Abstract

Nowadays, the sugar content detection of red globe grape is destructive, inefficient and cumbersome. In this study, in order to find a rapid non-destructive detection method for the sugar content of red globe grape, the experiment was conducted to study the relationship between sugar content of red globe grape and the near-infrared spectra. The near-infrared spectra of 160 red globe grapes were acquired with a wavelength of from 4000 to 10000 cm-1. The model established in all band was analyzed by using different spectral pretreatments combined with three quantitative analysis models which were Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR). The results illustrated that the reliability of PLSR was the best and PCR followed by. In order to get a better prediction model, the number of wavebands was reduced from 1557 to 650 by using quantum genetic algorithm and partial least squares regression (QGA-PLSR). At the same time, the correlation coefficient (RC) of prediction model and its Root-Mean-Square Error of Prediction (RMSEP) were improved obviously. RC was increased from 0.975 to 0.995 and RMSEP was decreased from 0.8 to 0.495. With the QGA-PLSR method, the number of wavebands was reduced greatly which made full use of the wavebands information. And the sugar content prediction model of red globe grape was established. This provided technical support for the quality classification of red globe grape.

Keywords:

Near-infrared spectroscopy, partial least squares regression, quantum genetic algorithm, red globe grape, sugar content,

References