可见/近红外光谱分析技术快速鉴别航天育种番茄  

Application of Vis/NIR Diffuse Reflectance Spectroscopy to the Rapid Detection and Identification of Tomato Fruit via Space Mutation Breeding

作　　者：施佳慧[1,2] 陈自力[3] 邵咏妮[1] 何勇[1] 冯盘[1] 朱加进[1] 

机构地区：[1]浙江大学生物系统工程与食品科学学院,浙江杭州310029 [2]浙江体育科学研究所,浙江杭州310004 [3]浙江省标准化研究院,浙江杭州310006

出　　处：《光谱学与光谱分析》2011年第2期387-389,共3页Spectroscopy and Spectral Analysis

基　　金：国家科技支撑项目(2006BAD27B02-03);国家高技术研究发展计划(863计划)项目(2007AA10Z436);国家重大科技专项项目(2009ZX08012-010B);浙江省重大科技招标项目(2007C02002-2)资助

摘　　要：提出了利用可见/近红外光谱技术快速无损鉴别航天育种番茄品种的方法,采用偏最小二乘法对光谱特征信息进行提取,与神经网络结合建立番茄品种的鉴别模型。该模型将提取后的主成分作为神经网络的输入,加速了神经网络的训练速度。同时采用小波变换对大量光谱数据进行压缩,并结合神经网络建立番茄品种鉴别模型,该模型将压缩后的数据作为神经网络的输入。通过对太空育种突变株M1,M2及其亲本番茄品种的共105个番茄果实样本建立训练模型,并用每个品种15个样本,共45个番茄果实的样本进行预测。两个模型的鉴别正确率分别达到95.6%和97.8%。说明本方法具有较高的鉴别准确度,为航天育种番茄品种的快速无损鉴别提供了新的方法。In order to quickly analyze varieties of tomato via space mutation breeding with near infrared spectra,characteristics of the pattern were analyzed by partial least square.The model was built with radial basis function neural network and regarded the compressed data as the input of neural network input vectors.The model regarded the compressed data as the input of neural network input vectors and the training process was speeded up.For one hundred and five fruit samples of CK,M1 and M2 the training model was built.Forty five samples formed the prediction set.The discrimination rate of these two models achieved 95.6% and 97.8%.It offered a new approach to the fast discrimination of varieties of tomato via space mutation breeding.

关 键 词：近红外光谱 航天育种番茄 偏最小二乘法 人工神经网络 

分 类 号：S123[农业科学—农业基础科学] S641.2