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Molecules 2018, 23(6), 1292; https://doi.org/10.3390/molecules23061292

Expectation-Maximization Model for Substitution of Missing Values Characterizing Greenness of Organic Solvents

1
Division of Applied Mathematics and Probability, Institute of Mathematics, Faculty of Mathematics, University of Gdansk, 8 J. Bażyńskiego St., 80-309 Gdańsk, Poland
2
Department of Nonlinear Analysis and Statistics, Faculty of Applied Mathematics, Gdańsk University of Technology (GUT), 11/12 G. Narutowicza St., 80-233 Gdańsk, Poland
3
Department of Analytical and Food Chemistry, Faculty of Chemistry, University of Vigo, Campus As Lagoas-Marcosende s/n, 36310 Vigo, Spain
4
Department of Analytical Chemistry, Chemical Faculty, Gdańsk University of Technology (GUT), 11/12 G. Narutowicza St., 80-233 Gdańsk, Poland
*
Author to whom correspondence should be addressed.
Received: 13 April 2018 / Revised: 21 May 2018 / Accepted: 25 May 2018 / Published: 28 May 2018
(This article belongs to the Special Issue Green Analytical Chemistry)
Full-Text   |   PDF [282 KB, uploaded 30 May 2018]

Abstract

Organic solvents are ubiquitous in chemical laboratories and the Green Chemistry trend forces their detailed assessments in terms of greenness. Unfortunately, some of them are not fully characterized, especially in terms of toxicological endpoints that are time consuming and expensive to be determined. Missing values in the datasets are serious obstacles, as they prevent the full greenness characterization of chemicals. A featured method to deal with this problem is the application of Expectation-Maximization algorithm. In this study, the dataset consists of 155 solvents that are characterized by 13 variables is treated with Expectation-Maximization algorithm to predict missing data for toxicological endpoints, bioavailability, and biodegradability data. The approach may be particularly useful for substitution of missing values of environmental, health, and safety parameters of new solvents. The presented approach has high potential to deal with missing values, while assessing environmental, health, and safety parameters of other chemicals. View Full-Text
Keywords: E-M algorithm; green analytical chemistry; missing data prediction; solvents; sustainability assessment E-M algorithm; green analytical chemistry; missing data prediction; solvents; sustainability assessment
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Łuczyńska, G.; Pena-Pereira, F.; Tobiszewski, M.; Namieśnik, J. Expectation-Maximization Model for Substitution of Missing Values Characterizing Greenness of Organic Solvents. Molecules 2018, 23, 1292.

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