Skip to main content
SpringerLink
Log in

Phytochemical studies and mass transfer phenomenon of raw soursop fruit at different drying temperatures and kinetics evaluation by ANN and mathematical modeling

  • Original Article
  • Published:
Biomass Conversion and Biorefinery Aims and scope Submit manuscript

Abstract

Evaluation of impact of temperatures (40°C, 50°C, and 60°C) on dried soursop fruit by determining the changes in thermal and mass transfer (MT) as well as nutritional and color changes and prediction of drying behavior by statistical tools were executed first time in this study. Interestingly, soursop involved many medicinal uses; therefore, it could be a healthy food substitute for the growing food industry, by being incorporated into fruit shakes, bakery products, capsules, and many more formulations. In this work, prediction ability was analyzed by an artificial neural network (ANN). TANSIGMOID transfer function along with Levenberg-Marquardt’s training algorithm proved a better prediction of moisture content (MC) and moisture ratio (MR). Thereafter, a comparative analysis of predicted ANN data with ten different mathematical models was done. Page model gave the best fit to the experimental data. The R2 value of the Page model (0.9697–0.999) revealed lower values than ANN (0.9999). As the temperature increased, the moisture diffusivity and MT coefficient increased as of 3.76 × 10−6 to 6.25 × 10−6 and 4.569 × 10−5 to 1.148 × 10−6, respectively. The activation energy (AE) was obtained to be 22.148 kJ/mol. At 60°C, maximum antioxidant activity in water extract by DPPH, FRAP, ABTS, and PA was found to be IC50 922 μg/ml, 34.086 mM TAE/g, 21.336 μg/g, and 15.46 mg/g, respectively. Total polyphenol content and flavonoid content were observed to be 11.662 mg GAE/g and 21.442 mg QE/g, respectively, along with the acceptable appearance of dried soursop fruit powder at 60°C. Hence, 60°C temperature was recommended for drying raw soursop fruit.

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

Similar content being viewed by others

Data availability 

The article includes the data which was utilized for the study.

References

  1. Ahmad A, Ali M, Barat G, Mohammad Hadi K, Saeidi M (2012) Effect of air velocity and temperature on energy and effective moisture diffusivity for Russian olive (Elaeagnusangastifolial L) in thin-layer drying. Iran J Chem Chem Eng 31(1):75–79

    Google Scholar 

  2. Pashazadeh H, Zannou O, Koca I (2020a) Modeling of drying and rehydration kinetics of Rosa pimpinellifolia fruits: towards formulation and optimization of a new tea with high antioxidant properties. J Food Process Eng 43(10). https://doi.org/10.1111/jfpe.13486

  3. Pashazadeh H, Zannou O, Koca I (2020b) Modeling and optimization of drying conditions of dog rose fr preparation of a functional tea. J Food Process Eng. https://doi.org/10.1111/jfpe.13632

  4. Zhu A, Zhao J, Wu Y (2020) Modeling and mass transfer performance of Dioscoreaalata L slices drying in convection air dryer. J Food Process Eng 43(7)

  5. Almeida G, Lancha JP, Pierre F, Casalinho J, Perre P (2016) Physical behavior of highly deformable products during convective drying assessed by a new experimental device. Dry Technol 35(8):906–917. https://doi.org/10.1080/07373937.2016.1233883

    Article  Google Scholar 

  6. Zi-Liang L, Zi-Yu W, Sriram K, Zhongli P, Magdalena Z, Li-Zhen D, Qing-Hui W, Qing W, Hong-Wei X (2020) Pulsed vacuum drying of kiwifruit slices and drying process optimization based on artificial neural network. Dry Technol 39(3):1–14. https://doi.org/10.1080/07373937.2020.1817063

    Article  Google Scholar 

  7. Zi-Liang L, Feng N, Xia Z, Magdalena Z, Xu D, Li-Zhen D, Jun W, Wei W, Zhen-Jiang G, Hong-Wei X (2020) Color prediction of mushroom slices during drying using Bayesian extreme learning machine. Dry Technol 38(14):1869–1881. https://doi.org/10.1080/07373937.2019.1675077

    Article  Google Scholar 

  8. Thant PP, Robi PS, Mahanta PS (2018) ANN modelling for prediction of moisture content and drying characteristics of paddy in fluidized bed. Int J Appl Sci Eng 5:118–123

    Google Scholar 

  9. Zi-Liang L, Jun-Wen B, Shu-Xi W, Jian-Sheng M, Hui W, Xian-Long Y, Zhen-Jiang G, Hong-Wei X (2019) Prediction of energy and energy of mushroom slices drying in hot air impingement dryer by artificial neural network. Dry Technol 38(15):1–13. https://doi.org/10.1080/07373937.2019.1607873

    Article  Google Scholar 

  10. Jun-Wen B, Hong-Wei X, Hai-Le M, Cun-Shan Z (2018) Artificial neural network modeling of drying kinetics and color changes of ginkgo biloba seeds during microwave drying process. J Food Qual 2018:1–8. https://doi.org/10.1155/2018/3278595

    Article  Google Scholar 

  11. Rodriguez J, Clemente G, Sanjuaan N, Bon J (2014) Modelling drying kinetics of thyme (Thymus vulgari s L.): theoretical and empirical models, and neural networks. Food Sci Technol Int. https://doi.org/10.1177/1082013212469614

  12. Brooker DB, Bakker-Arkema FW, Hall CW (1992) Drying and storage of grains and oilseeds. Springer, New York, NY. https://doi.org/10.1016/j.jfoodeng.2004.03.025

    Book  Google Scholar 

  13. Simal S, Femenia A, Garau M, Crosello C (2005) Use of exponential Page’s and diffusional models to simulate the drying kinetics of kiwi fruit. J Food Process Eng 66:323–332. https://doi.org/10.1016/j.jfoodeng.2004.03.025

    Article  Google Scholar 

  14. Babalis SJ, Papanicolaou E, Kyriakis N, Belessiotis VG (2006) Evaluation of thin layer drying models for describing drying kinetics of figs (Ficuscarica). J Food Process Eng 75:205–214. https://doi.org/10.1016/j.jfoodeng.2005.04.008

    Article  Google Scholar 

  15. Onwude DI, Hashim N, Janius RB, Nawi NM, Abdan K (2016) Modeling the thin layer drying of fruits and vegetables: a review. Compr Rev Food Sci Food Saf 15:599–618. https://doi.org/10.1111/1541-4337.12196

    Article  Google Scholar 

  16. Nadi F, Tzempelikos D (2018) Vacuum drying of apples (cv. Golden Delicious): drying characteristics, thermodynamic properties and mass transfer parameters. Heat Mass Transf 54:1853–1866. https://doi.org/10.1007/s00231-018-2279-5

    Article  Google Scholar 

  17. Dorofki M, Elshafie AH, Jaafar O, Karim OA, Mastura S (2012) In: Energy and Biotechnology, IPCBEE comparison of artificial neural network transfer functions abilities to simulate extreme runoff data. Int Conf Environ 33:39–44. https://doi.org/10.1515/1556-3758.1986

    Article  Google Scholar 

  18. Demiray E, Seker A, Tulek Y (2017) Drying kinetics of onion (Allium cepa L.) slices with convective and microwave drying. Heat Mass Transf 53(5):1817–1827. https://doi.org/10.1007/s00231-016-1943-x

    Article  Google Scholar 

  19. Dincer I, Hussian MM (2004) Development of a new biot number and lag factor correlation for drying applications. Int J Heat Mass Transf 47(4):653–658. https://doi.org/10.1016/j.ijheatmasstransfer.2003.08.006

    Article  MATH  Google Scholar 

  20. Sergio Giner A, Martin Torrez Irigoyren R, Sabrina C, Cecilia F (2010) The variable nature of Biot numbers in food drying. J Food Eng 101(2):214–222. https://doi.org/10.1016/j.jfoodeng.2010.07.005

    Article  Google Scholar 

  21. Hao-Yu J, Shi-Hao Z, Mujumdar AS, Xiao-Ming F (2018) Energy efficient improvements in hot air drying by controlling relative humidity based on Weibull and Bi-Di models. Food Biprod Process 111:1–45. https://doi.org/10.1016/j.fbp.2018.06.002

    Article  Google Scholar 

  22. Krokida MK, Karathanos VT, Maroulis ZB (2003) Drying kinetics of some vegetables. J Food Eng 59:391–403. https://doi.org/10.1016/S0260-8774(02)00498-3

    Article  Google Scholar 

  23. Mitra J, Shrivastava SL, Rao PS (2011) Vacuum dehydration kinetics of onion slices. Food Byprod Process 89:1–9. https://doi.org/10.1016/j.fbp.2010.03.009

    Article  Google Scholar 

  24. Kandhasamy S, Jince MJ, Karuppusamy A, Sellamuthu M (2010) Evaluation of Merremiatridentate (L.) hallier f. for invitro antioxidant activity. Journal of. Food Sci Biotechnol 19(3):663–669. https://doi.org/10.1007/s10068-010-0093-z

    Article  Google Scholar 

  25. Cheng C-s, Qing-HuiGu J-KZ, Tao J-H, Zhao T-R, Cao J-X, Cheng G-G, Lai G-F, Liu Y-P (2022) Phenolic constituents, antioxidant and cytoprotective activities, enzyme inhibition abilities of five fractions from vacciniumdunalianumwight. Molecules 27(11). https://doi.org/10.3390/molecules27113432

  26. Vuong QV, Hirun S, Roach PD, Bowyer MC, Phillips PA, Scarlett CJ (2013) Effect of extraction conditions on total phenolic compounds and antioxidant activities of Caricapapaya leaf aqueous extracts. J Herbal Med 3(3):104–111. https://doi.org/10.1016/j.hermed.2013.04.004

    Article  Google Scholar 

  27. Zielinska M, Markowski M (2012) Color characteristics of carrot: effect of drying and rehydration. Int J Food Prop 15(2):450–466. https://doi.org/10.1080/10942912.2010.489209

    Article  Google Scholar 

  28. Wang Z, Sun J, Liao X, Chen F, Zhao G, Wu J, Hu X (2006) Mathematical modelling on hot air drying of thin layer apple pomace. Food Res Int 40:39–46. https://doi.org/10.1016/j.foodres.2006.07.017

    Article  Google Scholar 

  29. Tarafdar A, Shahi NC, Singh A, Sirohi R (2018) Artificial neural network modeling of water activity: a low energy approach to freeze drying. Food Bioprocess Technol 11:164–171. https://doi.org/10.1007/s11947-017-2002-4

    Article  Google Scholar 

  30. Kumar Y, Lochan S, Vijay SS, Ayon T (2021) Artificial neural network (ANNs) and mathematical modeling of hydration of green chickpea. Inform Process Agric 8:75–86. https://doi.org/10.1016/j.inpa.2020.04.001

    Article  Google Scholar 

  31. Jafari AM, Ganje M, Dehnad D, Ghanbari V (2016) Mathematical, fuzzy logic and artificial neural network modeling techniques to predict drying kinetics of onion. J Food Process Preserv 40(2):329–339. https://doi.org/10.1111/jfpp.12610

    Article  Google Scholar 

  32. Mohammad K, ValiRasooli S, Reza AC, Ebrahim T, Yousef AG, Imam G (2015) ANFIS and ANNs model for prediction of moisture diffusivity and specific energy consumption potato, garlic and cantaloupe drying under convective hot air dryer. Inform Process Agric 5:372–387. https://doi.org/10.1016/j.inpa.2018.05.003

    Article  Google Scholar 

  33. Tripathy PP, Kumar S (2009) A methodology for determination of temperature dependent mass transfer coefficients from drying kinetics: application to solar drying. J Food Eng 90(2):212–218. https://doi.org/10.1016/j.jfoodeng.2008.06.025

    Article  Google Scholar 

  34. Olanipekun BF, Tunde-Akintunde TY, Oyelade OJ, Adenaya TA (2014) Mathematical modeling of thin-layer pineapple drying. Journal of Food Processing and Preservation 39(6):1431–1441. https://doi.org/10.1111/jfpp.12362

    Article  Google Scholar 

  35. Rizvi SSH, Rao MA (1986) Thermodynamic properties of food in dehydration in engineering properties of foods. Marcel Dekker, New York, USA

    Google Scholar 

  36. Fiorentini C, Demarchi SM, Quintero Ruiz NA, Irigoyen RMT, Giner SA (2015) Arrhenius activation energy for water diffusion during drying of tomato leathers: the concept of characteristic product temperature. Biosyst Eng 132:39–46. https://doi.org/10.1016/j.biosystemseng.2015.02.004

    Article  Google Scholar 

  37. McMinn WAM (2004) Prediction of moisture transfer parameter for microwave drying of lactose powder using Bi-G drying correlation. Food Res Int 37(10):1041–1047. https://doi.org/10.1016/j.foodres.2004.06.013

    Article  Google Scholar 

  38. Iloki-Assanga SB, Lewis-Lujan ML, Claudia LL, Armida AG, Daniela F, Jose LR, David DH (2014) Solvent effects on phytochemical constituent profiles and antioxidant activities using four different extraction formulations for analysis of Bucidabuceras L. and Phoradendroncolifornicum. BMC Res Notes 8(396):1–14. https://doi.org/10.1186/s13104-015-1388-1

    Article  Google Scholar 

  39. Hossain MB, Barry-Ryan C, Martin-Diana AB, Brunton NP (2010) Effect of drying method on the antioxidant capacity of six Lamiaceae herbs. Food Chem 1:85–91. https://doi.org/10.1016/j.foodchem.2010.04.003

    Article  Google Scholar 

  40. Sharma K, Ko EY, Assefa AD, Ha S, Nile SH, Lee ET, Park SW (2015) Temperature-dependent studies on the total phenolics, flavonoids, antioxidant activities, and sugar content in six onion varieties. J Food Drug Anal 23:24–252. https://doi.org/10.1016/j.jfda.2014.10.005

    Article  Google Scholar 

  41. Dewanto V, Wu X, Adom KK, Liu RH (2000) Thermal processing enhances the nutritional value of tomatoes by increasing total antioxidant activity. J Agric Food Chem 50(10):3010–3014. https://doi.org/10.1021/jf0115589

    Article  Google Scholar 

  42. Damian C, Oroian M (2013) Effect of thermal treatment on antioxidant activity and colour of carrot purées. Ovidius Univ Ann Chem 24(1):35–38. https://doi.org/10.2478/auoc-2013-0007

    Article  Google Scholar 

  43. Narmin YS, Rashid J, Reza H (2014) Antioxidant activities of two sweet pepper capsicum phenolic extracts and the effects of thermal treatment. J Phytomedicine 3(1):25–24

    Google Scholar 

  44. Wang L, Weller CL (2006) Recent advances in extraction of nutraceuticals from plants. Trends Food Sci Technol 17:300–312. https://doi.org/10.1016/j.tifs.2005.12.00

    Article  Google Scholar 

  45. Boeing JS, Barizoã EO, Silva BC, Montanher PF, de Cinque AV, Visentainer JV (2014) Evaluation of solvent effect on the extraction of phenolic compounds and antioxidant capacities from the berries: application of principal component analysis. Chem Cent J 8:1–9. https://doi.org/10.1186/s13065-014-0048-1

    Article  Google Scholar 

  46. Vega-Galvez A, Di Scala K, Rodriguez K, Lemus-Mondaca R, Miranda M, Lopez J, Perez-Won M (2009) Effect of air-drying temperature on physico-chemical properties, antioxidant capacity, colour and total phenolic content of red pepper (Capsicum annuum, L. var. Hungarian). Food Chem 1174:647–653. https://doi.org/10.1016/j.foodchem.2009.04.066

    Article  Google Scholar 

  47. Sofia RR, Dilip KR, Nisreen A (2012) Water at room temperature as a solvent for the extraction of apple pomace phenolic compound. Food Chem 135(3):1991–1998. https://doi.org/10.1016/j.foodchem.2012.06.068

    Article  Google Scholar 

  48. Lopez J, Vega-Galvez A, Torres MJ, Lemus-Mondaca R, Quispe-Fuentes I, Scala KD (2013) Effect of dehydration temperature on physic-chemical properties and antioxidant capacity of goldenberry (Physalisperuviana L.). Chilean. J Agric Res 73(3):293–300. https://doi.org/10.4067/S0718-58392013000300013

    Article  Google Scholar 

  49. Sturm B, Hensel O (2017) Pigments and nutrients during vegetables drying process, dried products storage and their associated colour changes. CRC Press, Taylor and Francis, Boca Raton

    Google Scholar 

  50. Sturm B, Hofacker WC, HenselO. (2012) Optimizing the drying parameters for hot air dried apples. Dry Technol 30:1570–1582. https://doi.org/10.1080/07373937.2012.698439

    Article  Google Scholar 

  51. Avila IMLB, Silva CLM (1999) Modeling kinetics of thermal degradation of colour in peach puree. J Food Eng 39:161–166. https://doi.org/10.1016/S0260-8774(98)00157-5

    Article  Google Scholar 

  52. Argyropoulos D, Muller J (2014) Kinetics of change in colour and rosmarinic acid equivalents during convective drying of lemon balm (Melissa officinalis L.). J Appl Res Med Aromat Plants 1(1):1–8. https://doi.org/10.1016/j.jarmap.2013.12.001

    Article  Google Scholar 

  53. Steel RGD, Torrie JH (1960) Principles and procedures of statistics. McGraw-Hill, New York

    MATH  Google Scholar 

Download references

Acknowledgements

The authors were thankful to Bannari Amman Institute of Technology for providing lab facilities and also would like to acknowledge Chhatrapati Research Training and Human Development Institute (SARTHI, Pune, India) for giving fellowship to the first author for the research work.

Author information

Authors and Affiliations

  1. Department of Biotechnology, Bannari Amman Institute of Technology, Anna University, Tamil Nadu, India

    Jadhav Snehal Mahesh & Balakrishnaraja Rengaraju

  2. Department of Food Technology, Rajalakshmi College of Engineering, Anna University, Tamil Nadu, India

    Ramalakshmi Kuathooran

  3. Agricultural Structures and Process Engineering, Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P., 243 122, India

    AyonTarafdar

Authors
  1. Jadhav Snehal Mahesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Balakrishnaraja Rengaraju
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ramalakshmi Kuathooran
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. AyonTarafdar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JSM had done the experimental work, statistical analysis, and manuscript writing work. BR had supervised the work, RK had given conceptualization, AT guided for the statistical analysis, and all the authors’ reviewed the article.

Corresponding author

Correspondence to Jadhav Snehal Mahesh.

Ethics declarations

Ethical approval

The research conducted did not involve any studies with animal or human participants, and it was not conducted in any private or protected areas.

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

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mahesh, J.S., Rengaraju, B., Kuathooran, R. et al. Phytochemical studies and mass transfer phenomenon of raw soursop fruit at different drying temperatures and kinetics evaluation by ANN and mathematical modeling. Biomass Conv. Bioref. (2023). https://doi.org/10.1007/s13399-023-04556-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13399-023-04556-4

Keywords

Search

Navigation