Odor threshold prediction by means of the Monte Carlo method

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

Research output: Contribution to journal › Article

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 languageEnglish
Pages (from-to)390-394
Number of pages5
JournalEcotoxicology and Environmental Safety
Volume133
DOIs
Publication statusPublished - Nov 1 2016

Fingerprint

Monte Carlo Method
Odors
Monte Carlo methods
Calibration
Organic compounds
Odorants

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

Cite this

Odor threshold prediction by means of the Monte Carlo method. / Toropov, Andrey A.; Toropova, Alla P.; Cappellini, Luigi; Benfenati, Emilio; Davoli, Enrico.

In: Ecotoxicology and Environmental Safety, Vol. 133, 01.11.2016, p. 390-394.

Research output: Contribution to journal › Article

Toropov, AA, Toropova, AP, Cappellini, L, Benfenati, E & Davoli, E 2016, 'Odor threshold prediction by means of the Monte Carlo method', Ecotoxicology and Environmental Safety, vol. 133, pp. 390-394. https://doi.org/10.1016/j.ecoenv.2016.07.039
Toropov AA, Toropova AP, Cappellini L, Benfenati E, Davoli E. Odor threshold prediction by means of the Monte Carlo method. Ecotoxicology and Environmental Safety. 2016 Nov 1;133:390-394. https://doi.org/10.1016/j.ecoenv.2016.07.039
Toropov, Andrey A. ; Toropova, Alla P. ; Cappellini, Luigi ; Benfenati, Emilio ; Davoli, Enrico. / Odor threshold prediction by means of the Monte Carlo method. In: Ecotoxicology and Environmental Safety. 2016 ; Vol. 133. pp. 390-394.
@article{0615110c6e9d4d998d935db25faf92fb,
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",
month = "11",
day = "1",
doi = "10.1016/j.ecoenv.2016.07.039",
language = "English",
volume = "133",
pages = "390--394",
journal = "Ecotoxicology and Environmental Safety",
issn = "0147-6513",
publisher = "Academic Press Inc.",

}

TY - JOUR

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

N2 - 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.

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

UR - http://www.scopus.com/inward/record.url?scp=84982732379&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84982732379&partnerID=8YFLogxK

U2 - 10.1016/j.ecoenv.2016.07.039

DO - 10.1016/j.ecoenv.2016.07.039

M3 - Article

AN - SCOPUS:84982732379

VL - 133

SP - 390

EP - 394

JO - Ecotoxicology and Environmental Safety

JF - Ecotoxicology and Environmental Safety

SN - 0147-6513

ER -

Access to Document