Modeling for SSC and firmness detection of persimmon based on NIR hyperspectral imaging by sample partitioning and variables selection

Nondestructive detecting for persimmon's internal quality is meaningful for post-harvest processing. This study focused on the modeling of soluble solid content (SSC) and firmness (FM) determination of persimmon with near-infrared (NIR) hyperspectral imaging within 900-1700 nm. Sample partitioning and variables selecting were used to optimize the partial least squares (PLS) regression detective models. Three sample partitioning methods included Kennard-stone (KS) algorithm, sample set partitioning joint x-y distances algorithm (SPXY) and random selection (RS) methods were adopted. Monte Carlo uninformative variables elimination (MC-UVE), competitive adaptive reweight sampling method (CARS) and successive projection algorithm (SPA) were applied for feature variables selection. For SSC and FM detecting, the best models were SPXY-MC-UVE-CARS-PLS model with 12 feature variables and Savitzky-Golay-RS-CARS-PLS with 7 feature variables respectively. The 12 and 7 feature variables are grouped together to build PLS models for SSC and FM determination and after the evaluation the regression coefficient of each variables, finally 10 and 9 selected wavelengths were selected to SSC and FM detection respectively. The final models obtained coefficient of determination (R-P(2)) of 0.757, root mean standard error of prediction (RMSEP) of 1.404 (circle)Brix and R-P(2) of 0.876, RMSEP of 0.395 kg/cm(2) for SSC and FM detection respectively. Meanwhile, we obtained the SSC and FM distribution maps which could give help to visual detection. The results in this study could provide reference for the development of online classification equipment with multi-indicators detection for persimmon.