基于高光谱技术的马铃薯外部品质检测

摘要

为了快速无损检测马铃薯外部品质,研究采用高光谱成像技术对马铃薯外部品质分级。选取合格、发芽、绿皮、孔洞4种马铃薯外部特征,获取光谱数据,采用不同预处理方法对光谱数据进行处理,并分别建立偏最小二乘判别模型,结果显示采用标准正态变量变换法(SNV)获得的模型效果最优。对预处理后的光谱数据利用连续投影算法(SPA)及加权权重法(WWM)分别优选出了13个和9个特征波段,对两种不同方法得出的特征波段分别建立了支持向量机判别模型,结果显示两种方法对预测集的判别准确率均达到了100%,WWM-SVM 判别模型对校正集的交叉验证率为99.5%,高于 SPA-SVM 判别模型的交叉验证率。利用高光谱成像技术结合 SPA-SVM 和 WWM-SVM 对马铃薯外部品质进行分级具有可行性。%In order to detect the external quality of potato quickly,the hyperspectral imaging technology was used.Potato with germination and other three kinds of common defects were studied.The partial least-squares discriminant model were built after different pretreat-ment methods for spectral data processing.The results showed that pretreatment method of SNV was the best.1 3 and 9 feature bands were selected after using successive projections algorithm (SPA)and weighted weight method (WWM)for spectral data preprocessed. The support vector machine (SVM)discriminant model were estab-lished for both SPA and WWM.Our results also showed that the two methods to predict the set of discriminant accuracy reached 100%. WWM-SVM discriminant model of calibration set of cross validation rate was 99.5%,higher than that of the SPA-SVM discriminant model.The study demonstrated the feasibility of using hyperspectral imaging technology combined with WWM-SVM and SPA-SVM for potato external quality grading.