|
|||||
|
|
| PLS-BP法近外光谱同时检测饲料组分的研究 | |
| 引用本文： | 刘波平,秦华俊,罗香,曹树稳,王俊德.PLS-BP法近外光谱同时检测饲料组分的研究[J].光谱学与光谱分析,2007,27(10):2005-2009. |
| 作者姓名： | 刘波平 秦华俊 罗香 曹树稳 王俊德 |
| 作者单位： | 1. 南京理工大学化工学院,南京,江苏,210094;江西省分析测试中心,南昌,江西,330029 2. 南昌大学食品科学教育部重点实验室,南昌,江西,330047 3. 江西省分析测试中心,南昌,江西,330029;南昌大学化学系,南昌,江西,330047 4. 南京理工大学化工学院,南京,江苏,210094 |
| 基金项目： | 教育部南昌大学食品科学重点实验室开放基金 , 江西省星火计划项目 |
| 摘 要： | 建立了用偏最小二乘(partial least squares,PLS)与人工神经网络(artificial neural networks,ANN)联用对饲料样品同时测定水分、灰分、蛋白质、磷含量的预测校正模型.光谱数据用二阶微分及标准归一化处理(SNV),用PLS法将原始数据压缩提取前10个主成分与2个特征峰值作为12个输入向量,采用单隐层的反向传播人工神经网络(Back-Propagation Network,BP),确定中间层的神经元个数为23,初始训练迭代次数为1 000.PLS-BP模型对样品四个组分含量的预测决定系数(r2)分别为:0.995 0,0.998 0,0.999 0和0.967 0;样品平行扫描光谱预测值的标准偏差分别为:0.027 74,0.048 53,0.032 92和0.022 04. |
| 关 键 词： | 近红外光谱 饲料 偏最小二乘 人工神经网络 BP网络 |
| 文章编号： | 1000-0593（2007）10-2005-05 |
| 修稿时间： | 2006-07-18 |
Determination of Four Contents of Feedstuff Powder Using Near Infrared Spectroscopy by PLS-BP Model |
|
| LIU Bo-ping,QIN Hua-jun,LUO Xiang,CAO Shu-wen,WANG Jun-de.Determination of Four Contents of Feedstuff Powder Using Near Infrared Spectroscopy by PLS-BP Model[J].Spectroscopy and Spectral Analysis,2007,27(10):2005-2009. | |
| Authors: | LIU Bo-ping QIN Hua-jun LUO Xiang CAO Shu-wen WANG Jun-de |
| Institution: | 1. College of Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; 2. Analytical and Testing Center of Jiangxi Province, Nanchang 330029, China; 3. Key Laboratory of Food Science of MOE, Nanchang University, Nanchang 330047, China ;4. Department of Chemistry, Nanchang University, Nanchang 330047, China |
| Abstract: | Partial least squares(PLS)and artificial neural networks(ANN)prediction model for four components of feedstuff has been established with good veracity and recurrence.The spectra put into the model should be processed by second derivative and standard normal variate(SNV).Ten principal components compressed from original data by PLS and two peak values were taken as the inputs of Back-Propagation Network(BP),while four predictive targets as outputs,according to Kolmogorov theorem and experiment,and twenty three nerve cells were taken as hidden nodes.Its training iteration times was supposed to be 10 000.Prediction deciding coefficient of four components by the model are 0.995 0,0.998 0,0.999 0 and 0.967 0,while the standard deviation of an unknown sample scanned parallelly are 0.027 74,0.048 53,0.032 92 and 0.022 04. |
| Keywords: | Near infrared spectroscopy(NIRS) Feedstuff PLS ANN BP |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |