An original approach for the quantitative characterization of saffron aroma strength using electronic nose

Abstract

ABSTRACTThis study was conducted to quantitatively characterize the aroma strength of saffron, Crocus sativus L. (Iridaceae), using an electronic nose (e-nose). Thirty-three saffron samples from different geographical regions were prepared for the experiments. The aroma strength of the saffron samples was determined based on the ISO 3632 method as directly relates to safranal concentration. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural network models were then applied for the prediction of using sensor array responses of the e-nose sensors. Principal Components Analysis (PCA) was also utilized for aroma feature selection and then dimensionality reduction of sensor array data sets for improving the neural models was applied. The results revealed that in the improved MLP model with lower-dimension data, the prediction accuracy of (R2 = 0.99, RMSE = 0.64) remained the same or slightly higher than the original model with higher-dimension data and also was better than those obtained using the RBF models. The overall results showed the suitability of e-nose as a nondestructive and online instrument for saffron aroma strength characterization.