腰肉
咀嚼度
食品科学
化学
热气腾腾的
烹调方法
作者
Stefania Sut,Karen Lacey,Rohini Dhenge,Irene Ferrarese,Saverio Santi,Antonio Cacchioli,Ferdinando Gazza,Stefano Dall’Acqua,Massimiliano Rinaldi
出处
期刊:Meat Science
[Elsevier]
日期:2023-12-01
卷期号:206: 109338-109338
被引量:1
标识
DOI:10.1016/j.meatsci.2023.109338
摘要
In this paper, the effects of four cooking procedures were evaluated, two occurring in atmospheric (in ventilated and steam oven) and two in subatmospheric (vacuum and sous vide cooking) conditions on pork Longissimus lumborum. The main objective of the study was to compare and evaluate the physical and chemical characteristics. Samples were cooked in four independent trials namely Oven (O), Steaming (ST), Vacuum Cooking (VC) and Sous Vide (SV). The analyses included temperature, cooking effect, percentage weight loss, texture (cutting and double compression tests), colour (superficially and inside the sample), microstructure (optical microscopy) and fibres shortening analysis. To assess cooking effects on significant nutritional constituents, the fatty acid composition and the content of B vitamins were analysed. Volatile profiles of samples were also compared using solid-phase microextraction. SV cooking resulted in the less favourable meat texture, presenting the highest hardness and chewiness. Moreover, high hardness values measured on SV samples is also related to the high weight loss. The technique of oven cooking (O) demonstrated superior results in terms of mechanical properties, which are closely associated with the cooking values. Specifically, the cook value C0 was significantly higher in the case of oven cooking compared to SV, VC, and ST. Mild temperature conditions and cooking times of the four considered cooking techniques did not induce significant variations in the fatty acid composition and volatile profile. Conversely, SV and VC allowed the highest amount of vitamin B retention in cooked meat. This work suggests that some differences emerged on the effects due to sub-atmospheric and atmospheric cooking compared to traditional ones.
科研通智能强力驱动
Strongly Powered by AbleSci AI