A Rapid Non-Destructive Hyperspectral Imaging Data Model for the Prediction of Pungent Constituents in Dried Ginger

Ginger is best known for its aromatic odour, spicy flavour and health-benefiting properties. Its flavour is derived primarily from two compound classes (gingerols and shogaols), with the overall quality of the product depending on the interaction between these compounds. Consequently, a robust method for determining the ratio of these compounds would be beneficial for quality control purposes. This study investigated the feasibility of using hyperspectral imaging to rapidly determine the ratio of 6-gingerol to 6-shogoal in dried ginger powder. Furthermore, the performance of several pre-processing methods and two multivariate models was explored. The best-performing models used partial least squares regression (PSLR) and least absolute shrinkage and selection operator (LASSO), using multiplicative scatter correction (MSC) and second derivative Savitzky-Golay (2D-SG) pre-processing. Using the full range of wavelengths (~400-1000 nm), the performance was similar for PLSR (R-2 >= 0.73, RMSE <= 0.29, and RPD >= 1.92) and LASSO models (R-2 >= 0.73, RMSE <= 0.29, and RPD >= 1.94). These results suggest that hyperspectral imaging combined with chemometric modelling may potentially be used as a rapid, non-destructive method for the prediction of gingerol-to-shogaol ratios in powdered ginger samples.

Automated summary

This study investigated the feasibility of using hyperspectral imaging and chemometric modeling to rapidly predict the ratio of gingerols to shogaols in dried ginger powder. The best-performing models used partial least squares regression and least absolute shrinkage and selection operator, with multiplicative scatter correction and second derivative Savitzky-Golay pre-processing.