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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10.1016/j.ecoenv.2016.07.039

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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.",

keywords = "CORAL software, Monte Carlo method, Odor threshold, Optimal descriptor, QSPR/QSAR",

author = "Toropov, {Andrey A.} and Toropova, {Alla P.} and Luigi Cappellini and Emilio Benfenati and Enrico Davoli",

year = "2016",

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T1 - Odor threshold prediction by means of the Monte Carlo method

AU - Toropov, Andrey A.

AU - Toropova, Alla P.

AU - Cappellini, Luigi

AU - Benfenati, Emilio

AU - Davoli, Enrico

PY - 2016/11/1

Y1 - 2016/11/1

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AB - 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.

KW - CORAL software

KW - Monte Carlo method

KW - Odor threshold

KW - Optimal descriptor

KW - QSPR/QSAR

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JO - Ecotoxicology and Environmental Safety

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Odor threshold prediction by means of the Monte Carlo method

Abstract

Keywords

ASJC Scopus subject areas

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