Effects of repeated deep-frying on fatty acid profiles of potato fries and frying oils: soybean oil, canola oil and their 1:1 blend

@#Introduction: This study looked at the fatty acid composition changes in potato fries fried in three different types of oils, namely soybean oil (SO), canola oil (CO), and a 1:1 blend of soybean oil and canola oil (SCO), throughout an intermittent frying process of 80 batches in five consecutive days. The study also examined the fatty acid composition changes in SO, CO and SCO during the frying process. Methods: Fat from potato fries (extracted by Soxtec system) and oil samples from the corresponding frying oil were analysed by gas chromatography-mass spectrometer (GC-MS) to examine the fatty acid profile changes during the deep-frying process. Results: Linoleic acid (LA) and a-linolenic acid (ALA) in all three oils decreased, while oleic acid (C18:1), stearic acid (C18:0), palmitic acid (C16:0) and octanoic acid (C8:0) increased. Formation of C18:1 trans fatty acid was observed as the frying time increased. The fatty acid composition of the potato fries was consistent with the fatty acid composition of the corresponding frying oils. Conclusion: Our results showed that blending soybean oil and canola oil did not significantly improve the frying stability of the resulting oil in terms of fatty acid profile. Due to the formation of trans fatty acids and the decrease in polyunsaturated fatty acids, our study also recommends not to use the same frying oil repeatedly and not to consume food products cooked in reused oil.

Equations to predict height and weight in Asian- Chinese adults

@#Introduction: Height and weight measurements are required for the assessment of nutritional status. However, it is difficult to measure these parameters in nonambulatory persons. Hence, simple predictive equations that estimate these measurements using various anthropometric measurements are necessary. Methods: A total of 441 Asian-Chinese adults (174 males, median age = 32.5, IQR: 27.8 years; 267 females, median age = 34.6, IQR: 28.5 years) were used to build height and weight sex-specific prediction equations. An additional 111 Asian- Chinese adults (44 males, median age = 31.1, IQR: 25.0 years; 67 females, median age = 30.6, IQR: 25.6 years) were used to validate the newly developed prediction equations. Results: The best predictive model for height included arm length, knee height measurements and age (R2 = 0.70, standard error of estimate [SEE] = 3.38 for males; R2 = 0.71, SEE = 3.14 for females). The best weight predictive model included age, arm circumference and waist circumference (R2 = 0.79, SEE = 4.66 for males; R2 = 0.78, SEE = 4.38 for females). The new predictive models for height and weight have non-significant prediction biases as compared to the Cereda et al. (2010) and Ross equations, respectively. Conclusion: Height and weight predictive equations with a higher degree of accuracy have been developed for Asian Chinese adults.