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 |
Artificial Colours, Chemometrics, De-Noising, Classification, ANN, PLS-DA
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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
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
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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Download@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}
}
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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