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QSSR prediction of HPLC retention time of triacylglycerols in soybean oil obtained by supercritical CO2 extraction

Soybean oil fractions were obtained by collecting extract at different time intervals during supercritical CO2 extraction. The influence of pressure (300–500 bar), temperature (40–60 °C), CO2 flow rate (0.194– 14.125 kg/h) and particle size (0.238–1.059 mm) were investigated. The concentration of triacylglycerols in different fractions of soybean oil obtained at different extraction process conditions was determined. Triacylglycerols were analyzed by reversed phase high performance liquid chromatography (HPLC) with refractive index detector and identified by comparing their retention time to standards. Quantitative structure-retention relationship (QSRR) is undoubtedly of great importance in modern chemistry and biochemistry. QSRR analysis is a useful technique, which quantify the relation between the structure of the molecule and the chromatographic retention times of the compounds, allowing the prediction of the retention times. QSRR currently are being applied in many disciplines, such as in drug design and environmental risk assessment. QSRR offers the possibility for screening a large number of chemicals in a short time and with low cost. In view of the above, the objective of this investigation was to establish a new QSRR model for predicting retention times of some triacylglycerols using chemometric linear least squares (LLS) procedure. QSRR study has been carried out for training set of 17 triacylglycerols to correlate and predict the HPLC retention time of studied compounds. Molecular modelling and QSRR analysis were performed to find the quantitative effects of the lipophilicity of the compounds on their retention behaviour. An accurate mathematical model was developed for predicting the HPLC retention time of some triacylglycerols. The validity of the model has been established using Leave-One-Out (LOO) cross validation. The statistical measures such as r2, r2CV, and F value of this model were 0.914, 0.885 and 158.98 respectively, revealing the credibility of the model. The established model was used to predict the retention time of the investigated compounds and close agreement between experimental and predicted values was obtained. The low residual values suggest a good predictive ability of the developed QSRR model. It indicates the retention time of series of triacylglycerols can be successfully modelled using lipophilicity descriptor, logP.

QSRR ; triacylglycerols ; soybean oil ; supercritical CO2 extraction

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