Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, F4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, H3-H4 I1.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, G3.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, M4.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, D4.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, H4.

Index fossilium analysibus chemicis examinatorum

Arkit: 2 arkintunnuksetonta lehteä, B4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, I4 K1.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, F4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, C4.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, A4.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, L4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, E4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, G4 H2.

Quantitative structure-retention relationships analysis of retention index of essential oils

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.