Prediction of hydrophile-lipophile balance values of anionic surfactants using a quantitative structure-property relationship

Journal of Colloid and Interface Science

Volumn 336, Issue 2, 2009, Pages 773-779

  Luan, Feng ^a   Liu, Huitao ^a   Gao, Yuan ^a   Li, Qingzhong ^a   Zhang, Xiaoyun ^b   Guo, Yun ^c  

^a YANTAI UNIVERSITY   (China)

^b LANZHOU UNIVERSITY   (China)

^c National Laboratory of Vacuum and Cryogenics Technology and Physics   (China)

Author keywords

Hydrophile lipophile balance (HLB); Quantitative structure property relationship (QSPR); Radial basis function neural network (RBFNN); Stepwise multiple linear regression (MLR)

Indexed keywords

DESCRIPTOR EQUATION; HYDROPHILE-LIPOPHILE BALANCE; HYDROPHILE-LIPOPHILE BALANCE (HLB); QSPR MODEL; QUANTITATIVE STRUCTURE PROPERTY RELATIONSHIPS; QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIP (QSPR); RADIAL BASIS FUNCTION NEURAL NETWORK (RBFNN); RADIAL BASIS FUNCTION NEURAL NETWORKS; ROOT MEAN SQUARE ERRORS; SQUARED CORRELATION COEFFICIENTS; STEPWISE MULTIPLE LINEAR REGRESSION; STEPWISE MULTIPLE LINEAR REGRESSION (MLR); TEST SETS; TRAINING SETS;

ATTITUDE CONTROL; BIOPHYSICS; INSTRUMENT SCALES; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS; SET THEORY; SOLUBILITY; STRUCTURAL PROPERTIES; SULFUR COMPOUNDS;

ANIONIC SURFACTANTS;

ANIONIC SURFACTANT;

ARTICLE; CONTROLLED STUDY; HYDROPHILICITY; INTERMETHOD COMPARISON; LIPOPHILICITY; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

EID: 67549106613     PISSN: 00219797     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jcis.2009.04.002     Document Type: Article

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