Location: Sugarbeet and Bean Research
Title: Analysis of Spatially-Resolved Hyperspectral Scattering Images for Assessing Apple Fruit Firmness and Soluble Solids Content Authors
|Peng, Yankun - MICHIGAN ST UNIVERSITY|
Submitted to: Postharvest Biology and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 2, 2007
Publication Date: April 1, 2008
Citation: Peng, Y., Lu, R. 2008. Analysis of Spatially-Resolved Hyperspectral Scattering Images for Assessing Apple Fruit Firmness and Soluble Solids Content. Postharvest Biology and Technology. 48(1):52-62. Interpretive Summary: Sorting and grading fruit for firmness and sugar (or soluble solids content) is critical to meeting the high quality requirements in today's competitive marketplace. It is challenging to measure apple fruit firmness nondestructively, and it is much more difficult to measure both firmness and sugar accurately and rapidly. We recently developed a novel optical technology that measures and characterizes light scattering in the fruit at multiple wavelengths, which shows superior performance in evaluating both firmness and soluble solids content of apples. The performance of the imaging system, however, depends on the mathematical model that describes light scattering characteristics. Moreover, fruit size and instrument response can affect the measurement of light scattering. This research evaluated and compared different mathematical models for describing light scattering characteristics in terms of assessing the firmness and soluble solids content of 'Golden Delicious' apples. Mathematical equations were also proposed for correcting the effect of fruit size and instrument response on the characterization of light scattering in apple fruit. With the optimal mathematical model coupled with the equations for correcting the fruit size and instrument setup effects, the imaging system achieved good measurements of both fruit firmness and soluble solids content with values of the correlation coefficient being 0.893 and 0.882, respectively. The mathematical model selected will be valuable in the development of an imaging system for sorting and grading fruit. Adoption of such technology in fruit packing houses will allow the fruit packer to differentiate good quality fruit from lower quality fruit and, thus, better meet consumer needs.
Technical Abstract: Hyperspectral scattering is a promising technique for nondestructive sensing of multiple quality attributes of apple fruit. This research evaluated and compared different mathematical models for describing the hyperspectral scattering profiles over the spectral region between 450 nm and 1000 nm in order to select an optimal model for predicting fruit firmness and soluble solids content (SSC) of 'Golden Delicious' apples. Ten modified Lorentzian distribution functions of various forms were proposed to fit the spectral scattering profiles at individual wavelengths, each of which gave superior fitting to the data with the average correlation coefficient (r) greater than 0.995. Mathematical equations were derived to correct the spectral scattering intensity and distance by taking into account the instrument response and individual apples' size. The 10 modified Lorentzian distribution functions were compared for predicting fruit firmness and SSC using multi-linear regression and cross-validation methods. The modified Lorentzian function with three parameters (representing scattering peak value, width and slope) had better predictions over the other functions with r = 0.893 and the standard error of prediction (SEP) of 6.14 N for firmness, and r = 0.882 and SEP = 0.73% for SSC. Twenty-one and 23 wavelengths were needed to obtain the best predictions of fruit firmness and SSC, respectively. This new function, coupled with the scattering profile correction methods, improved the hyperspectral scattering technique for measuring fruit quality.