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Feature Extraction for the Identification of Two-Class Mechanical Stability Test of Natural Rubber Latex

  • Weng Kin Lai
  • Kee Sum Chan
  • Chee Seng Chan
  • Kam Meng Goh
  • Jee Keen Raymond Wong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)

Abstract

Rubber latex concentrate is a popular raw material widely used for making many common household and industrial products. As its quality is not consistent due to either, the source, weather, storage time, etc. there is a need to be able to measure its quality. A common measure of its quality is the mechanical stability, which is defined as the time at the first onset of flocculation when the latex is subjected to physical stress. Currently, the assessment is performed manually by trained personnel, closely adhering to the specifications defined by the ISO 35 standard mechanical stability test that is widely adopted by the rubber industry. Nevertheless, there is some level of subjectivity involved as the test heavily depends on the human eyesight as well as the technician’s experience. In this paper, we proposed a new feature set for a computer vision-based mechanical stability classification system that is based on the current standard test. We investigated this with several features as well as a new feature set that is based on the particle size. These were classified with a feedforward neural network. Experimental results demonstrated that the proposed system was able to provide good classification accuracies for this two-class MST problem.

Keywords

Colloids Latex Quality testing Mechanical stability test Feature extraction Neural networks 

Notes

Acknowledgements

The authors are grateful to Ming Chieng TAN for her assistance in collecting the rubber latex MST images as well as Jaya Kumar Veellu, Chief Chemist of Sime Darby R&D for giving us access to the rubber latex testing facility. The work reported here was partially funded by the Malaysian Ministry of Education’s (MOE) Fundamental Research Grant Scheme (FRGS/1/2014/TK04/TARUC/02/1).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Weng Kin Lai
    • 1
    • 3
  • Kee Sum Chan
    • 1
  • Chee Seng Chan
    • 2
  • Kam Meng Goh
    • 1
  • Jee Keen Raymond Wong
    • 1
  1. 1.Tunku Abdul Rahman University CollegeKuala LumpurMalaysia
  2. 2.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  3. 3.Swinburne University of TechnologyKuchingMalaysia

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