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

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10637))

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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.

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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).

Author information

Authors and Affiliations

  1. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai, Kee Sum Chan, Kam Meng Goh & Jee Keen Raymond Wong

  2. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Chee Seng Chan

  3. Swinburne University of Technology, Sarawak Campus, Kuching, Malaysia

    Weng Kin Lai

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  1. Weng Kin Lai
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  3. Chee Seng Chan
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  4. Kam Meng Goh
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Corresponding author

Correspondence to Weng Kin Lai .

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Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Lai, W.K., Chan, K.S., Chan, C.S., Goh, K.M., Wong, J.K.R. (2017). Feature Extraction for the Identification of Two-Class Mechanical Stability Test of Natural Rubber Latex. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_54

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  • DOI: https://doi.org/10.1007/978-3-319-70093-9_54

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  • Publisher Name: Springer, Cham

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