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。
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.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2006年第5期850-853,共4页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(30270773)
高等学校优秀青年教师教学科研奖励计划(02411)
浙江省自然科学基金人才基金项目(RC02067)资助
关键词
近红外光谱
苹果
主成分分析
人工神经网络
聚类
Near infrared spectral
Apple
Principal component analysis （PCA）
Artificial neural network
Clustering
