Artificial neural network and multiple regression analysis models to predict essential oil content of ajowan (Carum copticum L.)
2018
Niazian, Mohsen | Sadat-Noori, Seyed Ahmad | Abdipour, Moslem
Ajowan is an important medicinal plant that grows in arid and semi-arid regions of central Europe, India, Egypt, Iran, Iraq, Afghanistan, and Pakistan. Essential oil is the most consumable product of ajowan in pharmaceutical and food industrials, and correct predict of oil content is one of the main goals in breeding programs of ajowan. Two methods namely artificial neural network (ANN) and multiple regression model (MLR) were conducted to predict the oil content of ajowan from readily measurable plant characters. According to simple correlation analysis, four characters (number of rays, number of pedicels, number of flowers per umbellet, and number of umbellets in an umbel) were selected as input variables in both artificial neural network and multiple linear regressions models. The essential oil content of ajowan was well predicted using SigmoidAxon transfer function and two hidden layers of artificial neural network with a root mean square error (RMSE) of 0.192%, a mean absolute error (MAE) of 0.112% and a determination coefficient (R²) of 0.901. The performance of ANN was better than MLR with a RMSE of 0.262 and a R² of 0.748. Based on stepwise regression and ANN analyses the most important characters for oil content of ajowan were number of umbellets in an umbel and number of flowers per umbellet and these traits can be assigned as selection criteria for essential oil content of ajowan.
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