Aim. The aim of this study was to assess the prevalence of computer vision syndrome (CVS) and its associated ergonomic factors among university administrative staff in Ghana. Methods. A cross-sectional survey was conducted among 200 administrative staff of the University of Cape Coast. The procedure included a self-administered questionnaire, comprehensive ocular health examination, and assessment of computer workstation and lighting conditions. The prevalence of CVS among the subjects and the association between CVS and ergonomic practices were determined. Results. The mean age of the study sample was 31.0 ± 4.7 years, and the majority were males (56.0%). The prevalence of CVS was among 103 (51.5%)participants. Over a third of the respondents used computers for 6 or more hours daily. Significant association was found between CVS and poor ergonomic practices (χ = 15.175, p=0.001). Conclusion. In addition to poor ergonomic office setup, university administrative staff spend several hours behind computer screens leading to the development of CVS. Increased awareness of CVS and adherence to recommended ergonomic practices are necessary to reduce the prevalence of CVS and ultimately enhance work satisfaction and productivity.

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The potential of predicting maturity using total soluble solids (TSS) and identifying organic from inorganic pineapple fruits based on near-infrared (NIR) spectra fingerprints would be beneficial to farmers and consumers alike. In this study, a portable NIR spectrometer and chemometric techniques were combined to simultaneously identify organically produced pineapple fruits from conventionally produced ones (thus organic and inorganic) and also predict total soluble solids. A total of 90 intact pineapple fruits were scanned with the NIR spectrometer while a digital refractometer was used to measure TSS from extracted pineapple juice. After attempting several preprocessing techniques, multivariate calibration models were built using principal component analysis (PCA), K-nearest neighbor (KNN), and linear discriminant analysis (LDA) to identify the classes (organic and conventional pineapple fruits) while partial least squares regression (PLSR) method was used to determine TSS of the fruits. Among the identification techniques, the MSC-PCA-LDA model accurately identified organic from conventionally produced fruits at 100% identification rate. For quantification of TSS, the MSC-PLSR model gave Rp = 0.851 and RMSEC = 0.950 °Brix, and Rc = 0.854 and RMSEP = 0.842 °Brix at 5 principal components in the calibration set and prediction set, respectively. The results generally indicated that portable NIR spectrometer coupled with the appropriate chemometric tools could be employed for rapid nondestructive examination of pineapple quality and also to detect pineapple fraud due to mislabeling of conventionally produced fruits as organic ones. This would be helpful to farmers, consumers, and quality control officers.

Laser-induced fluorescence (LIF) combined with multivariate techniques has been used in identifying antimalarial herbal plants (AMHPs) based on their geographical origin. The AMHP samples were collected from four geographical origins (Abrafo, Jukwa, Nfuom, and Akotokyere) in the Cape Coast Metropolis, Ghana. LIF spectra data were recorded from the AMHP samples. Utilizing multivariate techniques, a training set for the first two principal components of the AMHP spectra data was modeled through the use of K-nearest neighbor (KNN), support vector nachine (SVM), and linear discriminant analysis (LDA) methods. The SVM and KNN methods performed best with 100% success for the prediction data, while the LDA had a 99% success rate. The KNN and SVM methods are recommended for the identification of AMHPs based on their geographical origins. Deconvoluted peaks from the LIF spectra of all the AMHP samples revealed compounds such as quercetin and berberine as being present in all the AMHP samples.

Multi-spectral imaging (MSI) has made diagnosis of microscopic samples considerably easier and information abound. Most MSI systems use continuum light sources and filters for imaging purposes. However, these light sources and filters are relatively expensive, unstable due to extreme pressure and temperature and associated with prolong acquisition time. In this work, we present a metallurgical microscope retrofitted with light-emitting diodes (LEDs) as illumination sources for MSI microscopy. This multispectral LED imaging microscope (MSLEDIM) is relatively cheaper and capable of acquiring images in reflection, transmission and scattering modes at thirteen (13) different wavelengths ranging from ultraviolet to near infrared. The microscope has been demonstrated in biomedical and entomological research fields. The MSLEDIM can be used in various scientific research fields for imaging microscopic samples.

Cataracts are the leading cause of blindness worldwide. Current methods for discriminating cataractous lenses from healthy lenses of Sprague-Dawley rats during preclinical studies are based on either histopathological or clinical assessments which are weakened by subjectivity. In this work, both cataractous and healthy lens tissues of Sprague-Dawley rats were studied using multispectral imaging technique in combination with multivariate analysis. Multispectral images were captured in transmission, reflection and scattering modes. In all, five spectral bands were found to be markers for discriminating cataractous lenses from healthy lenses; 470 nm and 625 nm discriminated in reflection mode whereas 435 nm, 590 nm and 700 nm discriminated in transmission mode. With Fisher's Linear discriminant analysis, the midpoints for classifying cataractous from healthy lenses were found to be 14.718 × 10 −14 and 3.2374 × 10 −14 for the two spectra bands in the reflection mode and the three spectral bands in the transmission mode respectively. Images in scattering mode did not show significant discrimination. These spectral bands in reflection and transmission modes may offer potential diagnostic markers for discriminating cataractous lenses from healthy lenses thereby promising multispectral imaging applications for characterizing cataractous and healthy lenses.

Plasmodium falciparum (P. falciparum) malarial degree of infection, termed as parasite density (PD), estimation is vital for point-of-care diagnosis and treatment of the disease. In this work, we present application of optical techniques: optical absorption and multispectral imaging for P. falciparum malarial byproduct (hemozoin) detection in human‐infected blood samples to estimate PD. The blood samples were collected from volunteers who were tested positive for P. falciparum infections (i-blood), and after treatment, another set of blood samples (u-blood) were also taken. The i-blood samples were grouped based on PD (+, ++, +++, and ++++). Optical densities (ODs) of u-blood samples and i-blood samples at blood absorption bands of 405 nm, 541 nm, and 577 nm showed different optical absorption characteristics. Empirical computation of ratio of the ODs for the blood absorption bands revealed reduction in the ODs with increasing PD. Multispectral images containing uninfected red blood cells (u-RBCs) and P. falciparum‐infected red blood cells (i-RBCs) on unstained blood smear slides exhibited spectrally determined decrease in both reflected and scattered pixel intensities and increase in transmitted pixel intensities with increasing PD. We further propose a linear classification model based on Fisher’s approach using reflected, scattered, and transmitted pixel intensities for easy and inexpensive estimation of PD as an alternative to manual estimation of PD, currently, the widely used technique. Application of the optical techniques and the proposed linear classification model are therefore recommended for improved malaria diagnosis and therapy.

Onsite technique for determining drug integrity in Sub Saharan Africa is needed for ensuring drug integrity and enhancing public health. This current study presents the application of handheld NIR spectroscopic...