摘要
为了实现对牛肉嫩度品质的快速无损检测和评价。该文选取60头牛的眼肌部位作为试验样本，经在75～80℃的水浴中加热并煮至肉的内部温度达到70℃后取出，冷却至室温（20℃）。利用质构仪测得牛肉黏力、黏性、弹力、弹性长度、内聚性、弹性、胶着性和咀嚼性等质构特性参数，并分析各参数与牛肉嫩度等级之间的相关性，黏力和黏性与牛肉嫩度的相关性较低，相关系数为0.246和0.096；弹力、弹性长度、内聚性、弹性、胶着性和咀嚼性与牛肉嫩度相关性较大，且成负相关，说明上述流变学参数值会随着牛肉嫩度等级的增大而下降，相关系数为-0.92、-0.939、-0.771、-0.776、-0.815、-0.882。结合感官评定法构建BP网络模型、RBF网络模型和自组织竞争神经网络模型，用其预测牛肉嫩度等级，3种模型训练误差均为1×10-6。另选取20头牛的背最长肌中间部位作为测试样本，对3种网络模型进行比较分析。研究结果证明，自组织竞争神经网络预测模型较为准确，预测牛肉嫩度等级的准确率达到90%，说明此方法能够准确地对牛肉嫩度等级进行评定，研究结果为未来牛肉嫩度评定方法提供参考。
Tenderness is one of the important assessment indices of beef quality. Traditional assessment methods, such as the sensory evaluation method and the Warner-Bratzler shear force method, have artificial error at different degrees. One steak from the mid-region of each longissimus dorsi （LD） was collected from each of 60 cattle as the testing sample. The age of cattle （400-550 kg） was from 30 to 36 months, and the cattle were fattened for more than 6 months on the same farm. After starving for 24 h, the live cattle were weighed, showered, stunned, killed, and letting blood for 56 min. After electrical stimulation, 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. The LDs were weighed, placed into plastic bags individually, vacuum-sealed, packed on ice, and transported to the laboratory. Each steak was cut into 10 cm×10 cm×10 cm samples, but the intermuscular fat and connective tissues were deleted. The samples were rinsed in water to remove surface contamination, then placed into plastic bags individually in a 75-80℃ water bath, and cooked for 15 min after the internal temperature of meat reached 70℃. Then the samples were cooled to room temperature （20℃）. The 20 evaluators were healthy and dentally tidy adults with the age from 20 to 25 years old, without thirst or hunger. Each evaluator chewed the samples from each steak. After cooking, the samples within an LD were divided into 3 groups so as to run the experiments in triplicate. The samples freshly chewed for 0-20 times were measured using a Brookfield CT3 texture analyzer （Brookfield Engineering Laboratories, INC. Middleboro, Massachusetts, USA）. With a two-cycle texture profile analysis （TPA） model and a TA44 probe （cylinder diameter = 4 mm）, the size of testing surface of each sample was 10 mm ×10 mm × 10 mm. A Hold Time-pressure and keeping model was used throughout. The instrument settings were： pre-test speed of 2 mm/s, test speed of 5 mm/s, posttest speed of 5 mm/s, trigger force of 10 g, distance of probe movement on the sample of 7 mm, and hold time after downward movement of the probe of 2 s. For those samples, viscous force, stickiness, elastic force, elastic length, cohesiveness, resilience, gumminess, chewiness and other texture properties were measured using the texture analyzer. The correlations were analyzed between the parameters and beef tenderness level. The main texture properties decreased with the increase of beef tenderness grade, and the texture properties value also showed a downward trend with chewing more times. Combined with the sensory evaluation method, the BP （back propagation） network model, the RBF （radical basis function） network model and the self-organizing competition network model were built, and all the training errors were 1×10-6. Another steak from the mid-region of each LD collected from each of 20 cattle was selected as verification sample. Then the 3 network models were compared, and the self-organizing competition network model was the most accurate model with an accuracy rate of 90%, which showed that this method can accurately assess the level of beef tenderness.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2015年第18期262-268,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金青年科学基金项目(31101273)
国家自然科学基金面上项目(31271861)
吉林省科技发展计划项目(20140204035NY)
关键词
肉
模型
质构
嫩度
神经网络模型
meats
models
texture
tenderness
neural network model
