Gao Zili, Zhang Yuzhen, Harrington Laura C, Murdock Courtney C, Martin Elisabeth, Manbeck-Mosig Dalton, Vetrone Steve, Tremblay Nicolas, Barker Christopher M, Clark John M, He Lili, Zhu Wei
Department of Food Science, University of Massachusetts, Amherst, MA, 01003, USA.
Raman, IR, and XRF Core Facility, University of Massachusetts, Amherst, MA, 01003, USA.
Parasit Vectors. 2025 Jun 10;18(1):218. doi: 10.1186/s13071-025-06831-x.
Mosquito-borne diseases, such as malaria, dengue, and Zika, continue to pose significant threats to global health, resulting in millions of cases and thousands of deaths each year. Notably, only older mosquitoes can transmit these diseases. Therefore, accurate age estimation of mosquitoes is vital for targeted interventions and risk assessments. However, traditional methods, such as tracheole morphology analysis, are labor-intensive and have limited scalability. Surface-enhanced Raman spectroscopy (SERS), when coupled with artificial neural networks (ANNs), offers a robust and flexible alternative, facilitating accurate and efficient mosquito age determination even in diverse and complex environmental conditions.
We analyzed 124 Aedes aegypti mosquitoes from California (CA) and Thailand (TH) using SERS, each generating 20 spectra. The ANNs utilized a multilayer perceptron with two hidden layers of 100 neurons and rectified linear unit (ReLU) activation. Classification tasks used cross-entropy loss; regression applied mean squared error. Models were trained with a 70-30 training-validation split and optimized using the Adam optimizer over 10,000 iterations. Performance metrics included accuracy, correlation coefficient (R), and root mean square error (RMSE). t-Distributed stochastic neighbor embedding (t-SNE) visualizations and confusion matrices offered additional model insights into effectiveness.
The ANN models demonstrated superior performance in differentiating mosquito age relative to non-ANN methods. For female CA mosquitoes, the models classified ages from day 1 to day 21 with 84% accuracy and predicted age with an R of 0.96 and RMSE of 2.18 days. Similarly, the models achieved 86% accuracy and an R-value of 0.95 for female TH mosquitoes. While mosquito origin and sex influenced performance, the combined model maintained robust results, achieving 80% accuracy and an R-value of 0.93. Implementing a voting mechanism across multiple spectra for each mosquito significantly improved accuracy, increasing classification performance from approximately 80% at the spectrum level to 100% at the mosquito level.
This study demonstrates the effectiveness of SERS combined with ANN for accurate age classification and prediction of Ae. aegypti mosquitoes. The models achieved high accuracy across diverse populations, with a voting mechanism enhancing classification to 100%. These findings highlight the potential of SERS-ANN as a reliable tool for vector control and disease surveillance.
疟疾、登革热和寨卡等蚊媒疾病继续对全球健康构成重大威胁，每年导致数百万病例和数千人死亡。值得注意的是，只有老龄蚊子才能传播这些疾病。因此，准确估计蚊子的年龄对于有针对性的干预措施和风险评估至关重要。然而，传统方法，如气管形态分析，劳动强度大且可扩展性有限。表面增强拉曼光谱（SERS）与人工神经网络（ANN）相结合，提供了一种强大且灵活的替代方法，即使在多样和复杂的环境条件下也能促进准确、高效地确定蚊子的年龄。
我们使用SERS分析了来自加利福尼亚州（CA）和泰国（TH）的124只埃及伊蚊，每只蚊子产生20个光谱。人工神经网络采用具有两个包含100个神经元的隐藏层和整流线性单元（ReLU）激活的多层感知器。分类任务使用交叉熵损失；回归应用均方误差。模型采用70 - 30的训练 - 验证分割进行训练，并使用Adam优化器在10000次迭代中进行优化。性能指标包括准确率、相关系数（R）和均方根误差（RMSE）。t分布随机邻域嵌入（t - SNE）可视化和混淆矩阵为模型有效性提供了更多见解。
与非人工神经网络方法相比，人工神经网络模型在区分蚊子年龄方面表现出卓越性能。对于加利福尼亚州的雌性蚊子，模型对1日龄至第21日龄的年龄分类准确率为84%，预测年龄的相关系数R为0.96，均方根误差为2.18天。同样，对于泰国的雌性蚊子，模型的准确率达到86%，R值为0.95。虽然蚊子的来源和性别会影响性能，但组合模型保持了稳健的结果，准确率达到80%，R值为0.93。对每只蚊子的多个光谱实施投票机制显著提高了准确率，将光谱水平的分类性能从约80%提高到蚊子水平的100%。
本研究证明了SERS与人工神经网络相结合用于准确分类和预测埃及伊蚊年龄的有效性。这些模型在不同种群中都达到了高精度，投票机制将分类准确率提高到100%。这些发现突出了SERS - ANN作为病媒控制和疾病监测可靠工具的潜力。
