光谱技术预测牛肉嫩度研究进展

肉的嫩度是肉品品质的首要指标。多年来,牛肉嫩度一直是肉品学者关注的焦点,而牛肉嫩度的检测是近年来研究的热点之一。本文从光谱谱信息和牛肉内部成分的关系及光谱成像和牛肉外部特征的关系两方面,简要阐述牛肉嫩度检测的光谱技术特点。主要介绍国内外近十年来在牛肉嫩度检测方面所采用的近红外(NIR)、高光谱、多光谱、荧光光谱和可见光谱技术研究进展,讨论现有技术的局限性,并指出未来牛肉嫩度检测技术的发展方向。     

仿真技术检测牛肉嫩度方法研究

嫩度是牛肉品质的重要检测指标之一。检测牛肉嫩度的传统方法是感官评定法和剪切力测定法,然而这两种方法存在不同程度的人为误差。本文设计了基于压力传感器的仿真磨齿咀嚼装置,其由电机驱动,利用曲柄连杆机构及曲柄滑块机构带动下颚产生上、下的往复运动,上颚产生左右的往复运动,从而模拟磨齿咀嚼过程。结合感官评定法,建立了牛肉嫩度等级评定标准,验证了仿真技术检测牛肉嫩度的可行性。采用此方法,牛肉嫩度等级检测准确率达到85%,说明仿真磨齿咀嚼装置设计合理,具有实用价值。     

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.     

氯化钙处理对牛肉嫩度影响的研究

对宰后8h的牛肉分别注射200、250、300mmol／LCaCl2溶液(注射量为肉重的3％),然后将处理样品在4℃下分别腌制12h、24h、48h,通过对其剪切力值的测定,研究注射氯化钙及腌制时间对牛肉嫩度的影响。结果表明,与未注CaCl2溶液组相比,注射CaCl2溶液组牛肉嫩度显著提高(P<0.05),但不同水平CaCl2溶液处理之间牛肉嫩度差异不显著(P>005);不同腌制时间对牛肉嫩度的影响差异不显著(P>0.05)。综合分析,200mmol/L的CaCl2溶液处理浓度、48h的腌制时间改善牛肉的嫩度是可行的。     

氯化钙处理对牛肉嫩度影响的研究

对宰后8h的牛肉分别注射200、250、300mmol／LCaCl2溶液(注射量为肉重的3％),然后将处理样品在4℃下分别腌制12h、24h、48h,通过对其剪切力值的测定,研究注射氯化钙及腌制时间对牛肉嫩度的影响。结果表明,与未注CaCl2溶液组相比,注射CaCl2溶液组牛肉嫩度显著提高(P<0.05),但不同水平CaCl2溶液处理之间牛肉嫩度差异不显著(P>005);不同腌制时间对牛肉嫩度的影响差异不显著(P>0.05)。综合分析,200mmol/L的CaCl2溶液处理浓度、48h的腌制时间改善牛肉的嫩度是可行的。      

牛肉嫩化技术的研究进展

牛肉的品质受多方面因素的影响,其中最重要的一个因素就是嫩度。本文介绍了影响牛肉嫩度的因素,以及目前通常使用的嫩化方法,最后提出了展望。     

牛肉嫩度的终极者——烹调方法

在制作牛肉菜肴的过程中,牛肉经过严格的选料与恰当的加工方法之后,采取合适的“烹”法、“调”法,是保持并确保牛肉嫩度的“终极者”。一般讲,牛肉的烹法分为两类:使用干热(烤、炙、煎、炸、炒)和使用湿热(煮、烧、焖、炖、煨)。通常要根据肉的嫩度,来决定牛肉的烹法。对于分档取料上,部位较嫩的肉,经常采用干热法保持嫩度;对于部位较老的肉,常常使用湿热法来软化结缔组织以达到致嫩的目的;否则,物极必反,影响牛肉的嫩度。比如:在西餐中,牛肉类菜肴为了保证其特有的嫩度,通常把牛肉分成三成熟(Rare)、五成熟(m edium rare)、七成熟(m ediu…     

不同加热方式对牛肉嫩度和杀菌率的影响

本文研究变频微波和水浴两种低温加热方式对牛肉加热过程中嫩度、保水性和微生物杀死率的影响。以牛大腿腱子肉为实验材料,在两种低温加热方式下经不同加热温度和加热时间处理后,检测肉品各项指标。结果表明:微波加热时间短;微波加热温度每降低5℃,加热时间缩短且节能10%~15%。微波加热牛肉的肉汁渗出率明显小于水浴加热。两种加热方式下牛肉的剪切力都是随加热温度的升高呈波浪形趋势;加热温度为60℃时,剪切力达到最大值,继续升高到65℃,剪切力为最小值。微波加热牛肉的剪切力小于水浴加热;当微波加热温度为65℃保温4~6 min时,剪切力较小。牛肉水分保留率与肉汁渗出率密切相关,线性方程为y=-0.323x+99.49(R2=0.927)。两种加热方式下,温度高于60℃都能杀死99%以上的微生物。综合分析得出结论:在变频微波500 W、控温65℃条件下加热42 g(±2)牛肉4~6 min,肉品品质较好。     

牛肉嫩度的色差值预测模型研究

为了更快捷地甄选高品质牛肉,通过测定排酸24~48h育肥公牛和淘汰母牛部位肉的色差,探索用牛肉颜色预测牛肉嫩度的方法。实验表明,公牛和母牛的肉色及嫩度差异显著(p<0.05)。公牛和母牛以及公牛前、中、后躯的预测模型均显著(p<0.05)。各模型经过24个部位肉验证,所得剪切力值与实测值差异不显著(p<0.05),说明各模型在嫩度预测上具有较高的参考价值。     

牛肉嫩化机制的初步研究

对牛肉分别注射300mmol／LCaCl2溶液、EDTA溶液、ZnCl2溶液（注射量为肉重的5％），然后将处理样品在4℃下分别腌制24h、48h，通过对其剪切力值、MFI的测定，初步研究牛肉嫩化的机制。结果表明，注射CaCl2能促进肌原纤维的分解，进而加速肉的嫩化；而注射EDTA和ZnCl2却抑制肌原纤维的分解，阻止肉的嫩化。牛肉的嫩化是Ca^2＋通过促进肌原纤维的分解而实现的。     

食源性致病菌快速检测方法的探索研究

目的探讨传统细菌培养法及荧光定量PCR检测两种检测方法在食源性致病菌上的检出效果。方法对2015年采集的7类食品共600例样品采用传统细菌培养法及增菌后的增菌液荧光定量PCR检测,对检测结果进行分析比较。结果被检食品存在食源性致病菌污染,600例样品共检测出35株致病菌,食品致病菌的总检出率为5.83%,其中副溶血性弧菌与金黄色葡萄球菌检出率最高,达到15%。蜡样芽胞杆菌检出率较高,达到10.00%。传统细菌培养法致病菌阳性检出率为2.67%,荧光定量PCR检测致病菌阳性检出率为3.17%,两种检测方法下的检出率差异没有明显差异(X~2=1.882,P0.05)。结论检测中将传统细菌培养法同荧光定量PCR检测这两种检测方法结合起来,不仅提高了食源性致病菌检出率,也可缩短检测时间,提高检出率。后续研究将继续加大样本量并覆盖更多食品种类。     

不同品种牛肉新鲜度光谱检测模型的维护方法

目的 研究不同品种牛肉新鲜度挥发性盐基氮 (TVB-N)定量光谱检测模型的修正方法, 对模型进行维护, 从而提高光谱校正模型的通用性。方法 采用基于SPXY算法的代表性样本添加及光谱吸光度值修正两种方法对黄陂黄牛的TVB-N含量预测模型进行修正。结果 两种方法修正之后的模型使恩施水牛样本的TVB-N预测相关系数R从0.021分别提高至0.919、0.851, 预测误差从8.347分别降至2.182、2.872。结论 修正之后的黄牛模型可实现对黄陂黄牛和恩施水牛两个品种新鲜度TVB-N含量的定量检测, 样本添加的方法优于吸光度值修正方法。     

Partial least squares analysis of near-infrared hyperspectral images for beef tenderness prediction

Tenderness is a primary determinant of consumer satisfaction of beef steaks. The objective of this study was to implement and test near-infrared (NIR) hyperspectral imaging to forecast 14-day aged, cooked beef tenderness from the hyperspectral images of fresh ribeye steaks (n = 319) acquired at 3–5 day post-mortem. A pushbroom hyperspectral imaging system (wavelength range: 900–1700 nm) with a diffuse-flood lighting system was developed. After imaging, steaks were vacuum-packaged and aged until 14 days postmortem. After aging, the samples were cooked and slice shear force (SSF) values were collected as a tenderness reference. After reflectance calibration, a Region-of-Interest (ROI) of 150 × 300 pixels at the center of longissimus muscle was selected. Partial least squares regression (PLSR) was carried out on each ROI image to reduce the dimension along the spectral axis. Gray-level textural co-occurrence matrix analysis with two quantization levels (64 and 256) was conducted on the PLSR bands to extract second-order statistical textural features. These features were then used in a canonical discriminant model to predict three beef tenderness categories, namely tender (SSF ≤ 205.80 N), intermediate (205.80 N < SSF < 254.80 N), and tough (SSF ≥ 254.80 N). The model with a quantization level of 256 performed better than the one with a quantization level of 64. This model correctly classified 242 out of 314 samples with an overall accuracy of 77.0%. Fat, protein, and water absorption bands were identified between 900 and 1700 nm. Our results show that NIR hyperspectral imaging holds promise as an instrument for forecasting beef tenderness.     

生鲜牛肉嫩度的影响因素和嫩化技术

主要论述了生鲜牛肉嫩度的影响因素,诸如性别和年龄、肌内脂肪和胶原蛋白含量等,同时嫩化技术得到了重点阐述、包括了排酸成熟、机械法、电刺激等。希望为冷鲜牛肉的生产、品质的改善提供理论依据和技术支持。     

Optical scattering with hyperspectral imaging to classify longissimus dorsi muscle based on beef tenderness using multivariate modeling

The objective of this study was to develop a non-destructive method for classifying cooked-beef tenderness using hyperspectral imaging of optical scattering on fresh beef muscle tissue. A hyperspectral imaging system (λ = 922–1739 nm) was used to collect hyperspectral scattering images of the longissimus dorsi muscle (n = 472). A modified Lorentzian function was used to fit optical scattering profiles at each wavelength. After removing highly correlated parameters extracted from the Lorentzian function, principal component analysis was performed. Four principal component scores were used in a linear discriminant model to classify beef tenderness. In a validation data set (n = 118 samples), the model was able to successfully classify tough and tender samples with 83.3% and 75.0% accuracies, respectively. Presence of fat flecks did not have a significant effect on beef tenderness classification accuracy. The results demonstrate that hyperspectral imaging of optical scattering is a viable technology for beef tenderness classification.     

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。     

冷鲜牛肉嫩度的影响因素和嫩化技术

主要论述了冷鲜牛肉嫩度的影响因素,诸如性别和年龄、肌内脂肪和胶原蛋白含量等,同时嫩化技术得到了重点阐述,其包括了排酸成熟、机械法、电刺激等。希望为冷鲜牛肉的生产、品质的改善提供理论依据和技术支持。     

Effect of post-mortem temperature on beef tenderness

Beef adductor muscles were incubated for 4 h post mortem at 10°C and for 4 h and 6 h post mortem at 30°C, 37°C and 42°C. Half of the muscles were cooked just after incubation and the other half was first stored for two days at 4°C and then cooked. Meat kept for 4 h or 6 h at 42°C and for 6 h at 37°C and cooked at once had a significantly (p<0·05) lower shear force than meat kept for 4 h at 37°C, 4 h at 30°C, 6 h at 30°C or 4 h at 10°C. The respective significant differences were also found when the meat was cooked two days after incubation. Organoleptic evaluation showed that meat incubated for 6 h at 37°C or for 4 h at 42°C was not significantly more tender than other samples. However, meat kept for 6 h at 42°C was more tender (p<0·5) than the other samples. After two days of storage, meat incubated for 6 h at 37°C and for 6 h at 42° was more tender (p<0·05) than meat kept for 6 h at 30°C. It was concluded that high temperature conditioning at 37°C or higher for 6 h (4 h at 42°C) just after slaughter makes meat more tender than conventional cooling systems.