Visualizing distribution of pesticide residues in mulberry leaves using NIR hyperspectral imaging

Nowadays, hyperspectral imaging technique has been abroad and successfully used for quality inspection of food products. In this study, the microstructural changes of mulberry were investigated by scanning electron microscope (SEM). Furthermore, NIR hyperspectral imaging system was used to predict the distribution of Pesticide residues in mulberry leaves with the aid of Gas Chromatography. It is primary to extract the characteristic wavelengths in the prediction and visualization of Pesticide residues in mulberry leaves. Successive projections algorithm (SPA), Stepwise regression(SR) and Regression coefficients (RC) were utilized. MLR was applied to build prediction models based on spectral feature in characteristic wavelengths. The best model SPA-MLR ( Rp=0.859 and RMSEP=38.789) was implemented into the pesticide residues visualization procedure. This study demonstrated that pesticide residues distribution map clearly showed the distribution of Pesticide residues in mulberry leaves. Practical applicationsWell understanding the effect of Pesticide residues to biological structure is very important for revelation of novel biological function and mechanism of action of the protein. To facilitate more quickly and effectively detect the types of pesticide residues on the surface of mulberry leaves, the microstructural changes of mulberry were investigated by scanning electron microscope (SEM). Furthermore, NIR hyperspectral imaging system was used to predict the distribution of Pesticide residues in mulberry leaves with the aid of Gas Chromatography.