Classification models of nondestructive acoustic response for predicting translucent mangosteens

Nattapong Swangmuang, Kasemsak Uthaichana, Nipon Theera-Umpon, Hideyuki Sawada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Mangosteen export generates large revenue; however, translucent mangosteens, which contain undesirable internal condition, result in the shipment rejection and decrease the reliability of the export. This research investigates a novel non-destructive classification approach based on acoustic frequency response to detect mangosteens containing translucent fleshes. The set of uniform-distributed multi-frequency acoustic signal is generated and passed through each mangosteen under the test. The frequency responses, describing a feature space, for all mangosteens are computed via the discrete Fourier transform. To prevent intensive computation, a linear optimization is adopted to select relevant frequency contents, creating a compact classifying feature vector. To solve the classification problem, two proposed acoustic-based classification techniques are studied, namely linear classifier (LC), and non-linear classifier (NLC) based on an artificial neural network. Then the results from both classifiers are compared against the results from the conventional water-floating (WF) approach. Against the experimental data, it is found that the average flesh classification accuracy of good mangoteens achieved from the LC and the NLC are about 61% and 74% respectively, while the WF yields an accuracy of about 69%. It is evident that the acoustic-based approach possesses the convincing accuracy for solving the problem of export-grade translucent mangosteen classification. In addition, the paper shows that a mangosteen's physical density can possibly provide intuitive information for better classification performance in the future research study.

Original languageEnglish
Title of host publication2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2012
DOIs
Publication statusPublished - 2012 Oct 2
Event2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2012 - Phetchaburi, Thailand
Duration: 2012 May 162012 May 18

Publication series

Name2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2012

Other

Other2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2012
CountryThailand
CityPhetchaburi
Period12/5/1612/5/18

Keywords

  • acoustic
  • acoutic signal processing applications
  • mangosteens
  • neural networks
  • non-destructive testing
  • pattern classification

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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  • Cite this

    Swangmuang, N., Uthaichana, K., Theera-Umpon, N., & Sawada, H. (2012). Classification models of nondestructive acoustic response for predicting translucent mangosteens. In 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2012 [6254134] (2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2012). https://doi.org/10.1109/ECTICon.2012.6254134