Electronic nose and data analysis for detection of maize oil adulteration in sesame oil

An “electronic nose” has been used for the detection of adulterations of sesame oil. The system, comprising 10 metal oxide semiconductor ensors, was used to generate a pattern of the volatile compounds present in the samples. Prior to different supervised pattern recognition treatments, eature extraction techniques were employed to choose a set of optimal discriminant variables. Principal component analysis (PCA), Fisher linear ransformation (FLT), stepwise linear discriminant analysis (Step-LDA), selection by Fisher weights (SFW) were used, respectively. And then, inear discriminant analysis (LDA), probabilistic neural networks (PNN), back propagation neural networks (BPNN) and general regression neural etwork (GRNN) were applied as pattern recognition techniques for the electronic nose. As for LDA and PNN, FLT was the most effective eature extraction method, while Step-LDA was the most effective way for BPNN and FLT was more suitable for GRNN. With only one sample isclassified in our experiment, LDA is more powerful than PNN. Excellent results were obtained in the prediction of percentage of adulteration n sesame oil by BPNN and GRNN. After training for some time, BPNN could predict the adulteration quantitatively more precisely than GRNN, hereas with FLT as its feature extraction method and without iterative training, GRNN could also yield rather acceptable results. 2006 Elsevier B.V. All rights reserved.