# Hyperspectral Imaging Sorting of Refurbishment Plasterboard Waste
## 3 Results
### Performance Evaluation of the PLS-DA Model in Sorting Refurbishment Plasterboard Waste  
The confusion matrixes for the calibration model and for laboratory-scale refurbishment plasterboard waste sorting are presented in Tables 4 and 5, respectively. These tables show the performance of the supervised learning classification algorithm applied to the calibration samples (training set) and the validation materials (test set), respectively. The confusion matrix allows us to determine if the algorithm is misclassifying materials. It is a comparison between the actual and predicted classification sets. Each row represents the samples in an actual category or class while each column represents the samples in a predicted category. For instance, Table 4 shows that 51 plasterboard training samples (actual category) were predicted as plasterboard by the algorithm (0 as contaminants and 0 as non-classified). Table 5 indicates that the prediction rate was 97.45 wt%.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Total Number of Samples (Weight \%)** & **Plasterboard (Weight \%)** & **Contaminants (Weight \%)** & **Non-Classified (Weight \%)** \\ \hline Plasterboard & 51 (47.7) & 51 (100) & 0 (0) & 0 (0) \\ Contaminants & 56 (52.3) & 0 (0) & 56 (100) & 0 (0) \\ Correct & 107 (100) & 51 (100) & 56 (100) & 0 (0) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix for the calibration model.  
Figure 9: Chemical purity of gypsum obtained from refurbishment plasterboard waste fraction <10 mm in size and from HSI-sorted plasterboard in outputs 1 and 2.  
speed of the sliding scanning table to ensure that there is a homogeneous distribution of the samples for HSI classification. Specifically, the main goal of this optimization process is to achieve a sample monolayer with sufficient distance between different fragments to avoid co-ejection at the end of the HSI classification system and to maximize the HSI system processing capacity. The different shapes and densities of the materials (plasterboard, concrete, wood, ceramics, etc.) must also be considered.
3. Loose paper is not classified as a contaminant because plasterboard is constituted by a gypsum core sandwiched between two lining papers. Lining paper constitutes 4 wt% of the plasterboard and has a density of 0.25-1.52 g/cm\({}^{3}\), whereas the gypsum core constitutes around the remaining 96 wt% of the plasterboard and has a density of 2.3 g/cm\({}^{3}\)[22; 23]. Therefore, the different densities of loose paper and plasterboard fragments should allow for compressed air ejectors to separate these two materials at the end of the HSI system. The lining paper in plasterboard fragments should be removed at the grinding stage of the plasterboard recycling process [24].  
The expected economic, health and environmental benefits of using the HSI classification system instead of manual labour for plasterboard waste sorting at refurbishment plasterboard waste recycling sites are listed below:  
1. Higher efficiency, reliability and processing capacity;
2. Lower labour costs;
3. Lower handling and exposure to hazardous materials (e.g., sharp objects, dust).  
However, there would be additional equipment and energy costs that must be assessed prior to the industrial implementation of this sorting technology.