Skip to main content
SpringerLink
Log in

Effect of Drying on Insulin Plant Leaves for Its Sustainability and Modeling the Drying Kinetics by Mathematical Models and Artificial Neural Network

  • Research
  • Published:
Environmental Modeling & Assessment Aims and scope Submit manuscript

Abstract

Environmental hurdles, in the form of climatic crises, have taken a toll on prevalence of medicinal plants, which served as the foundation for traditional medicine from time immemorial. Drying preserves medicinal plants, and analyzing drying kinetics enhances resource efficiency, including energy and time utilization. This study is the first to examine the drying kinetics of insulin plant leaves. In this study, insulin plant leaves were dried at different temperatures (“40 °C,” “50 °C,” and “60 °C”) to determine optimal drying temperature and assess its mass transfer characteristics. Higher temperatures led to shorter drying times: 530 min at 40 °C, 290 min at 50 °C, and 155 min at 60 °C. Both mathematical models and artificial neural networks (ANN) were used to model drying characteristics, with the logarithmic model showing superior predictive performance among the mathematical models. ANN with the “Levenberg-Marquardt algorithm” and “TANSIGMOID transfer function” gave the best model with better prediction. Comparative analysis confirmed that ANN exhibited superior predictive capabilities. Effective moisture diffusivity followed an upward trend with temperature and 60 °C revealed a diffusivity of 2.4352 × 10−7 m2/s. Activation energy, at 42.124 kJ/mol, underscored utilization of a moderate level of energy to enhance moisture diffusivity within the sample. Color and microstructural analysis also revealed that 60 °C had better color attributes and agglomerative structures. Drying leaves at 60 °C expedited the drying process, enhanced mass transfer, and improved color characteristics. These results provided vital insights for utilizing dried insulin plant leaves in various nutraceutical products.

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

Similar content being viewed by others

Availability of Data and Materials

The datasets used and analyzed in the current study are available from the corresponding author on reasonable request.

References

  1. Maikhuri, R. K., Phondani, P. C., Dhyani, D., Rawat, L. S., Jha, N. K., & Kandari, L. S. (2018). Assessment of climate change impacts and its implications on medicinal plants-based traditional healthcare system in Central Himalaya, India. Iranian Journal of Science and Technology, Transactions A: Science, 42(4), 1827–1835. https://doi.org/10.1007/s40995-017-0354-2

    Article  Google Scholar 

  2. Hamby, D. M. (1994). A review of techniques for parameter sensitivity analysis of environmental models. Environmental monitoring and assessment, 32(2), 135–154. https://doi.org/10.1007/BF00547132

    Article  CAS  Google Scholar 

  3. Gunhan, T., Demir, V., Hancioglu, E., & Hepbasli, A. (2005). Mathematical modelling of drying of bay leaves. Energy Conversion and Management, 46(11–12), 1667–1679. https://doi.org/10.1016/j.enconman.2004.10.001

    Article  Google Scholar 

  4. Midilli, A., Kucuk, H., & Yapar, Z. (2002). A new model for single-layer drying. Drying Technology, 20(7), 1503–1513. https://doi.org/10.1081/DRT-120005864

    Article  Google Scholar 

  5. Dedecker, A. P., Goethals, P. L., D’heygere, T., & Pauw, N. D. (2006). Development of an in-stream migration model for Gammarus pulex L. (Crustacea, Amphipoda) as a tool in river restoration management. Aquatic Ecology, 40(2), 249–261. https://doi.org/10.1007/s10452-005-9022-2

    Article  Google Scholar 

  6. Mouton, A. M., Dedecker, A. P., Lek, S., & Goethals, P. L. (2010). Selecting variables for habitat suitability of Asellus (Crustacea, Isopoda) by applying input variable contribution methods to artificial neural network models. Environmental Modeling & Assessment, 15(1), 65–79. https://doi.org/10.1007/s10666-009-9192-8

    Article  Google Scholar 

  7. Aghbashlo, M., Hosseinpour, S., & Mujumdar, A. S. (2015). Application of artificial neural networks (ANNs) in drying technology: A comprehensive review. Drying Technology, 33(12), 1397–1462. https://doi.org/10.1080/07373937.2015.1036288

    Article  Google Scholar 

  8. Thant, P. P., Robi, P. S., & Mahanta, P. (2018). ANN modelling for prediction of moisture content and drying characteristics of paddy in fluidized bed. International Journal of Engineering and Applied Sciences, 5(3), 257245.

    Google Scholar 

  9. Selvi, K. Ç., Alkhaled, A. Y., & Yıldız, T. (2022). Application of artificial neural network for predicting the drying kinetics and chemical attributes of linden (Tilia platyphyllos Scop.) during the infrared drying process. Processes, 10(10), 2069. https://doi.org/10.3390/pr10102069

    Article  CAS  Google Scholar 

  10. Kalsi, B. S., Singh, S., Alam, M. S., & Sidhu, G. K. (2023). Comparison of ANN and ANFIS modeling for predicting drying kinetics of Stevia rebaudiana leaves in a hot-air dryer and characterization of dried powder. International Journal of Food Properties, 26(2), 3356–3375. https://doi.org/10.1080/10942912.2023.2283380

    Article  CAS  Google Scholar 

  11. Zalpouri, R., Singh, M., Kaur, P., Singh, S., Kumar, S., & Kaur, A. (2023). Mathematical and artificial neural network modelling for refractance window drying kinetics of coriander (Coriandrum sativum L.) followed by the determination of energy consumption, mass transfer parameters and quality. Biomass Conversion and Biorefinery. https://doi.org/10.1007/s13399-023-05013-y

    Article  Google Scholar 

  12. Boateng, I. D. (2023). A review of solar and solar-assisted drying of fresh produce: State of the art, drying kinetics, and product qualities. Journal of the Science of Food and Agriculture. https://doi.org/10.1002/jsfa.12660

    Article  Google Scholar 

  13. Midilli, A., & Kucuk, H. (2003). Mathematical modeling of thin layer drying of pistachio by using solar energy. Energy conversion and Management, 44(7), 1111–1122. https://doi.org/10.1016/S0196-8904(02)00099-7

    Article  Google Scholar 

  14. Naik, A., Adeyemi, S. B., Vyas, B., & Krishnamurthy, R. (2022). Effect of co-administration of metformin and extracts of Costus pictus D. Don leaves on alloxan-induced diabetes in rats. Journal of Traditional and Complementary Medicine, 12(3), 269–280. https://doi.org/10.1016/j.jtcme.2021.08.007

    Article  CAS  Google Scholar 

  15. Dean, E. W., & Stark, D. D. (1920). A convenient method for the determination of water in petroleum and other organic emulsions. Industrial & Engineering Chemistry, 12(5), 486–490. https://doi.org/10.1021/ie50125a025

    Article  CAS  Google Scholar 

  16. O’callaghan, J. R., Menzies, D. J., & Bailey, P. H. (1971). Digital simulation of agricultural drier performance. Journal of Agricultural Engineering Research, 16(3), 223–244. https://doi.org/10.1016/S0021-8634(71)80016-1

    Article  Google Scholar 

  17. Hendreson, S. M., & Pabis, S. (1961). Grain drying theory. I. Temperature effect on drying coefficients. Journal of Agricultural Engineering Research, 6, 169–174.

    Google Scholar 

  18. Zhang, Q., & Litchfield, J. B. (1991). An optimization of intermittent corn drying in a laboratory scale thin layer dryer. Drying technology, 9(2), 383–395. https://doi.org/10.1080/07373939108916672

    Article  Google Scholar 

  19. Overhults, D. G., White, G. M., Hamilton, H. E., & Ross, I. J. (1973). Drying soybeans with heated air. Transactions of the ASAE, 16(1), 112.

    Article  Google Scholar 

  20. Corrêa, P. C., Botelho, F. M., Oliveira, G. H. H., Goneli, A. L. D., Resende, O., & Campos, S. D. C. (2011). Mathematical modeling of the drying process of corn ears. Acta Scientiarum. Agronomy, 33, 575–581. https://doi.org/10.4025/actasciagron.v33i4.7079

    Article  Google Scholar 

  21. Thompson, T. L., Peart, R. M., & Foster, G. H. (1968). Mathematical simulation of corn drying a new model. Transaction of the ASAE, 11(4), 582–586.

    Article  Google Scholar 

  22. Kassem, A. S. (1998). Comparative studies on thin layer drying models for wheat. In 13th international congress on agricultural engineering, 6, 2–6. Moroco: ANAFID.

    Google Scholar 

  23. Wang, C. Y., & Singh, R. P. (1978). A single drying equation for rough rice. Transactions of American Society of Agricultural Engineers, 11, 668–672.

    Google Scholar 

  24. Sharaf-Elden, Y. I., Blaisdell, J. L., & Hamdy, M. Y. (1980). A model for ear corn drying. Transactions of American Society of Agricultural Engineers, 23, 1261–1265.

    Article  Google Scholar 

  25. Sahin, U., Ucan, O. N., Bayat, C., & Oztorun, N. (2005). Modeling of SO2 distribution in Istanbul using artificial neural networks. Environmental Modeling & Assessment, 10(2), 135–142. https://doi.org/10.1007/s10666-004-7262-5

    Article  Google Scholar 

  26. Lawal, A. I. (2020). An artificial neural network-based mathematical model for the prediction of blast-induced ground vibration in granite quarries in Ibadan, Oyo State, Nigeria. Scientific African, 8, e00413. https://doi.org/10.1016/j.sciaf.2020.e00413

    Article  Google Scholar 

  27. Lazarovitch, N., Poulton, M., Furman, A., & Warrick, A. W. (2009). Water distribution under trickle irrigation predicted using artificial neural networks. Journal of Engineering Mathematics, 64(2), 207–218. https://doi.org/10.1007/s10665-009-9282-2

    Article  Google Scholar 

  28. Murthy, T. P. K., & Manohar, B. (2014). Hot air drying characteristics of mango ginger: Prediction of drying kinetics by mathematical modeling and artificial neural network. Journal of Food Science and Technology, 51(12), 3712–3721. https://doi.org/10.1007/s13197-013-0941-y

    Article  CAS  Google Scholar 

  29. Lopez, A., Iguaz, A., Esnoz, A., & Virseda, P. (2000). Thin-layer drying behaviour of vegetable wastes from wholesale market. Drying Technology, 18(4–5), 995–1006. https://doi.org/10.1080/07373930008917749

    Article  CAS  Google Scholar 

  30. Dincer, I., & Hussain, M. M. (2002). Development of a new Bi–Di correlation for solids drying. International Journal of Heat and Mass Transfer, 45(15), 3065–3069. https://doi.org/10.1016/S0017-9310(02)00031-5

    Article  CAS  Google Scholar 

  31. Demirhan, E., & Özbek, B. (2015). Color change kinetics of tea leaves during microwave drying. International Journal of Food Engineering, 11(2), 255–263. https://doi.org/10.1515/ijfe-2014-0276

    Article  Google Scholar 

  32. Tian, Y., Zhao, Y., Huang, J., Zeng, H., & Zheng, B. (2016). Effects of different drying methods on the product quality and volatile compounds of whole shiitake mushrooms. Food Chemistry, 197, 714–722. https://doi.org/10.1016/j.foodchem.2015.11.029

    Article  CAS  Google Scholar 

  33. Guo, H. L., Chen, Y., Xu, W., Xu, M. T., Sun, Y., Wang, X. C., Wang, X. Y., Luo, J., Zhang, H., & Xiong, Y. K. (2022). Assessment of drying kinetics, textural and aroma attributes of Mentha haplocalyx leaves during the hot air thin-layer drying process. Foods, 11(6), 784. https://doi.org/10.3390/foods11060784

    Article  CAS  Google Scholar 

  34. Doymaz, İ, Tugrul, N., & Pala, M. (2006). Drying characteristics of dill and parsley leaves. Journal of Food Engineering, 77(3), 559–565. https://doi.org/10.1016/j.jfoodeng.2005.06.070

    Article  Google Scholar 

  35. Darvishi, H., Farhudi, Z., & Behroozi-Khazaei, N. (2016). Mass transfer parameters and modeling of hot air drying kinetics of dill leaves. Chemical Product and Process Modeling, 12(2), 20150079. https://doi.org/10.1515/cppm-2015-0079

    Article  Google Scholar 

  36. Pin, K. Y., Chuah, T. G., Rashih, A. A., Law, C. L., Rasadah, M. A., & Choong, T. S. Y. (2009). Drying of betel leaves (Piper betle L.): Quality and drying kinetics. Drying Technology, 27(1), 149–155. https://doi.org/10.1080/07373930802566077

    Article  CAS  Google Scholar 

  37. Kadam, D. M., Goyal, R. K., & Gupta, M. K. (2011). Mathematical modeling of convective thin layer drying of basil leaves. Journal of Medicinal Plants Research, 5(19), 4721–4730.

    Google Scholar 

  38. Bai, J. W., Xiao, H. W., Ma, H. L., & Zhou, C. S. (2018). Artificial neural network modeling of drying kinetics and color changes of ginkgo biloba seeds during microwave drying process. Journal of Food Quality. https://doi.org/10.1155/2018/3278595

    Article  Google Scholar 

  39. Dhurve, P., Tarafdar, A., & Arora, V. K. (2021). Vibro-fluidized bed drying of pumpkin seeds: Assessment of mathematical and artificial neural network models for drying kinetics. Journal of Food Quality. https://doi.org/10.1155/2021/7739732

    Article  Google Scholar 

  40. Kumar, Y., Singh, L., Sharanagat, V. S., & Tarafdar, A. (2021). Artificial neural network (ANNs) and mathematical modelling of hydration of green chickpea. Information Processing in Agriculture, 8(1), 75–86. https://doi.org/10.1016/j.inpa.2020.04.001

    Article  Google Scholar 

  41. Tarafdar, A., Jothi, N., & Kaur, B. P. (2021). Mathematical and artificial neural network modeling for vacuum drying kinetics of Moringa olifera leaves followed by determination of energy consumption and mass transfer parameters. Journal of Applied Research on Medicinal and Aromatic Plants, 24, 100306. https://doi.org/10.1016/j.jarmap.2021.100306

    Article  Google Scholar 

  42. Erbay, Z., & Icier, F. (2010). A review of thin layer drying of foods: Theory, modeling, and experimental results. Critical Reviews in Food Science and Nutrition, 50(5), 441–464. https://doi.org/10.1080/10408390802437063

    Article  Google Scholar 

  43. Moradi, M., Fallahi, M. A., & Mousavi Khaneghah, A. (2020). Kinetics and mathematical modeling of thin layer drying of mint leaves by a hot water recirculating solar dryer. Journal of Food Process Engineering, 43(1), e13181. https://doi.org/10.1111/jfpe.13181

    Article  Google Scholar 

  44. Zogzas, N. P., Maroulis, Z. B., & Marinos-Kouris, D. (1996). Moisture diffusivity data compilation in foodstuffs. Drying Technology, 14(10), 2225–2253. https://doi.org/10.1080/07373939608917205

    Article  CAS  Google Scholar 

  45. Dincer, I. (1998). Moisture transfer analysis during drying of slab woods. Heat and Mass Transfer, 34(4), 317–320. https://doi.org/10.1007/s002310050265

    Article  CAS  Google Scholar 

  46. Buchaillot, A., Caffin, N., & Bhandari, B. (2009). Drying of lemon myrtle (Backhousia citriodora) leaves: Retention of volatiles and color. Drying Technology, 27(3), 445–450. https://doi.org/10.1080/07373930802683740

    Article  CAS  Google Scholar 

  47. Mujaffar, S., & John, S. (2018). Thin-layer drying behavior of West Indian lemongrass (Cymbopogan citratus) leaves. Food Science & Nutrition, 6(4), 1085–1099. https://doi.org/10.1002/fsn3.642

    Article  CAS  Google Scholar 

Download references

Funding

None.

Author information

Authors and Affiliations

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

    Saranya Selvakumarasamy & Balakrishnaraja Rengaraju

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

    Ramalakshmi Kulathooran

Authors
  1. Saranya Selvakumarasamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ramalakshmi Kulathooran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Balakrishnaraja Rengaraju
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Saranya Selvakumarasamy performed the investigation, analysis, data curation, validation, writing, and review of the manuscript. Ramalakshmi Kulathooran contributed for the conceptualization and methodology. Balakrishnaraja Rengaraju did the supervision, review, and editing of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Saranya Selvakumarasamy.

Ethics declarations

Ethics Approval and Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing Interests

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

Selvakumarasamy, S., Kulathooran, R. & Rengaraju, B. Effect of Drying on Insulin Plant Leaves for Its Sustainability and Modeling the Drying Kinetics by Mathematical Models and Artificial Neural Network. Environ Model Assess (2024). https://doi.org/10.1007/s10666-024-09974-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10666-024-09974-w

Keywords

Search

Navigation