Determination Of Moisture Content Research Articles

Thermochemical Conversion of Biomass into 2nd Generation Biofuel

Bioenergy is considered the largest contributor to the renewable and sustainable energy sector worldwide, playing a significant role in various energy sectors such as heating, electricity supply, and even in replacing fossil fuels in the transportation sector. As part of renewable, low-carbon energy systems, bioenergy can also ensure atmospheric carbon sequestration, provide numerous environmental and socio-economic benefits, and thus contribute to achieving global climate change goals, as well as broader environmental, social, economic, and sustainable development objectives. The use of bioenergy can significantly reduce our carbon footprint and thus contribute to improving the environment. While bioenergy conversion of biomass produces some amount of carbon dioxide, similar to traditional fossil fuels, its impact can be minimized by replacing forest biomass with fast-growing trees and energy crops. Therefore, fast-growing trees and energy crops are the primary raw materials for bioenergy. The results of the research in this publication confirm the high efficiency of biomass depolymerization through thermochemical conversion. The principle of continuous biomass conversion was used at a process temperature of 520 °C. The experiments were carried out in the Biomass Gasification Laboratory at the AgroBioTech Research Center of the Slovak University of Agriculture in Nitra. The biomass used for the experiments was from energy-producing fast-growing willows, specifically the varieties Sven, Inger, and Express. The aim was to determine the amount of biochar produced from each of these tree species and subsequently to investigate its potential use for energy purposes. During the experiments, 0.106 kg of biochar was produced from 1 kg of Inger willow biomass, 0.252 kg from 1 kg of Express willow biomass, and 0.256 kg from 1 kg of Sven willow biomass. A subsequent goal was to determine the production of gas, which can also be used for energy purposes. The biofuel samples obtained were subsequently subjected to thermogravimetric analysis to determine moisture content, volatile matter, and ash content. The ash content in dry matter ranged from 6% to 7.28%, while the volatile matter in dry matter was between 92.72% and 94%. The moisture content in the samples ranged from 1.7% to 2.43%. These results may contribute to innovative insights into biomass depolymerization and help define optimized parameters for thermochemical conversion, as well as the required biomass composition, with the goal of generating second-generation biofuels in the most cost-effective way.

Moisture content determination along production line of ibuprofen soft gelatin capsule manufacturing by near infrared spectroscopy and ensemble deep neural networks

AbstractIn the manufacturing process of ibuprofen soft gelatin capsules, controlling moisture content at each production line stage, including in their components—gelatin, fill content, and shell—is vital to ensure quality and stability. This study developed and assessed an analytical method for rapid and non‐destructive moisture determination using near‐infrared spectroscopy (NIRS) coupled with deep neural networks (DNN) on all stages of the ibuprofen production line. The NIRS‐DNN classifier models were able to distinguish between the three components, achieving accuracy scores of up to 99%. The DNN models for moisture quantification also demonstrated high accuracy, with R2 values exceeding 0.997 across all production stages and prediction errors to those of previously reported models. A significant advantage of the NIRS‐DNN approach was its ability to maintain accuracy over a wide moisture concentration range, from 7% to 48%. The ensemble model, NIRS‐EDNN, seamlessly integrated classification and quantification, revealing its potential for real‐time process control in soft gel manufacturing. The comprehensive sampling approach ensured a diverse representation of moisture content, thereby enhancing the understanding of its impact on the final product stability, demonstrating that this methodology is potentially applicable to any soft gelatin capsule tracking worldwide.

Metrological traceability of moisture/water content measurements

This paper explains advantages and disadvantages of the measurement methods for moisture and water content determinations in plant-based materials, in order to identify the method providing the best metrological features.The term “moisture” is generic and it does not identify a specific measurand. In this sense, to declare proper Calibration and Measurement Capabilities (CMCs) and to develop Certified Reference Materials (CRMs), a better specification of the measurand should be given. Currently, no CMCs for moisture or water content measurements in the plant-origin bulk materials, as well as respective CRMs, are available in the Key Comparison Database (KCDB) published on the website of the Bureau International del Poids et Mesures (BIPM). Undoubtedly, those CMCs and CRMs, characterised for water content, are crucial and essential to provide metrological traceability of measurement results for the quality assessment of plant-origin bulk materials in the agricultural sector.

Comparative Analysis of XGB, CNN, and ResNet Models for Predicting Moisture Content in Porphyra yezoensis Using Near-Infrared Spectroscopy.

This study explored the performance and reliability of three predictive models-extreme gradient boosting (XGB), convolutional neural network (CNN), and residual neural network (ResNet)-for determining the moisture content in Porphyra yezoensis using near-infrared (NIR) spectroscopy. We meticulously selected 380 samples from various sources to ensure a comprehensive dataset, which was then divided into training (300 samples) and test sets (80 samples). The models were evaluated based on prediction accuracy and stability, employing genetic algorithms (GA) and partial least squares (PLS) for wavelength selection to enhance the interpretability of feature extraction outcomes. The results demonstrated that the XGB model excelled with a determination coefficient (R2) of 0.979, a root mean square error of prediction (RMSEP) of 0.004, and a high ratio of performance to deviation (RPD) of 4.849, outperforming both CNN and ResNet models. A Gaussian process regression (GPR) was employed for uncertainty assessment, reinforcing the reliability of our models. Considering the XGB model's high accuracy and stability, its implementation in industrial settings for quality assurance is recommended, particularly in the food industry where rapid and non-destructive moisture content analysis is essential. This approach facilitates a more efficient process for determining moisture content, thereby enhancing product quality and safety.

MoistNet: Machine vision-based deep learning models for wood chip moisture content measurement

Quick and reliable measurement of wood chip moisture content is an everlasting problem for numerous forest-reliant industries such as biofuel, pulp and paper, and bio-refineries. Moisture content is a critical attribute of wood chips due to its direct relationship with the final product quality. Conventional techniques for determining moisture content, such as oven-drying, possess some drawbacks in terms of their time-consuming nature, potential sample damage, and lack of real-time feasibility. Furthermore, alternative techniques, including NIR spectroscopy, electrical capacitance, X-rays, and microwaves, have demonstrated potential; nevertheless, they are still constrained by issues related to portability, precision, and the expense of the required equipment. Hence, there is a need for a moisture content determination method that is instant, portable, non-destructive, inexpensive, and precise. This study explores the use of deep learning and machine vision to predict moisture content classes from RGB images of wood chips. A large-scale image dataset comprising 1,600 RGB images of wood chips has been collected and annotated with ground truth labels, utilizing the results of the oven-drying technique. Two high-performing neural networks, MoistNetLite and MoistNetMax, have been developed leveraging Neural Architecture Search (NAS) and hyperparameter optimization. The developed models are evaluated and compared with state-of-the-art deep learning models. Results demonstrate that MoistNetLite achieves 87% accuracy with minimal computational overhead, while MoistNetMax exhibits exceptional precision with a 91% accuracy in wood chip moisture content class prediction. With improved accuracy (9.6% improvement in accuracy by MoistNetMax compared to the best baseline model ResNet152V2) and faster prediction speed (MoistNetLite being twice as fast as MobileNet), our proposed MoistNet models hold great promise for the wood chip processing industry to be efficiently deployed on portable devices, such as smartphones.

Integration of dual band radio waves and ensemble-based approach for rice moisture content determination and localisation

Maintaining optimal moisture content in grain storage is critical to ensuring adequate supply throughout the year, but it presents a significant challenge. Current moisture measurement methods often necessitate sophisticated and costly equipment. This paper introduces an approach employing real-time rice moisture content determination and detection of spoilage (specifically wet spots) within a storage facility achieved through the utilisation of radio waves operating at 2.4 GHz and 868 MHz, along with an ensemble-based machine learning algorithm. Experimental samples spanning from 12% to 30% moisture levels were collected, then subjected to pre-processing, and subsequently employed to train the Ensemble-based Rice Moisture Content and Localisation (eRMCL) algorithm. The eRMCL produced an effective prediction of both rice moisture content and the localisation of wet spots within the grain storage unit. The results show that compared to support vector machine, random forest, and machine learning methods, the eRMCL algorithm had the best performance metrics, with an accuracy of 94.8% in predicting the moisture content and location of spoilage in storage. The measurement of moisture content and the identification of wet spots in rice storage using the dual frequency wave approach were found to be more accurate than with a single frequency band. Thus, the dual frequency band is a novel method for the determination of the moisture content of stored rice and the localisation of the spoilage area.

Prediction model of moisture content in spray drying of ceramic slurry based on IMFO-BPNN

The final particle moisture content of ceramic slurry spray drying affects the processing, product quality, and process energy consumption. While the real-time moisture content determination is very difficult for spray drying process due to nonlinear, severe lag, interference factors, and complexity of the systems, the high temperature, high humidity drying environment and short drying process. In current work, an improved moth optimization (IMFO) algorithm combined with backpropagation neural network (BPNN) was proposed to predict the moisture content of ceramic slurry particles during spray drying. The performance of the model is trained and tested. Results demonstrate that IMFO has better convergence ability and speed compared to other algorithms such as particle swarm optimization (PSO) and gravitational search algorithm (GSA). The IMFO-BPNN model achieves a MAE of 0.0293, RMSE of 0.0383, and R2 value of 0.9113, outperforming the prediction performance of BPNN and MFO-BPNN models. The absolute error rate of the IMFO-BPNN model (0–5%) is lower compared to BPNN (5–10%) and MFO-BPNN model (>10%), showcasing superior accuracy in predicting moisture content. This mathematical model established in the study provides an efficient, accurate, and nondestructive method for predicting the drying endpoint of ceramic slurry spray drying.

Moisture penetration and distribution characterization of hard coal: a µ-CT study

Moisture content of rock/coal can change its mechanical properties and absorption capacities, which can directly affect gas diffusivity, change the stress distribution and hence cause significant impacts on the overall gas or coal extraction process. Observation of the water penetration process and water distribution in the coal matrix will be beneficial for the understanding of the fluid-solid coupling mechanism in hydraulic fracturing, aquifer cracking and coal seam infusion. However, the observation of water penetration process and the determination of water distribution mode were hard to be non-destructively achieved as coal is a non-uniform, inhomogeneous and un-transparent material. µ-CT imaging, which is based on variation of X-ray attenuation related to the density and atomic composition of the scanned objects, enables a four-dimensional (spatial-temporal) visualise of the heterogeneous and anisotropic coal samples. The primary aim of this paper is extending the application of µ-CT imaging to explore the moisture penetration and distribution within coal samples during water infusion process, which has been reported by very little literature. The working principle and procedures of CT imaging was firstly introduced. Then, the determination equation of moisture distribution based on density profile was established. The CT determined moisture content has been compared with weighting method for verification. The paper has demonstrated that µ-CT can be used for non-destructively imaging the moisture distribution within coal samples.

Moisture patterns and fluxes in evolving tafoni developed in arkosic sandstone in temperate climate

AbstractCavernous weathering forms have long been studied and discussed as enigmas in geomorphology. Recently, their evolution has been shown to be controlled by moisture patterns, which are still poorly understood. For the first time, capillary water and vapor fluxes were characterized in detail at tafone in a temperate climate of central Europe using a wide range of methods adapted from soil hydrology. Time domain reflectometry showed that moisture flows from the rock interior to the evaporation front in the shallow subsurface of both — the backwalls and the outer surface. When overland flow occurs on the outer surfaces (after heavy rains), 10 mm/day can infiltrate and flow toward the backwalls. The main sources of water for tafone are the influx of water from the rock interior and the infiltration of overland flow after heavy rains, while condensation of air humidity is a minor source. Influx from the rock interior is coupled to the evaporation rate, which varies between 100 and 300 kg/m2/year in summer and less than 15 kg/m2/year in winter. More water evaporates from the backwall of the tafone than from the outer surface, and more salt is deposited in the backwalls, resulting in predominant salt weathering in the backwalls. The tafoni studied thus evolve, and the cavities deepen. Tafoni in arid and semi‐arid environments generally show a much higher contrast between evaporation rates from backwalls and outer surfaces than tafoni and honeycombs in temperate and coastal environments. Tafoni in temperate settings are therefore more susceptible to degradation when evaporation decreases or inflow to the tafone increases. This study also shows that microtensiometers can be used to determine moisture content with high spatial resolution, while time domain reflectometry allows accurate characterization of moisture patterns with depth.

Determining Moisture Content of Laminated Veneer Lumber (LVL)

Wooden load-bearing structures are becoming more common as an eco-friendly alternative to steel and concrete in large buildings. In these buildings, laminated veneer lumber (LVL) is increasingly used in structural building elements, particularly in the flanges of wooden I-beams. However, as for all products made from wood, proper moisture control is important to ensure the long-term integrity of the elements. The purpose of this study is to investigate the moisture properties of LVL and the correlation between moisture sensor readings and the actual moisture content determined from accurate weighing of the samples. Laboratory measurements were made of two different wooden materials using 20 identical sensors. The test was conducted on samples of LVL flanges delivered by the Norwegian wood production company Hunton, and on samples of pine lumber. The moisture sensors were delivered by Omnisense. For the LVL samples, the test results show that the resistance values given by the resistance method were too high compared to the more accurate gravimetric method. Conversely, the measured values were too low for the pine samples. LVL also had a faster moisture sorption than pine under the same moisture conditions. The glue between the veneer layers affects the electric conductivity of the wood in LVL and interrupts the readings. The glue might also affect the moisture sorption.

A pin-based probe for electronic moisture meters to determine moisture content in a single wheat kernel

BackgroundOptimum moisture in straw and grain at maturity is important for timely harvesting of wheat. Grain harvested at the right time has reduced chance of being affected by adverse weather conditions which is important to maintain grain quality and end use functionality. Wheat varieties with a short dry down period could help in timely harvest of the crop. However, measuring single kernel moisture in wheat and other small grain crops is a phenotyping bottleneck which requires characterising moisture content of the developing kernel at physiological maturity.ResultsHere we report developing a pin-based probe to detect moisture in a developing wheat kernel required for determining physiological maturity. An in-house designed pin-based probe was used with different commercially available electronic moisture meters to assess the moisture content of the individual kernels in spikes with high accuracy (R2 = 0.73 to 0.94, P < 0.001) compared with a reference method of oven drying. The average moisture values varied among different electronic moisture meters and the oven-dry method and differences in values were minimized at low kernel moisture content (< 50%). The single kernel moisture probe was evaluated in the field to predict the physiological maturity in wheat using 38% moisture content as the reference and visible notes on kernel stage.ConclusionThe pin-based moisture probe is a reliable tool for wheat physiologists and breeders to conveniently and accurately measure moisture content in developing grain that will aid in identifying wheat germplasm with fast dry-down characteristics.

Rapid and non-destructive prediction of moisture content on intact gayo coffee beans using near infrared spectroscopy

Abstract Presented work explores the potential of Near-Infrared Spectroscopy (NIRS) as a rapid, non-destructive analytical method for predicting moisture content in intact Gayo coffee beans. The moisture content is an essential parameter affecting the quality, processing, and shelf life of coffee beans. While conventional moisture determination techniques involve invasive procedures and considerable time, NIRS emanates as an innovative technique offering non-invasive, swift analysis. Using a robust representative selection of Gayo coffee bean samples, we acquired numerous spectra and employed multivariate analysis to construct a calibration model correlating the spectral data with reference moisture values. The initial results exhibit a high correlation coefficient (r) of 0.93 and ratio prediction to deviation (RPD) index of 3, suggesting that the NIRS technique can effectively predict the moisture content within the beans. This research underpins the potential usage of NIRS which, if validated through further testing, could transform quality control operations in the coffee industry by offering a real-time, non-destructive approach for moisture content determination.

Exploring the circular prospects of cashew nutshells and avocado seeds as potential adsorbents for onboard natural gas storage

Natural gas (NG), known for its eco-friendly energy attributes, faces challenges such as low volumetric density, leakage susceptibility, and explosion risks during storage and transport. To narrow this gap, this study reports on the potential of bio-based adsorbents from cashew nut shells (CNS) and avocado seeds (AVS) as an alternative approach for the onboard storage of NG. CNS and AVS were characterized to determine moisture content, volatile matter content, ash content, and fixed carbon content. Additionally, the study determined the optimal loading of activating agents to produce activated carbons (ACs). ACs were produced through a dual process involving simultaneous chemical activation and carbonization. In the chemical activation phase, a sample of either cashew nut shells (CNS) or avocado seeds (AVS) was impregnated with phosphoric acid at various ratios. Subsequently, the properties of the ACs, including surface area, pore volume, and X-ray diffraction (XRD), were evaluated. The NG adsorption capacity of the ACs was assessed, and Freundlich and Langmuir isotherm adsorption equations were derived to elucidate the NG adsorption behavior on the ACs. The results revealed that the proximate analysis of CNS and AVS showed low moisture content (6.48% and 8.91%), high volatile matter (80.21% and 73.09%), low ash content (1.48% and 4.71%), and high fixed carbon (13.65% and 15.59%), indicating suitability for AC formulations. During synthesis, adding phosphoric acid to AVS or CNS in a 1:1 ratio resulted in significantly larger surface areas and pore volumes in the produced ACs. X-ray diffraction analysis of the ACs produced from CNS or AVS with phosphoric acid added at a 1:1 ratio indicated a broad diffraction background and a predominantly amorphous structure. The performance of the ACs in the adsorption of NG demonstrated that ACs produced from 1:1 mixtures of CNS with phosphoric acid and AVS with phosphoric acid exhibited higher adsorption capacities, 0.038 g/g and 0.031 g/g, respectively, at a low pressure of 5 bar, showcasing a novelty of this study. Furthermore, the Freundlich equation test yielded a constant R² exceeding 0.9, with positive adsorption intensity and adsorption capacity values, indicating a favorable fit for the model. In contrast, the Langmuir equation test resulted in a constant R² exceeding 0.9, but the adsorption capacity exhibited negative values, leading to inconclusive explanations. These findings affirm that bio-wastes can be an effective source of adsorbents for onboard natural gas storage , suggesting a means to reduce the take-dispose model and promote a circular economy in the energy sector.

Drying Kinetics and Quality Attributes of Solar-Dried Red Peppers: A Comparative Study with Traditional and Industrial Methods

Red pepper is a valuable ingredient known for its abundance of vitamins and antioxidants. But, it usually needs to be dried for longer preservation. Hence, this research is aimed at examining the drying kinetics and quality attributes of dried red peppers utilizing various solar drying methods, in comparison with traditional open sun drying (OSD) and industrial laboratory thin layer dryers (LTLD). Analysis parameters employed include determining moisture content, measuring color properties, evaluating antioxidant capacity, analyzing capsaicinoid content, and assessing microbial presence. The drying process took place in a sunny environment with fluctuations in temperature and relative humidity as evidence of the dynamic conditions experienced within the solar dryers. It was observed that the logarithmic model was the most accurate in predicting moisture ratio over time, estimating a drying time of 25 hours to achieve 10% moisture content. The result demonstrated that direct solar dryers (DSD), indirect solar dryers (ISD), and mix mode solar dryers (MMSD) methods showed moderate changes in color parameters, with average ΔL∗, Δa∗, and Δb∗ values of -5.08, -23.71, and -13.62, respectively. The average overall color difference (ΔE) for these methods was 27.96. In addition, after comparing it to the LTLD method, which showed the highest content of phenolic compounds at 47.89%, MMSD displayed a slightly lower content of 44.71%. Similarly, MMSD exhibited higher levels of DPPH radical scavenging activity and ferric reducing power, measuring at 44.22% and 1163.75 μmol Fe2+/L, respectively. The capsaicin content remained relatively consistent across all drying methods, with MMSD, DSD, and LTLD demonstrating similar levels of approximately 31 mg/g. Although MMSD had slightly higher mold and yeast counts compared to LTLD, ISD, and DSD, it remained lower than OSD (less than 0.56 log10cfu/g). Furthermore, MMSD showed a lower total microbial count in comparison to other drying methods. These findings suggest that MMSD shows promise as a drying technique for preserving the phenolic compounds and antioxidant activity of the dried product.

Rapid assessment of vanilla (Vanilla planifolia) quality parameters using portable near-infrared spectroscopy combined with random forest

The determination of moisture and vanillin content significantly influences the quality of vanilla. Currently, conventional chemical methods employed for assessing these parameters are time-consuming, involve complex sample preparation, are expensive, and environmentally unfriendly due to the use of chemical solutions. Portable Near-Infrared (NIR) spectroscopy emerges as a promising alternative, characterized by smaller dimensions and lower costs. This study investigates the performance of two portable NIR spectrometers with distinct wavelengths at 740–1070 nm and 1350–2550 nm, in conjunction with Random Forest (RF) and Partial Least Square (PLS) regression, and preprocessing techniques including min-max normalization, 1st derivative, standard normal variate (SNV), multiplicative scatter correction (MSC), 1st derivative + SNV, and 1st derivative + MSC for predicting moisture and vanillin content. At the wavelength range of 1350–2550 nm, RF coupled with 1st derivative produced the best moisture content prediction model with an R2 of 0.971, and RF paired with 1st derivative+SNV yielded the best vanillin content prediction with an R2 of 0.983. This work highlights that the integration of portable NIR and RF allows for rapid and non-destructive detection of moisture and vanillin content. This methodology provides a novel regression method for predicting vanilla qualities.

Determination of moisture content in coal using pulsed fast thermal neutron activation technique

The quality of coal is greatly influenced by the residual moisture content, which in turn affects the Gross Calorific Value (GCV) by limiting the maximum energy that can be extracted from a given mass of coal. Various methods have been developed to measure this residual moisture, including gravimetric techniques, near-infrared spectroscopy, microwave, and infrared moisture meters. Amongst these, neutron-based techniques offer distinct advantages for bulk and rapid characterization, making them suitable for online monitoring. This article presents the use of a Pulsed Fast Thermal Neutron Activation technique to determine moisture content in various coal samples acquired from Indian coal mines. A 14.1 MeV D-T neutron source coupled with a Bismuth Germanate (BGO) detector and associated electronics have been used. The hydrogen content in the sample has been used as an indicator for the moisture and the technique has been calibrated with samples of known moisture content. The results obtained for the coal samples demonstrate a remarkable agreement with the findings of proximate analysis. A minimum quantity of 2% moisture by weight has been detected with a relative error of 0.4%.

Electrical Properties for Non-destructive Determination of Free Fatty Acid and Moisture Content in Oil Palm Fruit

FFA content is one of the essential qualities of oil palm fruit. FFA content exceeding 5% is considered unsuitable for human consumption. Commonly, FFA content is determined by chemical methods in the laboratory, but it is destructive, time-consuming, and costly. Several non-destructive methods have been investigated, but a maturity prediction approach has been primarily used with no direct relation to FFA content. Thus, there is a pressing need for a non-destructive framework to assess the FFA levels in the oil palm fruit directly. An attempt has been explored using a non-destructive palm fruit quality assessment that relied on electrical properties (impedance, admittance, resistance, and capacitance) in the frequency range from 50 Hz to 5 MHz was investigated to predict FFA and moisture content directly. Two statistical analyses were employed: stepwise multiple linear regressions (MLR) and artificial neural networks (ANN) to calibrate and validate electrical properties with FFA and moisture content. The best-performing models ANN showcased significant results: r= 0.96, R2=0.92, SEC at 0.86%, SEP at 0.97%, CV at 19.45%, consistency at 88.54, and RPD at 3.43 for FFA prediction, and r= 0.99, R2=0.98, SEC at 3.09 %, SEP at 3.46 %, CV at 5.44 %, consistency at 89.08 and RPD at 7.02 for predicting moisture content. In modeling oil palm quality determination, applying the ANN method significantly improved model performances, demonstrating its efficacy in predicting non-destructively both FFA levels based on admittance and moisture content based on impedance.

Spatial variability of heavy metals concentrations in soil of auto-mechanic workshop clusters in Nsukka, Nigeria

The indiscriminate disposal of spent engine oils and other hazardous waste at auto mechanic workshops clusters in Nsukka, Enugu State, Nigeria is an environmental concern. This study examines the concentration of heavy metals in the soil inside the workshop cluster and in the unpolluted soil outside the workshop cluster at approximately 100 m. Ten sampling points were randomly selected from within the cluster and another ten from outside the cluster. Using a hand-held Global Positioning System, the coordinates of the selected points were established and used to create a digital map. Soil samples at depths of 0–30 cm and 30–60 cm, were analyzed for Cu, Fe, Zn, Pb, As and Cd using Spectrophotometer. Moisture content determination and particle size analysis were also done on the samples. Spatial variability of heavy metals concentrations of the studied site was also mapped with ArcGIS 10.2.2 using interpolation methods. Results showed that the soil ranged from sandy loam to sandy clay loam. Cadmium and Zinc had the lowest and highest concentration, respectively, in the studied area. Comparing the concentrations of heavy metals in soils within and outside the auto mechanic cluster revealed notable differences across various depths (0–30 cm and 30–60 cm). The analysis results for soil samples within the cluster exhibited concentration levels (mg/kg) ranging from 0.716–0.751 (Cu), 2.981–3.327 (Fe), 23.464–30.113 (Zn), 1.115–1.21 (Pb), 2.6–2.912 (As), and 0.133–0.365 (Cd) demonstrating a variation pattern in the order of Zn > Fe > As > Pb > Cu > Cd. Conversely, for soil samples outside the cluster, concentration levels (mg/kg) ranged from 0.611–0.618 (Cu), 2.233–2.516 (Fe), 12.841–15.736 (Zn), 0.887–0.903 (Pb), 1.669–1.911 (As), and 0.091–0.091 (Cd). To assess the disparity in heavy metal concentration levels between samples collected within and outside the clusters, ANOVA test was performed. The test showed significant difference in heavy metal concentrations between samples within and outside the auto mechanic cluster (p < 0.05), implying auto mechanic activities significantly impact heavy metal levels within the cluster compared to outside areas. The assessment of soil pollution utilized indices including the Geo-accumulation Index (Igeo), Contamination factor (Cf), and anthropogenic metal concentration (QoC). Zinc, Cadmium, and Arsenic showed the highest contamination factors, indicating significant soil contamination likely due to anthropogenic activities. The concentrations of the metals analyzed were within WHO permissible limits while the metals concentrations were also observed to decrease as depth was increased. Using ArcGIS 10.2.2, spatial maps showing heavy metal distribution were developed, with the Kriging method proving superior. This study suggests that heavy metal levels in the soil at the area be monitored on a regular basis.

Effects of Lime on Bagasse Ash-Stabilised Expansive Soils

Management of agricultural wastes and improvement of construction soils have become increasingly necessary for sustainable development. This study therefore stabilised selected expansive soils using bagasse ash (BA) and lime, with a view to determining the effect of lime on BA-stabilised soils. Two soil samples (identified as Sample A and Sample B) were collected from two identified locations characterised with expansive soils in Ile-Ife, Osun State, Southwestern Nigeria. Using standard procedures, the following preliminary and geotechnical tests were conducted on the soil samples in their natural state: moisture content determination, particle size analysis, specific gravity, Atterberg limits, compaction and unconfined compressive strength (UCS). BA was then introduced to the soils in 5 %, 10 % and 15 % proportions by weight of dry soil. Thereafter, Atterberg limits and UCS tests were conducted on the BA-stabilised soils. Also 2.5 % lime was introduced to each proportion of BA earlier used. Atterberg limits and UCS tests were then conducted on the BA-lime stabilised soils. Results showed that the two soils in their natural state have high plasticity and they both belong to the A-2-7 group. For both BA and BA-lime stabilised soil samples, plasticity reduced with increase in the stabilisers, which implies an improvement in the soil properties. Also, UCS of both BA and BA-lime stabilised soil samples increased with optimum values at 10 % BA content. Expectedly, addition of lime increased the values of UCS for each combination of stabilisers. It was concluded that BA could be used to improve the properties of expansive soils and that the addition of certain proportions of lime does not have a negative impact on the stabilisation properties of BA, but rather improves it.

Determination of hydration kinetic of pinto beans: A hyperspectral images application

Hydration is a typical operation applied to legumes before cooking, reducing time and the associated energy cost. To monitor the process, mass balance method is the most used methodology, despite this method is destructive, repetitive, and time-consuming. For that reason. hyperspectral techniques are presented as an alternative for assessing the hydration process since it is a noninvasive method. Therefore, the objective of this work was to evaluate the technique of hyperspectral imaging for studying the hydration kinetics of pinto beans. For this purpose, a sample of pinto beans was hydrated in distilled water, determining moisture content during the process and taking hyperspectral images by reflectance mode, in the range 400 to 800 nm until constant mass. The moisture content was modelled using Peleg and a sigmoidal model. Next, the images were pre-treated and the median spectral profile for each bean was obtained. Then, a regression model was fitted, using the wavelength that maximized the coefficient of determination (R2) and minimized the root mean square error (RMSE). The results show that Peleg model fit experimental data with R2 in the range of 0.974 to 0.989 while sigmoidal model of 0.997 to 0.999. On other hand, mean spectral profiles at 632 nm and sigmoidal model give the higher metrics 0.997 and 38.3 for R2 and RMSE respectively. The results showed that hyperspectral imaging in reflectance mode is a tool capable of measuring the hydration level of beans with higher performance at 632 nm, with a determination coefficient R2 higher than 0.98.