基于高光谱技术的马铃薯外部品质检测

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。

Detection of potato external quality based on hyperspectral technology

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 pretreatment methods for spectral data processing. The results showed that pretreatment method of SNV was the best. 13 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 established 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.