基于光谱技术的杨梅汁品种快速鉴别方法的研究  

Fast Discrimination of Varieties of Bayberry Juice Based on Spectroscopy Technology

作　　者：岑海燕[1] 鲍一丹[1] 何勇[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

出　　处：《光谱学与光谱分析》2007年第3期503-506,共4页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金(30671213);高等学校优秀青年教师教学科研奖励计划(02411);浙江省重大科技攻关项目(2005C12029)资助

摘　　要：为了实现杨梅汁品种的快速无损鉴别,提出了一种用可见和近红外光谱技术快速鉴别杨梅汁品种的新方法。首先采用偏最小二乘法进行模式特征分析,经过交互验证法判别,确定最佳主成分数为9。完成特征提取后,将这9个主成分作为神经网络的输入变量,建立了三层BP神经网络,实现类别预测的同时也完成了数学建模与优化分析工作。3个品种的杨梅汁样本数均为20,共计60个样本。在神经网络学习中,将其分成训练集样本51个和预测集样本9个。对9个未知样本进行预测,准确率为100%。说明本文提出的基于光谱技术和模式识别的方法具有很好的分类和鉴别能力。Visible and near-infrared reflectance spectroscopy （NIRS） was applied in the discrimination of bayberry juice varieties. Characteristics of the pattern were analyzed by partial least square. Through full cross validation, nine principal components presenting important information of spectra were confirmed as the best number of principal components. Then, these nine principal components were taken as the input of BP neural network. Through the training and prediction, three different varieties of bayberry juice were classified according to the outputs of BP neural network. Besides, the work on building mathematic model and optimizing the algorithm was completed. In the process of BP neural network modeling, 60 samples were gained from the local market and each species has 20 samples. Fifty one samples were used as the training set and the reminder samples （total 9 samples） formed the prediction set. With a proper network training parameter, a 100% accuracy was obtained by BP neural network. Thus, it is concluded that PLS analysis combined with BP neural network is an available alternative for pattern recognition based on the spectroscopy technology.

关 键 词：偏最小二乘法 BP神经网络 模式识别 杨梅汁 光谱技术 

分 类 号：TH744.1[机械工程—光学工程] S602.3[机械工程—仪器科学与技术]