Department of Physics, University of Nairobi, Nairobi, Kenya.
Department of Biology, University of Nairobi, Nairobi, Kenya.
Malar J. 2023 Nov 8;22(1):342. doi: 10.1186/s12936-023-04777-y.
Identification of malaria vectors is an important exercise that can result in the deployment of targeted control measures and monitoring the susceptibility of the vectors to control strategies. Although known to possess distinct biting behaviours and habitats, the African malaria vectors Anopheles gambiae and Anopheles arabiensis are morphologically indistinguishable and are known to be discriminated by molecular techniques. In this paper, Raman spectroscopy is proposed to complement the tedious and time-consuming Polymerase Chain Reaction (PCR) method for the rapid screening of mosquito identity.
A dispersive Raman microscope was used to record spectra from the legs (femurs and tibiae) of fresh anaesthetized laboratory-bred mosquitoes. The scattered Raman intensity signal peaks observed were predominantly centered at approximately 1400 cm, 1590 cm, and 2067 cm. These peaks, which are characteristic signatures of melanin pigment found in the insect cuticle, were important in the discrimination of the two mosquito species. Principal Component Analysis (PCA) was used for dimension reduction. Four classification models were built using the following techniques: Linear Discriminant Analysis (LDA), Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), and Quadratic Support Vector Machine (QSVM).
PCA extracted twenty-one features accounting for 95% of the variation in the data. Using the twenty-one principal components, LDA, LR, QDA, and QSVM discriminated and classified the two cryptic species with 86%, 85%, 89%, and 93% accuracy, respectively on cross-validation and 79%, 82%, 81% and 93% respectively on the test data set.
Raman spectroscopy in combination with machine learning tools is an effective, rapid and non-destructive method for discriminating and classifying two cryptic mosquito species, Anopheles gambiae and Anopheles arabiensis belonging to the Anopheles gambiae complex.
疟疾媒介的鉴定是一项重要的工作，它可以导致有针对性的控制措施的部署，并监测媒介对控制策略的敏感性。虽然已知具有不同的叮咬行为和栖息地，但非洲疟疾媒介冈比亚按蚊和阿拉伯按蚊在形态上无法区分，并且已知可以通过分子技术来区分。在本文中，拉曼光谱被提议作为聚合酶链反应（PCR）方法的补充，用于快速筛选蚊子的身份。
使用色散拉曼显微镜记录新鲜麻醉的实验室饲养的蚊子的腿（股骨和胫骨）的光谱。观察到的散射拉曼强度信号峰主要集中在约 1400 cm、1590 cm 和 2067 cm 左右。这些峰是昆虫表皮中黑色素色素的特征签名，对于区分这两种蚊子物种很重要。主成分分析（PCA）用于降维。使用以下技术构建了四个分类模型：线性判别分析（LDA）、逻辑回归（LR）、二次判别分析（QDA）和二次支持向量机（QSVM）。
PCA 提取了二十一个特征，占数据变化的 95%。使用二十一个主成分，LDA、LR、QDA 和 QSVM 在交叉验证上分别以 86%、85%、89%和 93%的准确率对两种隐种进行了区分和分类，在测试数据集上分别为 79%、82%、81%和 93%。
拉曼光谱与机器学习工具相结合是一种有效、快速和非破坏性的方法，可用于区分和分类两种隐种，冈比亚按蚊和阿拉伯按蚊，属于冈比亚按蚊复合体。
