title: [Research on the sugar content measurement of grape and berries by using Vis/NIR spectroscopy technique].
abstract: Aiming at the nonlinear correlation characteristic of Vis/NIR spectra and the corresponding sugar content of grape and berries, the Vis/NIR spectra of grape and berries were obtained by diffusion reflectance. A mixed algorithm was presented to predict sugar content of grape and berries. The original spectral data were processed using partial least squares (PLS), and three best principal factors were selected based on the reliabilities. The scores of these 3 principal factors would be taken as the input of the three-layer back-propagation artificial neural network (BP-ANN). Trained with the samples in calibration collection, the BP-ANN predicted the samples in prediction collection. The values of decision coefficient (r2), the root mean squared error of prediction (RMSEP), and bias were used to estimate the mixed model. The observed results using PLS-ANN (r2 = 0.908, RMSEP = 0.112 and Bias = 0.013) were better than those obtained by PLS (r2 = 0.863, RMSEP = 0.171, Bias = 0.024). The result indicted that the detection of internal quality of grape and berries such as sugar content by nondestructive determination method was very feasible and laid a solid foundation for setting up the sugar content forecasting model for grape and berries.
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
authors.forename: Gui-fang
authors.initials: GF
authors.lastname: Wu
authors.forename: Ling-xia
authors.initials: LX
authors.lastname: Huang
authors.forename: Yong
authors.initials: Y
authors.lastname: He
mesh_terms: D000465:Algorithms; D002241:Carbohydrates / Q000032:analysis*; D005638:Fruit / Q000737:chemistry*; D016571:Neural Networks, Computer; D019265:Spectroscopy, Near-Infrared; D013057:Spectrum Analysis; D027843:Vitis / Q000737:chemistry*
chemical_list: D002241:Carbohydrates