DOI: 10.3390/foods15213795 ISSN: 2304-8158

Effect of Drying Preconditioning and Particle Size on Extractable Constituents from Plectranthus amboinicus Leaves by Ultrasound-Assisted Extraction

Dayana Portillo-Izquierdo, Diego C. Durán, Oswaldo Osorio-Mora, Ramiro Torres-Gallo, Diego F. Tirado

Thymol and carvacrol are bioactive monoterpenoids of recognized pharmacological value, and Plectranthus amboinicus leaves are a promising natural source for their recovery through ultrasound-assisted extraction. This study evaluated how forced-convection drying temperature (36–64 °C) and particle size affect dehydration kinetics and the recovery of thymol and carvacrol (thymol-equivalent response), total phenolic compounds (TPC), and extraction yield. The Midilli–Küçük model best fitted the drying kinetics (R2 > 0.99), with effective moisture diffusivity ranging from 6.68 × 10−12 m2 s−1 to 1.72 × 10−11 m2 s−1 and an activation energy of 41.19 kJ mol−1. Response surface methodology using a central composite design indicated that particle size had the strongest influence on the evaluated extraction responses. Multi-response optimization identified 46 °C and 0.18 mm as a model-predicted candidate condition, for which the fitted cubic models predicted 785.3 ppm thymol equivalents, 206.4 mg GAE g−1 dry extract for TPC, and 10.1% extraction yield. Experimental verification yielded relative prediction errors of −18.6%, +5.6%, and −34.4% for the thymol-equivalent response, TPC, and extraction yield, respectively. These discrepancies, particularly for the thymol-equivalent response and extraction yield, indicate limitations in predictive accuracy. Furthermore, the UV–Vis assay did not establish the specific identity of thymol or carvacrol in the extracts. The findings provide insight into the effects of drying preconditioning and particle size on the measured extraction responses under the evaluated conditions, while further chromatographic analysis and experimental verification are needed to establish compound-specific recovery and the robustness of the predicted conditions.

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