|Noh, Hyun Kwon|
Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: October 1, 2006
Publication Date: N/A
Interpretive Summary: The maturity of apples has important ramifications on their postharvest storage life and quality and may also influence the way fruit should be handled, stored and marketed. Reliable assessment of apple fruit maturity will require measuring multiple quality parameters, including both external (color and size) and internal (firmness, sugar, acid, starch level, and ethylene production level). Currently, destructive methods are routinely used in maturity measurements. These measurements can only be performed on a few apple samples carefully selected from individual trees of the orchard. Fluorescence and interactance are two promising nondestructive techniques for measuring fruit quality and condition. The two techniques are based on different principles and can be complementary in measuring fruit quality. This research was conducted on developing a spectroscopic technique for measuring both fluorescence and interactance spectra from apples in the visible and near-infrared region and developing an algorithm integrating the two types of spectral data for predicting multiple maturity parameters. Fluorescence and interactance data were collected from Golden Delicious apples harvested over a period of four weeks. Maturity prediction models were developed using artificial neural networks. Interactance was consistently better than fluorescence in predicting most of the maturity parameters. Integrating interactance and fluorescence led to improved prediction results for individual maturity parameters; the improvements ranged from 4.1% to 23.5% compared with the better results of either fluorescence or interactance. The integrated technique can improve our ability of measuring apple maturity. The technique is useful for assessing the maturity of apples in the orchard and for sorting and grading multiple quality parameters of apples after harvest. This would enable fruit growers to better determine harvest time and fruit packers to optimize postharvest handling and storage operations to maximize the quality and consistency of apples.
Technical Abstract: Fluorescence and interactance are promising techniques for measuring fruit quality and condition. Our previous research showed that a hyperspectral imaging technique integrating fluorescence and reflectance could improve predictions of selected quality parameters compared to single sensing techniques. The objective of this research was to use a low cost spectrometer for rapid acquisition of fluorescence and interactance spectra from apples and develop an algorithm integrating the two types of data for predicting skin and flesh color, fruit firmness, starch index, soluble solids content, and titratable acid. Experiments were performed to measure UV light induced transient fluorescence and interactance spectra from ‘Golden Delicious’ apples that were harvested over a period of four weeks during the 2005 harvest season. Standard destructive tests were performed to measure maturity parameters from the apples. Principal component (PC) analysis was applied to the interactance and fluorescence data. A back-propagation feedforward neural network with the inputs of PC data was used to predict individual maturity parameters. Interactance mode was consistently better than fluorescence mode in predicting the maturity parameters. Integrating interactance and fluorescence improved predictions of all parameters except flesh chroma; values of the correlation coefficient for firmness, soluble solids content, starch index, and skin and flesh hue were 0.77, 0.77, 0.89, 0.99, and 0.96 respectively, with the corresponding standard errors of 6.93 N, 0.90%, 0.97 g/L, 0.013 rad, and 0.013 rad. These results represented 4.1% to 23.5% improvements in terms of standard error, in comparison with the better results from the two single sensing methods. Integrating interactance and fluorescence can better assess apple maturity and quality.