• Korean Journal of Food Science and Technology
• /
• v.41 no.6
• /
• pp.668-672
• /
• 2009

Cookies containing wheat flour mixed with 10, 30 or 50% esterified with octenylsuccinic anhydride (OSA, 3%) and dry-heated ($130^{\circ}C$, 2 hr) high amylose rice (Goami 2) starch (DH-OSAR) were prepared and then their physical and digestive properties were evaluated. When the amount of added DH-OSAR increased, the hardness and brittleness of the cookies decreased, and L (brightness) value increased. For the digestive properties, the cookies containing 50% DH-OSAR significantly increased the amount of slowly digestible starch (SDS), and decreased the amount of rapidly digestible starch (RDS), resulting in the lowest expected Glycemic Index (eGI) among tested cookies. Although the cookies containing DHOSAR were inferior to the control, the addition of xanthan gum (0.5% based on total powder amount) significantly improved their textural and sensory properties. Specially, the cookies containing 50% DH-OSAR and the addition of 0.5% xanthan gum showed the lowest eGI value, maintaining the improved textural and sensory properties.

• Molecular & Cellular Toxicology
• /
• v.1 no.3
• /
• pp.157-163
• /
• 2005

Because of their widespread occurrence and substantial biological activity, halogenated aromatic hydrocarbons are one of the important classes of contaminants in the environment. We have performed comparative molecular field analysis (CoMFA) on structurally diverse ligands of Ah (dioxin) receptor to explore the physico-chemical requirements for binding. All CoMFA models have given $q^{2}$ value of more than 0.5 and $r^{2}$ value of more than 0.83. The predictive ability of the models was validated by an external test set, which gave satisfactory predictive $r^{2}$ values. Best predictions were obtained with CoMFA model of combined modified training set ($q^{2}=0.631,\;r^{2}=0.900$), giving predictive residual value = 0.002 log unit for the test compound. We have suggested a model comprises of four structurally different compounds, which offers a good predictability for various ligands. Our QSAR model is consistent with all previously established QSAR models with less structurally diverse ligands. The implications of the CoMFA/QSAR model presented herein are explored with respect to quantitative hazard identification of potential toxicants.