Yang Cihang, Xie Junhao, Gowen Aoife, Xu Jun-Li
School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland.
Sci Rep. 2024 Feb 12;14(1):3464. doi: 10.1038/s41598-024-54003-1.
In recent years, the field of microplastic (MP) research has evolved significantly; however, the lack of a standardized detection methodology has led to incomparability across studies. Addressing this gap, our current study innovates a reliable MP detection system that synergizes sample processing, machine learning, and optical photothermal infrared (O-PTIR) spectroscopy. This approach includes examining high-temperature filtration and alcohol treatment for reducing non-MP particles and utilizing a support vector machine (SVM) classifier focused on key wavenumbers that could discriminate between nylon MPs and non-nylon MPs (1077, 1541, 1635, 1711 cm were selected based on the feature importance of SVM-Full wavenumber model) for enhanced MP identification. The SVM model built from key wavenumbers demonstrates a high accuracy rate of 91.33%. Results show that alcohol treatment is effective in minimizing non-MP particles, while filtration at 70 °C has limited impact. Additionally, this method was applied to assess MPs released from commercial nylon teabags, revealing an average release of 106 particles per teabag. This research integrates machine learning with O-PTIR spectroscopy, paving the way for potential standardization in MP detection methodologies and providing vital insights into their environmental and health implications.
近年来，微塑料（MP）研究领域取得了显著进展；然而，缺乏标准化的检测方法导致各项研究之间无法进行比较。为填补这一空白，我们当前的研究创新了一种可靠的MP检测系统，该系统将样品处理、机器学习和光学光热红外（O-PTIR）光谱技术相结合。这种方法包括研究高温过滤和酒精处理以减少非MP颗粒，并利用支持向量机（SVM）分类器聚焦于能够区分尼龙MP和非尼龙MP的关键波数（基于SVM全波数模型的特征重要性，选择了1077、1541、1635、1711 cm）以增强MP识别。基于关键波数构建的SVM模型显示出91.33%的高准确率。结果表明，酒精处理在最大限度减少非MP颗粒方面有效，而70°C过滤的影响有限。此外，该方法被应用于评估商业尼龙茶包释放的MP，结果显示每个茶包平均释放106个颗粒。这项研究将机器学习与O-PTIR光谱技术相结合，为MP检测方法的潜在标准化铺平了道路，并为其对环境和健康的影响提供了重要见解。
