@article{3225,
  author = {Mariyana Sestrimska, Tanya Titova, Veselin Nachev},
  title = {Artificial Neural Network for Identification of Multicomponent Mixtures of Tea},
  journal = {International Journal of Computational Linguistics Research},
  year = {2021},
  volume = {12},
  number = {1},
  doi = {https://doi.org/10.6025/jcl/2021/12/1/1-17-24},
  url = {https://www.dline.info/jcl/fulltext/v12n1/jclv12n1_3.pdf},
  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 Kohonen’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.},
}