Comparison of prediction power of three multivariate calibrations for estimation of leaf anthocyanin content with visible spectroscopy in Prunus cerasifera.

ABSTRACT: The anthocyanin content in leaves can reveal valuable information about a plant's physiological status and its responses to stress. Therefore, it is of great value to accurately and efficiently determine anthocyanin content in leaves. The selection of calibration method is a major factor which can influence the accuracy of measurement with visible and near infrared (NIR) spectroscopy. Three multivariate calibrations including principal component regression (PCR), partial least squares regression (PLSR), and back-propagation neural network (BPNN) were adopted for the development of determination models of leaf anthocyanin content using reflectance spectra data (450-600 nm) in Prunus cerasifera and then the performance of these models was compared for three multivariate calibrations.

SUBMITTER: Liu X 

PROVIDER: S-EPMC6825749 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature