# Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
## 3 Results and Discussion
### HSI Analyses
#### 3.2.3 Classification by HSI  
Figure 9 shows the results of the PLS-DA model, which was developed using the acquired NIR-HSI data, applying the _sand/plastic/other materials model_ and _polymer type_ model after plastic recognition. The obtained prediction maps clearly show that the classification models are effective at correctly recognizing different groups. Despite the sands having different elemental compositions as per the XRF analysis, PLS-DA successfully classified the plastic particles from the other materials and the three different sands. Furthermore, the subsequent polymer recognition model applied to the plastic particles in the images accurately identified the categories. Although some pixels are misclassified, most pixels for each item in the image are correctly assigned to their respective class, making them  
Figure 7: Raw (**a**) and pre-processed reflectance (**b**) spectra of the five different polymers (i.e., PP, PE, PS, PVC, and PET) in the NIR range. easily detectable. Additionally, setting a threshold on the number of classified pixels could enhance the clarity and readability of the prediction maps. Figure 8: PCA score plot related to the samples of the calibration set acquired to build the _sand/plastic/other materials_ model (**a**), _sand/plastic_ model (**b**), and _polymer type_ model (**c**) in the NIR range. The blue dotted line represents the 95% confidence level. Results shown in Figure 10 are related to the second experimental set-up. They confirm both the ability of the model to predict the plastic presence from the three different sand substrates and the capability to recognize the different analyzed polymer types. Figure 9: Digital image of other material and plastic samples on sand (**a**), sand/plastic/other materials, PLS-DA (**b**), and polymers type PLS-DA (**c**) prediction maps. In the sand/plastic model prediction map, some sporadic little particles identified as plastic on the sand are not classified in the second experimental setup (i.e., polymer-type recognition) since only particles checked at FT-IR were included in the second classification (i.e., polymer-type recognition) purpose. In fact, to ensure a correct classification, known plastic fragments with recognized polymer types by FT-IR were employed in the second classification model realization. Conversely, in the polymer-type experimental setup, some incorrectly classified pixels on the plastic fragment surface can be attributed to the very thin layer of plastic or the  
Figure 10: Digital image of unknown plastic samples on sand (**a**), sand/plastic PLS-DA (**b**), and polymers PLS-DA (**c**) prediction maps. presence of some sand grains. However, despite these misclassifications, identifying the polymer on the object is straightforward. Such results can be evaluated considering the values of sensitivity and specificity obtained for the classification models reported in Tables 2-4. Based on the classification models developed for each sand sample, the sensitivity and specificity for the 'plastic' and 'other material' classes reach values up to 0.9-1 (Table 2). For the'sand' class, while the sensitivity values are lower (ranging from 0.802 to 0.885), they are still considered acceptable. The characteristics of the sand could be the cause of this discrepancy. In contrast to 'plastic' and 'other materials', which can be easily classified with high sensitivity and specificity values due to their more pronounced and readily identifiable spectral features,'sand' is characterized by lesser pronounced features, which reduce sensitivity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Class 1** & **Class 2** & **Class 3** & **Class 4** & **Class 5** \\  & **PP** & **PE** & **PET** & **PS** & **PVC** \\ \hline
**Sensitivity (Cal)** & 1.000 & 1.000 & 1.000 & 1.000 \\
**Specificity (Cal)** & 1.000 & 1.000 & 0.991 & 1.000 & 1.000 \\
**Sensitivity (CV)** & 1.00 & 1.000 & 1.000 & 1.000 \\
**Specificity (CV)** & 0.964 & 0.932 & 0.970 & 1.000 & 1.000 \\ \hline \hline \end{tabular}
\end{table}