核桃仁脂肪含量的近红外光谱无损检测

Non-destructive Detection for Fat Content of Walnut Kernels by Near Infrared Spectroscopy

为了实现核桃仁脂肪含量的快速无损检测,在1040~2560nm光谱范围内采集了核桃仁近红外光谱。首先,通过多元散射校正和标准正态化组合方法对原始光谱信息进行预处理,采用马氏距离法剔除异常样本;然后,运用竞争性自适应重加权采样算法与相关系数法相结合,进行特征波段筛选;最后,采用偏最小二乘回归和支持向量机回归算法建立了核桃仁脂肪含量的预测模型。结果显示,以筛选出的6个特征波段为输入,采用偏最小二乘回归算法建立的核桃仁脂肪质量分数预测模型的验证集决定系数为0.86,均方根误差为1.5849%;采用支持向量机回归算法建立的模型验证集决定系数为0.88,均方根误差为1.3716%;表明支持向量机回归算法的建模质量优于偏最小二乘回归算法。采用特征波段建立的支持向量机回归预测模型能大幅降低建模复杂度,实现核桃仁脂肪含量的快速无损检测。

Fat content is an important indicator of the quality of walnuts.In order to achieve the rapid non-destructive detection of walnut fat content,the near infrared spectrum of walnut kernel was collected in the spectral range of 1 040~2 560 nm.Multivariate scatter correction and standard normalized variate were used to pre-processing the original spectral information.And abnormal samples were eliminated by the Mahalanobis distance method.Then the feature bands were screened by the method,which combined competitive adaptive re-weighting sampling (CARS) and correlation coefficient method (CCM) algorithm.Finally,the partial least squares regression and the support vector machine regression algorithm were used to establish prediction model for the fat content of walnut kernels.The results showed that with the six feature bands selected as input,the validation set coefficient of the walnut kernel fat content prediction model established by partial least squares regression algorithm was 0.86,and the root mean square error was 1.584 9%.The validation set coefficient of model established by the support vector machine regression algorithm was 0.88 and the root mean square error was 1.371 6%.It was showed that the modeling quality of the support vector machine regression algorithm was better than the partial least squares regression algorithm.The support vector machine regression prediction model established by the feature bands could sharply reduce the modeling complexity and realize the rapid non-destructive detection of the fat content of walnut kernel.