Optimization of ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using RSM and ANFIS modeling with machine learning algorithm
Abstract Plant materials are a rich source of polyphenolic compounds with interesting health-beneficial effects. The present study aimed to determine the optimized condition for maximum extraction of polyphenols from grape seeds through RSM (response surface methodology), ANFIS (adaptive neuro-fuzzy...
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| Format: | Article |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-49839-y |
| _version_ | 1797355823626715136 |
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| author | Selvaraj Kunjiappan Lokesh Kumar Ramasamy Suthendran Kannan Parasuraman Pavadai Panneerselvam Theivendren Ponnusamy Palanisamy |
| author_facet | Selvaraj Kunjiappan Lokesh Kumar Ramasamy Suthendran Kannan Parasuraman Pavadai Panneerselvam Theivendren Ponnusamy Palanisamy |
| author_sort | Selvaraj Kunjiappan |
| collection | DOAJ |
| description | Abstract Plant materials are a rich source of polyphenolic compounds with interesting health-beneficial effects. The present study aimed to determine the optimized condition for maximum extraction of polyphenols from grape seeds through RSM (response surface methodology), ANFIS (adaptive neuro-fuzzy inference system), and machine learning (ML) algorithm models. Effect of five independent variables and their ranges, particle size (X 1: 0.5–1 mm), methanol concentration (X 2: 60–70% in distilled water), ultrasound exposure time (X 3: 18–28 min), temperature (X 4: 35–45 °C), and ultrasound intensity (X 5: 65–75 W cm−2) at five levels (− 2, − 1, 0, + 1, and + 2) concerning dependent variables, total phenolic content (y1; TPC), total flavonoid content (y2; TFC), 2, 2-diphenyl-1-picrylhydrazyl free radicals scavenging (y3; %DPPH*sc), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) free radicals scavenging (y4; %ABTS*sc) and Ferric ion reducing antioxidant potential (y5; FRAP) were selected. The optimized condition was observed at X 1 = 0.155 mm, X 2 = 65% methanol in water, X 3 = 23 min ultrasound exposure time, X 4 = 40 °C, and X 5 = 70 W cm−2 ultrasound intensity. Under this situation, the optimal yields of TPC, TFC, and antioxidant scavenging potential were achieved to be 670.32 mg GAE/g, 451.45 mg RE/g, 81.23% DPPH*sc, 77.39% ABTS*sc and 71.55 μg mol (Fe(II))/g FRAP. This optimal condition yielded equal experimental and expected values. A well-fitted quadratic model was recommended. Furthermore, the validated extraction parameters were optimized and compared using the ANFIS and random forest regressor-ML algorithm. Gas chromatography-mass spectroscopy (GC–MS) and liquid chromatography–mass spectroscopy (LC–MS) analyses were performed to find the existence of the bioactive compounds in the optimized extract. |
| first_indexed | 2024-03-08T14:16:38Z |
| format | Article |
| id | doaj.art-eb7631b00cf94528bd52a8215627751d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T14:16:38Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-eb7631b00cf94528bd52a8215627751d2024-01-14T12:22:41ZengNature PortfolioScientific Reports2045-23222024-01-0114112210.1038/s41598-023-49839-yOptimization of ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using RSM and ANFIS modeling with machine learning algorithmSelvaraj Kunjiappan0Lokesh Kumar Ramasamy1Suthendran Kannan2Parasuraman Pavadai3Panneerselvam Theivendren4Ponnusamy Palanisamy5Department of Biotechnology, Kalasalingam Academy of Research and EducationSchool of Computer Science and Engineering, Vellore Institute of TechnologyDepartment of Information Technology, Kalasalingam Academy of Research and EducationDepartment of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied SciencesDepartment of Pharmaceutical Chemistry, Swamy Vivekanandha College of PharmacySchool of Mechanical Engineering, Vellore Institute of TechnologyAbstract Plant materials are a rich source of polyphenolic compounds with interesting health-beneficial effects. The present study aimed to determine the optimized condition for maximum extraction of polyphenols from grape seeds through RSM (response surface methodology), ANFIS (adaptive neuro-fuzzy inference system), and machine learning (ML) algorithm models. Effect of five independent variables and their ranges, particle size (X 1: 0.5–1 mm), methanol concentration (X 2: 60–70% in distilled water), ultrasound exposure time (X 3: 18–28 min), temperature (X 4: 35–45 °C), and ultrasound intensity (X 5: 65–75 W cm−2) at five levels (− 2, − 1, 0, + 1, and + 2) concerning dependent variables, total phenolic content (y1; TPC), total flavonoid content (y2; TFC), 2, 2-diphenyl-1-picrylhydrazyl free radicals scavenging (y3; %DPPH*sc), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) free radicals scavenging (y4; %ABTS*sc) and Ferric ion reducing antioxidant potential (y5; FRAP) were selected. The optimized condition was observed at X 1 = 0.155 mm, X 2 = 65% methanol in water, X 3 = 23 min ultrasound exposure time, X 4 = 40 °C, and X 5 = 70 W cm−2 ultrasound intensity. Under this situation, the optimal yields of TPC, TFC, and antioxidant scavenging potential were achieved to be 670.32 mg GAE/g, 451.45 mg RE/g, 81.23% DPPH*sc, 77.39% ABTS*sc and 71.55 μg mol (Fe(II))/g FRAP. This optimal condition yielded equal experimental and expected values. A well-fitted quadratic model was recommended. Furthermore, the validated extraction parameters were optimized and compared using the ANFIS and random forest regressor-ML algorithm. Gas chromatography-mass spectroscopy (GC–MS) and liquid chromatography–mass spectroscopy (LC–MS) analyses were performed to find the existence of the bioactive compounds in the optimized extract.https://doi.org/10.1038/s41598-023-49839-y |
| spellingShingle | Selvaraj Kunjiappan Lokesh Kumar Ramasamy Suthendran Kannan Parasuraman Pavadai Panneerselvam Theivendren Ponnusamy Palanisamy Optimization of ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using RSM and ANFIS modeling with machine learning algorithm Scientific Reports |
| title | Optimization of ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using RSM and ANFIS modeling with machine learning algorithm |
| title_full | Optimization of ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using RSM and ANFIS modeling with machine learning algorithm |
| title_fullStr | Optimization of ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using RSM and ANFIS modeling with machine learning algorithm |
| title_full_unstemmed | Optimization of ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using RSM and ANFIS modeling with machine learning algorithm |
| title_short | Optimization of ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using RSM and ANFIS modeling with machine learning algorithm |
| title_sort | optimization of ultrasound aided extraction of bioactive ingredients from vitis vinifera seeds using rsm and anfis modeling with machine learning algorithm |
| url | https://doi.org/10.1038/s41598-023-49839-y |
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