Signal processing for electronic nose

Name: Rahman, Md. Mizanur

keyword: Electronic nose

; Pattern recognition

; Signal processing

; False alarm

; Classification

Abstract: Ripeness identification of fruits with short lifetime is important to benefit both the cultivators and consumers. Recently electronic noses (E-Noses) have become popular for fruit quality checkup for its sturdiness and repetitive usability without fatigue dissimilar to human experts. The primary components of an E-Nose are a data acquisition device, a sensor panel and a classification algorithm. Most sensors which are used for E-Noses are expensive. In addition a sensor panel with large number of sensors increases design complexity. Thus to find a minimal set of sensors with maximum relevant data classification efficiency is of vital importance. To analyze the classification efficiencies of different classification methods fruits, such as banana, mango, sapodilla, and pineapple are chosen. Two novel methods for finding a minimal set of sensors are proposed in this thesis. One is a principal component loading and mutual information based approach, and the other is a threshold based approach. With these methods minimal set of sensors are found which show more than 90% classification accuracy while classifying each of the four fruit types at three ripeness states. Once a sensor panel is designed and a data acquisition device chosen, a simple, fast, efficient classification method is required for classifying data of relevant training classes, and to reject any irrelevant data. At present to classify E- Nose data, k-nearest neighbor (k-NN), support vector machine (SVM) and multilayer perceptron neural network (MLPNN) classification algorithms are often applied. Due to open ended hyperplane based classification boundaries, these algorithms falsely classify extraneous odor data. For reducing false classification error and thereby improve correct rejection performance classification algorithms with hyperspheric boundary such as generalized regression neural network (GRNN) and radial basis function neural network (RBFNN) should be used. Simulation results show that GRNN has better ability to overcome false classification problem compared to RBFNN. For large number of neurons requirement, designing a GRNN is complex and expensive. A simple hyperspheric classification method based on minimum, maximum, and mean (MMM) values of the training data is also proposed in this thesis. It is observed that the MMM algorithm is simpler, faster, and have higher accuravy for classifying data of training classes and correctly rejecting data of irrelevant classes.

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Chalie Charoenlarpnopparut

Role: advisor

Name: Prapan Suksompong

Role: co-advisor

Created: 2017

Modified: 2021-05-28

Issued: 2021-05-28

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2017.210

eng

DegreeName: Doctor of Philosophy

Level: Doctoral Degree

Descipline: Engineering and Technology

Grantor: Thammasat University

©copyrights Thammasat University

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