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A False Alarm Reduction Method for a Gas Sensor Based Electronic Nose.

Mohammad Mizanur Rahman1,2, Chalie Charoenlarpnopparut3, Prapun Suksompong4

  • 1School of Information Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Khlong Luang 12121, Thailand. mizan.ku@gmail.com.

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Summary
This summary is machine-generated.

Electronic noses (E-Noses) offer robust food quality assessment. A new Minimum, Maximum, Mean (MMM) algorithm improves classification accuracy and reduces false alarms in E-Nose systems.

Keywords:
classificationcorrect rejectionelectronic nosefalse alarmhyperspheric classification boundary

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Area of Science:

  • * Sensory science and artificial intelligence applications.
  • * Development of advanced analytical instrumentation for quality control.

Background:

  • * Electronic noses (E-Noses) are increasingly utilized for food and fruit quality assessment due to their reliability and non-fatiguing nature compared to human sensory panels.
  • * Conventional E-Nose classification algorithms like k-nearest neighbor (k-NN), support vector machine (SVM), and multilayer perceptron neural network (MLPNN) exhibit limitations in handling irrelevant odor data, leading to false classification and misclassification errors.
  • * Algorithms employing hyperspheric boundaries, such as radial basis function neural network (RBFNN) and generalized regression neural network (GRNN), show promise in reducing these errors.

Purpose of the Study:

  • * To evaluate the effectiveness of hyperspheric boundary algorithms (RBFNN and GRNN) in improving the performance of E-Noses for odor classification.
  • * To introduce and assess a simplified hyperspheric classification method based on minimum, maximum, and mean (MMM) values for enhanced E-Nose accuracy and efficiency.
  • * To reduce false classification and misclassification errors while improving the correct rejection of irrelevant odor data in E-Nose systems.

Main Methods:

  • * Comparative analysis of classification algorithms including k-NN, SVM, MLPNN, RBFNN, and GRNN for E-Nose data.
  • * Simulation of GRNN and RBFNN performance, focusing on correct classification efficiency and false alarm reduction.
  • * Development and implementation of a novel, simple hyperspheric classification algorithm (MMM) utilizing statistical properties (minimum, maximum, mean) of training data.

Main Results:

  • * GRNN demonstrated superior correct classification efficiency and false alarm reduction capabilities compared to RBFNN in simulations.
  • * The proposed simple MMM algorithm proved to be fast and efficient in correctly classifying training data.
  • * The MMM algorithm effectively rejected extraneous odor data, significantly reducing false alarms.
  • * GRNN and RBFNN, while effective, present design complexities and higher costs due to substantial neuron requirements.

Conclusions:

  • * Hyperspheric boundary algorithms, particularly GRNN, offer significant advantages over open-ended boundary classifiers for E-Nose applications by minimizing classification errors.
  • * The novel MMM algorithm provides a computationally simple, fast, and effective alternative for E-Nose data classification and outlier rejection, overcoming the complexity and cost associated with advanced neural networks.
  • * The MMM algorithm enhances the reliability of E-Noses for food quality assessment by improving both correct classification and the rejection of irrelevant odors.