基于主成分分析的苹果霉心病近红外漫反射光谱判别

为了探讨利用近红外漫反射光谱技术判别苹果霉心病的可行性，将健康苹果和霉心病苹果的近红外光谱经不同光谱预处理方法处理，将主成分分析提取的主成分作为自变量，对苹果霉心病进行了判别研究。结果表明，光谱经矢量归一化处理后提取到的20个主成分建立的Fisher判别函数判别率最高，性能稳定，建模集正确判别率为89.9%，对检验集正确判别率为87.8%。

Discriminant Analysis of Apple Moldy Core Using Near Infrared Diffuse Reflectance Spectroscopy Based on Principal Component Analysis

In order to evaluate the ability of near infrared spectroscopy (NIRS) to detect moldy core in apple fruits, the discriminant efficiency of Fisher function based on several spectra preprocessing methods and principal component analysis (PCA) were investigated. Results showed that the discriminant efficiency of Fisher function established by 20 principal components (PCs) after vector normalization preprocess was the highest, discriminant efficiency for training sample set and test set were 89.9% and 87.8%, respectively.