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Machine Learning Calibration for Near Infrared Spectroscopy Data: A Visual Programming Approach

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Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019 (NUSYS 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 666))

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Abstract

Spectroscopy including Near infrared spectroscopy (NIRS) is a non-destructive and rapid technique applied increasingly for food quality evaluation, medical diagnosis, manufacturing, etc. The qualitative or quantitative information using NIRS is only obtained after spectra data calibration process based mathematical knowledge in chemometrics and statistics. This process naturally involves multivariate statistical analysis. Machine learning as a subset of AI (artificial intelligence), in addition to conventional multivariate statistical tools, seems to get more popularity for chemometric calibration of NIRS data nowadays. However, often the software/toolboxes in chemometrics are commercialized version which is not free. For the free versions, programming skills are required to deal with applications of machine learning in spectra data calibration. Therefore, this paper introduces a different approach of spectra data calibration based on visual programming approach using Orange data mining, a free software which is still rarely used by the research community in spectroscopy. The data used namely: pesticide sprayed on cabbage (to classify between pure cabbage and pesticide-sprayed cabbage with different level of pesticide solution), mango sweetness assessment (to predict sugar soluble content in mango based on Brix degree value). These two data represent classification and regression respectively. This approach is intended more for researchers who want to apply machine learning calibration in their spectroscopy data but don’t want to have rigorous programming jobs, i.e. for non-programmers.

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Author information

Authors and Affiliations

  1. Mechatronics Engineering, Faculty of Engineering, UCSI University, Kuala Lumpur, Malaysia

    Mahmud Iwan Solihin, Chun Kit Ang & Fahri Heltha

  2. Electrical and Electronics Engineering, Faculty of Engineering, UCSI University, Kuala Lumpur, Malaysia

    Mohamed Rizon

  3. TUM (Technical University of Munich) Asia, Singapore, Singapore

    Zheng Zekui

Authors
  1. Mahmud Iwan Solihin
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  2. Zheng Zekui
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  3. Chun Kit Ang
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  4. Fahri Heltha
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  5. Mohamed Rizon
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Corresponding author

Correspondence to Mahmud Iwan Solihin .

Editor information

Editors and Affiliations

  1. Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Zainah Md Zain

  2. Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Hamzah Ahmad

  3. Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Dwi Pebrianti

  4. Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Mahfuzah Mustafa

  5. Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Nor Rul Hasma Abdullah

  6. Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Rosdiyana Samad

  7. Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Maziyah Mat Noh

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Solihin, M.I., Zekui, Z., Ang, C.K., Heltha, F., Rizon, M. (2021). Machine Learning Calibration for Near Infrared Spectroscopy Data: A Visual Programming Approach. In: Md Zain, Z., et al. Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019 . NUSYS 2019. Lecture Notes in Electrical Engineering, vol 666. Springer, Singapore. https://doi.org/10.1007/978-981-15-5281-6_40

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