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Artificial Neural Network for Identification of Multicomponent Mixtures of Tea

Mariyana Sestrimska, Tanya Titova, Veselin Nachev
26 Maritza Blvd Plovdiv, 4002 Bulgaria

Abstract: To recognise and detect the ingredients used in the tea, we have deployed Artificial intelligence techniques. The techniques that we have used are the VIS/NIR spectroscopy, measurement of color and pH. Principal components analysis (PCA). We have adapted the reduced high dimensional feature space to three specific factors. We further present the experimental results in the paper. The Back propagation Artificial Neural Network (BP-ANN) and KohonenÃ¢â‚¬â„¢s Self organizing map (SOM) shown the studied samples with an accuracy of 99.4% and 98%, which ensure the use of these techniques for future also.

Keywords: VIS/NIR Spectral Analysis, Principle Component Analysis, Artificial Neural Network, Self-organizing Maps, Tea Artificial Neural Network for Identification of Multicomponent Mixtures of Tea

DOI:https://doi.org/10.6025/jcl/2021/12/1/1-17-24

Full_Text PDF 1.69 MB Download: 252 times

References:

[1] He, Y., Li, X., Deng, X. (2009). Discrimination of Varieties of Tea Using Near Infrared Spectroscopy by Principal Component Analysis and BP-model, Journal of Food Engineering, vol. 79, p. 1238-1242, 2009.
[2] Liu, F., Ye, X., He, Y., Wang, L. (2009). Application of Visible/Near Infrared Spectroscopy and Chemometric Calibrations for Variety Discrimination of Instant Milk Teas, Journal of Food Engineering, vol. 93, p. 127-133, 2009.
[3] Gang, L., Yang, H. (2011). Discrimination of Different Brands of Nescafé Coffee Using VIS-NIR Spectroscopy and Comparative Study, International Conference on Agricultural and Biosystems Engineering, 2011.
[4] Damyanov, Ch. (2006). Non-Destructive Quality Recognition in Automatic Food Sorting Systems, UFT Academic Publishing House, Plovdiv, 2006 (in Bulgarian).
[5] Bieroza, M., Baker, A., Bridgeman, J. (2009). Exploratory Analysis of Excitation-Emission Matrix Fluorescence Spectra with Self- Organizing Maps as a Basis for Determination of Organic Matter Removal Efficiency at Water Treatment Works, Journal of Geophysical Research, 2009.
[6] Marique, T., Pennincx, S., Kharoubi, A. (2005). Image Segmentation and Bruise Identification on Potatoes Using a Kohonen’s Self-Organizing Map, Journal of Food Science, 2005.

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