FRAGRANCE MEASUREMENT OF SCENTED RICE USING ELECTRONIC NOSE

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

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VOLUME 8 , ISSUE 3 (September 2015) > List of articles

FRAGRANCE MEASUREMENT OF SCENTED RICE USING ELECTRONIC NOSE

Arun Jana * / Nabarun Bhattacharyya / Rajib Bandyopadhyay / Bipan Tudu / Subhankar Mukherjee / Devdulal Ghosh / Jayanta Kumar Roy

Keywords : Aromatic rice, Sensor, Principal Component Analysis, Probabilistic Neural Network, Back-propagation Multilayer Perceptron, Linear Discriminant Analysis, response surface
methodology.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 3, Pages 1,730-1,747, DOI: https://doi.org/10.21307/ijssis-2017-827

License : (CC BY-NC-ND 4.0)

Received Date : 03-July-2015 / Accepted: 10-August-2015 / Published Online: 01-September-2015

ARTICLE

ABSTRACT

This article describes about an instrument and method for aroma based quality detection of Basmati and other aromatic rice varieties. It comprises few modules such as odour delivery module, sniffing module, water bath module and computing module. Odour handling module helps to deliver odour to the sensor array; a sniffing unit comprising a sensor array module that includes a eight number of metal oxide semiconductor sensors assembled on a printed circuit board, said printed circuit board fitted into a sensor chamber; a water bath module for preparing rice sample, said water bath module including a heater attachment to facilitate cooking; a computing module to quantify the aroma data acquired by sensors; data acquisition module etc. Principal Component Analysis (PCA) implemented for clustering the data sets acquired from sensor array. Also data generated from sensor array was fed to Probabilistic Neural Network (PNN), Back-propagation Multilayer Perceptron (BPMLP) and Linear Discriminant Analysis (LDA) for identification of different rice varieties. Finally, for aroma quantifying, pure-quadratic response surface methodology model used with mean square error (MSE) 0.0028.

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