Near-infrared multispectral scattering for assessing internal quality of apple fruit

摘要

Firmness and sweetness are key quality attributes that determine the acceptability of apple fruit to the consumer. The objective of this research was to investigate a multispectral imaging system for simultaneous acquisition of multispectral scattering images from apple fruit to predict firmness and soluble solids content (SSC). A circular broadband light beam was used to generate light backscattering at the surface of apple fruit and scattering images were acquired, using a common aperture multispectral imaging system, from Red Delicious and Golden Delicious apple fruit for wavelengths at 680, 880, 905, and 940 nm. Scattering images were radially averaged to produce one-dimensional spectral scattering profiles, which were then input into a backpropagation neural network for predicting apple fruit firmness and SSC. It was found that the neural network performed best when 10 neurons and 20 epochs were used. With inputing three ratios of spectral profiles involving all four wavelengths, the neural network gave firmness predictions with the correlation (r) of 0.76 and the standard error for validation (SEV) of 6.2 N for Red Delicious apples and r=0.73 and SEV=8.9 N for Golden Delicious apples. Relatively good SSC predictions were obtained for both varieties with SEV=0.9 °Brix.