Abstract
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.