Correlate Toxicity Order Numbers with Metal Ion Characteristics through the Ridge Regression (RR) Method

Xiao Li Li; Lan Wang; Yu Li

doi:10.4028/www.scientific.net/AMR.599.155

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 599Correlate Toxicity Order Numbers with Metal Ion...

Correlate Toxicity Order Numbers with Metal Ion Characteristics through the Ridge Regression (RR) Method

Abstract:

In this paper, we use 9 ion characteristics (choose from 14 ion characteristics by correlation analysis) in 19 metals to construct the simulate model through ridge regression (RR) which can remove the high multicollinearity among the ion characteristics. Two multi-parameter regression models were established: the first multi-parameter QICAR model was used to distinguish the quantitative relationship between the ion characteristics and toxicity order numbers (TON). We can found that the parameters AN, X_m, AN/ΔIP, AW and X^m²r have positive coefficients, and AN and AW have the more contribution to the toxicity of heavy metals. The parameters ΔE₀, |logK_OH|, AR/AW and δ_P have negative coefficients, and δ_P does the most negatively influence to the toxicity. The second model we constructed to simulate the toxicity order numbers (TON) of metals that hard to test by experiment. The regression model provided the high simulate ability, with Nash-Suttcliffe simulation efficiency coefficients (NSC) of 0.94 for the modeling phase.

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Periodical:

Advanced Materials Research (Volume 599)

Pages:

155-158

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.599.155

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Online since:

November 2012

Authors:

Xiao Li Li, Lan Wang, Yu Li

Keywords:

Heavy Metal, Quantitative Ion Character-Activity Relationship (QICAR), Ridge Regression (RR), Toxicity Order Numbers (TON)

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