Odor threshold prediction by means of the Monte Carlo method

Andrey A. Toropov; Alla P. Toropova; Luigi Cappellini; Emilio Benfenati; Enrico Davoli

doi:10.1016/j.ecoenv.2016.07.039

Odor threshold prediction by means of the Monte Carlo method

Andrey A. Toropov, Alla P. Toropova, Luigi Cappellini, Emilio Benfenati, Enrico Davoli

Istituto di Ricerche Farmacologiche "Mario Negri"

Research output: Contribution to journal › Article › peer-review

Abstract

A large set of organic compounds (n=906) has been used as a basis to build up a model for the odor threshold (mg/m³). The statistical characteristics of the best model are the following: n=523, r²=0.647, RMSE=1.18 (training set); n=191, r²=0.610, RMSE=1.03, (calibration set); and n=192, r²=0.686, RMSE=1.06 (validation set). A mechanistic interpretation of the model is presented as the lists of statistical promoters of the increase and decrease in the odor threshold.

Original language	English
Pages (from-to)	390-394
Number of pages	5
Journal	Ecotoxicology and Environmental Safety
Volume	133
DOIs	https://doi.org/10.1016/j.ecoenv.2016.07.039
Publication status	Published - Nov 1 2016

Keywords

CORAL software
Monte Carlo method
Odor threshold
Optimal descriptor
QSPR/QSAR

ASJC Scopus subject areas

Medicine(all)
Pollution
Public Health, Environmental and Occupational Health
Health, Toxicology and Mutagenesis

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title = "Odor threshold prediction by means of the Monte Carlo method",

abstract = "A large set of organic compounds (n=906) has been used as a basis to build up a model for the odor threshold (mg/m3). The statistical characteristics of the best model are the following: n=523, r2=0.647, RMSE=1.18 (training set); n=191, r2=0.610, RMSE=1.03, (calibration set); and n=192, r2=0.686, RMSE=1.06 (validation set). A mechanistic interpretation of the model is presented as the lists of statistical promoters of the increase and decrease in the odor threshold.",

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AU - Toropov, Andrey A.

AU - Toropova, Alla P.

AU - Cappellini, Luigi

AU - Benfenati, Emilio

AU - Davoli, Enrico

PY - 2016/11/1

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KW - Optimal descriptor

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