Global Centre for Environmental Remediation (GCER), University of Newcastle, Callaghan, NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), University of Newcastle, Callaghan, NSW 2308, Australia.
Flinders Institute for NanoScale Science and Technology, College of Science and Engineering, Flinders University, South Australia 5042, Australia; Flinders Microscopy and Microanalysis, College of Science and Engineering, Flinders University, Bedford Park 5042, Australia.
Sci Total Environ. 2022 Dec 10;851(Pt 2):158293. doi: 10.1016/j.scitotenv.2022.158293. Epub 2022 Aug 27.
The characterisation of microplastics is still difficult, and the challenge is even greater for nanoplastics. A possible source of these particles is the scratched surface of a non-stick cooking pot that is mainly coated with Teflon. Herein we employ Raman imaging to scan the surfaces of different non-stick pots and collect spectra as spectrum matrices, akin to a hyperspectral imaging process. We adjust and optimise different algorithms and create a new hybrid algorithm to extract the extremely weak signal of Teflon microplastics and particularly nanoplastics. We use multiple characteristic peaks of Teflon to create several images, and merge them to one, using a logic-based algorithm (i), in order to cross-check them and to increase the signal-noise ratio. To differentiate the varied peak heights towards image merging, an algebra-based algorithm (ii) is developed to process different images with weighting factors. To map the images via the whole set of the spectrum (not just from the individual characteristic peaks), a principal component analysis (PCA)-based algorithm (iii) is employed to orthogonally decode the spectrum matrix to the PCA spectrum and PCA intensity image. To effectively extract the Teflon spectrum information, a new hybrid algorithm is developed to justify the PCA spectra and merge the PCA intensity images with the algebra-based algorithm (PCA/algebra-based algorithm) (iv). Based on these developments and with the help of SEM, we estimate that thousands to millions of Teflon microplastics and nanoplastics might be released during a mimic cooking process. Overall, it is recommended that Raman imaging, along with the signal recognition algorithms, be combined with SEM to characterise and quantify microplastics and nanoplastics.
微塑料的特征化仍然具有挑战性，而纳米塑料的挑战更大。这些颗粒的一个可能来源是主要涂有特氟龙的不粘锅的划伤表面。在此，我们采用 Raman 成像技术扫描不同不粘锅的表面，并收集光谱作为光谱矩阵，类似于高光谱成像过程。我们调整和优化不同的算法，并创建一种新的混合算法来提取特氟龙微塑料，特别是纳米塑料的极其微弱信号。我们使用特氟龙的多个特征峰创建多个图像，并使用基于逻辑的算法（i）将它们合并为一个图像，以交叉检查并提高信号噪声比。为了区分图像合并的不同峰值高度，开发了一种基于代数的算法（ii），用加权因子处理不同的图像。为了通过整个光谱集（不仅仅是从单个特征峰）映射图像，采用基于主成分分析（PCA）的算法（iii）将光谱矩阵进行正交解码到 PCA 光谱和 PCA 强度图像。为了有效地提取特氟龙光谱信息，开发了一种新的混合算法来验证 PCA 光谱，并使用基于代数的算法（PCA/基于代数的算法）（iv）合并 PCA 强度图像。基于这些发展，并借助 SEM，我们估计在模拟烹饪过程中可能会释放数千到数百万个特氟龙微塑料和纳米塑料。总之，建议将 Raman 成像与信号识别算法相结合，结合 SEM 来表征和量化微塑料和纳米塑料。
