DIA-based quantitative proteomic analysis on the meat quality of porcine Longissimus thoracis et lumborum cooked by different procedures. (1st March 2022)

• Record Type: Journal Article
• Authors: Song, Yu; Huang, Feng; Li, Xia; Zhang, Hongru; Liu, Jiqian; Han, Dong; Rui, Maoneng; Wang, Jipeng; Zhang, Chunhui
• Is Part Of: Food chemistry. Volume 371(2022)
• Publication Date: 2022-03-01
• Subjects: DIA -- Proteomics -- Meat quality -- Cooking procedures -- PRM; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2021.131206
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: In total of 103 proteins showed significant changes (FC > 1.5) between steam and boiling treatment. Cooked meat color mainly related to oxidation and denaturation of proteins. Proteins that related with meat texture mainly involved in protein structure and oxidative stress. Structural proteins, metabolic enzymes and oxidoreductases were linked with cooking loss of meat. Collagen connects the sarcoplasm, myofilament, and scaffold protein into network during cooking. 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.