A firm texture of dry onions is important for consumer acceptance. Both the texture and dry matter content decline during storage, influencing the market value of onions. The main goal of this study was to develop predictive models that in future might form the basis for automated sorting of onions for firmness and dry matter content in the industry. Hyperspectral scanning was conducted in reflectance mode for six commercial batches of onions that were monitored three times during storage. Mean spectra from the region of interest were extracted and partial least squares regression (PLSR) models were constructed. Feature wavelengths were identified using variable selection techniques resulting from interval partial least squares and recursive partial least squares analyses. The PLSR model for firmness gave a root mean square error of cross-validation (RMSECV) of 0.84 N, and a root mean square error of prediction (RMSEP) of 0.73 N, with coefficients of determination ( R2) of 0.72 and 0.83, respectively. The RMSECV and RMSEP of the PLSR model for dry matter content were 0.10% and 0.08%, respectively, with a R2 of 0.58 and 0.79, respectively. The whole wavelength range and selected wavelengths showed nearly similar results for both dry matter content and firmness. The results obtained from this study clearly reveal that hyperspectral imaging of onion bulbs with selected wavelengths, coupled with chemometric modeling, can be used for the noninvasive determination of the firmness and dry matter content of stored onion bulbs.
Keywords: Allium cepa L.; Hyperspectral imaging; PLS; iPLS; internal quality; interval partial least squares; partial least squares regression; rPLS; recursive partial least squares.