基于高光谱散射特征的牛肉品质参数的预测研究

研究利用光谱散射特性预测牛肉的pH值、嫩度和颜色。使用高光谱成像系统，获取400～1 100 nm波长范围内新鲜牛肉表面的高光谱散射图像，预测牛肉的品质参数。提取高光谱图像在400～1 100 nm波长范围内的散射特征，利用洛伦兹分布函数，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函数参数。使用逐步回归方法，选择优化波长及其相应的拟合参数，建立多元线性回归模型预测牛肉的品质参数，使用全交叉验证方法评价模型性能。对嫩度的预测相关系数达到0.86，预测标准差为11.7 N，分级准确率达到91%;pH值的预测相关系数为0.86，预测标准差为0.07;对颜色参数L*, a*, b*的预测相关系数分别达到0.92, 0.90和0.88，预测标准差分别为0.90，1.34和0.41。研究结果表明，利用光谱散射特征可以较好的预测牛肉的品质参数。

Study of Spatially Resolved Hyperspectral Scattering Images for Assessing Beef Quality Characteristics

Hyperspectral scattering techniques were used to predict beef pH, tenderness(i.e. WBSF: Warner-Bratzler Shear Force) and color parameters. Thirty-three fresh strip loin cuts were collected from 2-day postmortem carcass. After capturing scattering images and measuring pH values, the samples were vacuum packaged and aged to seventh day, then their color parameters (L*, a*, b*) and WBSF were measured as references. The optical scattering profiles were extracted from the hyperspectral images and fitted to the Lorentzian distribution (LD) function with three parameters. LD parameters, such as the peak height, full scattering width at half maximum (FWHM) and the scattering asymptotic were calculated at individual wavelength. Stepwise regression was used to determine optimal combinations of wavelengths for each of parameters. The optimal combinations were then used to establish multi-linear regression (MLR) models to predict the beef attributes. The full cross validation method was used to examine the performance of models. The models were able to predict beef WBSF with R_CV=0.86, and with the SE_CV (the standard error of cross validation) of 11.7 N, 91% classification accuracy could be obtained. Two-day pH values with R_CV=0.86, SE_CV=0.07 and color parameters (L*, a*, b*) with R_CV of 0.92, 0.90 and 0.88, with the SE_CV of 0.90, 1.34 and 0.41 were obtained respectively. This research provided available technique for the development of multispectral system, which could be implemented online to determine beef steaks color and tenderness.