Prediction of sweetness by multilinear regression analysis and support vector machine

Journal of Food Science

Volumn 78, Issue 9, 2013, Pages

  Zhong, Min ^a   Chong, Yang ^a   Nie, Xianglei ^a   Yan, Aixia ^a   Yuan, Qipeng ^a  

^a BEIJING UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

Author keywords

Food properties; Multilinear regression (MLR); Quantitative structure activity relationships (QSAR); Support vector machine (SVM); Sweeteners

Indexed keywords

SWEETENING AGENT;

ARTICLE; CHEMICAL MODEL; CHEMISTRY; EVALUATION STUDY; FOOD HANDLING; FOOD PROPERTIES; METHODOLOGY; MULTILINEAR REGRESSION (MLR); QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS (QSAR); REGRESSION ANALYSIS; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; SUPPORT VECTOR MACHINE (SVM); TASTE;

FOOD PROPERTIES; MULTILINEAR REGRESSION (MLR); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS (QSAR); SUPPORT VECTOR MACHINE (SVM); SWEETENERS;

EVALUATION STUDIES AS TOPIC; FOOD TECHNOLOGY; LINEAR MODELS; MODELS, CHEMICAL; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; SWEETENING AGENTS; TASTE;

EID: 84883828968     PISSN: 00221147     EISSN: 17503841     Source Type: Journal    
DOI: 10.1111/1750-3841.12199     Document Type: Article

References (34)

1. ADRIANA.Code. Molecular Networks GmbH, Erlangen, Germany. v2.2.2, http://www.molecular-networks.com/products/adrianacode.
2. Broto P, Moreau G, Vandycke C. 1984. Molecular structures: perception, autocorrelation descriptor and sar studies. Autocorrelation descriptor. Eur J Med Chem Chim Ther 19(1):66-70.
3. Chang C-C, Lin C-J. 2011. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):1-27.
4. CORINA. Molecular Networks GmbH, Erlangen, Germany. v3.4, http://www.molecular-networks.com/products/corina.
5. Deutsch EW, Hansch C. 1966. Dependence of relative sweetness on hydrophobic bonding. Nature 211(5044):75.
6. Douglas KA, Soejarto DD. 2002. Discovery of terpenoid and phenolic sweeteners from plants. Research Triangle Park, NC: Pure and Applied Chemistry.
7. Ertl P, Rohde B, Selzer P. 2000. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J Med Chem 43(20):3714-7.
8. Gao P, Fang Z. 2005. The relationship between the sweetness and structure of sucrose derivatives. Jiangsu Chem Ind 33:73-5.
9. Hemmer MC, Steinhauer V, Gasteiger J. 1999. Deriving the 3D structure of organic molecules from their infrared spectra. Vibrat Spectrosc 19(1):151-64.
10. Hu X, Yan A. 2011. In silico prediction of rhabdomyolysis of compounds by self-organizing map and support vector machine. Toxicol In Vitro 25(8):2017-24.
11. Hu X, Yan A. 2012. In Silico models to discriminate compounds inducing and non-inducing toxic myopathy. Mol Inf 31(1):27-39.
12. Iwamura H. 1980. Structure-taste relationship of perillartine and nitro- and cyanoaniline derivatives. J Med Chem 23(3):308-12.
13. Iwamura H. 1981. Structure-sweetness relationship of L-aspartyl dipeptide analogues. A receptor site topology. J Med Chem 24(5):572-83.
14. LIBSVM. 2011. v3.1, Chang, CC, Lin, CJ. http://www.csie.ntu.edu.tw/~cjlin/libsvm/. Accessed June 2013.
15. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. 1997. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23(1-3):3-25.
16. Mathlouthi M. 1994. Physico-chemical aspects of sweeteners: the ideal sugar substitute. Ind Alimentaires Agricoles 111(7-8):402-10.
17. Miller KJ. 1990. Additivity methods in molecular polarizability. J Am Chem Soc 112(23):8533-42.
18. MOE. Chemical Computing Group, Montreal, Canada. v2010.10, http://www.chemcomp.com/.
19. Moreau G, Broto P. 1980. The autocorrelation of a topological structure: a new molecular descriptor. Nouv J Chim 4:359-60.
20. Petitjean M. 1992. Applications of the radius-diameter diagram to the classification of topological and geometrical shapes of chemical compounds. J Chem Inf Comput Sci 32(4):331-7.
21. Shallenberger RS, Acree TE. 1967. Molecular theory of sweet taste. Nature 216(5114):480-2.
22. Spillane WJ. 2006. Optimising sweet taste in foods. Cambridge, England: Woodhead.
23. Tanford C. 1963. Physical chemistry of macromolecules. Hoboken, N.J.: John Wiley & Sons, Inc.
24. Tarko L, Lupescu I. 2006. QSAR Studies on amino-succinamic acid derivatives sweeteners. Arkivoc 13: 22-40.
25. Volkenshteǐn MV. 1963. Configurational statistics of polymeric chains. New York, N.Y.: Interscience Publishers.
26. Wagener M, Sadowski J, Gasteiger J. 1995. Autocorrelation of molecular surface properties for modeling corticosteroid binding globulin and cytosolic Ah receptor activity by neural networks. J Am Chem Soc 117:7769-75.
27. Wang M, Wang K, Yan A, Yu C. 2012. Classification of hcv ns5b polymerase inhibitors using support vector machine Intl J Mol Sci 13:4033-47.
28. Wilson R. 2007. Sweeteners. London, England: Blackwell Publishing.
29. Yan A, Chong Y, Wang L, Hu X, Wang K. 2011. Prediction of biological activity of Aurora-A kinase inhibitors by multilinear regression analysis and support vector machine. Bioorg Med Chem Lett 21(8):2238-43.
30. Yan A, Gasteiger J. 2003. Prediction of aqueous solubility of organic compounds based on a 3D structure representation. J Chem Inf Comput Sci 43(2):429-34.
31. Yan A, Wang K. 2012. Quantitative structure and bioactivity relationship study on human acetylcholinesterase inhibitors. Bioorg Med Chem Lett 22(9):3336-42.
32. Yan A, Wang Z, Cai Z. 2008. Prediction of human intestinal absorption by GA feature selection and support vector machine regression. Intl J Mol Sci 9(10):1961-76.
33. Yang X, Chong Y, Yan A, Chen J. 2011. In-silico prediction of sweetness of sugars and sweeteners. Food Chem 128(3):653-8.
34. Zheng J, Rao Z, Jia C. 2003. Study on the relationship between structure and sweetness of sucrose derivatives. Food Sci 24:29-33.