可见/近红外光谱分析技术快速鉴别航天育种番茄

摘要

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.%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.