摘要
提出了一种用近红外光谱技术快速鉴别酸奶品种的新方法。首先应用光谱仪获得5种典型酸奶品种的光谱曲线,用主成分分析法对5种酸奶品种进行聚类分析,再结合人工神经网络技术建立模型进行品种鉴别。主成分分析表明,主成分1和主成分2的累积可信度已达98.96%,前7个主成分的累积可信度达到99.97%。以每一个样品的前7个主成分作为神经网络的输入,品种类型作为神经网络的输出,建立三层BP人工神经网络模型。每个品种各27个样本,5个品种共135个样本用来建立神经网络模型,余下每个品种各5个共25个用于预测。建模品种的拟合率和预测品种的识别率均为100%。说明该方法能快速无损的检测酸奶品种,为酸奶的品种鉴别提供了一种新方法。
A new method for the discrimination of varieties of near acidophilous milk by means of near infrared spectroscopy （NIRS） was developed. Firstly, through the principal component analysis （PCA） of spectroscopic curves of 5 typical kinds of acidophilous milk, the clustering of acidophilous milk varieties was processed. The analysis results showed that the cumulate reliabilities of PC1 and PC2 （the first two principal components） reached 98. 96%, and the cumulate reliabilities of PC1 to PC7 （the first seven principal components） were 99.97%. Secondly, a discrimination model of artificial neural network （ANN-BP） was set up. The first seven principal components of the samples were applied as ANN-BP inputs, and the values of type of aei dophilous milk were applied as outputs, then the three layer ANN-BP model was build. In this model, every variety of acidophb lous milk includes 27 samples, the total number of samples is 135, and the rest 25 samples were used as prediction set. Calculation results showed that the distinguishing rate of the five aeidophilous milk varieties was 100M. This model is reliabile and practicable. So a new approach to the rapid and lossless discrimination of varieties of acidophilous milk was put forward.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2006年第11期2021-2023,共3页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金
高等学校优秀青年教师教学科研奖励计划项目(02411)
浙江省自然科学基金人才基金(RC02067)资助项目
关键词
近红外光谱
酸奶
主成分分析
人工神经网络
鉴别
Near infrared spectral
Acidophilous milk
Principal component analysis （PCA）
Artificial neural network
Discrimination
