农业工程学报
農業工程學報
농업공정학보
Transactions of the Chinese Society of Agricultural Engineering
2015年
18期
262-268
,共7页
王笑丹%刘爱阳%孙永海%王莹%韩云秀%王洪美
王笑丹%劉愛暘%孫永海%王瑩%韓雲秀%王洪美
왕소단%류애양%손영해%왕형%한운수%왕홍미
肉%模型%质构%嫩度%神经网络模型
肉%模型%質構%嫩度%神經網絡模型
육%모형%질구%눈도%신경망락모형
meats%models%texture%tenderness%neural network model
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。
為瞭實現對牛肉嫩度品質的快速無損檢測和評價。該文選取60頭牛的眼肌部位作為試驗樣本，經在75～80℃的水浴中加熱併煮至肉的內部溫度達到70℃後取齣，冷卻至室溫（20℃）。利用質構儀測得牛肉黏力、黏性、彈力、彈性長度、內聚性、彈性、膠著性和咀嚼性等質構特性參數，併分析各參數與牛肉嫩度等級之間的相關性，黏力和黏性與牛肉嫩度的相關性較低，相關繫數為0.246和0.096；彈力、彈性長度、內聚性、彈性、膠著性和咀嚼性與牛肉嫩度相關性較大，且成負相關，說明上述流變學參數值會隨著牛肉嫩度等級的增大而下降，相關繫數為?0.92、?0.939、?0.771、?0.776、?0.815、?0.882。結閤感官評定法構建BP網絡模型、RBF網絡模型和自組織競爭神經網絡模型，用其預測牛肉嫩度等級，3種模型訓練誤差均為1×10-6。另選取20頭牛的揹最長肌中間部位作為測試樣本，對3種網絡模型進行比較分析。研究結果證明，自組織競爭神經網絡預測模型較為準確，預測牛肉嫩度等級的準確率達到90%，說明此方法能夠準確地對牛肉嫩度等級進行評定，研究結果為未來牛肉嫩度評定方法提供參攷。
위료실현대우육눈도품질적쾌속무손검측화평개。해문선취60두우적안기부위작위시험양본，경재75～80℃적수욕중가열병자지육적내부온도체도70℃후취출，냉각지실온（20℃）。이용질구의측득우육점력、점성、탄력、탄성장도、내취성、탄성、효착성화저작성등질구특성삼수，병분석각삼수여우육눈도등급지간적상관성，점력화점성여우육눈도적상관성교저，상관계수위0.246화0.096；탄력、탄성장도、내취성、탄성、효착성화저작성여우육눈도상관성교대，차성부상관，설명상술류변학삼수치회수착우육눈도등급적증대이하강，상관계수위?0.92、?0.939、?0.771、?0.776、?0.815、?0.882。결합감관평정법구건BP망락모형、RBF망락모형화자조직경쟁신경망락모형，용기예측우육눈도등급，3충모형훈련오차균위1×10-6。령선취20두우적배최장기중간부위작위측시양본，대3충망락모형진행비교분석。연구결과증명，자조직경쟁신경망락예측모형교위준학，예측우육눈도등급적준학솔체도90%，설명차방법능구준학지대우육눈도등급진행평정，연구결과위미래우육눈도평정방법제공삼고。
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.
