title: Improvement of the identification of four heavy metals in environmental samples by using predictive decision tree models coupled with a set of five bioluminescent bacteria.
abstract: A primary statistical model based on the crossings between the different detection ranges of a set of five bioluminescent bacterial strains was developed to identify and quantify four metals which were at several concentrations in different mixtures: cadmium, arsenic III, mercury, and copper. Four specific decision trees based on the CHAID algorithm (CHi-squared Automatic Interaction Detector type) which compose this model were designed from a database of 576 experiments (192 different mixture conditions). A specific software, 'Metalsoft', helped us choose the best decision tree and a user-friendly way to identify the metal. To validate this innovative approach, 18 environmental samples containing a mixture of these metals were submitted to a bioassay and to standardized chemical methods. The results show on average a high correlation of 98.6% for the qualitative metal identification and 94.2% for the quantification. The results are particularly encouraging, and our model is able to provide semiquantitative information after only 60 min without pretreatments of samples.
authors.affiliation: University of Nantes, UMR CNRS 6144 GEPEA, France.
authors.forename: Sulivan
authors.initials: S
authors.lastname: Jouanneau
authors.forename: Marie-José
authors.initials: MJ
authors.lastname: Durand
authors.forename: Philippe
authors.initials: P
authors.lastname: Courcoux
authors.forename: Thomas
authors.initials: T
authors.lastname: Blusseau
authors.forename: Gérald
authors.initials: G
authors.lastname: Thouand
mesh_terms: D001419:Bacteria / Q000187:drug effects*; D003661:Decision Support Techniques; D003663:Decision Trees; D004784:Environmental Monitoring / Q000379:methods*; D008163:Luminescent Measurements; D019216:Metals, Heavy / Q000032:analysis / Q000633:toxicity*; D015233:Models, Statistical; D014874:Water Pollutants, Chemical / Q000032:analysis / Q000633:toxicity*; D014877:Water Pollution, Chemical / Q000706:statistics & numerical data
chemical_list: D019216:Metals, Heavy; D014874:Water Pollutants, Chemical