Rapid urbanization and its associated pollution can affect water quality in mosquito breeding habitats and, as a result, the ecology and control of mosquito vectors. To understand the effects of pollution on mosquito vectors, an accurate assessment of water quality in breeding habitats is needed. Presently, water quality assessment of mosquito breeding habitats is usually based on the measurement of individual physicochemical parameters. However, several parameters are sometimes difficult to interpret or may not give a clear picture of the overall water quality of the breeding habitats, especially when the pollutants are in complex mixtures. This study employed the use of Laser-Induced Fluorescence (LIF) spectroscopy to assess water quality in breeding habitats of Anopheles, Aedes, and Culex mosquitoes in urban areas in Cape Coast, Ghana. The LIF spectra, using a 445-nm diode laser, were measured from field-collected water samples in the laboratory. The LIF spectra showed the presence of dissolved organic matter (DOM) and chlorophyll in the breeding habitats. The DOM and chlorophyll fluorescence signals were normalised by the Raman vibrational signals to determine water quality in each habitat. The overall water quality was better in Aedes breeding habitats than in Anopheles and Culex breeding habitats. The poor water quality in Anopheles and Culex breeding habitats was due to the presence of high fulvic acid and chlorophyll content, which often reflect pollutants from anthropogenic sources. Anopheles and Aedes habitats were made up of mainly An. coluzzii and Ae. aegypti respectively while Culex species were identified to genus level. The results add up to the growing concern about the breeding of Anopheles in polluted habitats. The study demonstrated for the first time the ability of LIF spectroscopy to assess water quality in mosquito breeding habitats.

We report on a computational technique that recovers Raman peaks embedded in highly fluorescent contaminated spectra. The method uses a second derivative technique to identify the most intense Raman peak, and a modified Savisty Golay algorithm to filter and recover the embedded Raman peaks iteratively. This technique is an improvement on existing background removal algorithms in both performance and user objectivity.

Spectral imaging and lidar methods for the characterization of vegetation are vital for understanding plants and, in turn, food security, biodiversity, and vegetation health in a changing climate. While novel hyperspectral imaging, canopy lidar,and solar-induced fluorescence provide details on species, state, structure, and plant physiology, such data come from different instruments. Thus, post-processing and data fusion struggles with synchronization, spatial overlap, and resolution issues. Especially in the tropical regions of sub-Saharan Africa, these complex, expensive, and bulky instruments remain inaccessible. Here, in Ghana, we have built a low-cost, lightweight, and realistic instrument for simultaneously acquiring hyperspectral data of vegetation fluorescence and canopy structure with perfect spatial overlap. In this paper, we demonstrate the application of the hyperspectral fluorescence lidar for diagnostics and species specificity of locally significant crops. We demonstrate simultaneous range and fluorescence measurements of forest canopy, conducted in full sunlight. Our results indicate that the upper side of the leaves shows a more substantial deviation for stressed plants, whilethe lower side shows greater contrast for plant species. This new and simple tool provides a combined method for hyperspectral classification and assessment of the physiological state while also reporting the vegetation height over ground and its diversity.

Rice is a staple food in sub-Saharan Africa, including Ghana. Local production is hindered by the use of rice grains for cultivation, which directly affects both yield and grain quality. This study employed laser-induced fluorescence spectroscopy (LIFS) combined with multivariate analysis to rapidly and non-destructively discriminate between rice seeds and grains, offering an alternative to conventional methods. Fluorescence spectra from rice seeds and grains from six locally cultivated rice varieties were analyzed using three pre-processing techniques (Z-score, first derivative, and second derivative) across four machine learning models: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), K-nearest neighbor (KNN), and support vector machine (SVM). Principal component analysis score plots and the Davies–Bouldin index were used to assess the separation between seeds and grains. The first and second derivatives outperformed the Z-score, with SVM performing well under the second derivative and KNN excelling under the first derivative. LDA and QDA varied depending on the rice variety and the pre-processing method. The best accuracy was achieved using the first derivative with KNN, achieving test accuracy and F1 scores ranging from 0.74 to 0.97 and 0.76 to 0.97, respectively, confirming that LIFS combined with multivariate techniques is an effective method for discriminating rice seeds and grains.

Shelf life of bottled natural fruit juice (BNFJ) provides relevant information on quality and authenticity for consumer protection. However, existing techniques for monitoring the shelf life of BNFJ are destructive and time-consuming. We report on using laser-induced autofluorescence (LIAF) spectroscopic technique in combination with multivariate analysis for shelf life monitoring of BNFJ. The LIAF spectra data were acquired for nine (9) continuous days on three batches of BNFJ samples purchased from a certified retailer. Deconvolution of the LIAF spectra revealed underlying peaks representing constituents of the BNFJ. Principal component analysis (PCA) was able to monitor the trend in the changes of the BNFJ as it aged. Partial least square regression (PLSR) predicted the exact day from the production of the BNFJ accurately at 96.6% and 98.8% in the training and testing sets, respectively. We, therefore, propose the LIAF combined with multivariate analysis as a potential tool for nondestructive, rapid, and relatively inexpensive monitoring of the shelf life of BNFJ.