颗粒分类在各种科学和技术应用中发挥着至关重要的作用，例如在医疗保健应用中区分细菌和病毒或识别和分类癌细胞。该技术需要对颗粒特性进行准确有效的分析。在这项研究中，我们通过多模态颗粒分类方法研究了电学和光学特征的集成。应用机器学习分类器算法来评估组合这些测量的影响。我们的结果证明了多模态方法相对于独立分析电学或光学特征的优越性。通过集成两种模态，我们实现了 94.9% 的平均测试准确度，而单独电气特征的平均测试准确度为 66.4%，单独光学特征的平均测试准确度为 90.7%。这凸显了电学和光学信息的互补性及其增强分类性能的潜力。通过利用电传感和光学成像技术，我们的多模式方法可以更深入地了解颗粒特性，并提供对复杂生物系统更全面的理解。© 2024。作者。

Particle classification plays a crucial role in various scientific and technological applications, such as differentiating between bacteria and viruses in healthcare applications or identifying and classifying cancer cells. This technique requires accurate and efficient analysis of particle properties. In this study, we investigated the integration of electrical and optical features through a multimodal approach for particle classification. Machine learning classifier algorithms were applied to evaluate the impact of combining these measurements. Our results demonstrate the superiority of the multimodal approach over analyzing electrical or optical features independently. We achieved an average test accuracy of 94.9% by integrating both modalities, compared to 66.4% for electrical features alone and 90.7% for optical features alone. This highlights the complementary nature of electrical and optical information and its potential for enhancing classification performance. By leveraging electrical sensing and optical imaging techniques, our multimodal approach provides deeper insights into particle properties and offers a more comprehensive understanding of complex biological systems.© 2024. The Author(s).