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Optimizing sonication-assisted hydrodistillation of Cinnamomum tamala essential oil using response surface methodology and artificial neural network modeling
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  • Published: 18 March 2026

Optimizing sonication-assisted hydrodistillation of Cinnamomum tamala essential oil using response surface methodology and artificial neural network modeling

  • Parvej Hasan Jon1,
  • Jahid Hasan Shourove1,
  • Md. Kashem Ali1,
  • Oliur Rahman1,
  • Mostak Uddin Thakur2 &
  • …
  • G. M. Rabiul Islam1 

Scientific Reports , Article number:  (2026) Cite this article

  • 652 Accesses

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Chemistry
  • Plant sciences

Abstract

This study optimized the sonication-assisted hydrodistillation (SAHD) process for extracting essential oil (EO) from Cinnamomum tamala leaves, aiming to maximize yield and antioxidant activity. Both response surface methodology (RSM) and artificial neural network (ANN) models were used to predict extraction performance. The interpretability of the ANN model was enhanced using a neural interpretation diagram (NID), Olden’s algorithm, and sensitivity analysis, and it demonstrated higher accuracy and generalization than RSM. Under optimized conditions, the EO yield reached 1.67 ± 0.13%, with strong antioxidant activity indicated by a total phenolic content (TPC) of 79.24 ± 0.82 mg GAE/g and 81.54 ± 0.88% inhibition of the 2,2-diphenyl-1-picrylhydrazyl radical (DPPH). Residual analysis showed that both models satisfied key regression assumptions, including normality, independence, homoscedasticity, and lack of bias. Gas chromatography–mass spectrometry (GC–MS) analysis identified linalool (47.37%), eugenol (18.34%), and cinnamaldehyde (16.45%) as key constituents. Physicochemical characterization verified EO quality and stability. The integrated modeling approach provides a robust framework for enhancing EO extraction efficiency.

Data availability

Data will be made available on request from the corresponding author.

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Authors and Affiliations

  1. Department of Food Engineering and Tea Technology, Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh

    Parvej Hasan Jon, Jahid Hasan Shourove, Md. Kashem Ali, Oliur Rahman & G. M. Rabiul Islam

  2. Department of Chemistry, University of Chittagong, Chittagong, 4331, Bangladesh

    Mostak Uddin Thakur

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Contributions

This work was carried out in collaboration among all authors. P.H.J. led the conceptualization, conducted the formal analysis, developed the methodology, performed the investigation, and wrote the first draft of the manuscript. P.H.J. and J.H.S. implemented the software, and both contributed to data validation. J.H.S. also contributed to formal analysis & methodology and critically reviewed & edited the manuscript. M.K.A. and O.R. participated in methodology development, experimental investigations, and manuscript reviewing. M.U.T. supported the research through the provision of resources, methodological assistance, and conducting investigations. G.M.R.I. conceptualized and designed the study, supervised the entire project, provided necessary resources, assisted in manuscript revision, and managed the overall project administration. All authors read and approved the final manuscript.

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Correspondence to G. M. Rabiul Islam.

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Jon, P.H., Shourove, J.H., Ali, M.K. et al. Optimizing sonication-assisted hydrodistillation of Cinnamomum tamala essential oil using response surface methodology and artificial neural network modeling. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42869-2

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  • Received: 10 July 2025

  • Accepted: 27 February 2026

  • Published: 18 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-42869-2

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Keywords

  • Cinnamomum tamala
  • Sonication-assisted hydrodistillation
  • Essential oil
  • Response surface methodology
  • Artificial neural network
  • GC–MS analysis
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