Serranti Silvia, Capobianco Giuseppe, Cucuzza Paola, Bonifazi Giuseppe
Department of Chemical Engineering, Materials & Environment, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy.
Department of Chemical Engineering, Materials & Environment, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy.
Sci Total Environ. 2024 Dec 1;954:176630. doi: 10.1016/j.scitotenv.2024.176630. Epub 2024 Oct 2.
Microplastics (MPs) pollution is a global and challenging issue, necessitating the development of efficient analytical strategies for their detection to monitor their environmental impact. This study aims to define an optimal analytical protocol for characterizing MPs by hyperspectral imaging (HSI), comparing different setups based on spatial resolution, spectral range and classification models. The investigated MPs include polymers commonly found in the environment, such as polystyrene (PS), polypropylene (PP) and high-density polyethylene (HDPE), subdivided in three size classes (1000-2000 μm, 500-1000 μm, 250-500 μm). Furthermore, MP particles with diameters ranging from 30 to 250 μm were assessed to determine the limit of detection (LOD) in the different configurations. Hyperspectral images were acquired with two spatial resolutions, 150 and 30 μm/pixel, and two spectral ranges, 1000-1700 nm (NIR) and 1000-2500 nm (SWIR). Three classification models, Partial Least Square-Discriminant Analysis (PLS-DA), Error Correction Output Coding-Support Vector Machine (ECOC-SVM) and Neural Network Pattern Recognition (NNPR) were tested on the acquired images. The correctness of these models was evaluated by prediction maps and statistical parameters (Recall, Specificity and Accuracy). The results demonstrated that for MP particles larger than 250 μm, the optimal setup is a spatial resolution of 150 μm/pixel and a spectral range of 1000-1700 nm, utilizing a linear classification model like PLS-DA. This approach offers accurate predictions while being time- and cost-efficient. For MPs smaller than 250 μm, a higher spatial resolution of 30 μm/pixel with a spectral range of 1000-2500 nm and a non-linear classification method like ECOC-SVM is preferable. The LOD is 250 μm for the 150 μm/pixel resolution and ranges from 100 to 200 μm for the 30 μm/pixel resolution. These findings provide a valuable guide for selecting the appropriate HSI acquisition conditions and data processing methods to optimally characterize MPs of different sizes.
微塑料（MPs）污染是一个全球性的具有挑战性的问题，因此需要开发高效的分析策略来检测微塑料，以监测其对环境的影响。本研究旨在通过高光谱成像（HSI）定义一种用于表征微塑料的最佳分析方案，比较基于空间分辨率、光谱范围和分类模型的不同设置。所研究的微塑料包括环境中常见的聚合物，如聚苯乙烯（PS）、聚丙烯（PP）和高密度聚乙烯（HDPE），并细分为三个尺寸等级（1000 - 2000μm、500 - 1000μm、250 - 500μm）。此外，还对直径范围为30至250μm的微塑料颗粒进行了评估，以确定不同配置下的检测限（LOD）。使用两种空间分辨率（150和30μm/像素）以及两个光谱范围（1000 - 1700nm（近红外）和1000 - 2500nm（短波红外））采集高光谱图像。在采集的图像上测试了三种分类模型，即偏最小二乘判别分析（PLS - DA）、纠错输出编码支持向量机（ECOC - SVM）和神经网络模式识别（NNPR）。通过预测图和统计参数（召回率、特异性和准确率）评估这些模型的正确性。结果表明，对于大于250μm的微塑料颗粒，最佳设置是150μm/像素的空间分辨率和1000 - 1700nm的光谱范围，采用像PLS - DA这样的线性分类模型。这种方法在提供准确预测的同时具有时间和成本效益。对于小于250μm的微塑料，更高的30μm/像素的空间分辨率、1000 - 2500nm的光谱范围以及像ECOC - SVM这样的非线性分类方法更为可取。对于150μm/像素分辨率，检测限为250μm，对于30μm/像素分辨率，检测限范围为100至200μm。这些发现为选择合适的高光谱成像采集条件和数据处理方法以最佳地表征不同尺寸的微塑料提供了有价值的指导。
