DR. ANGEYO HUDSON KALAMBUKA

Nuclear forensics (NF) is an analytical methodology that involves analysis of intercepted nuclear and radiological materials (NRM) so as to establish their nuclear attribution. The critical challenge in NF currently is the lack of suitable microanalytical methodologies for direct, rapid, minimally invasive detection and quantification of NF signatures. Laser-induced breakdown spectroscopy (LIBS) has the potential to overcome these limitations with the aid of machine-learning (ML) techniques. In this paper, we report the development of ML-enabled LIBS methodology for rapid NF analysis and attribution in support of nuclear security. The atomic uranium lines at 385.464 nm, 385.957 nm, and 386.592 nm were identified as NF signatures of uranium for rapid qualitative detection of trace uranium concealed in organic binders and uranium-bearing mineral ores. The limit of detection of uranium using LIBS was determined to be 34 ppm. A multivariate calibration strategy for the quantification of trace uranium in cellulose and uranium-bearing mineral ores was developed using an artificial neural network (ANN, a feed forward back-propagation algorithm) and spectral feature selection: (1) uranium lines (348 nm to 455 nm), (2) uranium lines (380 nm to 388 nm), and (3) subtle uranium peaks (UV range). The model utilizing category 2 was able to predict the 48 ppm of uranium with a relative error prediction (REP) of 10%. The calibration model utilizing subtle uranium peaks, that is, category 3, could predict uranium in the pellets prepared from certified reference material (CRM) IAEA-RGU-1, with an REP of 6%. This demonstrates the power of ANN to model noisy LIBS spectra for trace quantitative analysis. The calibration model we developed predicted uranium concentrations in the uranium-bearing mineral ores in the range of 54–677 ppm. Principal component analysis (PCA) was performed on the LIBS spectra (200–980 nm) utilizing feature selection of the uranium-bearing samples collected from different regions of Kenya clustered into groups related to their geographic origins. The PCA loading spectrum revealed that the groupings of these samples were mainly due to rare earth elements, namely, cerium, dysprosium, praseodymium, promethium, neodymium, and samarium. ML-enabled LIBS therefore has utility in field NF analysis and attribution of uranium in NRM under concealed conditions.

Direct diagnosis and characterization of cancer in tissue via trace biometals analyzed by energy dispersive X-ray fluorescence (EDXRF) spectrometry is challenging, as it requires sensitive detection and accurate quantitative analysis of the appropriate cancer biomarkers. The EDXRF spectrometry technique is not directly applicable due to the complexity of the tissue biopsy samples that are of limited size and irregular geometry, enhanced scatter from the sample dark matrix and extreme matrix effects as well as spectral overlaps and prominent Bremsstrahlung that masks the subtle biomarker analyte peaks. We report on the direct determination of biometals namely Cu, Mn, Fe, Zn; Mg, Co and Na and associated speciation (for Cu, Mn, Fe) in soft body tissue in the context of disease diagnostics utilizing a robust chemometrics enabled energy dispersive X-ray fluorescence and scattering (EDXRFS) spectrometric method. The EDXRFS method exploits, in addition to multiple fluorescence spectral signatures, scatter profiles associated with the trace metals and dark matrix to determine through hybridized multivariate chemometrics calibration models, the biometals in thin (10 μm) tissue sections. Wavelet transform (WT), principal component analysis (PCA) and independent component analysis (ICA) were used for spectral preprocessing towards model optimization using con-jointly artificial neural network (ANN) and partial least squares (PLS) based on paraffin wax ‘standards’ spiked with the cancer biomarker trace metals. Results obtained from applying oyster tissue standard validated models (to ≤6% accuracy) to dog tissues (used here as human body tissue analogues) show that both prostate and mammary malignant tissues have significantly high concentration of Zn i.e. 301 ± 4 μg/g and 301 ± 4 μg/g respectively when compared to benign tissues i.e. 160 ± 3 μg/g and 171 ± 10 μg/g. The same is the case for Fe and Cu. The concentrations of Fe, Zn, Cu and Mg in malignant (mammary) as compared to benign tissues occur in the ratios 3:1, 2:1, 3:1 and 2:1. On the other hand, for prostate malignant compared to benign tumor the corresponding ratios are 5:2, 2:1, 2:1 and 2:1 respectively. Prostate cancer was found to be characterized by strong positive correlation between Cu and Mg (0.999) and Mn and Fe (0.999) while mammary cancer is characterized by strong negative correlations between Cu and Mg (−0.994), Mn and Fe (−0.974). ICA and PCA were further used to successfully discriminate the dog tissue to 97% accuracy as either cancerous or non-cancerous based on validated pattern recognition PCA-ICA models for the determination of speciation of Cu, Fe and Mn in soft body tissue. For both mammary and prostate cancer malignancy was characterized by higher speciation of Cu, Fe and Mn (i.e. Cu2+, Fe3+, and Mn7+) compared to the benign. The results of this study demonstrate that robust chemometrics enabled EDXRFS spectrometry not only determine directly and rapidly but also accurately in a diagnostics manner cancer biomarker trace metals in soft body tissue. The technique has an additional advantage in that it has inbuilt multivariate capability to model the determined levels, their ratios and correlations as well as alterations in the speciation of the biometals to detect and characterize cancer (according to severity) as well discriminate among different types of cancer in the same tissue in a simple methodology that has potential for clinical applications.

Direct rapid energy dispersive X-ray fluorescence and scattering (EDXRFS) analysis of trace biometals in soft body tissues is important because it has an immense potential for biomedical applications. Unfortunately this is challenging because soft body tissues are characterized by dark matrix problems, weak analyte fluorescence, scattering, poor signal-to-noise ratio (SNR) of the analyte and spectral overlaps due to the properties of the detector and detection process. We report on hybridized utility of robust chemometrics approach for spectral preprocessing towards improving the quality of spectra towards quantitative analysis of trace biometals in soft body tissue. The study was based on (5–20 µm thick) paraffin wax model ‘standards’ spiked with biometals Fe, Cu, Mn, Zn, Co, Na and Mg. Wavelet transform (WT) and principal component analysis (PCA) were used conjointly for de-noising and mathematical enhancement of resolution. There was improved SNR of spectra by a factor of 3 compared to use of WT alone. The preprocessed spectra were used as input to artificial neural network (ANN) and partial least squares (PLS) models for developing multivariate calibration strategies for quantitative analysis. Both models predicted the concentrations of the biometals better than when raw spectra were utilized (R2 ~ 0.892–0.954 before, and ~ 0.990–0.998 after preprocessing for ANNs; and R2 ~ 0.876–0.931 before, and ~ 0.977–0.992 after preprocessing for PLS). There was also improvement in prediction of Na and Mg in model tissue when both fluorescence and scatter were utilized conjointly (EDXRFS) i.e. R2 = 0.970 for fluorescence alone and R2 = 0.998 for both fluorescence and scatter for Na; and R2 = 0.934 for fluorescence alone and R2 = 0.993 for both fluorescence and scatter for Mg for ANN model. The accuracy of the calibration model was tested using Oyster tissue (NIST 1566b). The results of all analyzed elements were in agreement with certified values to ≤ 6%. This shows proof-of-concept for use of hybridized robust chemometrics approaches for direct rapid determination of trace biometals in soft tissue utilizing EDXRFS spectrometry; an approach that has potential for biomedical applications of EDXRF.

Nuclear forensics (NF) is an analytical methodology that involves analysis of intercepted nuclear and radiological materials (NRM) so as to establish their nuclear attribution. The critical challenge in NF currently is the lack of suitable microanalytical methodologies for direct, rapid, minimally invasive detection and quantification of NF signatures. Laser-induced breakdown spectroscopy (LIBS) has the potential to overcome these limitations with the aid of machine-learning (ML) techniques. In this paper, we report the development of ML-enabled LIBS methodology for rapid NF analysis and attribution in support of nuclear security. The atomic uranium lines at 385.464 nm, 385.957 nm, and 386.592 nm were identified as NF signatures of uranium for rapid qualitative detection of trace uranium concealed in organic binders and uranium-bearing mineral ores. The limit of detection of uranium using LIBS was determined to be 34 ppm. A multivariate calibration strategy for the quantification of trace uranium in cellulose and uranium-bearing mineral ores was developed using an artificial neural network (ANN, a feed forward back-propagation algorithm) and spectral feature selection: (1) uranium lines (348 nm to 455 nm), (2) uranium lines (380 nm to 388 nm), and (3) subtle uranium peaks (UV range). The model utilizing category 2 was able to predict the 48 ppm of uranium with a relative error prediction (REP) of 10%. The calibration model utilizing subtle uranium peaks, that is, category 3, could predict uranium in the pellets prepared from certified reference material (CRM) IAEA-RGU-1, with an REP of 6%. This demonstrates the power of ANN to model noisy LIBS spectra for trace quantitative analysis. The calibration model we developed predicted uranium concentrations in the uranium-bearing mineral ores in the range of 54–677 ppm. Principal component analysis (PCA) was performed on the LIBS spectra (200–980 nm) utilizing feature selection of the uranium-bearing samples collected from different regions of Kenya clustered into groups related to their geographic origins. The PCA loading spectrum revealed that the groupings of these samples were mainly due to rare earth elements, namely, cerium, dysprosium, praseodymium, promethium, neodymium, and samarium. ML-enabled LIBS therefore has utility in field NF analysis and attribution of uranium in NRM under concealed conditions.

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This paper presents the radiometric survey results of the Mrima-Kiruku high
background radiation (HBR) anomaly complex of south coastal Kenya. Utilizing a portable γ-
ray spectrometer consisting of a 2.0 l NaI (Tl) backpack detector integrated with GPS to
perform the relevant in-situ radiometric measurements, a novel geospatial gating method
was devised to represent the measurements. The goal of this study was to assess radiation
exposure and associated natural radioactivity levels in the complex and to compare the

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This work considered the environmental impact of artisanal mining gold activity in the Migori–Transmara area (Kenya). From artisanal gold mining, mercury is released to the environment, thus contributing to degradation of soil and water bodies. High mercury contents have been quantified in soil (140 μg kg−1), sediment (430 μg kg−1) and tailings (8,900 μg kg−1), as expected. The results reveal that the mechanism for transporting mercury to the terrestrial ecosystem is associated with wet and dry depositions. Lichens and mosses, used as bioindicators of pollution, are related to the proximity to mining areas. The further the distance from mining areas, the lower the mercury levels. This study also provides risk maps to evaluate potential negative repercussions. We conclude that the Migori–Transmara region can be considered a strongly polluted area with high mercury contents. The technology used to extract gold throughout amalgamation processes causes a high degree of mercury pollution around this gold mining area. Thus, alternative gold extraction methods should be considered to reduce mercury levels that can be released to the environment.

Carbonatites and alkaline intrusive complexes are rich in a variety of mineral deposits such as rare earth elements (REEs), including Nb, Zr and Mn. These are often associated with U and Th bearing minerals, including monazite, samarskite and pyrochlore. Mining waste resulting from mineral processing activities can be highly radioactive and therefore poses a risk to human health and environment. The Jombo complex located in Kenya's south coastal region is potentially one of the richest sources of Nb and REEs in the world. It consists of the main intrusion at Jombo hill, three associated satellite intrusions at Mrima, Kiruku and Nguluku hills, and several dykes. The complex is highly heterogeneous with regard to its geological formation as it is characterized by alkaline igneous rocks and carbonatites which also influence its radio-ecological dynamics. In-situ gamma spectrometry offers a low-cost, rapid and spatially representative radioactivity estimate across a range of landscapes compared to conventional radiometric techniques. In this work, a wide ranging radiological survey was conducted in the Jombo complex as follow up on previous studies[1,2], to determine radiation exposure levels and source distributions, and perform radiological risk assessments. The in-situ measurements were carried out using a 2.0 l NaI(Tl) PGIS-2 portable detector from Pico Envirotec Inc integrated with GPS, deployed for ground (back-pack) and vehicular gamma-ray spectrometry. Preliminary results of radiological distribution and mapping will be presented. [1] Patel, J. P. (1991). Discovery and Innovation, 3(3): 31-35. [2] Kebwaro, J. M. et. al. (2011). J. Phys. Sci., 6(13): 3105-3110.

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There is, especially in precision agriculture, an increasing demand world over for affordable sensors for in situ (field deployable) soil quality assessment (SQA) applicable at an ecological scale due to the interplay between soil quality and environmental degradation. Although spectrometric (particularly optical) techniques offer the opportunity to meet this demand due to their high analytical versatility, their utility in rapid SQA is limited by the complexity of the soil matrix, and the interpretation of the resulting spectra and (usually) multivariate quality assurance (i.e. SQA) data. In this paper, we examine the utility of spectrometric techniques for soil analysis and critique their applicability to rapid SQA; in particular, we appraise their potential for development towards intelligent portable SQA systems for in situ application. We then evaluate in this perspective the applicability of a new method we have recently developed namely chemometrics energy dispersive X-ray fluorescence and scattering spectrometry (EDXRFS) for SQA, emphasizing its potential for realizing rapid intelligent sensor architecture for in situ SQA. We conclude that a point of care soil sensor that infers soil properties, and intelligently modulates precision agriculture may be realized by integrating the EDXRFS spectroscopy method to a portable XRF spectrometer.

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We exploited multivariate chemometric methods to reduce the spectral complexity and to retrieve trace heavy metal analyte concentration signatures directly from the LIBS spectra as well as, to extract their latent characteristics in two important environmental samples i.e. soils and rocks from a geothermal field lying in a high background radiation area (HBRA). As, Cr, Cu, Pb and Ti were modeled for direct trace (quantitative) analysis using partial least squares (PLS) and artificial neural networks (ANNs). PLS performed better in soils than in rocks; the use of ANN improved the accuracies in rocks because ANNs are more robust than PLS at modeling spectral non-linearities and correcting matrix effects. The predicted trace metal profiles together with atomic and molecular signatures acquired using single ablation in the 200–545 nm spectral range were utilized to successfully classify and identify the soils and rocks with regard to whether they were derived from (i) a high background radiation area (HBRA)-geothermal, (ii) HBRA-non-geothermal or (iii) normal background radiation area (NBRA)-geothermal field using principal components analysis (PCA) and soft independent modeling of class analogy (SIMCA).

The greatest challenge to material characterization by XRF technique is encountered in direct trace analysis of complex matrices. We exploited partial least squares (PLS) in conjunction with energy dispersive X-ray fluorescence and scattering (EDXRFS) spectrometry to rapidly (200 s) analyze lubricating oils. The PLS–EDXRFS method affords non-invasive quality assurance (QA) analysis of complex matrix liquids as it gave optimistic results for both heavy- and low-Z metal additives. Scatter peaks may further be used for QA characterization via the light elements.

A preliminary study of microbiological quality of honey was carried out using 26 samples obtained from the National Bee Keeping Research Station. Total viable counts (TVC) of aerobic bacteria, yeasts and moulds, and Clostridium species were done. Of the 26 samples, 24 (92.3%) had a TVC ranging from 3 x10 -87 x 10 colony forming units (cfu) per gram of honey. Two samples did not yield any microorganisms. Of the 24 positive samples, 9 (37.5%) were found to contain Clostridium species per gram while eight (33.3%) were positive for moulds with counts ranging from 10-100 c.f.u / g. No yeasts were detected. In addition, three samples yielded the three types of microorganisms.

A preliminary study of microbiological quality of honey was carried out using 26 samples obtained from the National Bee Keeping Research Station. Total viable counts (TVC) of aerobic bacteria, yeasts and moulds, and Clostridium species were done. Of the 26 samples, 24 (92.3%) had a TVC ranging from 3 x10 -87 x 10 colony forming units (cfu) per gram of honey. Two samples did not yield any microorganisms. Of the 24 positive samples, 9 (37.5%) were found to contain Clostridium species per gram while eight (33.3%) were positive for moulds with counts ranging from 10-100 c.f.u / g. No yeasts were detected. In addition, three samples yielded the three types of microorganisms.

A preliminary study of microbiological quality of honey was carried out using 26 samples obtained from the National Bee Keeping Research Station. Total viable counts (TVC) of aerobic bacteria, yeasts and moulds, and Clostridium species were done. Of the 26 samples, 24 (92.3%) had a TVC ranging from 3 x10 -87 x 10 colony forming units (cfu) per gram of honey. Two samples did not yield any microorganisms. Of the 24 positive samples, 9 (37.5%) were found to contain Clostridium species per gram while eight (33.3%) were positive for moulds with counts ranging from 10-100 c.f.u / g. No yeasts were detected. In addition, three samples yielded the three types of microorganisms.

A preliminary study of microbiological quality of honey was carried out using 26 samples obtained from the National Bee Keeping Research Station. Total viable counts (TVC) of aerobic bacteria, yeasts and moulds, and Clostridium species were done. Of the 26 samples, 24 (92.3%) had a TVC ranging from 3 x10 -87 x 10 colony forming units (cfu) per gram of honey. Two samples did not yield any microorganisms. Of the 24 positive samples, 9 (37.5%) were found to contain Clostridium species per gram while eight (33.3%) were positive for moulds with counts ranging from 10-100 c.f.u / g. No yeasts were detected. In addition, three samples yielded the three types of microorganisms.

A preliminary study of microbiological quality of honey was carried out using 26 samples obtained from the National Bee Keeping Research Station. Total viable counts (TVC) of aerobic bacteria, yeasts and moulds, and Clostridium species were done. Of the 26 samples, 24 (92.3%) had a TVC ranging from 3 x10 -87 x 10 colony forming units (cfu) per gram of honey. Two samples did not yield any microorganisms. Of the 24 positive samples, 9 (37.5%) were found to contain Clostridium species per gram while eight (33.3%) were positive for moulds with counts ranging from 10-100 c.f.u / g. No yeasts were detected. In addition, three samples yielded the three types of microorganisms.

A preliminary study of microbiological quality of honey was carried out using 26 samples obtained from the National Bee Keeping Research Station. Total viable counts (TVC) of aerobic bacteria, yeasts and moulds, and Clostridium species were done. Of the 26 samples, 24 (92.3%) had a TVC ranging from 3 x10 -87 x 10 colony forming units (cfu) per gram of honey. Two samples did not yield any microorganisms. Of the 24 positive samples, 9 (37.5%) were found to contain Clostridium species per gram while eight (33.3%) were positive for moulds with counts ranging from 10-100 c.f.u / g. No yeasts were detected. In addition, three samples yielded the three types of microorganisms.

A preliminary study of microbiological quality of honey was carried out using 26 samples obtained from the National Bee Keeping Research Station. Total viable counts (TVC) of aerobic bacteria, yeasts and moulds, and Clostridium species were done. Of the 26 samples, 24 (92.3%) had a TVC ranging from 3 x10 -87 x 10 colony forming units (cfu) per gram of honey. Two samples did not yield any microorganisms. Of the 24 positive samples, 9 (37.5%) were found to contain Clostridium species per gram while eight (33.3%) were positive for moulds with counts ranging from 10-100 c.f.u / g. No yeasts were detected. In addition, three samples yielded the three types of microorganisms.

A preliminary study of microbiological quality of honey was carried out using 26 samples obtained from the National Bee Keeping Research Station. Total viable counts (TVC) of aerobic bacteria, yeasts and moulds, and Clostridium species were done. Of the 26 samples, 24 (92.3%) had a TVC ranging from 3 x10 -87 x 10 colony forming units (cfu) per gram of honey. Two samples did not yield any microorganisms. Of the 24 positive samples, 9 (37.5%) were found to contain Clostridium species per gram while eight (33.3%) were positive for moulds with counts ranging from 10-100 c.f.u / g. No yeasts were detected. In addition, three samples yielded the three types of microorganisms.