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基于主成分分析和神经网络的近红外光谱苹果品种鉴别方法研究
引用本文:何勇,李晓丽,邵咏妮.基于主成分分析和神经网络的近红外光谱苹果品种鉴别方法研究[J].光谱学与光谱分析,2006,26(5):850-853.
作者姓名:何勇  李晓丽  邵咏妮
作者单位:浙江大学生物工程与食品科学学院,浙江,杭州,310029
基金项目:中国科学院资助项目 , 高等学校优秀青年教师教学科研奖励计划 , 浙江省自然科学基金
摘    要:提出了一种用近红外光谱技术快速鉴别苹果品种的新方法,首先用主成分分析法对苹果品种进行聚类分析并获取苹果的近红外指纹图谱,再结合人工神经网络技术进行品种鉴别.主成分分析表明,主成缮分1和主成分2的累积可信度已达98%,以主成分1和2对所有建模样本的得分值做出的得分图,对不同种类苹果具有很好的聚类作用.利用主成分分析得到的载荷图可以得到对于苹果品种敏感的特征波段,用特征波段图谱作为神经网络的输入建立三层BP人工神经网络模型.每个品种各25个苹果共75个用来建立神经网络模型,余下的共15个用于预测.对未知的15个样本进行预测,品种识别准确率达到100%.说明文章提出的方法具有很好的分类和鉴别作用,为苹果的品种鉴别提供了一种新方法.

关 键 词:近红外光谱  苹果  主成分分析  人工神经网络  聚类
文章编号:1000-0593(2006)05-0850-04
收稿时间:2005-08-01
修稿时间:2005-11-08

Discrimination of Varieties of Apple Using Near Infrared Spectra Based on Principal Component Analysis and Artificial Neural Network Model
HE Yong,LI Xiao-li,SHAO Yong-ni.Discrimination of Varieties of Apple Using Near Infrared Spectra Based on Principal Component Analysis and Artificial Neural Network Model[J].Spectroscopy and Spectral Analysis,2006,26(5):850-853.
Authors:HE Yong  LI Xiao-li  SHAO Yong-ni
Institution:College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China
Abstract:A new method for the discrimination of varieties of apple by means of near infrared spectroscopy(NIRS) was developed. First, principal component analysis (PCA) was used to compress thousands of spectral data into several variables and describe the body of spectra, the analysis suggested that the cumulate reliabilities of PC1 and PC2 (the first two principle components) were more than 98%, and the 2-dimentional plot was drawn with the scores of PC1 and PC2. It appeared to provide the best clustering of the varieties of apple. The loading plot was drawn with PC1 and PC2 through the whole wavelength region. The fingerprint spectra, which were sensitive to the variety of apple, were obtained from the loading plot. The fingerprint spectra were applied as ANN-BP inputs. Seventy five samples from three varieties were selected randomly, then they were used to build discrimination model. This model was used to predict the varieties of 15 unknown samples; the distinguishing rate of 100% was achieved. This model is reliable and practicable. So the present paper could offer a new approach to the fast discrimination of varieties of apple.
Keywords:Near infrared spectral  Apple  Principal component analysis (PCA)  Artificial neural network  Clustering
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