Multimodal close range hyperspectral imaging combined with multiblock sequential predictive modelling for fresh produce analysis

Multimodal measurements are increasingly becoming common in the domain of spectral sensing and imaging for fresh produce. Often multiple sensors are expected to carry complementary information which allows precise estimation of responses. In this study, a novel case of multimodal hyperspectral imaging is described where two different spectral cameras working in the complementary spectral ranges were integrated into a fully standalone system for spectral imaging for fresh produce analysis. Furthermore, a comparative analysis of different multiblock predictive modelling approaches for fusing data from these two complementary spectral cameras is demonstrated. Both multiblock latent space and multiblock variable selection approaches to identify key variables of interest was examined and compared with the analysis carried out on individual data blocks. Prediction of the soluble solids content in grapes was used to demonstrate the application. The presented approach can increase the applications of multimodal hyperspectral imaging for non-destructive analysis.

Automated summary

This study describes a novel method of multimodal hyperspectral imaging, where two different spectral cameras in complementary ranges are integrated into a standalone system for spectral imaging of fresh produce. A comparative analysis of various multiblock predictive modeling approaches for data fusion is demonstrated, and the application of predicting soluble solids content in grapes is used as an example. This approach expands the applications of multimodal hyperspectral imaging for non-destructive analysis.