Prediction of Secondary Metabolites Content of Laurel (Laurus nobilis L.) with Artificial Neural Networks Based on Different Temperatures and Storage times

Table 3

Actual secondary metabolites measured GC/MS and predicted secondary metabolites with ANN.

Secondary metabolites	Actual secondary metabolites measured GC/MS	Predicted secondary metabolites with ANN
α-pinene	1.76	1.65
β-pinene	1.39	1.36
Sabinene	2.40	2.16
1.8-cineole	12.60	11.60
γ-terpinene	0.72	0.73
Cymenol	0.52	0.49
Linalool	1.90	1.88
Borneol	1.22	1.21
4-terpineol	2.86	2.83
Caryophyllene	1.37	1.35
Sabinene	0.83	0.73
α-terpineol	15.50	15.30
Germacrene-D	0,61	0.59
α-selinene	1.70	1.67
Methyl eugenol	5.08	5.04
Caryophyllene oxide	1.66	1.64
Spathulenol	2.43	2.36
Eugenol	3.83	3.80