Optimization studies on batch extraction of phenolic compounds from Azadirachta indica using genetic algorithm and machine learning techniques

被引:1
|
作者
Patil, Sunita S. [1 ]
Deshannavar, Umesh B. [2 ,3 ]
Gadekar-Shinde, Shambala N. [4 ]
Gadagi, Amith H. [5 ]
Kadapure, Santosh A. [2 ]
机构
[1] Dr DY Patil Inst Engn Management & Res, Dept Chem Engn, Pune, Maharashtra, India
[2] KLE Dr MS Sheshgiri Coll Engn & Technol, Dept Chem Engn, Belagavi, Karnataka, India
[3] Dr JJ Magdum Coll Engn, Jaysingpur, India
[4] Bharati Vidyapeeth, Coll Engn, Dept Chem Engn, Pune, India
[5] KLE Dr MS Sheshgiri Coll Engn & Technol, Dept Mech Engn, Belagavi, Karnataka, India
关键词
Batch extraction; Optimization; Total phenolic content; Genetic algorithm; Machine learning; ULTRASOUND-ASSISTED EXTRACTION; SOLID-LIQUID EXTRACTION; CRUDE EXTRACTS; MASS-TRANSFER; GRAPE MARC; POLYPHENOLS; ANTIOXIDANTS; PRINCIPLES; LEAVES; L;
D O I
10.1016/j.heliyon.2023.e21991
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Phenolic compounds play a crucial role as secondary metabolites due to their substantial biological activity and medicinal value. These compounds are present in various parts of plant species. This study focused on solid-liquid batch extraction to recover total phenolic compounds from Azadirachta indica leaves. The experimental design was based on the Taguchi L16 array, considering four independent factors: extraction time, temperature, particle size, and solid-to -solvent ratio. Among these factors, the particle size exerted the maximum influence. Particle size inversely affects the yield of total phenolic content (TPC), while temperature, time, and solid -to-liquid ratio have a direct impact. The process factors concerned were investigated both experimentally and through machine learning techniques. Support vector regression (SVR) and random forest method (RFM) algorithms were utilized for predicting TPC, while a genetic algorithm (GA) was employed to derive optimal process parameters. The GA predicts the optimal extraction factors, yielding the maximum TPC. During this study, these factors were the following: particle size of 0.15 mm, extraction time of 40 min, solid-to-liquid ratio of 1:25 g/mL, and a temperature of 55 degrees C, with a predicted value of 23.039 mg GAE/g of plant material. Notably, in this study, the SVR values of TPC yield closely matched the experimental values for the training and test data set when compared with the random forest method values.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Modeling and optimisation studies on the ultrasound-assisted extraction of phenolic compounds from Azadirachta indica
    Patil, Sunita S.
    Deshannavar, Umesh B.
    Ramasamy, M.
    Hegde, Prasad G.
    CHEMICAL ENGINEERING COMMUNICATIONS, 2022, 209 (10) : 1423 - 1438
  • [2] Intensification of total phenolic compounds extraction from Azadirachta indica (Neem) leaves by ultrasound
    Shewale, Sandeep P.
    Kapadia, Miraj
    Rathod, Virendra K.
    CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION, 2022, 181
  • [3] Growth of MWCNTs from Azadirachta indica oil for optimization of chromium(VI) removal efficiency using machine learning approach
    Uthayakumar, Haripriyan
    Radhakrishnan, Pravina
    Shanmugam, Kalaiselvan
    Kushwaha, Omkar Singh
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (23) : 34841 - 34860
  • [4] Growth of MWCNTs from Azadirachta indica oil for optimization of chromium(VI) removal efficiency using machine learning approach
    Haripriyan Uthayakumar
    Pravina Radhakrishnan
    Kalaiselvan Shanmugam
    Omkar Singh Kushwaha
    Environmental Science and Pollution Research, 2022, 29 : 34841 - 34860
  • [5] Optimization of Microwave-Assisted Extraction of Phenolic Compounds from Opuntia ficus-indica Cladodes
    Oufighou, Amira
    Brahmi, Fatiha
    Achat, Sabiha
    Yekene, Sofiane
    Slimani, Sara
    Arroul, Younes
    Boulekbache-Makhlouf, Lila
    Blando, Federica
    PROCESSES, 2025, 13 (03)
  • [6] Studies on the Formulation of Anti-fungal Drugs from Azadirachta Indica Leaves Using Sonication Techniques
    Ravichandran, S.
    Ramanathan, Thulasya
    Da, Gary Wong Jian
    Choon, Ong Wee
    Begum, Nilfer
    Siti, Nadirah
    Senthil, Kumar
    6TH WORLD CONGRESS OF BIOMECHANICS (WCB 2010), PTS 1-3, 2010, 31 : 1439 - +
  • [7] Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization
    Mao, Tuo
    Mihaita, Adriana-Simona
    Chen, Fang
    Vu, Hai L.
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (07) : 7112 - 7141
  • [8] Optimization of extraction of phenolic compounds from scum using response surface methodology
    Zhao, Zhen-Gang
    Zhu, Li-Cai
    Yu, Shu-Juan
    Han, Zhong
    Song, Guo-Sheng
    INTERNATIONAL SUGAR JOURNAL, 2009, 111 (1321): : 13 - 19
  • [9] Optimization of extraction of phenolic compounds from wheat using response surface methodology
    Liyana-Pathirana, C
    Shahidi, F
    FOOD CHEMISTRY, 2005, 93 (01) : 47 - 56
  • [10] Optimization of extraction of phenolic compounds from scum using response surface methodology
    College of Food Science and Technology, South China University of Technology, 381 Wushan Road, Guangzhou 510640, China
    Int. Sugar J., 2009, 1321 (13-19):