Signal processing for electronic nose

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.

搜集汇总

背景与挑战

背景概述

该数据集是2017年泰国Thammasat大学的一篇博士论文，专注于电子鼻信号处理技术。研究提出了两种新方法来优化传感器选择，以实现对香蕉、芒果等水果成熟度的高精度分类，并开发了一种简单的MMM分类算法以提高效率和准确性。