Unsupervised pattern-recognition techniques to investigate metal pollution in estuaries

TY - JOUR

T1 - Unsupervised pattern-recognition techniques to investigate metal pollution in estuaries

AU - Gredilla, A.

AU - de Vallejuelo, S. Fdez-Ortiz

AU - Amigo Rubio, Jose Manuel

AU - de Diego, A.

AU - Madariaga, J.M.

PY - 2013/5

Y1 - 2013/5

N2 - We provide a clear classification of pattern-recognition techniques.•The main features of unsupervised pattern-recognition techniques are summarized.•Pattern recognition applied to pollution monitoring is an increasing area of concern.•The most important papers on pattern recognition in estuarine sediments are reviewed.•Multivariate techniques are mandatory to handle environmental data tables. There has been a significant increase in the application of unsupervised pattern-recognition techniques to the analysis of long datasets emerging from the monitoring of metal pollution in estuaries. In this work, we thoroughly review the most important articles published on this topic in recent years.

AB - We provide a clear classification of pattern-recognition techniques.•The main features of unsupervised pattern-recognition techniques are summarized.•Pattern recognition applied to pollution monitoring is an increasing area of concern.•The most important papers on pattern recognition in estuarine sediments are reviewed.•Multivariate techniques are mandatory to handle environmental data tables. There has been a significant increase in the application of unsupervised pattern-recognition techniques to the analysis of long datasets emerging from the monitoring of metal pollution in estuaries. In this work, we thoroughly review the most important articles published on this topic in recent years.

U2 - 10.1016/j.trac.2013.01.014

DO - 10.1016/j.trac.2013.01.014

M3 - Journal article

SN - 0165-9936

VL - 46

SP - 59

EP - 69

JO - Trends in Analytical Chemistry

JF - Trends in Analytical Chemistry

ER -