Using MLP networks to classify red wines and water readings of an electronic tongue

被引:2
|
作者
de Sousa, HC [1 ]
Carvalho, ACPLF [1 ]
Riul, A [1 ]
Mattoso, LHC [1 ]
机构
[1] Univ Sao Paulo, Inst Ciencias Matemat & Computacao, BR-13560970 Sao Carlos, SP, Brazil
来源
VII BRAZILIAN SYMPOSIUM ON NEURAL NETWORKS, PROCEEDINGS | 2002年
关键词
D O I
10.1109/SBRN.2002.1181428
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Feasible efforts have been made to mimic the human gustatory system through an "artificial tongue". This device comprises an array of sensing units that is able to differentiate tastes with a higher sensitivity than the biological system. Experimental results indicate that when the data generated by such sensing units are handled by artificial neural networks, this "artificial tongue" can successfully discriminate wines of different winemakers, vintage and grapes, as well as different brands of mineral water, distilled water and Milli-Q water. The accuracy achieved by the experiments suggests that the sensing units may be used to detect abnormal chemical substances in a production line or even set a new approach to control quality standards in food industry.
引用
收藏
页码:13 / 18
页数:6
相关论文
共 46 条
  • [41] Chlorophenols identification in water using an electronic nose and ANNs (artificial neural networks) classification
    Vázquez, MJ
    Lorenzo, RA
    Cela, R
    WATER SCIENCE AND TECHNOLOGY, 2004, 49 (09) : 99 - 105
  • [42] An electronic tongue using potentiometric all-solid-state PVC-membrane sensors for the simultaneous quantification of ammonium and potassium ions in water
    J. Gallardo
    S. Alegret
    R. Muñoz
    M. De-Román
    L. Leija
    P. R. Hernández
    M. del Valle
    Analytical and Bioanalytical Chemistry, 2003, 377 : 248 - 256
  • [43] An electronic tongue using potentiometric all-solid-state PVC-membrane sensors for the simultaneous quantification of ammonium and potassium ions in water
    Gallardo, J
    Alegret, S
    Muñoz, R
    De-Román, M
    Leija, L
    Hernández, PR
    del Valle, M
    ANALYTICAL AND BIOANALYTICAL CHEMISTRY, 2003, 377 (02) : 248 - 256
  • [44] Convolutional neural networks for water segmentation using sentinel-2 red, green, blue (RGB) composites and derived spectral indices
    James, Thomas
    Schillaci, Calogero
    Lipani, Aldo
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2021, 42 (14) : 5342 - 5369
  • [45] Differentiation of two Canary DO red wines according to their metal content from inductively coupled plasma optical emission spectrometry and graphite furnace atomic absorption spectrometry by using Probabilistic Neural Networks
    Moreno, Isabel M.
    Gonzalez-Weller, Dailos
    Gutierrez, Valerio
    Marino, Marino
    Camean, Ana M.
    Gonzalez, A. Gustavo
    Hardisson, Arturo
    TALANTA, 2007, 72 (01) : 263 - 268
  • [46] Understanding Selectivity of Hard and Soft Metal Cations within Biological Systems Using the Subvalence Concept. 1. Application to Blood Coagulation: Direct Cation-Protein Electronic Effects versus Indirect Interactions through Water Networks
    de Courcy, B.
    Pedersen, L. G.
    Parisel, O.
    Gresh, N.
    Silvi, B.
    Pilme, J.
    Piquemal, J. -P.
    JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2010, 6 (04) : 1048 - 1063