Analysis of Hyperspectral Scattering Profiles Using Generalized Gaussian Distribution for Prediction of Apple Firmness and Soluble Solids Content

Abstract

Hyperspectral scattering provides an effective means for characterizing light scattering in the fruit and is thus promising for noninvasive assessment of apple firmness and soluble solids content (SSC). A critical problem encountered in application of hyperspectral scattering technology is analyzing and modeling hyperspectral scattering profiles. A generalized Gaussian distribution (GGD) function, coupled with mean reflectance (GGD-mean), was proposed to describe the spectral scattering profiles of 600 Golden Delicious apples for the spectral region of 5001000 nm. The three-parameter GGD-mean model included mean parameter from mean reflectance, variance parameter and shape parameter from the GGD probability density function. A fast estimation algorithm was utilized for the variance and shape parameters. Calibration models for fruit firmness and SSC were developed for 400 apples, using multi-linear regression (MLR) and partial least squares (PLS), and the models were validated using the remaining 200 fruit. The GGD-mean model yielded better prediction results for fruit firmness and SSC with the average values of r obtained with PLS being equal to 0.854 and 0.864, respectively, for 10 validation runs, compared with those obtained using mean reflectance (r = 0.843 and 0.849 for firmness and SSC, respectively) and Lorentzian function (r = 0.847 and 0.802 for firmness and SSC, respectively). The GGD-mean method is recommended for firmness and SSC prediction because it can accurately characterize spectral scattering profiles for apples.