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Discrimination of Soils and Assessment of Soil Fertility Using Information from an Ion Selective Electrodes Array and Artificial Neural Networks
Author(s) -
Mimendia Aitor,
Gutiérrez Juan M.,
Alcañiz Josep M.,
del Valle Manel
Publication year - 2014
Publication title -
clean – soil, air, water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 66
eISSN - 1863-0669
pISSN - 1863-0650
DOI - 10.1002/clen.201300923
Subject(s) - extraction (chemistry) , artificial neural network , acetic acid , electronic tongue , soil water , biological system , computer science , artificial intelligence , chemistry , analytical chemistry (journal) , pattern recognition (psychology) , chromatography , environmental science , soil science , organic chemistry , food science , taste , biology
Multichannel sensor measurements combined with advanced treatment is the departure point for a new concept in sensorics, the electronic tongue. Our setup worked with an array of 20 ion selective electrodes plus an artificial neural network used as a pattern recognition method applied to soil analysis. With this design, we got a versatile tool which was able to perform qualitative and quantitative determinations. As first application, the qualitative discrimination between six distinct soil types based on their extractable components was attempted. The procedure was simplified to a single extraction step before measurements. Water, a BaCl 2 saline solution and an acetic acid extract were evaluated as extracting agents. The best performance was reached with the acetic acid extraction method with a correct classification rate and sensitivity both of 94%, and a specificity of 100%. In addition, a quantitative determination of several physicochemical properties of agricultural interest, such as organic carbon content and selected cations (like K + or Mg 2+ ) and anions (like NO 3 − or Cl − ) was also demonstrated, showing satisfactory agreement with the reference methods.