  Hyperspectral Imaging (HSI) combines microscopy and spectroscopy to assess
the spatial distribution of spectroscopically active compounds in objects, and
has diverse applications in food quality control, pharmaceutical processes, and
waste sorting. However, due to the large size of HSI datasets, it can be
challenging to analyze and store them within a reasonable digital
infrastructure, especially in waste sorting where speed and data storage
resources are limited. Additionally, as with most spectroscopic data, there is
significant redundancy, making pixel and variable selection crucial for
retaining chemical information. Recent high-tech developments in chemometrics
enable automated and evidence-based data reduction, which can substantially
enhance the speed and performance of Non-Negative Matrix Factorization (NMF), a
widely used algorithm for chemical resolution of HSI data. By recovering the
pure contribution maps and spectral profiles of distributed compounds, NMF can
provide evidence-based sorting decisions for efficient waste management. To
improve the quality and efficiency of data analysis on hyperspectral imaging
(HSI) data, we apply a convex-hull method to select essential pixels and
wavelengths and remove uninformative and redundant information. This process
minimizes computational strain and effectively eliminates highly mixed pixels.
By reducing data redundancy, data investigation and analysis become more
straightforward, as demonstrated in both simulated and real HSI data for
plastic sorting.
