Rapid Nondestructive Detection And Instrumentation Of Grape Internal Quality Based On Spectroscopy And Multispectral Imaging Technology

In recent years, fruit industry in China is facing huge challenge from abroad. Fruit grading becomes necessary to meet with market requirement for high-quality fruit.. Traditionalfruit grading has been labor-intensive and time-consuming Thus, it is necessary to develop fast, non-destructive, environment-frendly method of fruit quality detection. In this paper, visible near infrared spectroscopy and multi-spectral imaging technology were used for grape soluable solid content (SSC) model calibration and intruementation. Main points are as follows:(1) Visible-near infrared spectroscopy was used to determine the SSC of4grape varietis (Bianco, Red Globe, Muscat Kyoho and Fujimineri) Spectral data were scanned by a USB4000optic fiber spectrometer and were calibrated against grape SSC by such techniques as partial least squares regression(PLSR), back propagation artificial neural network(BP-ANN), least squares support vector machine(LS-SVM) and particle swarm optimized support vector machine(PSO-SVM). The result shows that the PSO-SVM model developed best prediction performance with coefficient of determination (R2p) of0.87-0.95and root-mean squares of error (RMSEP) of0.77-1.23°Brix.(2) Multi-spectral imaging technology was applied to determine the SSC of the same4grape varietis. In the study, a multi-spectral camera was used to obtain visible and near-infrared images of a grape which were then transformed into13optictal indeies in RGB, HSI and CIE XYZ colour spaces.The selected indeies were used to establish a single-variable linear regressive model and multi-variable regressive models The results indicates that a PSO-SVM model for the CIE XYZ and RGB colour indies developed best performed best prediction accuracy with R"p of0.82-0.93and RMSEP of0.99-1.17°Brix.(3) An instrument of grape SSC measurement based on wavelength-specific LEDs was proposed. Firsely, characteristic wavelengths of grape SSC were extracted from the spectra by successive project algorithm (SPA). Next, the LEDs with similar light-emitting wavelengths were sorted out as light source and combined with a micro-chip processing system The instrumental test shows that the relative error of prediction wasless than±10%...