Abstract：In order to grade Gannan navel oranges by internal qualities, on-line and fast detection method of soluble solids content (SSC) was established. Semi-transmission spectra of Gannan navel oranges were acquired at moving speed of 0.3 m?s-1 by USB4000 micro fiber spectrometer (470~1 150 nm). The important wavelength variables for SSC were selected by CARS variable selection method to establish on-line prediction model by partial least squares (PLS) regression. The prediction model was used to predict SSC of navel oranges in fully independent prediction set. The results indicate that CARS can effectively select wavelength variables for SSC of navel oranges with improved model prediction precision. Compared to full-spectrum PLS, the model performance of CARS-PLS is improved. The correlation coefficient of cross validation is increased from 0.871 to 0.934, and the root mean square error of cross validation (RMSECV) is decreased from 0.560% to 0.412%. For fully independent prediction set, the root mean square error of prediction (RMSEP) of SSC is 0.649%. The samples that have predicted residual errors of SSC within the limits of ±1.0% account for 86.3% of total prediction samples. The proposed method can basically satisfy the requirement of on-line detection and grading for SSC of navel oranges.
2013, Vol. 34 
