek Kumar RSM and ANN approach for optimization of ultrasonic assisted extraction of pumpkin seed oil and their quality assessment Food Chemistry Advances Pumpkin seed oil Ultrasonic assisted extraction Squalene content Oil induction time RSM ANN |
| title | RSM and ANN approach for optimization of ultrasonic assisted extraction of pumpkin seed oil and their quality assessment |
| title_full | RSM and ANN approach for optimization of ultrasonic assisted extraction of pumpkin seed oil and their quality assessment |
| title_fullStr | RSM and ANN approach for optimization of ultrasonic assisted extraction of pumpkin seed oil and their quality assessment |
| title_full_unstemmed | RSM and ANN approach for optimization of ultrasonic assisted extraction of pumpkin seed oil and their quality assessment |
| title_short | RSM and ANN approach for optimization of ultrasonic assisted extraction of pumpkin seed oil and their quality assessment |
| title_sort | rsm and ann approach for optimization of ultrasonic assisted extraction of pumpkin seed oil and their quality assessment |
| topic | Pumpkin seed oil Ultrasonic assisted extraction Squalene content Oil induction time RSM ANN |
| url | http://www.sciencedirect.com/science/article/pii/S2772753X23003738 |
| work_keys_str_mv | AT arunimasingh rsmandannapproachforoptimizationofultrasonicassistedextractionofpumpkinseedoilandtheirqualityassessment AT vivekkumar rsmandannapproachforoptimizationofultrasonicassistedextractionofpumpkinseedoilandtheirqualityassessment |