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

Parvej Hasan Jon, Jahid Hasan Shourove, Md Kashem Ali, Oliur Rahman, Mostak Uddin Thakur, G M Rabiul Islam

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Parvej Hasan JonDepartment of Food Engineering and Tea Technology, Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh.

Jahid Hasan ShouroveDepartment of Food Engineering and Tea Technology, Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh.

Md Kashem AliDepartment of Food Engineering and Tea Technology, Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh.

Oliur RahmanDepartment of Food Engineering and Tea Technology, Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh.

Mostak Uddin ThakurDepartment of Chemistry, University of Chittagong, Chittagong, 4331, Bangladesh.

G M Rabiul IslamDepartment of Food Engineering and Tea Technology, Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh. rabi-ttc@sust.edu.

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6 · The paper itself

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.

Indexed as

Artificial neural networkCinnamomum tamalaEssential oilGC–MS analysisResponse surface methodologySonication-assisted hydrodistillation

Identifiers

PMID41851264

DOI10.1038/s41598-026-42869-2

PMCPMC13136388

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