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United States Department of Agriculture

Agricultural Research Service

Research Project: TECHNOLOGIES FOR ASSESSING AND GRADING QUALITY AND CONDITION OF CUCUMBERS AND TREE FRUITS

Location: Sugarbeet and Bean Research

Title: Analysis of Hyperspectral Scattering Characteristics for Predicting Apple Fruit Firmness and Soluble Solids Content

Authors
item LU, RENFU
item Huang, Min -
item Qin, J -

Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: March 27, 2009
Publication Date: May 15, 2009
Citation: Lu, R., Huang, M., Qin, J. 2009. Analysis of Hyperspectral Scattering Characteristics for Predicting Apple Fruit Firmness and Soluble Solids Content. Proceedings of SPIE. April 13-17, 2009, Orlando, FL. 7315:7315-17.

Interpretive Summary: Firmness and soluble solids content (SSC) are two important quality attributes for apple. Despite significant progress in nondestructive sensing technology over the past decades, a viable system or method which can measure and sort apples for firmness and SSC has not been available. Our laboratory recently developed a new spectral scattering technique, which enables better quantification of light scattering in the fruit and has shown great potential for measuring fruit firmness and SSC. This research compared three mathematical methods for quantifying spectral scattering characteristics for prediction of fruit firmness and SSC. They included a mathematical model based on radiation transfer theory, a four-parameter empirical function, and a simple method of calculating mean reflectance. Spectral scattering images were acquired using a hyperspectral imaging system from 600 ‘Golden Delicious’ apples over the visible and short-wave near-infrared region of 500-1000 nm. The spectral scattering profiles were described using each of the three mathematical methods. Firmness and SSC prediction models were developed based on each mathematical method. Results showed that although the more sophisticated theoretical model was useful for quantification of the fundamental optical properties, the method had poorer results for firmness and SSC prediction, compared to the two other simpler methods. The method of calculating mean reflectance from the spectral scattering profiles produced good prediction results for firmness and SSC. The research suggested that for the purpose of assessing and grading apples for firmness and SSC using spectral scattering technique, the mean reflectance method is recommended because it is simple and fast in the data processing and analysis.

Technical 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.

Last Modified: 7/25/2014
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