Given the growing urge for plastic management and regulation in the world, recent studies have investigated the problem of plastic material identification for correct classification and disposal. Recent works have shown the potential of machine learning techniques for successful microplastics classification using Raman signals. Classification techniques from the machine learning area allow the identification of the type of microplastic from optical signals based on Raman spectroscopy. In this paper, we investigate the impact of high-frequency noise on the performance of related classification tasks. It is well-known that classification based on Raman is highly dependent on peak visibility, but it is also known that signal smoothing is a common step in the pre-processing of the measured signals. This raises a potential trade-off between high-frequency noise and peak preservation that depends on user-defined parameters. The results obtained in this work suggest that a linear discriminant analysis model cannot generalize properly in the presence of noisy signals, whereas an error-correcting output codes model is better suited to account for inherent noise. Moreover, principal components analysis (PCA) can become a must-do step for robust classification models, given its simplicity and natural smoothing capabilities. Our study on the high-frequency noise, the possible trade-off between pre-processing the high-frequency noise and the peak visibility, and the use of PCA as a noise reduction technique in addition to its dimensionality reduction functionality are the fundamental aspects of this work.

中文翻译：

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使用拉曼光谱和机器学习进行微塑料分类的高频噪声研究

鉴于世界各地对塑料管理和监管的呼声日益高涨，最近的研究调查了塑料材料识别问题，以便正确分类和处置。最近的工作表明，机器学习技术在使用拉曼信号成功进行微塑料分类方面具有潜力。机器学习领域的分类技术可以根据拉曼光谱从光信号中识别微塑料的类型。在本文中，我们研究了高频噪声对相关分类任务性能的影响。众所周知，基于拉曼的分类高度依赖于峰值可见度，但也众所周知，信号平滑是测量信号预处理中的常见步骤。这就提出了高频噪声和峰值保留之间的潜在权衡，这取决于用户定义的参数。这项工作中获得的结果表明，线性判别分析模型在存在噪声信号的情况下无法正确概括，而纠错输出代码模型更适合解释固有噪声。此外，鉴于主成分分析（PCA）的简单性和自然平滑功能，它可以成为稳健分类模型的必备步骤。我们对高频噪声的研究、高频噪声预处理和峰值可见度之间可能的权衡，以及使用 PCA 作为除降维功能之外的降噪技术是以下基本方面：这项工作。