Microwave-extraction kinetics of organic peppermint (Mentha piperita L.) leaves
2018
Pavlić, Branimir | Bera, Oskar (https://orcid.org/0000-0003-4654-038X) | Jovičić, Nebojša | Zeković, Zoran (https://orcid.org/0000-0002-5432-7071)
Peppermint (Mentha piperita L.) has been recognized worldwide as important industrial plant due to its medicinal, aromatic and culinary properties. Terpenoids from essential oil and polyphenols have been recognized as the most important bioactives from peppermint, therefore, it is important to establish novel extraction protocols in order to improve yield of these compounds. Microwave-assisted extraction (MAE) has been recently extensively used for recovery of bioactive compounds from plant material, therefore, this green and environmentally friendly technique was applied for recovery of polyphenols from peppermint leaves. The main goal of this research was to obtain different empirical kinetic models which would be able to adequately describe MAE process and to evaluate effect of irradiation power on yield of bioactive compounds, as well as on model parameters. Influence of microwave irradiation power (90, 180, 360, 600 i 800 W) on total extraction yield (Y) and polyphenols content (TP and TF) was evaluated. Commonly used empirical models (first-order, Peleg‟s hyperbolic, Elovich‟s logarithmic and power-law model) were applied for modeling of extraction process. Experimentally obtained values of TP were 9.1731 – 9.6110 g GAE/100 g and the highest yield at the end of extraction process was achieved with irradiation power of 90 W. First-order kinetics model provided the best fit with experimentally obtained values of Y and TP according to the highest R2 and the lowest SSer and AARD. Influence of irradiation power on extraction rate constant (k) has been fitted using cubic spline model and predicted maximum (2.36 min-1) has been roughly estimated at 665 W. MAE was successfully utilized as novel extraction technique for the recovery of polyphenolic compounds from organic Mentha piperita leaves. Further investigation would be performed towards optimization of MAE of same plant material by experimental design and artificial neural networks.
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