Rapid detection of anthocyanin content in lychee pericarp during storage using hyperspectral imaging coupled with model fusion

• A new approach was proposed to evaluate anthocyanin content of lychee pericarp.
• Two sets of optimal wavelengths were selected by two different algorithms.
• Both image and spectral data was utilized for developing prediction model.
• Model fusion method was used for improving prediction accuracy of optimized models.
• Hyperspectral imaging can effectively predict and visualize anthocyanin evolution.

A quantitative approach was proposed to evaluate anthocyanin content of lychee pericarp using hyperspectral imaging (HSI) technique. A HSI system working in the range of 350–1050 nm was used to acquire a 3-D lychee image. Successive projection algorithm (SPA) and stepwise regression (SWR) algorithm were utilized to reduce data dimensionality and search for optimal wavelengths related with anthocyanin content in pericarp. Radial basis function support vector regression (RBF-SVR) was adopted to establish quantitative relationship between hyperspectral image information in two sets of optimal wavelengths and anthocyanin content of pericarp. Finally, in order to improve prediction accuracy, SPA-RBF-SVR and SWR-RBF-SVR models were fused into a single model by radial basis function neural network (RBF-NN) algorithm. The results revealed that the fused model possessed a better performance than either SPA-RBF-SVR or SWR-RBF-SVR models alone, as the fused model showed higher coefficients of determination (R2) of 0.891 and 0.872, and lower root mean square errors (RMSEs) of 0.567% and 0.610% for the training and the testing sets, respectively. Visualization maps based on the fused model were generated to display the anthocyanin distribution within lychee pericarp. This study demonstrates that HSI is capable of predicting and visualizing anthocyanin evolution in the pericarp of lychee during storage.