E-nose Screening of Pesticide Residue on Chilli and Double-Checked Analysis through Different Data-Recognition Algorithms

This paper reports on the application of a custom-designed electronic nose (e-nose) for the detection and screening of pesticide residue from vegetable samples, together with a discussion of the analysis performed using two different data-recognition algorithms. Chilli samples were provided by the Agri-Food and Veterinary Authority Singapore, with different concentrations of a known pesticide named profenofos. The e-nose system used in this experiment was made up of 7 different types of Figaro sensors. Principal Component Analysis (PCA) and Fuzzy C Means (FCM) techniques were used to analyse the sensor responses obtained in the experiment. Unlike other previous methods based on multi-residue analysis, the e-nose technique here described does not require of gas chromatography techniques nor controlled laboratory conditions, and the results can be obtained within minutes. The results of the experiment show that the e-nose was able to detect and classify the different chilli samples when either data-recognition method was used.