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Chemometrics Assisted Method for Classification of Mango Juice as Adulterated or Safe with over Use of Artificial Colours by UV Spectroscopic Data

Received: 10 December 2016     Accepted: 15 March 2017     Published: 28 March 2017
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Abstract

Tartazine, Sunset Yellow and Beta Carotine are commonly used artificial colours in commercial mango juices in order to make them attractive to the consumers, even though these synthetic colours have hazardous effect on health. Therefore, it is very often necessary to classify juices as adulterated with heavy use of these colours or not. In the present study, two chemometric techniques, Artificial Neural Network (ANN) and Partial Least Square-Discrimination Analysis (PLS-DA) have been assessed for their efficiencies for classification. Here, UV spectroscopic data are used as input. Three techniques, Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Savitzky–Golay (S-G) filtering have been evaluated for their do-noising performance, and select the best one. Before calibration, spectral data are de-noised with MSC as is proved most efficient for de-noising UV spectral data. Spectral range 371-533 nm has been used for calibration ultimately. ANN shows better classification results than PLS-DA for all colours. Finally, the study is proposing a simpler and cheaper method for classification of mango juice as adulterated or safe with over use of artificial colours by applying ANN in de-noised spectroscopic data.

Published in Journal of Food and Nutrition Sciences (Volume 5, Issue 2)
DOI 10.11648/j.jfns.20170502.15
Page(s) 51-56
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Artificial Colours, Chemometrics, De-Noising, Classification, ANN, PLS-DA

References
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Cite This Article
  • APA Style

    Mohammad Nashir Uddin, Ajit Kumar Majumder, Abu Tareq Mohammad Abdullah, Md. Alamgir Kabir. (2017). Chemometrics Assisted Method for Classification of Mango Juice as Adulterated or Safe with over Use of Artificial Colours by UV Spectroscopic Data. Journal of Food and Nutrition Sciences, 5(2), 51-56. https://doi.org/10.11648/j.jfns.20170502.15

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    ACS Style

    Mohammad Nashir Uddin; Ajit Kumar Majumder; Abu Tareq Mohammad Abdullah; Md. Alamgir Kabir. Chemometrics Assisted Method for Classification of Mango Juice as Adulterated or Safe with over Use of Artificial Colours by UV Spectroscopic Data. J. Food Nutr. Sci. 2017, 5(2), 51-56. doi: 10.11648/j.jfns.20170502.15

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    AMA Style

    Mohammad Nashir Uddin, Ajit Kumar Majumder, Abu Tareq Mohammad Abdullah, Md. Alamgir Kabir. Chemometrics Assisted Method for Classification of Mango Juice as Adulterated or Safe with over Use of Artificial Colours by UV Spectroscopic Data. J Food Nutr Sci. 2017;5(2):51-56. doi: 10.11648/j.jfns.20170502.15

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  • @article{10.11648/j.jfns.20170502.15,
      author = {Mohammad Nashir Uddin and Ajit Kumar Majumder and Abu Tareq Mohammad Abdullah and Md. Alamgir Kabir},
      title = {Chemometrics Assisted Method for Classification of Mango Juice as Adulterated or Safe with over Use of Artificial Colours by UV Spectroscopic Data},
      journal = {Journal of Food and Nutrition Sciences},
      volume = {5},
      number = {2},
      pages = {51-56},
      doi = {10.11648/j.jfns.20170502.15},
      url = {https://doi.org/10.11648/j.jfns.20170502.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jfns.20170502.15},
      abstract = {Tartazine, Sunset Yellow and Beta Carotine are commonly used artificial colours in commercial mango juices in order to make them attractive to the consumers, even though these synthetic colours have hazardous effect on health. Therefore, it is very often necessary to classify juices as adulterated with heavy use of these colours or not. In the present study, two chemometric techniques, Artificial Neural Network (ANN) and Partial Least Square-Discrimination Analysis (PLS-DA) have been assessed for their efficiencies for classification. Here, UV spectroscopic data are used as input. Three techniques, Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Savitzky–Golay (S-G) filtering have been evaluated for their do-noising performance, and select the best one. Before calibration, spectral data are de-noised with MSC as is proved most efficient for de-noising UV spectral data. Spectral range 371-533 nm has been used for calibration ultimately. ANN shows better classification results than PLS-DA for all colours. Finally, the study is proposing a simpler and cheaper method for classification of mango juice as adulterated or safe with over use of artificial colours by applying ANN in de-noised spectroscopic data.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Chemometrics Assisted Method for Classification of Mango Juice as Adulterated or Safe with over Use of Artificial Colours by UV Spectroscopic Data
    AU  - Mohammad Nashir Uddin
    AU  - Ajit Kumar Majumder
    AU  - Abu Tareq Mohammad Abdullah
    AU  - Md. Alamgir Kabir
    Y1  - 2017/03/28
    PY  - 2017
    N1  - https://doi.org/10.11648/j.jfns.20170502.15
    DO  - 10.11648/j.jfns.20170502.15
    T2  - Journal of Food and Nutrition Sciences
    JF  - Journal of Food and Nutrition Sciences
    JO  - Journal of Food and Nutrition Sciences
    SP  - 51
    EP  - 56
    PB  - Science Publishing Group
    SN  - 2330-7293
    UR  - https://doi.org/10.11648/j.jfns.20170502.15
    AB  - Tartazine, Sunset Yellow and Beta Carotine are commonly used artificial colours in commercial mango juices in order to make them attractive to the consumers, even though these synthetic colours have hazardous effect on health. Therefore, it is very often necessary to classify juices as adulterated with heavy use of these colours or not. In the present study, two chemometric techniques, Artificial Neural Network (ANN) and Partial Least Square-Discrimination Analysis (PLS-DA) have been assessed for their efficiencies for classification. Here, UV spectroscopic data are used as input. Three techniques, Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Savitzky–Golay (S-G) filtering have been evaluated for their do-noising performance, and select the best one. Before calibration, spectral data are de-noised with MSC as is proved most efficient for de-noising UV spectral data. Spectral range 371-533 nm has been used for calibration ultimately. ANN shows better classification results than PLS-DA for all colours. Finally, the study is proposing a simpler and cheaper method for classification of mango juice as adulterated or safe with over use of artificial colours by applying ANN in de-noised spectroscopic data.
    VL  - 5
    IS  - 2
    ER  - 

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Author Information
  • BCSIR Laboratories, Dhaka, Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh

  • Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh

  • Institute of Food Science and Technology (IFST), Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh

  • Institute of Food Science and Technology (IFST), Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh

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