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United States Department of Agriculture

Agricultural Research Service


item Lu, Renfu
item Ariana, Diwan

Submitted to: American Society of Agricultural Engineers Meetings Papers
Publication Type: Abstract Only
Publication Acceptance Date: 7/29/2001
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Sorting fruit for internal quality, especially firmness and sugar content, is key to ensuring fruit consistency and increasing consumer confidence and satisfaction. Consistency in quality would improve the fruit industry's competitiveness and profitability. Near-infrared spectroscopy (NIRS) is a promising technique for nondestructive sensing of apple fruit with the potential for on-line sorting for multiple quality attributes. The objective of this research was to study a different NIR sensing method that acquires spectral information in interactance mode at different locations over an apple fruit to predict its firmness and sugar content. A NIRS system was assembled, with a specially designed sensing probe designed for simultaneous acquisition of interactance spectral data at two different distances from the light source in the spectral region between 900 nm and 1500 nm. Experiments on the apple cultivars 'Empire', 'Golden Delicious', and 'Red Delicious' were performed to develop chemometric models using partial least squares regression. Results showed that NIR data from the location closer to the light source gave better predictions of both firmness and sugar content of apples than those from the location further away from the light source. The correlations of prediction for sugar content were equal to or greater than 0.81, and the prediction error was between 0.5 and 0.7% for the three cultivars. Firmness predictions with the r-value of about 0.63 and prediction error of 10.6 N were not as good as those for sugar content. The ratio spectra did not give good predictions of the fruit sugar content and firmness. NIR sensing allows for rapid evaluation of sugar content of apple fruit, but it does not give accurate prediction of fruit firmness.

Last Modified: 06/27/2017
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