An electronic nose has various applications such as in medical, agriculture and industrial sectors. Quality control and classification of fruits and vegetables are the most important parameters in this field. We are proposing an electronic nose system to classify the status of tomatoes and mints via odour sensing mechanism. This is a creative and challenging area of research through gas sensing devices and embedded technologies. Low cost portable gas sensor array with six SnO2 based gas sensors was implemented for the detection of aroma emitted by the tomato and mint. Pattern recognition and ANN training provided a backbone for the future data sets from the collected data sets. Principal Component Analysis (PCA) and radar plots were used for the excellent interactive representation with the LabVIEW and MATLAB software as stand-alone application. PCA analysis represented 83% and 12% variations respectively in fresh and spoiled tomato data sets. The proposed system also covered 97.7 % and 1.2 % data variations respectively for PCA classification for tomato and mint datasets.

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