In clinical practice, infections caused by Aspergillus species are common and associated with high mortality rates. However, isolating Aspergillus from clinical samples and cultures is challenging, and serological tests cannot differentiate between strains. Furthermore, the complex cell wall structure in Aspergillus species hinders DNA extraction and subsequent nucleic acid detection. Aspergillus fumigatus, Aspergillus flavus, and Aspergillus niger are common and frequently encountered pathogenic species. This study aimed to establish an efficient and rapid method for detecting Aspergillus species in clinical samples. Fe₃O₄@PEI.NH₂ nanoparticles with large particle sizes were employed to capture Aspergillus from the samples. Flexible silver nanowires (AgNWs) were prepared and used as the surface-enhanced Raman scattering (SERS) substrate for Aspergillus detection. Through SERS spectroscopy, the identification of captured Aspergillus species was confirmed. Furthermore, the SERS spectrum underwent principal component analysis employing orthogonal partial least-squares discrimination analysis (OPLS-DA) as a multivariate analysis technique to distinguish between the various Aspergillus species effectively. After 10-fold cross-validation, the trained model achieved a testing accuracy of 99.15%, indicating excellent classification performance. By utilizing pre-prepared Fe₃O₄@PEI.NH₂ and AgNWs, the detection process was completed within 60 min without disrupting the cell wall of Aspergillus.

在临床实践中，由曲霉属物种引起的感染很常见，并且死亡率很高。然而，从临床样本和培养物中分离曲霉属具有挑战性，并且血清学测试无法区分菌株。此外，曲霉属物种复杂的细胞壁结构阻碍了DNA提取和随后的核酸检测。烟曲霉、黄曲霉和黑曲霉是常见且经常遇到的致病菌。本研究旨在建立一种高效、快速的方法来检测临床样本中的曲霉属物种。采用大粒径的Fe ₃ O ₄ @PEI.NH ₂纳米粒子捕获样品中的曲霉菌。制备了柔性银纳米线（AgNW）并用作曲霉检测的表面增强拉曼散射（SERS）基底。通过SERS光谱，确认了捕获的曲霉属物种的鉴定。此外，SERS 光谱采用正交偏最小二乘判别分析 (OPLS-DA) 作为多变量分析技术进行主成分分析，以有效地区分各种曲霉属物种。经过10倍交叉验证，训练后的模型测试准确率达到99.15%，分类性能优异。利用预先制备的Fe ₃ O ₄ @PEI.NH _{2和AgNWs，在不破坏}曲霉细胞壁的情况下，在60分钟内完成检测过程。