Combined Effects of Sous-Vide Cooking Conditions on Meat and Sensory Quality Characteristics of Chicken Breast Meat

C. H. Park,B. Lee, E. Oh, Y. S. Kim,Y. M. Choi

Poultry Science（2020）SCI 2区SCI 1区

Cited 50|Views29

Abstract

This study investigated the combined effects of cooking temperature and time on the meat and eating quality characteristics of the sous-vide chicken breast. For the control group, chicken breast samples were cooked in a convection oven until the internal temperature reached 71°C. Each sample for sous-vide cooking was vacuum packaged and then cooked under continuous thermocontrolled conditions in a water bath at 6 combinations of cooking temperature (60 and 70°C) and time (1, 2, and 3 h). Sous-vide cooked chicken meat at 60°C for 1 h (SV60-1h) showed lower cooking loss (6.58 vs. 26.5%, P < 0.05), Warner-Bratzler shear force (21.7 vs. 29.1 N, P < 0.05), and hardness (9.40 vs. 17.3 N, P < 0.05) than meat cooked by conventional oven. Similar to the objective tenderness parameters, cooked chicken meat from the SV60 treatments for all cooking times showed higher scores in all the tenderness attributes than the control group (P < 0.05). However, a higher flavor intensity was observed in the SV70-3h and control groups than in the SV60 treatments (P < 0.05). Owing to a lesser developed flavor in chicken meat from the SV60-1h treatment, the SV60-2h and 3h treatments were assigned a higher acceptability rating for overall impression (P < 0.05). Therefore, cooking temperature and time of sous-vide significantly influenced the physicochemical and palatability characteristics of chicken breast. In this study, the optimum conditions for the sous-vide chicken breast are to continuously cook at 60°C for 2 to 3 h to improve sensory quality characteristics without reducing the water-holding capacity.

Translated text

Key words

sous-vide,cooking condition,meat quality,sensory quality,chicken breast

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper