caa a22 4500 463156382 CHVBK 20180406164735.0 cr unu---uuuuu 170326e20070901xx s 000 0 eng 10.1007/s00217-006-0470-2 doi (NATIONALLICENCE)springer-10.1007/s00217-006-0470-2 Combination and comparison of multivariate analysis for the identification of orange varieties using visible and near infrared reflectance spectroscopy [Elektronische Daten] [Haiyan Cen, Yong He, Min Huang] A study of multivariate analysis for orange varieties was carried out, and the potential of visible and near infrared reflectance spectroscopy (Vis/NIRS) for its ability to nondestructively differentiate orange varieties was evaluated. A total of 320 orange samples (80 for each variety) were investigated for Vis/NIRS on 325-1075nm using a field spectroradiometer. Multivariate classification methods including principal component analysis (PCA), back propagation neural network (BPNN) and partial least squares discriminant analysis (PLSDA) were adopted to classify oranges. Sixteen principal components from PCA were used as the input of BPNN model, and the identification accuracy of four orange varieties reached 100%. The prediction result of PLSDA, i.e., standard error of prediction (SEP) 0.24497, correlation coefficient (R) 0.97843, root mean square error of prediction (RMSEP) 0.24268, and identification accuracy 90% indicate that PLSDA is an alternative model for orange identification. With the comparison of these two models, it shows that BPNN combined with PCA obtained better classification effect than that of PLSDA. The overall results demonstrate that Vis/NIRS technology with multivariate analysis models is promising for the rapid and reliable determination for identification of orange varieties. Springer-Verlag, 2006 Orange nationallicence Vis/NIRS nationallicence Principal component analysis nationallicence Back propagation neural network nationallicence Partial least squares discriminant analysis nationallicence Cen Haiyan College of Biosystems Engineering and Food Science, Zhejiang University, 310029, Hangzhou, China aut He Yong College of Biosystems Engineering and Food Science, Zhejiang University, 310029, Hangzhou, China aut Huang Min College of Biosystems Engineering and Food Science, Zhejiang University, 310029, Hangzhou, China aut European Food Research and Technology Springer-Verlag 225/5-6(2007-09-01), 699-705 1438-2377 225:5-6<699 2007 225 217 https://doi.org/10.1007/s00217-006-0470-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00217-006-0470-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Cen Haiyan College of Biosystems Engineering and Food Science, Zhejiang University, 310029, Hangzhou, China aut NATIONALLICENCE 700 1- He Yong College of Biosystems Engineering and Food Science, Zhejiang University, 310029, Hangzhou, China aut NATIONALLICENCE 700 1- Huang Min College of Biosystems Engineering and Food Science, Zhejiang University, 310029, Hangzhou, China aut NATIONALLICENCE 773 0- European Food Research and Technology Springer-Verlag 225/5-6(2007-09-01), 699-705 1438-2377 225:5-6<699 2007 225 217 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer