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Reduction of Variations Using Chemometric Model Transfer: A Case Study Using FT-NIR Miniaturized Sensors

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The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) (AMLTA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 921))

Abstract

The aim of this paper is to study the unit-to-unit variations in miniaturized Fourier Transform Near-InfraRed (FT-NIR) spectral sensors and the effects of these variations on a classification model developed on a single reference calibration sensor. The paper introduces a simple technique to transfer a classification model from the reference calibration sensor to any other target sensor taking into account variations that might occur. The unit-to-unit variations of the sensors usually result from changes in the signal to noise ratio (SNR) of the sensor due to changes in the mode of operation, variations due to aging, variations due to production tolerances, or changes that occur due to the setup and usage scenario such as scanning through a different medium. To prove the effectiveness of the model transfer technique, we use a Gaussian process classification (GPC) model developed using spectral data from ultra-high temperature (UHT) pasteurized milk with different levels of fat content. The model aims to classify the milk samples based on their fat content. After the model is developed, three experiments are held to mimic each type of the variations and to test how far this will influence the GPC model accuracy after applying the transfer technique.

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Acknowledgment

We would like to express our appreciation to Si-Ware Systems for supporting this research with their state-of-the-art spectrometers and allowing us to use their facilities and laboratories.

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Authors and Affiliations

  1. Computer and Systems Engineering Department, Ain Shams University, Cairo, 11517, Egypt

    Mohamed Hossam & M. Watheq El-Kharashi

  2. Computer Engineering Department, Cairo University, Cairo, 12613, Egypt

    Amr Wassal

Authors
  1. Mohamed Hossam
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  2. Amr Wassal
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  3. M. Watheq El-Kharashi
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Corresponding authors

Correspondence to Mohamed Hossam , Amr Wassal or M. Watheq El-Kharashi .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

  3. School of Computing, Science and Engineering, University of Salford, Salford, Greater Manchester, UK

    Tarek Gaber

  4. Department of Computer Science and Engineering, School of Computing and IT, Faculty of Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Roheet Bhatnagar

  5. Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

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Hossam, M., Wassal, A., El-Kharashi, M.W. (2020). Reduction of Variations Using Chemometric Model Transfer: A Case Study Using FT-NIR Miniaturized Sensors. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_27

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