牛排食用品质保证关键控制点研究

牛肉的食用品质主要包括嫩度、风味、多汁性,受遗传、宰前管理、宰后处理等因素的影响.本文探讨了嫩度、风味、多汁性在牛排食用品质中的权重,消费者对原料肉剪切力的接受程度,研究了大理石花纹等级对牛排食用品质的影响,以及不同分割部位肉食用品质的差异.结果表明,嫩度、风味、多汁性的权重分别为0.50、0.38、0.12,煎牛排时消费者能接受剪切力低于4.39kg的原料肉;大理石花纹(P<0.01)、不同分割部位(P<0.05)对牛肉的食用品质影响显著.     

大理石花纹、生理成熟度对牛肉品质的影响

依据我国农业行业标准《牛肉质量等级》,对不同大理石花纹和不同生理成熟度的牛肉品质进行试验分析,研究显示,大理石花纹对蒸煮损失有显著影响(p<0.05),对pH和剪切力值无显著影响,大理石花纹越丰富,蒸煮损失越小;生理成熟度对pH和蒸煮损失有显著影响(p<0.05),对剪切力值有较为显著影响(p<0.1),且随着生理成熟度的变老,即牛龄增加,牛肉的蒸煮损失和剪切力值呈增大趋势。     

基于VIS/NIR高光谱散射特征预测牛肉的嫩度

本文研究利用VIR/NIR光谱散射特征预测成熟 7 天牛肉的嫩度.开发高光谱散射成像系统,获取新鲜牛肉 400～1100 nm 波长范围高光谱散射图像,对牛肉嫩度进行预测和分级.利用洛伦兹函数,拟合各个波长处的散射曲线,获取不同波长散射曲线的洛伦兹分布函数参数.使用逐步回归方法,选择最佳波长及相应的拟合参数,建立线性回归模型预测牛肉的嫩度,使用全交叉验证方法评价模型的性能.使用散射曲线的峰值建立的模型对嫩度的预测结果最好,预测相关系数为0.86,预测残差为11.7 N.以嫩度剪切力值 58.8 N 为界将牛肉分为粗糙牛肉组和嫩牛肉组,对嫩度的分级准确率是 91%.该研究表明,利用牛肉的散射特征可以对牛肉嫩度预测和分级.     

基于决策树雪花牛肉大理石花纹分级模型

为建立雪花牛肉大理石花纹等级评价方法,根据不同等级雪花牛肉大理石花纹图像特征及人工评级的标准,确定了影响大理石花纹的等级主要因素。本研究提出影响大理石花纹等级的几何参数特征、几何分布参数特征和统计参数特征。其中几何参数特征主要反映大理石花纹面积、周长等;几何分布特征主要反映大理石花纹图像中脂肪颗粒沉积的密度,根据脂肪颗粒沉积情况可分为大颗粒脂肪、中颗粒脂肪、小颗粒脂肪等;统计参数特征主要反映大理石花纹丰富程度以及大理石花纹分布均匀性。利用相关性分析提取影响雪花牛肉大理石花纹等级的特征参数。建立基于C4.5和CART算法的决策树模型,结果表明：对于C4.5算法建立的决策树分级模型,三级和五级大理石花纹分级预测精度分别为91.80%、92.31%,而该模型针对四级样本建立的模型无效,其结果多数误判为三级;对于CART算法建立的决策树模型同样存在这样的问题,即三级和五级大理石花纹分级预测精度高,而对四级样本分级无效。     

牛肉品质检测技术的研究进展

牛肉品质检测在牛肉生产、加工和消费过程中起着重要的作用。本文介绍了剪切力测定、近红外光谱分析(NIR)和机器视觉三种牛肉品质测定方法的技术特点和国内外研究现状,着重介绍了机器视觉技术在牛肉颜色、大理石花纹、纹理和生理成熟度等品质参数测定方面的研究进展情况,指出机器视觉技术是未来牛肉品质检测研究的主要发展方向。     

清真屠宰工艺对牛肉品质的影响机制研究

通过检测分析清真屠宰后牛肉排酸后的pH值、压力失水率、肌肉色泽、大理石花纹、嫩度、微生物等指标,同时与普通非清真屠宰牛肉品质的检测结果做对比,分析评价清真屠宰工艺对牛肉品质的影响。结果显示,与普通非清真屠宰牛肉相比,清真屠宰牛肉在排酸成熟后pH值偏高(P0.05),色泽L*值a*值升高(P0.05),b*值降低(P0.05),剪切力偏低(P0.05),压力失水率偏低(P0.05)。微生物检测结果中,细菌总数较低(P0.05),大肠杆菌检出率较低,沙门氏菌检出率无明显变化,两者差异显著。与非清真屠宰牛肉相比,清真屠宰牛肉的色泽、嫩度和系水力较好,微生物污染程度得到改善,表明清真屠宰工艺能显著改善牛肉品质和食用卫生质量。     

牛肉大理石花纹分级系统软件设计

目的 运用机器视觉技术, 开发牛肉大理石花纹自动检测系统。方法 该软件设计以VC++6.0为开发工具, 首先从牛肉大理石花纹检测系统人机界面着手, 通过对CCD相机的控制, 得到大理石花纹图像, 并最终用图像处理的方法得到牛肉大理石花纹的分级结果。结果 该软件运行效率高, 对牛肉大理石花纹的分级准确率达到87.5%。结论 该系统可以满足企业实际需要。     

基于反射光谱特征的牛肉嫩度预测模型研究

旨在研究牛肉反射光谱在不同波段范围内的特征,筛选与牛肉剪切力值相关性大的敏感波段,优选牛肉剪切力值的预测模型。实验采用双通道光谱仪采集72块牛肉样品的反射光谱曲线,对原始光谱及剪切力值进行相关性分析,应用联合区间偏最小二乘法(Si-PLS)和遗传算法(GA)筛选敏感波段,建立牛肉剪切力值的偏最小二乘回归(PLSR)预测模型,并对模型进行了优选。结果表明:实验所用2个波段光谱均能实现对牛肉剪切力值的预测,在(400~1000)nm波段建立的预测模型结果较好,在可见光范围内光谱信号与剪切力值呈较大的正相关性,当联合9个子区间作为输入变量时建立PLSR模型的预测相关系数R和均方根误差分别达到0.8598和6.0141,模型的RPD值为1.91。应用遗传区间偏最小二乘法(GA-i PLS)在已选的9个子区间内再次筛选变量,所建立模型的验证集相关系数为0.8883,均方根误差为5.5665,RPD值达到了2.06。研究结果表明,可见近红外反射光谱可以实现对牛肉嫩度的较好预测,基于特征波段的预测模型可以为牛肉嫩度在线快速检测提供理论依据。     

黄原胶对牛肉品质影响的研究

本实验研究了黄原胶添加水平及冷藏时间对冷冻牛肉色泽、保水、嫩度及pH值等品质特性的影响。结果表明:利用0.5%的黄原胶溶液浸泡牛肉,可显著提高牛肉的保水性、pH值,并可降低牛肉的最大剪切力(p<0.05),改善了牛肉的嫩度,但也带来牛肉色泽的明显劣变。     

基于支持向量机的牛肉嫩度等级评价模型研究

目的 利用VIS/NIR反射光谱建立基于支持向量机的生鲜牛肉嫩度等级的评价模型。方法 以牛肉背最长肌为研究对象, 选取样本58个, 在牛肉新鲜切口处采集波长范围400~1700 nm的反射光谱信息, 使用肉类嫩度测量仪测量牛肉剪切力值, 应用支持向量机(SVM)模型评价牛肉嫩度等级。结果 应用SVM模型可以较好地实现对牛肉嫩度等级的评价。尤其是经主成分分析降维预处理, 结合径向基核函数SVM, 对牛肉训练集嫩度等级的回判率达到95%, 对样品校正集判别的准确率进一步提高至83.3%。结论 SVM模型对牛肉嫩度等级评价结果较好, 进行主成分分析后, 判别结果有所提高。     

Image texture features as indicators of beef tenderness

Image processing techniques were developed to predict cooked-beef tenderness from fresh-beef image characteristics. Cattle from different finishing treatments were processed in a commercial plant. Two short loin steaks were sampled from each carcass; one used for sensory evaluation and the other for imaging. The samples varied significantly in both US quality grades and sensory tenderness scores. Color, marbling and texture features were extracted from the beef images. Statistical and neural network analyses were performed to relate the image features to sensory tenderness scores. Image texture features were found to be useful indicators of beef tenderness. Partial least squares and neural network models were able to predict beef tenderness from color, marbling and image texture features to R(2)-values up to 0.70.     

Predicting beef tenderness using color and multispectral image texture features

The objective of this study was to investigate the usefulness of raw meat surface characteristics (texture) in predicting cooked beef tenderness. Color and multispectral texture features, including 4 different wavelengths and 217 image texture features, were extracted from 2 laboratory-based multispectral camera imaging systems. Steaks were segregated into tough and tender classification groups based on Warner-Bratzler shear force. The texture features were submitted to STEPWISE multiple regression and support vector machine (SVM) analyses to establish prediction models for beef tenderness. A subsample (80%) of tender or tough classified steaks were used to train models which were then validated on the remaining (20%) test steaks. For color images, the SVM model correctly identified tender steaks with 100% accurately while the STEPWISE equation identified 94.9% of the tender steaks correctly. For multispectral images, the SVM model predicted 91% and STEPWISE predicted 87% average accuracy of beef tender.     

Predicting beef tenderness using color and multispectral image texture features

The objective of this study was to investigate the usefulness of raw meat surface characteristics (texture) in predicting cooked beef tenderness. Color and multispectral texture features, including 4 different wavelengths and 217 image texture features, were extracted from 2 laboratory-based multispectral camera imaging systems. Steaks were segregated into tough and tender classification groups based on Warner–Bratzler shear force. The texture features were submitted to STEPWISE multiple regression and support vector machine (SVM) analyses to establish prediction models for beef tenderness. A subsample (80%) of tender or tough classified steaks were used to train models which were then validated on the remaining (20%) test steaks. For color images, the SVM model correctly identified tender steaks with 100% accurately while the STEPWISE equation identified 94.9% of the tender steaks correctly. For multispectral images, the SVM model predicted 91% and STEPWISE predicted 87% average accuracy of beef tender.     

Considerations for Beef Tenderness Evaluations

SUMMARY: Data collected from 690 beef rib steaks were utilized to evaluate the methodology of beef tenderness measurements. The information obtained in this study was critically compared to existing research and evaluated in relation to sample handling procedures. Frozen storage of steaks, especially if they are unprotected from dehydration or are stored for long periods of time, has important implications if such data are to be compared to steaks which were evaluated in the unfrozen state. Cooking frozen steaks with or without thawing apparently has little effect on tenderness or cooking loss. Anatomical location of the sample should be carefully controlled to prevent spurious differences from affecting tenderness measurements. Evidence indicates that a tenderness gradient exists over the cross-section of the I. dorsi and suggests that core samples should be taken from as many and as varied positions as is feasible for existing research conditions. The use of marbling score as an indicator of the tenderness of beef rib steaks resulted in the explanation of 28 percent of the variation in average shear force requirements. The shear force value of the diaphragm muscle was only moderately related (r = 0.40) to that of the I. dorsi. Cooking loss percentages were significantly increased as a result of freezing at–34°C and storage of steaks unwrapped at −23°C when compared to unfrozen steaks and by selecting I. dorsi samples from more anterior locations in the wholesale rib.     

Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region

The objective of this research is to develop a non-destructive method for predicting cooked beef tenderness using optical scattering of light on fresh beef muscle tissue. A hyperspectral imaging system (λ = 496–1,036 nm) that consists of a CCD camera and an imaging spectrograph, was used to acquire beef steak images. The hyperspectral image consisted of 120 bands with spectral intervals of 4.54 nm. Sixty-one fresh beef steaks, including 44 strip loin and 17 tenderloin cuts, were collected. After imaging, the steaks were cooked and Warner-Bratzler shear (WBS) force values were collected as tenderness references. The optical scattering profiles were derived from the hyperspectral images and fitted to the modified Lorentzian function. Parameters, such as the peak height, full scattering width at half maximum (FWHM), and the slope around the FWHM were determined at each wavelength. Stepwise regression was used to identify 7 key wavelengths and parameters. The parameters were then used to predict the WBS scores. The model was able to predict WBS scores with an R = 0.67. Optical scattering implemented with hyperspectral imaging shows limited success for predicting current status of tenderness in beef steak.     

基于不变矩和改进SVM的牛肉大理石纹评级

针对牛肉大理石纹人工评级过程中人为误差干扰大的问题,研究利用图像处理技术提高牛肉大理石纹评级的客观性并增强自动化程度,提出基于不变矩、灰度共生矩阵和混沌蜂群优化混合核函数支持向量机(Support Vector Machine,SVM)的牛肉大理石纹评级法。首先计算牛肉大理石纹图像的不变矩和灰度共生矩阵统计量并由此构造特征向量;然后将训练和测试样本输入到混合核函数SVM,采用混沌蜂群算法优化SVM中的惩罚因子及核参数,使其分类识别性能达到最优;最后输入待评级样本进行分类识别,得到最优评级结果。大量实验结果表明:根据我国NY/T676-2010牛肉大理石纹标准图谱,评级正确率分别达到100%(一级)、93.3%(二级)、93.3%(三级)、96.7%(四级)、100%(五级)。与基于灰度矩和SVM法、基于灰度共生矩阵和BP(Back Propagation)神经网络法相比,本文所得评级正确率最高,且与专业评级师的实际评级情况最相符。     

Comparison of instrumental and sensory evaluation of texture of cured and cooked beef meat

The instrumental and sensory texture attributes of beef muscles (M. longissimus dorsi and M. semimembranosus) were compared after curing and thermal treatments. Shear, compression and puncture tests were carried out with an UTM Instron 4301 and the sensory evaluation was made with the score method. The force values obtained for puncture test gave a greater degree of correlation with the sensory tenderness, hardness and elasticity than the shear test forces. The shear test forces were found to significantly correlate with sensory tenderness only for muscles with perpendicular orientation of fibres to the direction of shear blade movement. The evaluation of beef texture with compression test was dependent on the level of sample deformation degree. The force values of compression were found to correlate significantly with elasticity and juiciness of meat up to 60% deformation levels, but at deformation levels higher than 60% appeared significant correlation with tenderness and hardness. The obtained results showed the usefulness of puncture test with proposed parameters for the instrumental measurements of beef texture.     

原子力显徽镜在牛肉嫩度测定中的应用研究

【目的】探讨原子力显微镜在牛肉嫩度测定中的应用,为肉品嫩度提供可靠、直观的测定方法。【方法】用100、150、200mmol/L不同浓度氯化钙(CaCl2)处理牛肉,肉样真空包装后于4℃下成熟72h后测定剪切力、肌原纤维指数(MFI),并用原子力显微镜观察肌原纤维小片,比较三者结果。【结果】三种测定方法结果基本一致,但有一定差异,原子力显微镜观察肌原纤维小片直观地反映了肌原纤维小片化程度。【结论】原子力显微镜能直观地反映肉的嫩化情况,是对剪切力和MFI测定方法在形态学上的完善。     

SHEAR PROPERTIES AND WARNER-BRATZLER TENDERNESS MEASUREMENT OF BEEF 1

The shear properties of raw and cooked beef and their relationship with Warner-Bratzler (WB) tenderness measurement were evaluated. Shear tests were performed on four muscles, biceps femoris (BF), longissimus dorsi (LD), semimembranosus (SM), and semitendinosus (ST), with three different aging treatments (0, 1, and 2 weeks), using a specially designed device and the WB shear device. Raw beef exhibited a nonlinear behavior characterized by linearity at small deformations, stress yield at intermediate deformations, and work hardening at large deformations. Cooked beef had a different nonlinear behavior with the slope of the force-displacement curve increasing with displacement before the peak force. Cooking resulted in a large increase (p=0.01) in maximum shear force and strain energy. The effect of muscle type and aging on shear properties was mostly significant (p=0.05). The maximum shear force of cooked beef was correlated with WB measurement with the R² value ranging from 0.65 to 0.34 for LD, SM, and BF except for ST with a R² value of 0.10. Correlations of the shear properties of raw meat with WB measurement were low (R²<0.36), indicating the difficulty of predicting WB tenderness from the shear properties of raw meat.