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Arkit: 1 arkintunnukseton lehti, D4.

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Arkit: 2 arkintunnuksetonta lehteä, B4.

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Arkit: 1 arkintunnukseton lehti, I4 K1.

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Arkit: 1 arkintunnukseton lehti, G4 H2.

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Genetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention index (RI) and descriptors for 116 diverse compounds in essential oils of six Stachys species. The correlation coefficient LGO-CV (Q²) between experimental and predicted RI for test set by GA-MLR, GA-PLS, GA-KPLS and L-M ANN was 0.886, 0.912, 0.937 and 0.964, respectively. This is the first research on the QSRR of the essential oil compounds against the RI using the GA-KPLS and L-M ANN.

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Croton zehntneri, a plant native to northeastern Brazil, is widely used in folk medicine to treat gastrointestinal problems and has rich essential oil content. The essential oil of C. Zehntneri was analyzed by GC-MS, and its inclusion complex with β-cyclodextrin (β-CD) was characterized by both vibrational spectroscopy and differential scanning calorimetry (DSC). Estragol was the major component identified in the essential oil by the study. IR spectra indicated an interaction of β-CD with essential oil from C. zehntneri, a finding corroborated by the stability constant and scanning calorimetry. Microencapsulation within β-CD has the potential to mask sensory attributes and increase aqueous solubility of oils, thereby improving their applicability as drugs.

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The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.