To detect pesticide residue on navel orange surface by nondestructive means, five group oranges sprayed water,
fenvalerate, isocarbophos, fenpropathrin, carbendazim pesticides respectively were chosen as experimental samples.
Laser imaging system was built for acquiring images of fruits. Unitary nonlinear regression function was fitted by
analyzing gray histogram curves of images within 12-40 range. The coefficient or eigenvalue of functions was different
about every navel orange. The threshold coefficient was confirmed by data processing, which can establish fruits surface
sprayed pesticide or not. The result showed that laser imaging technique is feasible for detecting pesticide residue on
navel orange surface.
In this study, a hyperspectral imaging system using a laser source was developed and two experiments were carried
out. The first experiment was detection of pesticide residue on navel orange surface. We calculated the mean intensity of
regions of interest to plot the curves between 629nm to 638nm. The analysis of the mean intensity curves showed that
the mean intensity can be described by a characteristic Gaussian curve equation. The coefficients a in characteristic
equations of 0%, 0.1% and 0.5% fenvalerate residue images were more than 2400, 1570-2400 and less than 1570,
respectively. So we suggest using equation coefficient a to detect pesticide residue on navel orange surface. The second
experiment was predicting firmness, sugar content and vitamin C content of kiwi fruit. The optimal wavelength range of
the kiwi fruit firmness, sugar content, vitamin C content line regressing prediction model were 680-711nm, 674-708nm,
669-701nm. The correlation coefficients (R) of prediction models for firmness, sugar content and vitamin C content were
0.898, 0.932 and 0.918. The mean errors of validation results were 0.35×105Pa, 0.32%Brix and 7mg/100g. The experimental results indicate that a hyperspectral imaging system based on a laser source can detect fruit quality effectively.
This paper describes an experimental study on non-destructive methods for predicting quality of kiwifruits using
fluorescence imaging. The method is based on hyperspectral laser-induced fluorescence imaging in the region between
700 and 1110 nm, and estimates the kiwifruits quality in terms of internal sugar content and firmness. A station for
acquiring hyperspectral laser-induced fluorescence imaging has been designed and carefully choosing each component.
The fluorescence imaging acquired by the station has been pre-processed by selecting regions of interest (ROIs) of
50 100 × pixels. A line regressing prediction method estimates the quality of kiwifruit samples. The results obtained in
classification show that the station and prediction model enables the correct discrimination of kiwifruits internal sugar
content and firmness with a percentage of r= 98.5%, SEP=0.4 and r=99.9%, SEP=0.62.