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基于 DIA 的定量蛋白质组学分析不同烹饪程序下猪背最长肌肉质的变化。

DIA-based quantitative proteomic analysis on the meat quality of porcine Longissimus thoracis et lumborum cooked by different procedures.

机构信息

Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China.

Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China.

出版信息

Food Chem. 2022 Mar 1;371:131206. doi: 10.1016/j.foodchem.2021.131206. Epub 2021 Sep 21.

DOI:10.1016/j.foodchem.2021.131206
PMID:34619635
Abstract

A DIA-based quantitative proteomic strategy was used to investigate the effects of different cooking procedures (steaming and boiling) on pork meat quality. Results showed that steamed meats had higher redness, cohesion, springiness, but lower lightness, yellowness, shear force, hardness, chewiness and cooking loss than boiled meats. In total of 1608 proteins were identified and 103 proteins exhibited significant difference (fold change > 1.5, P < 0.05). These DAPs mainly involved in protein structure, metabolic enzyme, protein turnover and oxidation stress. ALDOC, PVALB, PPP1R14C, AMPD1, CRYAB and SOD1 were validated as potential indicators of color variations in cooked meat. CFL1, COL1A1, COL3A1, RTN4, NRAP, NT5C3A, and SOD1 might be potential biomarker for texture changes of cooked meats. Moreover, these validated proteins exhibited significant (P < 0.05) correlation with cooking loss and could be serve as candidate predictors for cooking loss changes of meats in different cooking procedures.

摘要

一种基于 DIA 的定量蛋白质组学策略被用于研究不同烹饪程序(蒸和煮)对猪肉品质的影响。结果表明,蒸肉的红色度、粘性、弹性较高,而亮度、黄度、剪切力、硬度、咀嚼性和烹饪损失较低。共鉴定出 1608 种蛋白质,其中 103 种蛋白质表现出显著差异(倍数变化>1.5,P<0.05)。这些 DAPs 主要涉及蛋白质结构、代谢酶、蛋白质周转和氧化应激。ALDOC、PVALB、PPP1R14C、AMPD1、CRYAB 和 SOD1 被验证为烹饪肉色变化的潜在指标。CFL1、COL1A1、COL3A1、RTN4、NRAP、NT5C3A 和 SOD1 可能是烹饪肉质地变化的潜在生物标志物。此外,这些经过验证的蛋白质与烹饪损失呈显著(P<0.05)相关,可作为不同烹饪程序下肉类烹饪损失变化的候选预测因子。

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