Skip to main content
SpringerLink
Log in

Quantitative structure–activity relationships to predict sweet and non-sweet tastes

  • Regular Article
  • Published:
Theoretical Chemistry Accounts Aims and scope Submit manuscript

Abstract

The aim of this work was the calibration and validation of mathematical models based on a quantitative structure–activity relationship approach to discriminate sweet, tasteless and bitter molecules. The sweet-tasteless and the sweet-bitter datasets included 566 and 508 compounds, respectively. A total of 3763 conformation-independent Dragon molecular descriptors were calculated and subsequently reduced through both unsupervised reduction and supervised selection coupled with the k-nearest neighbors classification technique. A model based on nine descriptors was retained as the optimal one for sweet and tasteless molecules, while a model based on four descriptors was calibrated for the sweetness-bitterness dataset. Models were properly validated through cross-validation and external test sets. The applicability domain of models was investigated, and the interpretation of the role of the molecular descriptors in classifying sweet and non-sweet tastes was evaluated. The classification and the performance of the models presented in this paper are simple but accurate. They are based on a relatively small number of descriptors and a straightforward classification approach. The results presented here indicate that the proposed models can be used to accurately select new compounds as potential sweetener candidates.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Shallenberger RS (1993) Taste chemistry. Springer Science & Business Media, Berlin

    Book  Google Scholar 

  2. Hugot E, Jenkins GH (1972) Handbook of cane sugar engineering, vol 114. Elsevier, Philadelphia

    Google Scholar 

  3. Asadi M (2006) Beet-sugar handbook. Wiley, NewYork

    Book  Google Scholar 

  4. Birch GG (1999) Modulation of sweet taste. BioFactors 9(1):73–80

    Article  CAS  Google Scholar 

  5. deMan JM (1999) Principles of food chemistry, 3rd edn. Berlin, Springer

    Book  Google Scholar 

  6. Oertly E, Myers RG (1919) A new theory relating constitution to taste. Simple relations between the constitution of aliphatic compounds and their sweet taste. J Am Chem Soc 41(6):855–867

    Article  CAS  Google Scholar 

  7. Shallenberger RS, Acree TE (1967) Molecular theory of sweet taste. Nature 216:480–482

    Article  CAS  Google Scholar 

  8. Kier LB (1972) A molecular theory of sweet taste. J Pharm Sci 61(9):1394–1397

    Article  CAS  Google Scholar 

  9. Nofre C, Tinti J-M (1996) Sweetness reception in man: the multipoint attachment theory. Food Chem 56(3):263–274

    Article  CAS  Google Scholar 

  10. Ellis JW (1995) Overview of sweeteners. J Chem Educ 72(8):671

    Article  CAS  Google Scholar 

  11. Katritzky AR, Lobanov VS, Karelson M (1995) QSPR: the correlation and quantitative prediction of chemical and physical properties from structure. Chem Soc Rev 24:279–287

    Article  CAS  Google Scholar 

  12. Trinajstic N (1992) Chemical graph theory. CRC Press, Boca Raton

    Google Scholar 

  13. Diudea MV (2001) QSPR/QSAR studies by molecular descriptors. Nova Science Publishers, New York

    Google Scholar 

  14. Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics, vol 2. Wiley-VCH, Weinheim

    Book  Google Scholar 

  15. Iwamura H (1980) Structure-taste relationship of perillartine and nitro-and cyanoaniline derivatives. J Med Chem 23(3):308–312

    Article  CAS  Google Scholar 

  16. van der Wel H, van der Heijden A, Peer H (1987) Sweeteners. Food Rev Int 3(3):193–268

    Article  Google Scholar 

  17. Kier LB (1980) Molecular structure influencing either a sweet or bitter taste among aldoximes. J Pharm Sci 69(4):416–419

    Article  CAS  Google Scholar 

  18. Takahashi Y, Miyashita Y, Tanaka Y, Abe H, Sasaki S (1982) A consideration for structure-taste correlations of perillartines using pattern-recognition techniques. J Med Chem 25(10):1245–1248

    Article  CAS  Google Scholar 

  19. Takahashi Y, Abe H, Miyashita Y, Tanaka Y, Hayasaka H, Sasaki SI (1984) Discriminative structural analysis using pattern recognition techniques in the structure-taste problem of perillartines. J Pharm Sci 73(6):737–741

    Article  CAS  Google Scholar 

  20. Miyashita Y, Takahashi Y, Takayama C, Ohkubo T, Funatsu K, Sasaki S-I (1986) Computer-assisted structure/taste studies on sulfamates by pattern recognition methods. Anal Chim Acta 184:143–149

    Article  CAS  Google Scholar 

  21. Miyashita Y, Takahashi Y, Takayama C, Sumi K, Nakatsuka K, Ohkubo T, Abe H, Sasaki S (1986) Structure-taste correlation of L-aspartyl dipeptides using the SIMCA method. J Med Chem 29(6):906–912

    Article  CAS  Google Scholar 

  22. Okuyama T, Miyashita Y, Kanaya S, Katsumi H, S-i Sasaki, Randić M (1988) Computer assisted structure-taste studies on sulfamates by pattern recognition method using graph theoretical invariants. J Comput Chem 9(6):636–646

    Article  CAS  Google Scholar 

  23. Spillane WJ, McGlinchey G (1981) Structure-activity studies on sulfamate sweeteners II: semiquantitative structure-taste relationship for sulfamate (RNHSO3 −) sweeteners-the role of R. J Pharm Sci 70(8):933–935

    Article  CAS  Google Scholar 

  24. Spillane WJ, McGlinchey G, Muircheartaigh IÓ, Benson GA (1983) Structure-activity studies on sulfamate sweetners III: structure-taste relationships for heterosulfamates. J Pharm Sci 72(8):852–856

    Article  CAS  Google Scholar 

  25. Spillane WJ, Sheahan MB (1989) Semi-quantitative and quantitative structure-taste relationships for carboand hetero-sulphamate (RNHSO3 −) sweeteners. J Chem Soc, Perkin Trans 2(7):741–746

    Article  Google Scholar 

  26. Spillane WJ, Sheahan M (1991) Structure-taste relationships for sulfamate sweeteners (RNHSO3 −). Phosphorus Sulfur Silicon Relat Elem 59(1–4):255–258

    Article  Google Scholar 

  27. Spillane WJ, Sheahan MB, Ryder CA (1993) Synthesis and taste properties of sodium disubstituted phenylsulfamates. Structure-taste relationships for sweet and bitter/sweet sulfamates. Food Chem 47(4):363–369

    Article  CAS  Google Scholar 

  28. Drew MGB, Wilden GRH, Spillane WJ, Walsh RM, Ryder CA, Simmie JM (1998) Quantitative structure-activity relationship studies of sulfamates RNHSO3Na: distinction between sweet, sweet-bitter, and bitter molecules. J Agric Food Chem 46(8):3016–3026

    Article  CAS  Google Scholar 

  29. Spillane WJ, Ryder CA, Curran PJ, Wall SN, Kelly LM, Feeney BG, Newell J (2000) Development of structure-taste relationships for sweet and non-sweet heterosulfamates. J Chem Soc Perkin Trans 2(7):1369–1374

    Article  Google Scholar 

  30. Spillane WJ, Feeney BG, Coyle CM (2002) Further studies on the synthesis and tastes of monosubstituted benzenesulfamates. A semi-quantitative structure-taste relationship for the meta-compounds. Food Chem 79(1):15–22

    Article  CAS  Google Scholar 

  31. Spillane WJ, Kelly LM, Feeney BG, Drew MG, Hattotuwagama CK (2003) Synthesis of heterosulfamates. Search for structure-taste relationships. Arkivoc 7:297–309

    Google Scholar 

  32. Kelly DP, Spillane WJ, Newell J (2005) Development of structure-taste relationships for monosubstituted phenylsulfamate sweeteners using classification and regression tree (CART) analysis. J Agric Food Chem 53(17):6750–6758

    Article  CAS  Google Scholar 

  33. Spillane WJ, Kelly DP, Curran PJ, Feeney BG (2006) Structure-taste relationships for disubstituted phenylsulfamate tastants using classification and regression tree (CART) Analysis. J Agric Food Chem 54(16):5996–6004

    Article  CAS  Google Scholar 

  34. Spillane WJ, Coyle CM, Feeney BG, Thompson EF (2009) Development of structure-taste relationships for thiazolyl-, benzothiazolyl-, and thiadiazolylsulfamates. J Agric Food Chem 57(12):5486–5493

    Article  CAS  Google Scholar 

  35. Spillane WJ (1993) Structure taste studies of sulphamates. In: Mathlouthi M, Kanters JA, Birch GG (eds) Sweet-taste chemoreception. Elsevier Science Publishers, Philadelphia, p 283

    Google Scholar 

  36. Spillane WJ, Ryder CA, Walsh MR, Curran PJ, Concagh DG, Wall SN (1996) Sulfamate sweeteners. Food Chem 56(3):255–261

    Article  CAS  Google Scholar 

  37. Walters DE (2006) Analysing and predicting properties of sweet-tasting compounds. In: Spillane WJ (ed) Optimising sweet taste in foods. pp 283–291

  38. Rojas C, Duchowicz PR, Pis Diez R, Tripaldi P (2016) Applications of quantitative structure-relative sweetness relationships in food chemistry. In: Mercader AG, Duchowicz PR, Sivakumar PM (eds) Chemometrics applications and research: QSAR in medicinal chemistry. CRC Press, Taylor & Francis Group, pp 317–339

    Google Scholar 

  39. van der Heijden A (1997) Historical overview on structure-activity relationships among sweeteners. Pure Appl Chem 69(4):667–674

    Google Scholar 

  40. Spillane W, Malaubier J-B (2014) Sulfamic acid and its N-and O-substituted derivatives. Chem Rev 114(4):2507–2586

    Article  CAS  Google Scholar 

  41. Organisation for Economic Co-operation and Development (2007) Guidance document on the validation of (quantitative)structure-activity relationships [(Q)SAR] models. OECD Publishing, Paris

    Google Scholar 

  42. Arnoldi A, Bassoli A, Borgonovo G, Drew MG, Merlini L, Morini G (1998) Sweet isovanillyl derivatives: synthesis and structure-taste relationships of conformationally restricted analogues. J Agric Food Chem 46(10):4002–4010

    Article  CAS  Google Scholar 

  43. Arnoldi A, Bassoli A, Borgonovo G, Merlini L (1995) Synthesis and sweet taste of optically active (−)-haematoxylin and of some (±)-haematoxylin derivatives. J Chem Soc Perkin Trans 1(19):2447–2453

    Article  Google Scholar 

  44. Arnoldi A, Bassoli A, Borgonovo G, Merlini L, Morini G (1997) Synthesis and structure-activity relationships of sweet 2-benzoylbenzoic acid derivatives. J Agric Food Chem 45(6):2047–2054

    Article  CAS  Google Scholar 

  45. Arnoldi A, Bassoli A, Merlini L (1996) Progress in isovanillyl sweet compounds. Food Chem 56(3):247–253

    Article  CAS  Google Scholar 

  46. Arnoldi A, Bassoli A, Merlini L, Ragg E (1991) Isovanillyl sweeteners. Synthesis, conformational analysis, and structure-activity relationship of some sweet oxygen heterocycles. J Chem Soc Perkin Trans 2(9):1399–1406

    Article  Google Scholar 

  47. Arnoldi A, Bassoli A, Merlini L, Ragg E (1993) Isovanillyl sweeteners. Synthesis and sweet taste of sulfur heterocycles. J Chem Soc Perkin Trans 1(12):1359–1366

    Article  Google Scholar 

  48. Bassoli A, Borgonovo G, Drew MG, Merlini L (2000) Enantiodifferentiation in taste perception of isovanillic derivatives. Tetrahedron Asymmetry 11(15):3177–3186

    Article  CAS  Google Scholar 

  49. Bassoli A, Drew MGB, Hattotuwagama CK, Merlini L, Morini G, Wilden GRH (2001) Quantitative structure-activity relationships of sweet isovanillyl derivatives. Quant Struct-Act Relat 20(1):3–16

    Article  CAS  Google Scholar 

  50. Belitz H-D, Grosch W, Schieberle P (2009) Food chemistry, 4th edn. Springer-Verlag, Heidelberg

    Google Scholar 

  51. Nanayakkara NPD, Hussain RA, Pezzuto JM, Soejarto DD, Kinghorn AD (1988) An intensely sweet dihydroflavonol derivative based on a natural product lead compound. J Med Chem 31(6):1250–1253

    Article  CAS  Google Scholar 

  52. O’Brien-Nabors L (2001) Alternative sweeteners, 3rd edn. New York, Marcel Dekker Inc

    Google Scholar 

  53. Yamato M, Hashigaki K (1979) Chemical structure and sweet taste of isocoumarins and related compounds. Chem Senses 4(1):35–47

    Article  CAS  Google Scholar 

  54. Yang X, Chong Y, Yan A, Chen J (2011) In-silico prediction of sweetness of sugars and sweeteners. Food Chem 128(3):653–658

    Article  CAS  Google Scholar 

  55. Zhong M, Chong Y, Nie X, Yan A, Yuan Q (2013) Prediction of sweetness by multilinear regression analysis and support vector machine. J Food Sci 78(9):S1445–S1450

    Article  CAS  Google Scholar 

  56. Paulus K, Reisch AM (1980) The influence of temperature on the threshold values of primary tastes. Chem Senses 5(1):11–21

    Article  CAS  Google Scholar 

  57. Open Babel, Open Babel: The Open Source Chemistry Toolbox. http://openbabel.org/

  58. Berthold M, Cebron N, Dill F, Gabriel T, Kötter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B (2008) KNIME: the konstanz information miner. In: Preisach C, Burkhardt H, Schmidt-Thieme L, Decker R (eds) Data analysis, machine learning and applications. Studies in classification, data analysis, and knowledge organization. Springer, Berlin Heidelberg, pp 319–326

    Chapter  Google Scholar 

  59. Hypercube, Inc., HyperChem. http://www.hyper.com

  60. TALETE, srl., Dragon (version 6) (2015). Software for Molecular Descriptor Calculation, http://www.talete.mi.it/

  61. Pearlman RS (1993) 3D molecular structures: generation and use in 3D searching. In: Kubinyi H (ed) 3D QSAR in drug design. Theory and applications, Springer Science & Business Media, pp 41–79

    Google Scholar 

  62. Doweyko AM (2004) 3D-QSAR illusions. J Comput Aided Mol Des 18(7–9):587–596

    Article  CAS  Google Scholar 

  63. Hechinger M, Leonhard K, Marquardt W (2012) What is wrong with quantitative structure-property relations models based on three-dimensional descriptors? J Chem Inf Model 52(8):1984–1993

    Article  CAS  Google Scholar 

  64. Kowalski B, Bender C (1972) k-Nearest neighbor classification rule (pattern recognition) applied to nuclear magnetic resonance spectral interpretation. Anal Chem 44(8):1405–1411

    Article  CAS  Google Scholar 

  65. Ballabio D, Consonni V, Mauri A, Claeys-Bruno M, Sergent M, Todeschini R (2014) A novel variable reduction method adapted from space-filling designs. Chemometr Intell Lab Syst 136:147–154

    Article  CAS  Google Scholar 

  66. Leardi R, Gonzalez AL (1998) Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemometr Intell Lab Syst 41(2):195–207

    Article  CAS  Google Scholar 

  67. Ballabio D, Consonni V (2013) Classification tools in chemistry. Part 1: linear models. PLS-DA. Anal Methods 5(16):3790–3798

    Article  CAS  Google Scholar 

  68. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometr Intell Lab Syst 2(1):37–52

    Article  CAS  Google Scholar 

  69. Jolliffe IT (1986) Principal component analysis. Springer Science + Business Media, Berlin

    Book  Google Scholar 

  70. Krzanowski W (1988) Principles of multivariate analysis: a user’s perspective. Oxford University Press, Oxford

    Google Scholar 

  71. Bro R, Smilde AK (2014) Principal component analysis. Anal Methods 6(9):2812–2831

    Article  CAS  Google Scholar 

  72. Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R (2012) Comparison of different approaches to define the applicability domain of QSAR models. Molecules 17(5):4791–4810

    Article  CAS  Google Scholar 

  73. Sahigara F, Ballabio D, Todeschini R, Consonni V (2013) Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions. J Cheminfor 5:27

    Article  CAS  Google Scholar 

  74. Cassotti M, Consonni V, Mauri A, Ballabio D (2014) Validation and extension of a similarity-based approach for prediction of acute aquatic toxicity towards Daphnia magna. SAR QSAR Environ Res 25(12):1013–1036

    Article  CAS  Google Scholar 

  75. Cassotti M, Ballabio D, Consonni V, Mauri A, Tetko IV, Todeschini R (2014) Prediction of acute aquatic toxicity toward Daphnia magna by using the GA-kNN method. Altern Lab Anim 42:31–41

    CAS  Google Scholar 

  76. Cassotti M, Ballabio D, Todeschini R, Consonni V (2015) A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas). SAR QSAR Environ Res 26(3):217–243

    Article  CAS  Google Scholar 

  77. Ballabio D (2015) A MATLAB toolbox for principal component analysis and unsupervised exploration of data structure. Chemometr Intell Lab Syst 149:1–9

    Article  CAS  Google Scholar 

  78. MathWorks: Natick, MatLab (version 7.13.0.564) (2011). http://www.mathworks.com

  79. Carhart RE, Smith DH, Venkataraghavan R (1985) Atom pairs as molecular features in structure-activity studies: definition and applications. J Chem Inf Comput Sci 25(2):64–73

    Article  CAS  Google Scholar 

  80. Birch GG, Karim R, Lopez A (1994) Novel aspects of structure-activity relationships in sweet taste chemoreception. Food Qual Prefer 5(1):87–93

    Article  Google Scholar 

  81. Rojas C, Tripaldi P, Duchowicz PR (2016) A new QSPR study on relative sweetness. Int J Quant Struct Prop Relatsh 1(1):76–90

    Google Scholar 

  82. Ghose AK, Viswanadhan VN, Wendoloski JJ (1998) Prediction of hydrophobic (lipophilic) properties of small organic molecules using fragmental methods: an analysis of ALOGP and CLOGP methods. J Phys Chem A 102(21):3762–3772

    Article  CAS  Google Scholar 

  83. Baek N-I, Chung M-S, Shamon L, Kardono LBS, Tsauri S, Padmawinata K, Pezzuto JM, Soejarto DD, Kinghorn AD (1993) Selligueain A, a novel highly sweet proanthocyanidin from the rhizomes of Selliguea feei. J Nat Prod 56(9):1532–1538

    Article  CAS  Google Scholar 

  84. Birch GG (1987) Sweetness and sweeteners. Endeavour 11(1):21–24

    Article  CAS  Google Scholar 

  85. Birch G, Mylvaganam A (1976) Evidence for the proximity of sweet and bitter receptor sites. Nature 260:632–634

    Article  CAS  Google Scholar 

  86. van der Heijden A, van der Wel H, Peer HG (1985) Structure-activity relationships in sweeteners. I. Nitroanilines, sulphamates, oximes, isocoumarins and dipeptides. Chem Senses 10(1):57–72

    Article  Google Scholar 

  87. Katritzky AR, Petrukhin R, Perumal S, Karelson M, Prakash I, Desai N (2002) A QSPR study of sweetness potency using the CODESSA program. Croat Chem Acta 75(2):475–502

    CAS  Google Scholar 

Download references

Acknowledgments

Cristian Rojas is grateful for his PhD Fellowship from the National Secretary of Higher Education, Science, Technology and Innovation (SENESCYT) from the Republic of Ecuador, as well as for the financial support provided by the Ministry of Foreign Affairs and International Cooperation (FARNESINA) from the Italian Government for the PhD research conducted at the University of Milano-Bicocca.

Author information

Authors and Affiliations

  1. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (CCT La Plata-CONICET, UNLP), Diag. 113 y 64, C.C. 16, Sucursal 4, 1900, La Plata, Argentina

    Cristian Rojas

  2. Decanato General de Investigaciones, Universidad del Azuay, Av. 24 de Mayo 7-77 y Hernán Malo, Apartado Postal 01.01.981, Cuenca, Ecuador

    Cristian Rojas

  3. Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.za della Scienza 1, 20126, Milan, Italy

    Davide Ballabio, Viviana Consonni, Andrea Mauri & Roberto Todeschini

  4. Laboratorio de Química-Física de Alimentos, Facultad de Ciencia y Tecnología, Universidad del Azuay, Av. 24 de Mayo 7-77 y Hernán Malo, Apartado Postal 01.01.981, Cuenca, Ecuador

    Piercosimo Tripaldi

Authors
  1. Cristian Rojas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Davide Ballabio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Viviana Consonni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Piercosimo Tripaldi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrea Mauri
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Roberto Todeschini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cristian Rojas.

Additional information

Published as part of the special collection of articles “CHITEL 2015 - Torino - Italy”.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (XLSX 63 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rojas, C., Ballabio, D., Consonni, V. et al. Quantitative structure–activity relationships to predict sweet and non-sweet tastes. Theor Chem Acc 135, 66 (2016). https://doi.org/10.1007/s00214-016-1812-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00214-016-1812-1

Keywords

Search

Navigation