Rapid and non-destructive identification of Anopheles gambiae and Anopheles arabiensis mosquito species using Raman spectroscopy via machine learning classification models.

BACKGROUND: 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. METHODS: 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-1, 1590 cm-1, and 2067 cm-1. 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). RESULTS: 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. CONCLUSION: 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.

Fabrication of surface-enhanced Raman spectroscopy substrates using silver nanoparticles produced by laser ablation in liquids.

This research describes the use of surface-enhanced Raman spectroscopy (SERS) substrates based on colloidal silver nanoparticles (AgNPs) produced by laser ablation of silver granules in pure water that are inexpensive, easy to make, and chemically stable. Here, the effects of the laser power, pulse repetition frequency, and ablation duration on the Surface Plasmon Resonance peak of AgNPs solutions, were used to determine the optimal parameters. Also, the effects of the laser ablation time on both ablation efficiency and SERS enhancement were studied. The synthesized AgNPs were characterized by UV-Vis spectrophotometer, Scanning Electron Microscope (SEM), and Raman spectrometer. The Surface Plasmon Resonance peak of AgNP solutions was centered at 404 nm confirming their synthesis and they were noted to be spherical with 34 nm in diameter. Using Raman spectroscopy, they had main bands centered at 196 cm-1 (O = Ag2/Ag-N stretching vibrations), 568 cm-1 (NH out of plane bending); 824 cm-1 (symmetric deformation of the NO2); 1060 cm-1 (NH out of plane bending); 1312 cm-1 (symmetric stretching of NO2); 1538 cm-1 (NH in-plane bending); and 2350 cm-1 (N2 vibrations). Their Raman spectral profiles remained constant within the first few days of storage at room temperature implying chemical stability. The Raman signals from blood were enhanced when mixed with AgNPs and this depended on colloidal AgNPs concentration. Using those generated by 12 h ablation time, an enhancement of 14.95 was achieved. Additionally, these substrates had an insignificant impact on the Raman profiles of samples of rat blood when mixed with them. The Raman peaks noted were attributed to CC stretching of glucose (932 cm-1); CC stretching of Tryptophan (1064 cm-1); CC stretching of ß Carotene (1190 cm-1); CH2 wagging of proteins (1338 and 1410 cm-1); carbonyl stretch for proteins (1650 cm-1); CN vibrations for glycoproteins (2122 cm-1). These SERS substrates can be applied to areas such as forensics to distinguish between human and other animal blood, monitoring of the efficacy of drugs, disease diagnostics such as diabetes, and pathogen detection. All this can be achieved by comparing the Raman spectra of the biological samples mixed with the synthesized SERS substrates for different samples. Thus, the results on the use of inexpensive, simple-to-prepare Raman substrates have the possibility of making surface-enhanced Raman spectroscopy available to laboratories with scarce resources in developing nations.

Application of principal component analysis to multispectral-multimodal optical image analysis for malaria diagnostics.

BACKGROUND: Multispectral imaging microscopy is a novel microscopic technique that integrates spectroscopy with optical imaging to record both spectral and spatial information of a specimen. This enables acquisition of a large and more informative dataset than is achievable in conventional optical microscopy. However, such data are characterized by high signal correlation and are difficult to interpret using univariate data analysis techniques. METHODS: In this work, the development and application of a novel method which uses principal component analysis (PCA) in the processing of spectral images obtained from a simple multispectral-multimodal imaging microscope to detect Plasmodium parasites in unstained thin blood smear for malaria diagnostics is reported. The optical microscope used in this work has been modified by replacing the broadband light source (tungsten halogen lamp) with a set of light emitting diodes (LEDs) emitting thirteen different wavelengths of monochromatic light in the UV-vis-NIR range. The LEDs are activated sequentially to illuminate same spot of the unstained thin blood smears on glass slides, and grey level images are recorded at each wavelength. PCA was used to perform data dimensionality reduction and to enhance score images for visualization as well as for feature extraction through clusters in score space. RESULTS: Using this approach, haemozoin was uniquely distinguished from haemoglobin in unstained thin blood smears on glass slides and the 590-700 spectral range identified as an important band for optical imaging of haemozoin as a biomarker for malaria diagnosis. CONCLUSION: This work is of great significance in reducing the time spent on staining malaria specimens and thus drastically reducing diagnosis time duration. The approach has the potential of replacing a trained human eye with a trained computerized vision system for malaria parasite blood screening.