Molecular Diversity

, Volume 22, Issue 2, pp 397–403 | Cite as

QSPR analysis of threshold of odor for the large number of heterogenic chemicals

  • Andrey A. Toropov
  • Alla P. Toropova
  • Luigi Cappellini
  • Emilio Benfenati
  • Enrico Davoli
Short Communication
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Abstract

Quantitative structure–property relationships for odor thresholds based on representation of the molecular structure by the simplified molecular input-line entry system were established using the CORAL software. The total set of compounds with numerical data on the so-called arithmetic odor thresholds (\(n=1259\)) was distributed into the training and validation sets, three times. The average statistical quality of these models is (1) for training set \(\tilde{n}=967\pm 20\;({\approx }\,80\%), {\mathop {{r}}\limits ^\frown }^{2}=0.62\pm 0.02\); and (2) for validation set \(\tilde{n}=290\pm 20\;({\approx }\,20\%), {\mathop {{r}}\limits ^\frown }^{2}=0.62\pm 0.04\). Thus, the predictive potential of this approach was confirmed for three different splits into training and validation sets. Domain of applicability and mechanistic interpretation of these models are defined from the probabilistic point of view. The suggested models are built up according to OECD principles.

Keywords

Odor threshold QSPR Monte Carlo method CORAL software OECD principles 
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Notes

Acknowledgements

Authors thank Mr. Daniele Zinetti and Gruppo Gabeca for supplying the odour thresholds database and LIFE-COMBASE contract (LIFE15 ENV/ES/000416) for financial support.

Supplementary material

11030_2017_9800_MOESM1_ESM.docx (222 kb)
Supplementary material 1 (docx 221 KB)

References

  1. 1.
    Kiechle M (2015) Navigating by nose: fresh air, stench nuisance, and the urban environment, 1840–1880. J Urban Hist.  https://doi.org/10.1177/0096144214566981 Google Scholar
  2. 2.
    Davoli E, Zuccato E, Bianchi G, Grande MI, Bonati S, Rossi AN, Porporato C, Pereno M, Soldati S (2016) Dynamic olfactometry and sample toxicity. A case study for a MSW incinerator odour assessment project. Chem Eng Trans 54:13–18.  https://doi.org/10.3303/CET1654003 Google Scholar
  3. 3.
    Palmiotto M, Fattore E, Paiano V, Celeste G, Colombo A, Davoli E (2014) Influence of a municipal solid waste landfill in the surrounding environment: toxicological risk and odor nuisance effects. Environ Int 68:16–24.  https://doi.org/10.1016/j.envint.2014.03.004 CrossRefPubMedGoogle Scholar
  4. 4.
    Davoli E, Gangai ML, Morselli L, Tonelli D (2003) Characterisation of odorants emissions from landfills by SPME and GC/MS. Chemosphere 51:357–368.  https://doi.org/10.1016/S0045-6535(02)00845-7 CrossRefPubMedGoogle Scholar
  5. 5.
    Bianchi G, Celeste G, Palmiotto M, Davoli E (2010) Source identification of odours and VOCs from a composting plant by multivariate analysis of trace volatile organic compounds. Chem Eng Trans 23:279–284.  https://doi.org/10.3303/CET1023047 Google Scholar
  6. 6.
    Tromelin A (2016) Odour perception: a review of an intricate signalling pathway. Flavour Fragr J 31:107–119.  https://doi.org/10.1002/ffj.3295 CrossRefGoogle Scholar
  7. 7.
    Toropova AP, Toropov AA, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2011) CORAL: quantitative structure-activity relationship models for estimating toxicity of organic compounds in rats. J Comp Chem 32:2727–2733.  https://doi.org/10.1002/jcc.21848 CrossRefGoogle Scholar
  8. 8.
    Toropova AP, Toropov AA, Raskova M, Raska I (2016) Improved building up a model of toxicity towards Pimephales promelas by the Monte Carlo method. Environ Toxicol Pharmacol 48:278–285.  https://doi.org/10.1016/j.etap.2016.11.010 CrossRefPubMedGoogle Scholar
  9. 9.
    Toropov AA, Toropova AP, Cappellini L, Benfenati E, Davoli E (2016) Odor threshold prediction by means of the Monte Carlo method. Ecotoxicol Environ Saf 133:390–394.  https://doi.org/10.1016/j.ecoenv.2016.07.039 CrossRefPubMedGoogle Scholar
  10. 10.
    Rodríguez O, Teixeira MA, Rodrigues AE (2011) Prediction of odour detection thresholds using partition coefficients. Flavour Fragr J 26:421–428.  https://doi.org/10.1002/ffj.2076 CrossRefGoogle Scholar
  11. 11.
    Korichi M, Gerbaud V, Floquet P, Meniai A-H, Nacef S, Joulia X (2008) Computer aided aroma design I—molecular knowledge framework. Chem Eng Process 47:1902–1911.  https://doi.org/10.1016/j.cep.2008.02.008 CrossRefGoogle Scholar
  12. 12.
    Korichi M, Gerbaud V, Talou T, Floquet P, Meniai A-H, Nacef S (2008) Computer-aided aroma design. II. Quantitative structure-odour relationship. Chem Eng Process 47:1912–1925.  https://doi.org/10.1016/j.cep.2008.02.009 CrossRefGoogle Scholar
  13. 13.
    Dearden JC (1994) Quantitative structure-activity relationships (QSAR) and odour. Food Qual Prefer 5:81–86.  https://doi.org/10.1016/0950-3293(94)90011-6 CrossRefGoogle Scholar
  14. 14.
    van Gemert LJ (2011) Compilations of odour threshold values in air and water and other media. Oliemans Punter & Partners Publisher, Amsterdam. http://www.leffingwell.com/bacis2.htm
  15. 15.
    Ojha PK, Roy K (2011) Comparative QSARs for antimalarial endochins: importance of descriptor-thinning and noise reduction prior to feature selection. Chemometr Intell Lab 109:146–161.  https://doi.org/10.1016/j.chemolab.2011.08.007
  16. 16.
    Toropov AA, Toropova AP, Puzyn T, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2013) QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells. Chemosphere 92:31–37.  https://doi.org/10.1016/j.chemosphere.2013.03.012 CrossRefPubMedGoogle Scholar
  17. 17.
    Toropova AP, Toropov AA, Benfenati E, Leszczynska D, Leszczynski J (2015) QSAR model as a random event: a case of rat toxicity. Bioorg Med Chem 23:1223–1230.  https://doi.org/10.1016/j.bmc.2015.01.055 CrossRefPubMedGoogle Scholar
  18. 18.
    Toropova AP, Toropov AA, Veselinović JB, Veselinović AM (2015) QSAR as a random event: a case of NOAEL. Environ Sci Pollut Res Int 22:8264–8271.  https://doi.org/10.1007/s11356-014-3977-2 CrossRefPubMedGoogle Scholar
  19. 19.
    OECD Environment Health and Safety Publications Series on Testing and Assessment (2007) No. 69. Guidance document on the validation of (quantitative) structure—activity relationship [(Q)SAR] models. http://search.oecd.org/officialdocuments

Copyright information

© Springer International Publishing AG, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health SciencesIRCCS Istituto di Ricerche Farmacologiche Mario NegriMilanItaly
  2. 2.Laboratory of Mass Spectrometry, Department of Environmental Health SciencesIRCCS Istituto di Ricerche Farmacologiche Mario NegriMilanItaly

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