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Regressional models that describe oil absolute viscosity
Author(s) -
ToroVazquez J. F.,
InfanteGuerrero R.
Publication year - 1993
Publication title -
journal of the american oil chemists' society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.512
H-Index - 117
eISSN - 1558-9331
pISSN - 0003-021X
DOI - 10.1007/bf02632152
Subject(s) - iodine value , saponification , viscosity , thermodynamics , saponification value , degree of unsaturation , quadratic equation , quadratic model , chemistry , mathematics , chromatography , physics , organic chemistry , geometry , response surface methodology
Equations that describe the temperature dependence (298–338°K) of absolute viscosity (μ) of 21 oils and oil‐liquid fat mixtures were obtained based on two different approaches. Fitting each particular viscosity profile to a quadratic extension of the Andrade equation provided the best predictive models (R 2 >0.96). However, the coefficients associated with temperature effect did not have a physical‐chemical meaning. In contrast, the multiple variable regressional approach fitted, in just one equation, the μ of all 21 oil systems (R 2 ≈0.93). This equation included terms associated with structural parameters of acylglycerides, namely the degree of unsaturation ( i.e. , iodine value) and chainlength ( i.e. , saponification value) of the fatty acids. The models described effects of the cis double bonds and fatty acid chainlength on the acylglycerides’ interactions that determine both the μ of the system and its capability to crystallize. Therefore, multiple variable regressional analysis might be an excellent tool to better understand the quantitative structure‐functional property relationships in lipids systems.