Lim Kaeul, Ardekani Arezoo
School of Mechanical Engineering, Purdue University West Lafayette Indiana USA
Nanoscale Adv. 2024 Aug 16;6(20):5171-80. doi: 10.1039/d4na00205a.
Nanoparticle (NP)-based technologies have gained significant attention in targeted drug delivery, encompassing chemotherapies, photodynamic therapy, and immunotherapy. Hyperspectral imaging (HSI) emerges as a label-free, minimally invasive, and high-throughput technique for quantitative NP analysis. Despite its growing importance, the application of HSI to nanoparticle analysis, especially for label-free characterization and classification, remains limited. Here, we propose a novel method integrating hyperspectral imaging with a spectral noise reduction method and machine learning (ML) for robust nanoparticle classification. There are many challenges to extracting information from noisy and overlapping particles in HSI data. To surmount these challenges, we propose a spectral angle matching (SAM) algorithm to effectively denoise hyperspectral datasets. Complementing this, we employ a support vector machine (SVM) algorithm for classification, leveraging preprocessed HSI data to extract unique spectral signatures. Our hyperspectral imaging classification of multiple nanoparticle types reveals distinct spectral characteristics inherent to each class. The classification accuracy reaches 99.9% for single nanoparticle types, highlighting the efficiency of our method. In the case of classifying multiple particle types, the overall accuracy also reaches 99.9%. Visualization of the NP classification map further demonstrates the efficacy of our model. The application of the SAM-SVM algorithm in hyperspectral analysis outperforms traditional SVM methods in classifying multiple samples, highlighting the potential of our nanoparticle analysis. Our findings not only address the challenges posed by noisy and overlapping particles but also demonstrate the potential of hyperspectral imaging in advancing real-time and label-free detection systems for diverse biomedical applications.
基于纳米颗粒（NP）的技术在靶向药物递送领域备受关注，涵盖化疗、光动力疗法和免疫疗法。高光谱成像（HSI）作为一种用于定量NP分析的无标记、微创且高通量的技术应运而生。尽管其重要性与日俱增，但HSI在纳米颗粒分析中的应用，尤其是在无标记表征和分类方面，仍然有限。在此，我们提出一种将高光谱成像与光谱降噪方法及机器学习（ML）相结合的新方法，用于稳健的纳米颗粒分类。从HSI数据中嘈杂且重叠的颗粒提取信息存在诸多挑战。为克服这些挑战，我们提出一种光谱角匹配（SAM）算法，以有效降低高光谱数据集的噪声。作为补充，我们采用支持向量机（SVM）算法进行分类，利用预处理后的HSI数据提取独特的光谱特征。我们对多种纳米颗粒类型的高光谱成像分类揭示了每个类别固有的独特光谱特征。单一纳米颗粒类型的分类准确率达到99.9%，凸显了我们方法的有效性。在对多种颗粒类型进行分类时，总体准确率也达到99.9%。NP分类图的可视化进一步证明了我们模型的有效性。SAM-SVM算法在高光谱分析中的应用在对多个样本进行分类时优于传统SVM方法，凸显了我们纳米颗粒分析的潜力。我们的研究结果不仅解决了由嘈杂且重叠的颗粒带来的挑战，还展示了高光谱成像在推进用于各种生物医学应用的实时无标记检测系统方面的潜力。
