title: [Discrimination of varieties of apple using near infrared spectra based on principal component analysis and artificial neural network model].
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.
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: He
authors.forename: Xiao-Li
authors.initials: XL
authors.lastname: Li
authors.forename: Yong-Ni
authors.initials: YN
authors.lastname: Shao
mesh_terms: D027845:Malus / Q000737:chemistry*; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D011786:Quality Control; D019265:Spectroscopy, Near-Infrared / Q000379:methods*