Abstract:For rapid and non-destructive detection of Lang jujubes with subtle bruises, dynamic visible/near-infrared (NIR) spectral data was collected to study intact and subtly bruised Lang jujubes. Based on the definition of different spectral ranges, the spectral data obtained were divided into six spectral ranges, namely, visible (Vis), short-wave NIR (SW-NIR), long-wave NIR (LW-NIR), Vis/SW-NIR, NIR, and Vis/NIR. The optimal pretreatment method for each range was selected. Successive projections algorithm (SPA) and principal component analysis (PCA) were used to reduce the dimensions of the full spectrum (FS). Using the characteristic wavelengths extracted by SPA, the principal component extracted by PCA and FS of the 6 spectral ranges as input variables, a partial least squares regression (PLSR) model and a least-squares support vector machines (LS-SVM) model were established. The optimal model was identified by comparing the discrimination accuracy of the prediction set. The results indicated that PLSR was more preferable than LS-SVM and the SW-NIR spectral range was optimal in comparison to the other five spectral ranges in terms of discriminatory power. The optimal model was identified as SW-NIR-SNV-SPA-PLSR and the discrimination accuracy of the prediction set was 93.3%. This study provide a reasonable theoretical basis for the discrimination of subtly bruised Lang jujubes and development of relevant instruments.
