Optimal Wavelength Selection for Hyperspectral Scattering Prediction of Apple Firmness and Soluble Solids Content

Abstract

Hyperspectral scattering is a promising technique for nondestructive quality measurement of apple fruit, and extraction of the most useful information from the hyperspectral scattering data is critical for accurate assessment of fruit firmness and soluble solids content (SSC). In this article, a hierarchical evolutionary algorithm (HEA) approach coupled with subspace decomposition and partial least squares regression is proposed to select the optimal wavelengths from hyperspectral scattering profiles of 'Golden Delicious' apples for predicting fruit firmness and SSC. Six hundred apples were tested in the experiment, 400 of which were used for calibration and the remaining 200 apples for validation. Seventeen optimal wavelengths were selected for firmness prediction, which nearly spanned the entire spectral range of 500 to 1000 nm, and 16 optimal wavelengths, all of which were above 600 nm, were selected in the SSC prediction model. The model using the 17 optimal wavelengths for predicting firmness yielded better results (r = 0.857, root mean square error of prediction or RMSEP = 6.2 N) than the full spectrum model (r = 0.848, RMSEP = 6.4 N). For predicting SSC, the model using the 16 optimal wavelengths also yielded better results (r = 0.822, RMSEP = 0.78%) than the full spectrum model (r = 0.802, RMSEP = 0.83%). The HEA approach provided an effective means for optimal wavelength selection and improved the prediction of firmness and SSC in apples compared with the approach using the full spectrum.