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.

Malaria parasite, Plasmodium falciparum, uses haemoglobin in host red blood cells (RBCs) as a major source of nutrient in ring and trophozoite stages. This brings about changes in the morphology and functional characteristics of the RBCs. We investigate malaria infected RBCs and uninfected RBCs-ring and trophozoite stages using multispectral imaging technique. Four spectral bands were found to be markers for identifying infected and uninfected RBCs: 435 nm and 660 nm were common markers for the two stages whiles 590 nm and 625 nm were markers for the ring and the trophozoite stages respectively. These four spectral bands may offer potential diagnostic markers for identifying infected and uninfected RBCs, as well as distinguishing ring and trophozoite stages.

<p class="1Body">Malaria parasites, <em>Plasmodium falciparum</em> (<em>P.falciparum</em>) infections are taking a great toll on the lives of people worldwide, especially in developing countries. Recently, haemozoin detection using optical techniques tends to provide comparable parasite densities (PDs) estimation. We conducted feasibility studies on <em>P.falciparum</em> infected blood (<em>i</em>-blood) and uninfected blood (<em>u</em>-blood) samples from volunteers employing laser-induced fluorescence technique for PDs estimation. Fluorescence results show high intensity in <em>u</em>-blood than<em> i</em>-blood. PeakFit analysis with Loess smoothing under Lorentzian curve shows that fluorescence peak of <em>i</em>-blood appears red-shifted with increasing PDs. The Lorentzian curves depict that fluorescence peak intensity ratio increases with increasing PDs in <em>i</em>-blood samples. This technique may be potentially applied in PDs estimation to improve malaria diagnosis.</p>

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.