Effects of repeated frying on physicochemical characteristics,oxidative stress,and free radical scavenging potential of canola oil and ghee

The repeated use of cooking oils and ghee for the deep frying of food materials may affect their nutritional quality. The present study evaluated the effect of repeated frying on the physicochemical characteristics and antiradical potential of canola oil and ghee. The oil and ghee were used for frying of fish and chicken for 2, 4, 6, 8, and 10 frying cycles followed by the analysis of physicochemical, oxidative stress, and antiradical parameters. Regression analysis of the data showed a frying cycle-dependent significant linear increase in saponification (R² = 0.9507–0.9748), peroxide and acid values (R² = 0.956–0.9915), and malondialdehyde (MDA) production (R² = 0.9058–0.9557) of canola oil and ghee subjected to fish and chicken frying but exponential increase in saponification value (R² = 0.9778) and MDA production (R² = 0.7407) of canola oil and ghee used for fish frying. The increase in the number of frying cycles linearly decreased the iodine value (R² = 0.9781–0.9924), and 1, 1-diphenyl-2-picrylhydrazyl, hydroxyl, and 2, 2′-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) radical scavenging potential (R² = 0.9089–0.9979) of canola oil and ghee. Repeated frying in cooking oil and ghee increases oxidative stress and decreases their physicochemical and antioxidant qualities. Canola oil was comparatively more oxidative resistant than canola ghee. The regression equations derived from regression analysis will guide researchers to conduct similar types of univariate studies.     

PCMD: personality‐characterized mood dynamics model toward personalized virtual characters

How to endow the virtual characters with personalized behavior patterns remains a challenging problem. Instead of heuristically designing behaviors for certain personalities, this paper bridges the gap between personalities and behaviors using a medium concept, mood, to make the behaviors of the characters consistent enough to convey their personalities, while flexible enough to make appropriate response in various situations. We propose a personality‐characterized mood dynamics model, in which the emotion weights are computed as a solution of a convex optimization problem that is constructed to make the overall mood approaches the personality after sufficient interactions. The convergence of the algorithm is demonstrated by numerical simulations. The implementation of the personality‐characterized mood dynamics model enables an emotion‐oriented virtual human, Sophie, to show personalized behaviors in the emotional interactions with users. Copyright © 2015 John Wiley & Sons, Ltd.