为实现生鲜肉水分含量的快速无损检测, 在波长350～1 700 nm范围内采集生鲜猪肉98个样本的可见近红外反射光谱。 经中值平滑滤波、 多元散射校正和一阶微分复合预处理方法对原始光谱进行降噪处理。 将样本数据随机分为训练集和测试集, 以训练集交叉验证网格搜索法确定最佳惩罚参数, 利用径向基核函数的支持向量机算法建立了支持向量机预测模型, 并与偏最小二乘回归建模法进行比较。 用径向基核函数的支持向量机算法所建模型对生鲜肉水分含量进行预测的结果为: 训练集的预测相关系数Rc为0.96、 标准差SEC为0.32, 测试集的预测相关系数Rv为0.87、 标准差SEV为0.67。 实验结果证实用支持向量机所建模型适合于生鲜猪肉水分含量的无损快速检测。

Visible near infrared reflectance spectra in the range of 350 nm to 1700 nm were collected from 98 pork samples to develop online, rapid and nondestructive detection system for water content in fresh pork. Median smoothing filter (M-filter), multiplication scatter correlation (MSC) and first derivative (FD) were used as compound preprocessing method to reduce noise present in the original spectrum. Seventy four samples were randomly selected to develop training model and remaining 24 samples were used to test the model. The optimal punishment parameters for the support vector machine (SVM) were determined by using cross-validation and grid-search in the training set. SVM prediction model was developed with the radial basis function (RBF) and the developed model was compared with the model developed by partial least squares regression (PLSR) method. SVM prediction model based on RBF had the correlation coefficient and root mean standard error of 0.96 and 0.32 respectively in the training set. The model obtained correlation coefficient of 0.87 and root mean square error of 0.67 in the test set. The result thus obtained demonstrates the applicability of SVM model for rapid, nondestructive detection of water content in pork.