| 基于离散元法的牛肉咀嚼破碎模型构建 |
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| 引用本文： | 王笑丹,王洪美,韩云秀,焦娜,才英明,金佳慧,徐丽萍,刘爱阳.基于离散元法的牛肉咀嚼破碎模型构建[J].农业工程学报,2016,32(4):228-234. |
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| 作者姓名： | 王笑丹 王洪美 韩云秀 焦娜 才英明 金佳慧 徐丽萍 刘爱阳 |
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| 作者单位： | 1. 吉林大学食品科学与工程学院,长春,130062;2. 同济大学生命科学与技术学院,上海,200092 |
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| 基金项目： | 吉林省科技发展计划项目（20140204035NY）。 |
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| 摘 要： | 为了实现准确、便捷、客观地牛肉嫩度分级检测。该文利用离散单元法构建牛肉块在口腔中咀嚼破碎模型,并进行仿真模拟。研究中选取50头牛的眼肉作为试验样品,30头牛用于构建模型,20头牛用于验证仿真模型的准确性。试验牛样品在75~80℃的恒温水浴中加热至样品内部温度达到70℃时取出,冷却至室温(20℃)。利用质构仪测得牛肉样品的剪切模量、法向刚度等参数,同时测得密度、碰撞恢复系数、摩擦系数等构建离散元模型需要的相关参数。利用测得的参数构建牛肉咀嚼破碎的离散元仿真模型。将咀嚼破碎的牛肉微颗粒作为离散单元模拟仿真中的最小单位,利用Hertz-Mindlin with bonding模型中的颗粒体积力将牛肉块替换为微颗粒黏结的颗粒簇。在咀嚼作用下,当任意2个微颗粒之间的法向应力和切向应力超过最大极限值时,颗粒簇就开始破碎,仿真牛肉的咀嚼破碎过程。研究中利用离散元模拟仿真4个咀嚼周期并记录力和牛肉颗粒的变化。采用感官评定法和剪切力测定分别验证仿真的准确性,准确率都达到90%。研究表明,利用离散元模拟仿真能有效地实现牛肉嫩度等级的预测评定,为牛肉嫩度品质检测提供新方法。
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| 关 键 词： | 肉 模型 质构 嫩度 离散单元 |
| 收稿时间： | 2015/11/8 0:00:00 |
| 修稿时间： | 2016/1/18 0:00:00 |
Structure of beef chewing model based on discrete element method |
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| Wang Xiaodan,Wang Hongmei,Han Yunxiu,Jiao N,Cai Yingming,Jin Jiahui,Xu Liping and Liu Aiyang.Structure of beef chewing model based on discrete element method[J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(4):228-234. |
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| Authors: | Wang Xiaodan Wang Hongmei Han Yunxiu Jiao N Cai Yingming Jin Jiahui Xu Liping and Liu Aiyang |
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| Institution: | 1. School of Food Science and Engineering, Jilin University, Changchun 130062, China,1. School of Food Science and Engineering, Jilin University, Changchun 130062, China,1. School of Food Science and Engineering, Jilin University, Changchun 130062, China,2. School of Life Science and Technology ,Tongji University ,Shanghai 200092,China,1. School of Food Science and Engineering, Jilin University, Changchun 130062, China,1. School of Food Science and Engineering, Jilin University, Changchun 130062, China,1. School of Food Science and Engineering, Jilin University, Changchun 130062, China and 1. School of Food Science and Engineering, Jilin University, Changchun 130062, China |
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| Abstract: | Tenderness is one of the most important factors influencing the quality of beef. Traditional evaluation methods have some disadvantages and limitations more or less. In order to predict beef tenderness accurately, conveniently and objectively, in this research, the discrete element method was used to establish the beef chewing model. Beef from the mid-region of longissimus dorsi (LD) was collected from 50 cattle as the samples, in which 30 cattle were used for structuring the beef chewing model, and 20 cattle were prepared for verifying the accuracy. The age of cattle (400-550 kg) was from 30 to 36 months, and the cattle were fattened for more than 6 months. After starving for 24 h, the live cattle were weighed, showered, stunned, killed, and bled blood. The 4 limbs and head of each animal were cut off, and the body of cattle was split into halves, cooled at 4℃for 24 h, and then the carcasses were divided. Each piece of beef was cut into 10 mm × 10 mm × 10 mm sample, but the inter-muscular fat, connective tissues and tendon were deleted. The samples were placed into plastic bags individually in a 75-80℃water bath, and cooked for 15 min until the internal temperature of beef sample reached 70℃. The samples were divided into 3 groups so as to carry out the experiments in triplicate after the samples were cooled to room temperature (20℃). Shear modulus and normal stiffness were detected by Brookfield CT3 texture analyzer (Brookfield Engineering Laboratories, INC. Middleboro Massachusetts, USA). With a two-cycle texture profile analysis (TPA) model (a compression model for normal stiffness) and a TA44 probe (cylinder diameter=4 mm), the size of testing surface of each sample was 10 mm ×10 mm × 10 mm (for normal stiffness). The related parameters settings were: test speed of 0.5 mm/s and deformation quantity of 2.5 mm for shear modulus detection, and test speed of 0.5 mm/s and preload of 2 N for detecting normal stiffness. In addition, density, restitution coefficient, friction coefficient and other parameters were also gained by the experiments to establish the beef chewing model. This chewing model made use of the particle body force in the Hertz-Mindlin with bonding model to replace the beef with micro particles which was the minimum unit in simulation. During the chewing process, the particle cluster would be not broken until the normal stress and tangential stress between random 2 micro particles exceeded the maximum limit values. The enhanced discrete element method (EDEM) recorded the exchange of force and beef particles of samples during the chewing cycles. Chewing simulation continued for 4 cycles and got the average shear force to judge the level of tenderness. Then the sensory evaluation and texture analysis were used to verify the results of simulation and compare their accuracy. Ten healthy and dentally tidy adults were chosen to evaluators who had the age from 20 to 25 years old and without thirst or hunger. Each evaluator chewed the samples and estimated the level. Accuracy of broken process and stress change during beef chewing by the EDEM software achieved 90% compared with the sensory evaluation method. On the other hand, the texture analysis showed that the parameters were test speed of 0.5 mm/s, moving distance of 2.5 mm and preload of 2 N. Compared with texture analyzer’s results, the accuracy achieved 90% as well. The results prove that the discrete element method is a new efficient method for beef tenderness quality inspection. |
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| Keywords: | meats models textures tenderness discrete element method |
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