苹果霉心病可见/近红外透射能量光谱识别方法  

Detection of Moldy Core of Apples Based on Visible/Near Infrared Transmission Energy Spectroscopy

作　　者：雷雨[1] 何东健[1] 周兆永[1] 张海辉[1] 苏东[1] 

机构地区：[1]西北农林科技大学机械与电子工程学院,陕西杨凌712100

出　　处：《农业机械学报》2016年第4期193-200,共8页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家高技术研究发展计划(863计划)项目(2013AA10230402);陕西省科技统筹创新工程计划项目(2014KTCL02-15)

摘　　要：针对苹果霉心病从外表无法识别的难题,提出基于可见/近红外透射能量光谱进行快速无损识别的模型和方法。在200-1 100 nm波段内采集了200个苹果的透射能量光谱数据,随机选取140个样品作为训练集,剩余60个样品作为测试集。用平滑法和多元散射校正对光谱数据进行预处理。基于全光谱、连续投影算法（SPA）提取的12个特征波长、主成分分析（PCA）提取的9个主成分,分别建立了偏最小二乘判别法、误差反向传播人工神经网络和支持向量机（SVM）识别模型。实验结果说明,应用PCA-SVM建立的模型识别性能最优,该模型对测试集和训练集中霉心病果和健康果的识别正确率分别为99.3%和96.7%。基于SPA和PCA所建模型的输入变量数仅相当于基于全光谱所建模型输入变量数的0.99%和0.74%,极大降低了模型的复杂度。研究结果表明,该方法是可行的且具有较高识别准确度,为苹果在线内部品质分级和便携式苹果霉心病检测仪的研究提供了技术依据。In order to solve the problem of identification moldy core of apples from the surface,a quick and non-destructive detection method was proposed based on visible / near infrared transmission energy spectroscopy. Visible / near infrared transmission energy spectra of 200 apples were collected in the wavelength range of 200 - 1 100 nm. Totally 140 samples were used for the calibration set,and 60 samples for the validation set. Smoothing method and multiple scattering correction were used to preprocess the original spectra. Totally 12 characteristic wavelengths and 9 principal components were selected by successive projections algorithm（ SPA） and principal component analysis（ PCA）,respectively. Partial least squares discriminant analysis,error back propagation artificial neural networks,and support vector machine（ SVM） measurement model were established based on SPA and PCA,respectively. The results showed that the best model was PCA-SVM,and its recognition accuracy rate reached 99. 3% for the calibration set and 96. 7% for the validation set. The models established based on SPA and PCA were much simpler than those based on full spectra,since the numbers of input variable of them were only about 0. 99% and 0. 74% of that of full spectra,respectively. The results showed that the method was available and had high identification accuracy. Meanwhile, the results would provide theoretical basis for the research and development of on-line detection of internal quality in apples and portable moldy core apple detector.

关 键 词：苹果 霉心病 能量光谱 连续投影算法 主成分分析 支持向量机 

分 类 号：O657.33[理学—分析化学] S436.611[理学—化学]