Quantitative evaluation of impact damage to apples using NIR hyperspectral imaging

Abstract

Impact damage to apples is one of the most crucial quality factors and needs to be detected in postharvest quality sorting processes. In this study, the impact damage of the ‘Red Fuji’ apple fruit was investigated quantitatively by hyperspectral imaging technology. A total of 240 samples were prepared with six groups for different damage degrees. The hyperspectral imaging technique based on near-infrared (NIR) spectrometry in the range of 900–1700 nm was used to measure mechanical parameters, such as the average pressure, contact load, damaged area, absorbed energy, and damaged firmness. Four types of spectral pre-treatment, including the standard normal variate, multiplicative scatter correction, first-order derivative, and second-order derivative, were adopted to improve the model’s predictive performance. The quantitative relationships between spectra and mechanical parameters were successfully modeled based on partial least squares (PLS) regression. For ‘Red Fuji’ apples, raw spectral data without pre-treatment performed better than those after spectral pre-treatments. In this model, the characteristic wavelengths were selected by the Savitzky–Golay second-order derivative (SG 2nd Der) and competitive adaptive reweighted sampling (CARS) method. The results indicate that the CARS-PLS regression model produced better results than the SG 2nd Der-PLS regression model. The good prediction performances were presented by the coefficient of determination (RP2) and root mean square errors of prediction (RMSEP) values. The RP2 and RMSEP results of the average pressure, contact load, damaged area, absorbed energy, and damaged firmness are 0.66 and 0.02 MPa, 0.86 and 53.80 N, 0.83 and 116.37 mm2, 0.81 and 0.24 J, and 0.64 and 0.19 N, respectively. This study demonstrates the potential of the NIR hyperspectral imaging technique as a highly accurate way to quantitatively predict the mechanical parameters of apples.

Keywords

• apple
• hyperspectral imaging
• impact damage
• mechanical parameter