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Proceedings Paper

Analysis of hyperspectral scattering characteristics for predicting apple fruit firmness and soluble solids content
Author(s): Renfu Lu; Min Huang; Jianwei Qin
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Paper Abstract

Spectral scattering is useful for assessing the firmness and soluble solids content (SSC) of apples because it provides an effective means for characterizing light scattering in the fruit. This research compared three methods for quantifying the spectral scattering profiles acquired from 'Golden Delicious' apples using a hyperspectral imaging system for the spectral region of 500-1000 nm. The first method relied on a diffusion theory model to describe the scattering profiles, from which the absorption and reduced scattering coefficients were obtained. The second method utilized a four-parameter Lorentzian function, an empirical model, to describe the scattering profiles. And the third method was calculation of mean reflectance from the scattering profiles for a scattering distance of 10 mm. Calibration models were developed, using multi-linear regression (MLR) and partial least squares (PLS), relating function parameters for each scattering characterization method to the fruit firmness and SSC of 'Golden Delicious' apples. The diffusion theory model gave poorer prediction results for fruit firmness and SSC (the average values of r obtained with PLS were 0.837 and 0.664 respectively for the validation samples). Lorentzian function and mean reflectance performed better than the diffusion theory model; their average r values for PLS validations were 0.860 and 0.852 for firmness and 0.828 and 0.842 for SSC respectively. The mean reflectance method is recommended for firmness and SSC prediction because it is simple and much faster for characterizing spectral scattering profiles for apples.

Paper Details

Date Published: 28 April 2009
PDF: 11 pages
Proc. SPIE 7315, Sensing for Agriculture and Food Quality and Safety, 73150I (28 April 2009); doi: 10.1117/12.819287
Show Author Affiliations
Renfu Lu, USDA Agricultural Research Service (United States)
Min Huang, USDA Agricultural Research Service (United States)
Jiangnan Univ. (China)
Jianwei Qin, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 7315:
Sensing for Agriculture and Food Quality and Safety
Moon S. Kim; Shu-I Tu; Kaunglin Chao, Editor(s)

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