期刊论文

Universal Models for Determining Oil Contents in Three Woody Oil Plant Seeds by Using Near Infrared Spectroscopy: A Feasibility Study

中文

林产化学与工业

0253-2417

2017

37

4

137-142

10.3969/j.issn.0253-2417.2017.04.020

湖南省教育厅重点项目（14A155）；

本校为第一机构

为了构建湖南常见3种木本油料植物种子含油率近红外光谱通用模型,收集了98个油桐、96个油茶和96个核桃样本,采集了粉碎后种仁的近红外光谱(NIR),测定了样本含油率,分别采用偏最小二乘法(PLS)及径向基神经网络法(RBFNN)建立油桐+油茶+核桃、油桐+油茶、油桐+核桃和油茶+核桃4个混合样本集含油率的NIR通用模型。对PLS模型,4个样本集(验证集)的相关系数(R_p)分别为0.963、0.881、0.965和0.967,预测均方根误差(RMSEP)分别为2.78、3.31、2.47和2.70,相对标准偏差(RSD)分别为4.87%、6.51%、4.03%和4.55%;RBFNN模型的R_p分别为0.958、0.877、0.959和0.966,RMSEP分别为3.34、2.55、...

In order to build a universal model of near infrared spectroscopy for determining oil content in three woody oil plant seeds in Hunan, 98 Vernicia fordii seed samples, 96 Camellia oleifera seed samples and 96 Juglans regia seed samples were collected. Near infrared spectra (NIR) of their crushed seed kernel were recorded. Oil content was determined. Partials quare least (PLS) and radical basis function neural networks (RBFNN) were used to develop the universal NIR models for determining oil content for each of 4 sample sets (i. e. V. fordii+C. ...