Skip to main content
SpringerLink
Log in

Predictive Modeling Coupled with Multiple Optimization Techniques for Assessing the Effect of Various Process Parameters on Oil and Pectin Extraction from Watermelon Rind

  • Original Research Paper
  • Published:
Process Integration and Optimization for Sustainability Aims and scope Submit manuscript

Abstract

This study combines artificial neural network (ANN) with a novel metaheuristic technique, satin bowerbird optimizer (SBO) for predicting oil, and pectin yield from watermelon rind. The experimental design was based on two-level factors, drying temperature (°C) and heating time (hours) for oil yield (%) and three-level factors, drying temperature (°C), extraction time (min), and pH for pectin yield (%) using response surface methodology (RSM). The RSM and SBO optimization resulted oil yield of 17.7–26.6% at conditions of drying temperature (90–100°C), and heating time (8.7–9 h), while pectin yield is between 21 and 37.6% at extraction temperature (80–100), extraction time (46.3–60 min), and pH (1–3). However, ANN predicted more accurately than the RSM model, with a lower percentage relative error. It was observed that pH and extraction time are pertinent process parameters for predicting pectin yield. Similarly, drying temperature is significant for oil extraction from watermelon.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Availability of Data and Material

All data generated or analyzed during this study are included in this article and its additional files. The experimental values of response surface methodology (RSM) design as well as the artificial neural networks (ANN) data used are contained in an Excel File. Requests for material should be made to the corresponding author.

Abbreviations

W:

weight of sample (g)

P:

amount of dry pectin (g)

RSM:

response surface optimization

ANN:

artificial neural network

SBO:

satin bower optimizer

SBBOA:

satin bower bird optimization algorithm

RMSE:

root mean square error

MAPE:

mean absolute percent error

MSE:

mean square error

CCRD:

central composite rotatable design

2FI:

two factorial interaction

k :

number of independent variables

h :

layer

out :

output

i :

initial

1 :

empty flask

2 :

flask and extracted oil

n:

number of repetition of experiments

V:

vector network input

References

  • Adepoju TF, Esu IO, Olu-Arotiowa OA, Blessed E (2019) Oil extraction from butter fruit (Dacryodes edulis) seeds and its optimization via response surface and artificial neural network. Nigerian J Technol Dev 16(2):56. https://doi.org/10.4314/njtd.v16i2.2

    Article  Google Scholar 

  • Agu CM, Menkiti MC, Ekwe EB, Agulanna AC (2020) Modeling and optimization of Terminalia catappa L. kernel oil extraction using response surface methodology and artificial neural network. Artif Intell Agric 4:1–11. https://doi.org/10.1016/j.aiia.2020.01.001

    Article  Google Scholar 

  • Aravantinos-Zafiris GVO (1991) The effect of nitric acid extraction variables on orange pectin. J Sci Food Agric 60:127–129

    Article  Google Scholar 

  • Babu BV (2004) Process plant simulation. Oxford University Press, New Delhi, India, p 257e290

    Google Scholar 

  • Betiku E, Ajala SO (2014) Modeling and optimization of Thevetia peruviana (yellow oleander) oil biodiesel synthesis via Musa paradisiacal (plantain) peels as heterogeneous base catalyst: a case of artificial neural network vs. response surface methodology. Ind Crop Prod 53:314–322

    Article  Google Scholar 

  • Betiku E, Okunsolawo SS, Ajala SO, Odedele OS (2015) Performance evaluation of artificial neural network coupled with generic algorithm and response surface methodology in modeling and optimization of biodiesel production process parameters from shea tree (Vitellaria paradoxa) nut butter. Renew Energy. Elsevier Ltd. https://doi.org/10.1016/j.renene.2014.11.049

  • Chan SY, Choo WS, Young DJ, Loh XJ (2017) Pectin as a rheology modifier: origin, structure, commercial production and rheology. Carbohydr Polym 161:118–139

    Article  Google Scholar 

  • Charles AL, Sriroth K, Huang TC (2005) Proximate composition, mineral contents, hydrogen cyanide and phytic acid of 5 cassava genotypes. Food Chem 92:615–620

    Article  Google Scholar 

  • Chen W, Chen X, Peng J, Panahi M, Lee S (2021) Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer. Geosci Front 12(1):93–107. https://doi.org/10.1016/j.gsf.2020.07.012

    Article  Google Scholar 

  • El-Hay EA, El-Hameed MA, El-Fergany AA (2018) Steady-state and dynamic models of solid oxide fuel cells based on satin bowerbird optimizer. Int J Hydrog Energy 43(31):14751–14761

    Article  Google Scholar 

  • Esonye C, Onukwuli OD, Anadebe VC, Ezeugo JNO, Ogbodo NJ (2021) Application of soft-computing techniques for statistical modeling and optimization of Dyacrodes edulis seed oil extraction using polar and non-polar solvents. Heliyon 7(3):e06342. https://doi.org/10.1016/j.heliyon.2021.e06342

    Article  Google Scholar 

  • Fakayode OA, Abobi KE (2018) Optimization of oil and pectin extraction from orange (Citrus sinensis) peels: a response surface approach. J Anal Sci Technol 9(1):1–16. https://doi.org/10.1186/s40543-018-0151-3

    Article  Google Scholar 

  • Gama B, De-Farias-Silva CE, Oliveira-Da-Silva LM, Abud AKS (2015) Extraction and characterization of pectin from citric waste. Chem Eng Trans 44:259–264

    Google Scholar 

  • Garg A, Jain S (2020) Process parameter optimization of biodiesel production from algal oil by response surface methodology and artificial neural networks. Fuel 277:118254. https://doi.org/10.1016/j.fuel.2020.118254

    Article  Google Scholar 

  • Hartati I, Riwayati I, Subekti E (2014) Microwave assisted extraction of watermelon rind pectin with different kind of acid solution. ICETIA 8(11):27–30

    Google Scholar 

  • Jagadeeswar RC, Saripiralla B, Geetha V (2021) Satin bowerbird optimization algorithm for the application of optimal power flow of power system with FACTS devices. Turk J Comp Math Educ (TurcomatOrg) 12(11):1641–1659 Retrieved from https://turcomat.org/index.php/turkbilmat/article/view/6099. Accessed 22 Oct 2021

  • Jiang LN, Shang JJ, He LB, Dan JM (2012) Comparisons of microwave-assisted and conventional heating extraction of pectin from seed watermelon peel. Adv Mater Res 550–553:1801–1806. https://doi.org/10.4028/www.scientific.net/AMR.550-553.1801

    Article  Google Scholar 

  • Kant G, Sangwan KS (2015) Predictive modelling and optimization of machining parameters to minimize surface roughness using artificial neural network coupled with genetic algorithm. Proc CIRP 31:453–458. https://doi.org/10.1016/j.procir.2015.03.043

    Article  Google Scholar 

  • Kant G, Rao VV, Sangwan KS (2013) Predictive modeling of turning operations using response surface methodology. Appl Mech Mater 307:170

    Article  Google Scholar 

  • Khan M, Bibi N, Zeb A (2015) Optimization of process conditions for pectin extraction from citrus peel. Sci Technol Dev 34(1):9–15

    Article  Google Scholar 

  • Leong LP, Shui G (2002) An investigation of antioxidant capacity of fruits in Singapore markets. Food Chem 76:69–75

    Article  Google Scholar 

  • Maran JP, Priya B (2015) Ultrasound-assisted extraction of pectin from sisal waste. Carbohydr Polym 115:732–738. https://doi.org/10.1016/j.carbpol.2014.07.058

    Article  Google Scholar 

  • Mellinas C, Ramos M, Jiménez A, Garrigós MC (2020) Recent trends in the use of pectin from agro-waste residues as a natural-based biopolymer for food packaging applications. Mater MDPI AG. https://doi.org/10.3390/ma13030673

  • Noreen A, Nazli Z-H, Akram J, Rasul I, Mansha A, Yaqoob N, Iqbal R, Tabasum S, Zuber M, Zia KM (2017) Pectins functionalized biomaterials; a new viable approach for biomedical applications: a review. Int J Biol Macromol 101:254–272

    Article  Google Scholar 

  • Olatunji OM, Charles K, Ekanem JT, Horsfall IT, Okon SK (2021a) Oil and pectin extraction from citrus paradise (grape) peels: a case of response surface optimization. MOJ Food Process Technol 9(2):44–55

    Google Scholar 

  • Olatunji OM, Horsfall IT, Ubom EV (2021b) Response surface optimization approach to predict the maximum %biodiesel yield via transesterification of esterified shea butter oil by utilizing bio-catalysts. Curr Res Green Sustain Chem 4:100167. https://doi.org/10.1016/j.crgsc.2021.100167

    Article  Google Scholar 

  • Onoji SE, Iyuke SE, Igbafe AI, Daramola MO (2019) Hevea brasiliensis (rubber seed) oil: modeling and optimization of extraction process parameters using response surface methodology and artificial neural network techniques. Biofuels 10(6):677–691. https://doi.org/10.1080/17597269.2017.1338122

    Article  Google Scholar 

  • Perez J, Gomez K, Vega L (2021) Optimization and physicochemical characterization of pectin extraction from watermelon rind. Res Square. https://doi.org/10.21203/rs.3.rs-268492/v1

  • Petkowicz CLO, Vriesmann LC, Williams PA (2017) Pectins from food waste: extraction, characterization and properties of watermelon rind pectin. Food Hydrocoll 65:57–67. https://doi.org/10.1016/j.foodhyd.2016.10.040

    Article  Google Scholar 

  • Reddy CJ, Basamma S, Geetha V (2021) Satin bowerbird optimization algorithm for the application of optimal power flow of power system with FACTS devices. Turk J Comp Math Educ 12(11):1641–1659

    Google Scholar 

  • Samareh MSH, Bardsiri VK (2017) Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Eng Appl Artif Intell 60:1–15

    Article  Google Scholar 

  • Sangwan KS, Kant G, Deshpande A, Sharma P (2013) Modeling of stresses and temperature in turning using finite element method. Appl Mech Mater 307:174

    Article  Google Scholar 

  • Sodeke VA (2005) Extraction of oil from watermelon seed. Anal Q Res Serv 2005:25–30

    Google Scholar 

  • Tanarslan HM, Secer M, & Kumanlioglu A (2012) An approach for estimating the capacity of RC beams strengthened in shear with FRP reinforcements using artificial neural networks. Construct Build Mater 30, 556–568

  • Temitayo OD (2017) Optimization of oil extraction from Thevetia peruviana (yellow oleander) seeds: a case study of two statistical models. Int J Eng Modern Technol 3(4):2504–8856 Retrieved from www.iiardpub.org. Accessed 4 Nov 2021

  • Uzoh FC, Onukwuli DO (2016) Extraction, analysis and desaturation of gmelina seed oil using different soft computing approaches. S Afr J Chem Eng 22:6–16. https://doi.org/10.1016/j.sajce.2016.07.001

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Agricultural Engineering, Akwa Ibom State University, Ikot Akpaden, Mkpat-Enin LGA, Akwa Ibom State, Nigeria

    Ololade Moses Olatunji & Godwin Edem Akpan

  2. Department of Civil & Environmental Engineering, Faculty of Engineering & Technology, University of Calabar, Calabar, Nigeria

    Daniel Hogan Itam

  3. Department of Agricultural and Bioresources Engineering, Michael Okpara University of Agriculture, Umudike, P.M.B 7267, Umuahia, Abia State, Nigeria

    Ibiba Taiwo Horsfall

  4. Department of Environmental Engineering, IBIBATH MULTI SERVICES, Port Harcourt, Nigeria

    Ibiba Taiwo Horsfall

Authors
  1. Ololade Moses Olatunji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Hogan Itam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Godwin Edem Akpan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ibiba Taiwo Horsfall
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ibiba Taiwo Horsfall.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Olatunji, O.M., Itam, D.H., Akpan, G.E. et al. Predictive Modeling Coupled with Multiple Optimization Techniques for Assessing the Effect of Various Process Parameters on Oil and Pectin Extraction from Watermelon Rind. Process Integr Optim Sustain 6, 765–779 (2022). https://doi.org/10.1007/s41660-022-00248-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41660-022-00248-0

Keywords

Search

Navigation