|Noh, Hyun Kwon|
Submitted to: Postharvest Biology and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/4/2006
Publication Date: 2/1/2007
Citation: Noh, H., Lu, R. 2007. Hyperspectral laser-induced fluorescence imaging for assessing apple quality. Postharvest Biology and Technology. 43(2):193-201.
Interpretive Summary: Reliable, nondestructive measurement of fruit quality is critical in determining optimal harvest time and implementing appropriate postharvest handling procedures. Currently, destructive methods are routinely used to measure apple fruit maturity, including fruit firmness, sugar, acid, starch, and skin and flesh color. The measurements are time consuming and also prone to operational error. Recent research showed that chlorophyll fluorescence has potential for assessing postharvest quality and condition of fruit. Chlorophyll fluorescence measures light of longer wavelengths emitting from the plant tissue after it absorbs the light of short wavelengths. This research explored a new technique of using hyperspectral imaging to measure fluorescence from apples induced by a blue laser for assessing fruit quality. The hyperspectral imaging system was capable of acquiring both spatial and spectral information from ‘Golden Delicious’ fruit over the wavelengths of 500-1040 nm covering both visible and near-infrared (invisible) spectral range. Mathematical models were developed on relating laser induced fluorescence to fruit firmness, soluble solids content, acid, and skin and flesh color. Hyperspectral fluorescence was correlated well with fruit skin and flesh color. Relatively good correlations were found between fluorescence spectra and fruit firmness. However, lower or poor correlations were obtained for the soluble solids and acid. Hyperspectral fluorescence is potentially useful for assessing the maturity and condition of apples. The technique is fast and relatively easy to implement, and it can be complementary with other nondestructive techniques to improve measurement of fruit maturity and postharvest quality. Nondestructive sensing technology will help the fruit industry in producing high quality, consistent fruit and thus improving its competitiveness and profitability.
Technical Abstract: Chlorophyll fluorescence is useful for assessing fruit postharvest quality and condition. The objective of this research was to investigate the potential of using hyperspectral imaging to measure laser induced fluorescence for assessing apple fruit quality. A blue laser of 408 nm was used as an excitation source for inducing fluorescence in apples. Laser induced fluorescence spectra from ‘Golden Delicious’ apples were measured by using a hyperspectral imaging system after zero, one, two, three, four, and five minutes of continuous illumination. Standard destructive tests were performed to measure fruit firmness, skin and flesh color, soluble solids and acid content from the apples. Principal component analyses and neural networks were performed to extract critical information from the hyperspectral fluorescence data and this information was then related to fruit quality indexes. Calibration models for each of the six illumination time periods were developed to predict fruit quality indexes. The results showed that fluorescence emission decreased steadily during the first four minutes of laser illumination and was stable within five minutes. The differences in the model prediction results were minimal based on the fluorescence data at one, two, three, four or five minutes of illumination. Overall, better predictions were obtained for apple skin chroma and hue with values for the correlation coefficient of validation between 0.75 and 0.94. Relatively good predictions were obtained for fruit firmness and poorer results for soluble solids content, titrational acid, and flesh chroma. This research demonstrated that fluorescence spectroscopy is potentially useful for assessing selected quality attributes of apple fruit and further research is needed to improve fluorescence measurements so that better predictions of fruit quality can be achieved.