Hyperspectral imaging (HSI) provides a powerful, non-destructive method for obtaining spatially and spectrally resolved information across diverse applications. However, conventional systems remain limited by high cost, complexity, and bulkiness, restricting their use in resource-constrained environments. This study presents a cost-effective, modular hyperspectral and polarimetric imaging platform developed using 3D-printed components and open-source software control. The system integrates a linear variable long-pass filter (LV-LPF) mounted on a motorized translation stage with a monochrome CMOS camera and LED-based diffuse illumination, enabling continuous spectral scanning between 450-720 nm. Calibration and validation using laser and LED sources yielded a mean absolute error (MAE) of 10.0 nm, a spectral precision of 10 +/- 1 nm FWHM, and a stable wavelength registration drift of +/- 4 nm. Radiometric analysis achieved an average signal-to-noise ratio (SNR) of 20 +/- 3 dB across the visible spectrum. Application to healthy and diseased rice seeds demonstrated clear spectral and polarization-dependent contrasts, with mean degrees of linear polarization (DoLP) of 0.18 +/- 0.05, revealing distinct structural and subsurface variations. The results confirm the system's potential as an adaptable, low-cost optical imaging tool for crop-health assessment. (c) 2026 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

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.

Variability in antimalarial herbal drugs (AMHDs) poses a challenge to quality control and efficacy, especially in low-resource regions where malaria is prevalent. This study employs a non-destructive laser-induced autofluorescence (LIAF) technique combined with chemometrics to assess spectral fingerprint consistency across six (6) AMHD batches. The LIAF spectra reveal distinct Gaussian fluorescence profiles of secondary metabolites with associated specific fluorescence peaks. Results indicate a significant level of uniformity in metabolite composition with 99.46% and 98.67% averaged cosine similarity for intra-batch and inter-batch consistency respectively. This study characterized the spectral signature of batch-to-batch AMHDs, which manufacturers can leverage to prevent inconsistencies in AMHD production. These inconsistencies could potentially lead to counterfeiting and pose direct and indirect threats to public health, clinical care, and socio-economic development.

The objective of the study is to evaluate natural radioactivity and its radiological impact on the health of the populace within Cape Coast North. Soil and water samples were taken and analysed using a high purity germanium (HPGe) detector. Results for the average activity concentrations of 226Ra, 232Th, and 40K in soil samples range from 15.0 to 60.8 Bq/kg with a mean of 20.9 ± 7.2 Bq/kg, 16.3 to 97.2 Bq/kg with a mean of 43.8 ± 2.4 Bq/kg, and 4.7 to 411.4 Bq/kg with an average of 140.6 ± 4.2 Bq/kg, respectively. The absorbed dose rate in air and outdoor annual effective dose to the public were estimated to be 46.6 nGyh-1 and 0.1 mSv, respectively, which fell below the recommended average. The average activity concentrations of 226Ra, 232Th, and 40K in water samples were 1.4, 0.4, and 1.2 Bq/L, respectively. The annual effective dose is 0.4 mSv, which is greater than the WHO recommended level of 0.1 mSv/y.

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.

Exposure of antimalarial herbal drugs (AMHDs) to ultraviolet radiation (UVR) affects the potency and integrity of the AMHDs. Instant classification of the AMHDs exposed to UVR (UVR-AMHDs) from unexposed ones (Non-UVR-AMHDs) would be beneficial for public health safety, especially in warm regions. For the first time, this work combined laser-induced autofluorescence (LIAF) with chemometric techniques to classify UVR-AMHDs from Non-UVR-AMHDs. LIAF spectra data were recorded from 200 ml of each of the UVR-AMHDs and Non-UVR-AMHDs. To extract useful data from the spectra fingerprint, principal components (PCs) analysis was used. The performance of five chemometric algorithms: random forest (RF), neural network (NN), support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbour (KNN), were compared after optimization by validation. The chemometric algorithms showed that KNN, SVM, NN, and RF were superior with a classification accuracy of 100% for UVR-AMHDs while LDA had a classification accuracy of 98.8% after standardization of the spectra data and was used as an input variable for the model. Meanwhile, a classification accuracy of 100% was obtained for KNN, LDA, SVM, and NN when the raw spectra data was used as input except for RF for which a classification accuracy of 99.9% was obtained. Classification accuracy above 99.74 ± 0.26% at 3 PCs in both the training and testing sets were obtained from the chemometric models. The results showed that the LIAF, combined with the chemometric techniques, can be used to classify UVR-AMHDs from Non-UVR-AMHDs for consumer confidence in malaria-prone regions. The technique offers a non-destructive, rapid, and viable tool for identifying UVR-AMHDs in resource-poor countries.

In malaria-prone developing countries the integrity of Anti-Malarial Herbal Drugs (AMHDs) which are easily preferred for treatment can be compromised. Currently, existing techniques for identifying AMHDs are destructive. We report on the use of non-destructive and sensitive technique, Laser-Induced-Autofluorescence (LIAF) in combination with multivariate algorithms for identification of AMHDs. The LIAF spectra were recorded from commercially prepared decoction AMHDs purchased from accredited pharmacy shop in Ghana. Deconvolution of the LIAF spectra revealed secondary metabolites belonging to derivatives of alkaloids and classes of phenolic compounds of the AMHDs. Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were able to discriminate the AMHDs base on their physicochemical properties. Based on two principal components, the PCA- QDA (Quadratic Discriminant Analysis), PCA-LDA (Linear Discriminant Analysis), PCA-SVM (Support Vector Machine) and PCA-KNN (K-Nearest Neighbour) models were developed with an accuracy performance of 99.0, 99.7, 100.0, and 100%, respectively, in identifying AMHDs. PCA-SVM and PCA-KNN provided the best classification and stability performance. The LIAF technique in combination with multivariate techniques may offer a non-destructive and viable tool for AMHDs identification.

The craving for organic cocoa beans has resulted in fraudulent practices such as mislabeling, adulteration, all known as food fraud, prompting the international cocoa market to call for the authenticity of organic cocoa beans before export. In this study, we proposed robust models using laser-induced fluorescence (LIF) and chemometric techniques for rapid classification of cocoa beans as either organic or conventional. The LIF measurements were conducted on cocoa beans harvested from organic and conventional farms. From the results, conventional cocoa beans exhibited a higher fluorescence intensity compared to organic ones. In addition, a general peak wavelength shift was observed when the cocoa beans were excited using a 445 nm laser source. These results highlight distinct characteristics that can be used to differentiate between organic and conventional cocoa beans. Identical compounds were found in the fluorescence spectra of both the organic and conventional ones. With preprocessed fluorescence spectra data and utilizing principal component analysis, classification models such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Network (NN) and Random Forest (RF) models were employed. LDA and NN models yielded 100.0% classification accuracy for both training and validation sets, while 99.0% classification accuracy was achieved in the training and validation sets using SVM and RF models. The results demonstrate that employing a combination of LIF and either LDA or NN can be a reliable and efficient technique to classify authentic cocoa beans as either organic or conventional. This technique can play a vital role in maintaining integrity and preventing fraudulent practices in the cocoa bean supply chain.

This paper presents the Intertropical Convergence Zone (ITCZ) as the possible source mechanism of the medium-scale traveling ionospheric disturbances (MSTIDs) propagating to the southeast direction over the South American region. Using the data collected by the GNSS dual-frequency receivers network from January 2014 to December 2019, detrended TEC maps were generated to identify and characterize 144 MSTIDs propagating southeastward over the South American low-latitude and equatorial region. We also used images from the Geostationary Operational Environmental Satellite (GOES) 13 and 16 in the infrared (IR) and water vapor (WV) channel, and reanalisys data from the National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA) to study the daily features and seasonal migration of ITCZ. In the winter, when ITCZ migrates to the northern hemisphere around 10–15° N, 20 MSTIDs propagated southeastward. During summer, when the ITCZ lies within the continent, around 0–5° S 80 MSTIDs were observed to propagate southeastward; in the equinoxes (spring and fall), 44 MSTIDs were observed. Again, the MSTIDs propagating southeastward showed a clear seasonality of their local time dependence; in summer, the MSTIDs occurred frequently in the evening hours, whereas those in winter occurred during the daytime. We also found for the first time that the day-to-day observation of ITCZ position and MSTIDs propagation directions were consistent. With regard to these new findings, we report that the MSTIDs propagating southeastward over the South American region are possibly induced by the atmospheric gravity waves, which are proposed as being generated by the ITCZ in the troposphere. The mean distribution of the horizontal wavelength, period, and phase velocity are 698 ± 124 km, 38 ± 8 min, and 299 ± 89 m s−1, respectively. For the first time, we were able to use MSTID propagation directions as a proxy to study the source region.

Diagnosing Alzheimer’s Disease (AD) in older people using magnetic resonance imaging (MRI) is quite hard since it requires the extraction of highly discriminative feature representation from similar brain patterns and pixel intensities. However, deep learning techniques possess the capability of extracting relevant representations from data. In this work, we designed a novel spiking deep convolutional neural network-based pipeline to classify AD using MRI scans. We considered three MRI scan groups (patients with AD dementia, Mild Cognitive Impairment (MCI), and healthy controls (NC)). We developed a three-binary classification task (AD vs. NC, AD vs. MCI, and NC vs. MCI) for the AD classification tasks. Specifically, an unsupervised convolutional Spiking Neural Networks (SNN) is pre-trained on the MRI scans. Finally, a supervised deep Convolution Neural Network (CNN) is trained on the output of the SNN for the classification tasks. Experiments are performed using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and promising results are obtained for the AD classification tasks. We present our proposed model results for both the unsupervised spike pre-training technique and the case where the pre-training technique was not considered, thus serving as a baseline. The accuracy of the proposed model with spike pre-training techniques for the three-binary classification are 90.15%, 87.30%, and 83.90%, respectively, and the accuracy of the model without the spike are 86.90%, 83.25%, and 76.70%, respectively, with a noticeable increase in accuracy and thus, reveals the effectiveness of the proposed method. We also evaluated the robustness of our proposed approach by running experiment on six baseline methods using our preprocessed MRI scans. Our model outperformed almost all the comparable methods due to the robust discriminative capability of the SNN in extracting relevant AD features for the AD classification task.

Laser-induced autofluorescence (LIAF), combined with multivariate techniques, has been used to discriminate a cataractous lens from healthy lens tissues. In this study, 405 nm and 445 nm were used as excitation sources to induce the autofluorescence. Results show higher autofluorescence intensity in cataractous lens tissues than in healthy ones. Cataractous lens tissues show a red shift of 0.9 nm and 1.2 nm at 405 nm and 445 nm excitations, respectively. Using principal component analysis (PCA), three principal components (PCs) gave more than 99% variability for both 405 nm and 445 nm excitation sources. Based on the three PCs, Fisher's linear discriminant model was developed. An accuracy of 100% was obtained in classifying the lens tissues using Fisher's linear discriminant analysis (FLDA). The LIAF technique assisted by PCA and FLDA may be used for objective discrimination of cataractous lens from healthy lens tissues of Sprague-Dawley rats.

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.

Deep learning architecture has shown remarkable performance in machine learning and AI applications. However, training a spiking Deep Convolutional Neural Network (DCNN) while incorporating traditional CNN properties remains an open problem for researchers. This paper explores a novel spiking DCNN consisting of a convolutional/pooling layer followed by a fully connected SNN trained in a greedy layer-wise manner. The feature extraction of images is done by the spiking DCNN component of the proposed architecture. And in achieving the feature extraction, we leveraged on the SAILnet to train the original MNIST data. To serve as input to the convolution layer, we process the raw MNIST data with bilateral filter to get the filtered image. The convolution kernel trained in the previous step is used to calculate the filtered image’s feature map, and carry out the maximum pooling operation on the characteristic map. We use BP-STDP to train the fully connected SNN for prediction. To avoid over fitting and to further improve the convergence speed of the network, a dynamic dropout is added when the accuracy of the training sets reaches 97% to prevent co-adaptation of neurons. In addition, the learning rate is automatically adjusted in training, which ensures an effective way to speed up training and slow down the rising speed of the training accuracy at each epoch. Our model is evaluated on the MNIST digit and Cactus3 shape datasets, with the recognition performance on test datasets being 96.16% and 97.92% respectively. The level of performance shows that our model is capable of extracting independent and prominent features in images using spikes.

The Bat Algorithm (BA) is a meta-heuristic algorithm based on echolocation behavior of microbats. The authors propose BA based Spiking Neural Network (SNN) model, where the advantages of BA and efficiency of SNN are exploited for classification tasks using some benchmark datasets. The advantages of the BA have been well exploited in the Artificial Neural Networks (ANN) domain particularly with the adjustment of weights. We therefore, leveraged on the BA as a learning strategy to train an SNN using the Leaky Integrate and Fire (LIF) and Izhikevich models to solve non-linear pattern classification tasks. In order to successfully discriminate between the various classes, the models are trained to fire at the same or similar firing rate for inputs from the same class, and inputs patterns from different classes to also spike or fire at different rate. To justify how efficient and how powerful the proposed model is, only one neuron is used. Finally, the model is tested on different non-linear pattern recognition tasks and comparison is made between our model and other similar existing models and our proposed model outperformed some of the state-of-the-art-models. To the best of our knowledge, this is the first work to implement BA in SNN.

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.

In real world, entities may occur several times in a database. These duplicates may have varying keys and/or include errors that make deduplication a difficult task. Deduplication cannot be solved accurately using either machine-based or crowdsourcing techniques only. Crowdsourcing were used to resolve the shortcomings of machine-based approaches. Compared to machines, the crowd provided relatively accurate results, but with a slow execution time and very expensive too. A hybrid technique for data deduplication using a Euclidean distance and a chromatic correlation clustering algorithm was presented. The technique aimed at: reducing the crowdsourcing cost, reducing the time the crowd use in deduplication and finally providing higher accuracy in data deduplication. In the experiments, the proposed algorithm was compared with some existing techniques and outperformed some, offering an utmost deduplication accuracy efficiency and also incurring low crowdsourcing cost.

In this paper, we present an extension on a hybrid-based deduplication technique in entity reconciliation (ER), by proposing an algorithm that builds clusters upon receiving a pre-specified K number of clusters, and second developing a crowd-based procedure for refining the results of the clusters produced after the clustering generation phases. With the clusters refined, we aim to minimize the cost metric Λ'(R) of the solitary and compound cluster generation algorithms, to achieve an improved and efficient deduplication method, to have an increase in accuracy in identifying duplicate records, and finally, further reduce the crowdsourcing overheads incurred. In this paper, in the experiments, we made use of three datasets commonly known to hybrid-based deduplication such as paper, product, and restaurant. The performance results and evaluations demonstrate clear superiority to the methods compared with our work offering low-crowdsourcing cost and high accuracy of deduplication, as well as better deduplication efficiency due to the clusters being refined.

In real world, databases often have several records representing the same entity and these duplicates have no common key, thus making deduplication difficult. Machine-based and crowdsourcing techniques were disjointly used in improving quality in data deduplication. Crowdsourcing were used for solving tasks that the machine-based algorithms were not good at. Though, the crowds, compared with machines, provided relatively more accurate results, both platforms were slow in execution and hence expensive to implement. In this paper, a hybrid human-machine system was proposed where machines were firstly used on the data set before the humans were further used to identify potential duplicates. We performed experiments using three benchmark datasets; paper, restaurant and product datasets. Our algorithm was compared with some existing techniques and our approach outperformed some methods by achieving a high accuracy of deduplication and good deduplication efficiency while incurring low crowdsourcing costs.

Entity resolution (ER) classifies records that refer to the same real-world entity and is fundamental to data cleaning. Identifying approximate but not exact duplicates in database records is a vital task. These duplicates may refer to the same real-world entity due to; 1. data entry errors, 2. differences in the detailed schemas of records from multiple databases, 3. unstandardized abbreviations, among several reasons. Machine-based techniques have been improving in quality, but still are a long way from being perfect. On the other hand, crowdsourcing platforms are widely accepted as a means for resolving tasks that machine-based approaches are not good at. Though they also offer relatively more accurate results, they are expensive and are slow in bringing human perception into the process. In this paper, we present a machine-based technique for data deduplication which adopts a novel clustering algorithm under a crowd-based environment. We propose a non-trivial technique; 1. To reduce the time the crowd (humans) use in performing deduplication. 2. To reduce the crowdsourcing overheads and 3. For higher accuracy in data deduplication. Comparing with some existing human-machine based approaches; the experiments proved that our proposed approach outperforms some methods by incurring low crowdsourcing cost and offering a high accuracy of deduplication as well as deduplication efficiency.

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.