Effect of Different Cooking Methods on Proton Dynamics and Physicochemical Attributes in Spanish Mackerel Assessed by Low-Field NMR

Foods

Biao Yuan¹  Mingqian Tan¹  Zhixiang Wang²  Shan Sun³  Siqi Wang³  Rong Lin³  Shasha Cheng³ 

[1] College of Engineering/National R&D Center for Chinese Herbal Medicine Processing, China Pharmaceutical University, Nanjing 211198, Jiangsu, China;School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, Liaoning, China;

关键词: spanish mackerel;    protons migration;    protein oxidation;    lipid oxidation;    cooking methods;    texture;   

DOI  :  10.3390/foods9030364

来源: DOAJ

【 摘 要 】

The states of protons within food items are highly related to their physical attributes. In this study, the effect of cooking methods including boiling, steaming, roasting and frying on proton dynamics, physicochemical parameters and microstructure of Spanish mackerel was assessed by low-field nuclear magnetic resonance (LF-NMR) and magnetic resonance imaging (MRI) techniques. The treatment of cooking resulted in a significant reduction of proton mobility and declined freedom of protons. The state changes of protons can be monitored easily in an intuitive and non-destructive manner during various cooking process. The treatments of boiling, steaming, roasting and frying resulted in different cooking loss and similar water-holding capability. A significant increase of total carbonyl content and thiobarbituric acid reactive substances was found, while a decrease of the values for free thiols and surface hydrophobicity was observed. The analysis of circular dichroism spectroscopy and cryo-scanning electron microscopy showed significant structural change. The correlation coefficients of R_cal² and R_cv² from partial least squares (PLS) regression models were more than 0.980, suggesting good correlation between LF-NMR data and hardness, resilience, springiness, chewiness, gumminess, and adhesiveness. Good recoveries and a relatively small coefficient of variation (CV) were obtained from the PLS regression models, indicating good reliability and accuracy in predicting texture parameters for mackerel samples.

【 授权许可】

Unknown