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United States Department of Agriculture

Agricultural Research Service

Title: Prediction of Apple Quality by Optical Properties

item Lu, Renfu
item Qin, Jianwei

Submitted to: Michigan State University Controlled Atmosphere Clinic
Publication Type: Experiment Station
Publication Acceptance Date: 6/10/2008
Publication Date: 6/13/2008
Citation: Lu, R., Qin, J. 2008. Prediction of Apple Quality by Optical Properties. Michigan State University Controlled Atmosphere Clinic. Volume 6. 10 p.

Interpretive Summary:

Technical Abstract: Optical properties (i.e., absorption and scattering) are useful for assessing the internal quality of apples such as firmness and soluble solids content (SSC). A spatially-resolved hyperspectral imaging technique was developed to measure the optical properties of apples for predicting fruit firmness and SSC. Absorption and scattering coefficients over the visible and near-infrared region of 500-1,000 nm were determined for 600 ‘Golden Delicious’ apples using the spatially-resolved hyperspectral imaging method. The absorption spectra of the apple samples were featured by major pigments (i.e., chlorophylls and carotenoids) and water in the fruit tissue, whereas the scattering spectra generally showed a steady decrease with the increase of wavelength. The measured absorption and scattering spectra were correlated with the firmness and SSC of ‘Golden Delicious’ apples; better correlations were obtained using the absorption spectra than using the scattering spectra. The combined absorption and scattering data gave better prediction results for both fruit firmness and SSC with the correlation coefficient (r) of 0.857 and 0.754, respectively. In comparison, a simpler method of calculating relative mean spectra from the hyperspectral scattering images was found to give comparable firmness prediction results (r=0.844) and better SSC predictions (r=0.864). The spatially-resolved hyperspectral imaging method provides an effective means for measuring the optical properties of apples and their quality attributes.

Last Modified: 06/28/2017
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