van Hoorn Hedde, Pourmohammadi Fahimeh, de Leeuw Arie-Willem, Vasulkar Amey, de Vos Jerry, van den Berg Steven
Photonics Research Group, The Hague University of Applied Sciences, 2628 AL Delft, The Netherlands.
AI & Data Science Expert Group, The Hague University of Applied Sciences, 2628 AL Delft, The Netherlands.
Sensors (Basel). 2025 Jun 17;25(12):3777. doi: 10.3390/s25123777.
Plastic waste and pollution is growing rapidly worldwide and most plastics end up in landfill or are incinerated because high-quality recycling is not possible. Plastic-type identification with a low-cost, handheld spectral approach could help in parts of the world where high-end spectral imaging systems on conveyor belts cannot be implemented. Here, we investigate how two fundamentally different handheld infrared spectral devices can identify plastic types by benchmarking the same analysis against a high-resolution bench-top spectral approach. We used the handheld Plastic Scanner, which measures a discrete infrared spectrum using LED illumination at different wavelengths, and the SpectraPod, which has an integrated photonics chip which has varying responsivity in different channels in the near-infrared. We employ machine learning using SVM, XGBoost, Random Forest and Gaussian Naïve Bayes models on a full dataset of plastic samples of PET, HDPE, PVC, LDPE, PP and PS, with samples of varying shape, color and opacity, as measured with three different experimental approaches. The high-resolution spectral approach can obtain an accuracy (mean ± standard deviation) of (0.97 ± 0.01), whereas we obtain (0.93 ± 0.01) for the SpectraPod and (0.70 ± 0.03) for the Plastic Scanner. Differences of reflectance at subsequent wavelengths prove to be the most important features in the plastic-type classification model when using high-resolution spectroscopy, which is not possible with the other two devices. Lower accuracy for the handheld devices is caused by their limitations, as the spectral range of both devices is limited-up to 1600 nm for the SpectraPod, while the Plastic Scanner has limited sensitivity to reflectance at wavelengths of 1100 and 1350 nm, where certain plastic types show characteristic absorbance bands. We suggest that combining selective sensitivity channels (as in the SpectraPod) and illuminating the sample with varying LEDs (as with the Plastic Scanner) could increase the accuracy in plastic-type identification with a handheld device.
全球范围内，塑料垃圾和污染正迅速增加，由于无法进行高质量回收，大多数塑料最终被填埋或焚烧。在世界上一些无法使用传送带上的高端光谱成像系统的地区，采用低成本手持式光谱方法进行塑料类型识别可能会有所帮助。在此，我们通过将两种截然不同的手持式红外光谱设备与高分辨率台式光谱方法进行相同分析的基准测试，来研究它们如何识别塑料类型。我们使用了手持式塑料扫描仪，它利用不同波长的LED照明来测量离散红外光谱；还有SpectraPod，它有一个集成光子芯片，在近红外的不同通道具有不同的响应度。我们在PET、HDPE、PVC、LDPE、PP和PS塑料样品的完整数据集上，采用支持向量机、极端梯度提升、随机森林和高斯朴素贝叶斯模型进行机器学习，这些样品具有不同的形状、颜色和不透明度，通过三种不同的实验方法进行测量。高分辨率光谱方法可获得的准确率（均值±标准差）为(0.97±0.01)，而我们得到SpectraPod的准确率为(0.93±0.01)，塑料扫描仪的准确率为(0.70±0.03)。在使用高分辨率光谱时，后续波长处的反射率差异被证明是塑料类型分类模型中最重要的特征，而其他两种设备无法做到这一点。手持式设备准确率较低是由其局限性导致的，因为这两种设备的光谱范围都有限——SpectraPod可达1600纳米，而塑料扫描仪在1100纳米和1350纳米波长处对反射率的灵敏度有限，某些塑料类型在这些波长处显示出特征吸收带。我们建议，结合选择性敏感通道（如SpectraPod）并用不同LED照射样品（如塑料扫描仪），可以提高手持式设备在塑料类型识别中的准确率。
