American Society of Agricultural and Biological Engineers



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Quantitative Determination of Onion Internal Quality Using Reflectance, Interactance, and Transmittance Modes of Hyperspectral Imaging

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Transactions of the ASABE. 56(4): 1623-1635. (doi: http://dx.doi.org/10.13031/trans.56.9883) @2013
Authors:   Haihua Wang, Changying Li, Maohua Wang
Keywords:   Hyperspectral imaging, Interactance, Internal quality, Near-infrared, Onion.
<italic>Abstract.</italic>

The internal quality of onions is important to both consumers and onion processors, but current methods for evaluating the internal quality are mostly destructive. The overall goal of this study was to investigate the feasibility of using hyperspectral imaging technology to quantitatively predict the amount of dry matter, the soluble solids content, and the firmness of onions. A total of 308 onions were scanned using a line-scan hyperspectral imaging system with three sensing modes (reflectance, interactance, and transmittance) in the spectral region of 400-1000 nm. An ellipsoidal model was developed to correct the uneven illumination caused by the curvature of the surface in the reflectance images. The spectra extracted from onion spectral images were used to develop partial least squares (PLS) regression models. Results showed that interactance achieved comparable or even better results than transmittance, and the two modes performed significantly better than diffuse reflectance. In interactance mode, soluble solids content [coefficient of determination (R2) = 0.93, standard error of prediction (SEP) = 1.46 °Brix] and dry matter (R2 = 0.93, SEP = 1.61%) can be estimated better than firmness (R2 = 0.59, SEP = 9.75 N). This study demonstrated for the first time that the interactance mode of the hyperspectral imaging technique can be used to quantitatively predict the internal quality properties of an onion. The lab-based hyperspectral imaging system has the potential to be used in an automated online quality inspection system for predicting the internal quality of onions.

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