Search Results (16)

This study evaluated the potential of a miniaturized portable near-infrared spectroscopy (NIRS) device for the non-destructive prediction of the physicochemical and functional quality attributes of red berries. A total of 145 samples from three berry species (aronia, haskap and goji), representing different harvest years and ripening stages, were analyzed. Spectra were acquired over the 908–1676 nm range using a MicroNIR™ 1700 OnSite-W spectrophotometer, and partial least squares regression models were developed to predict total soluble solids, moisture content, pH, total phenolic content and antioxidant capacity. The calibration models achieved coefficients of determination in cross-validation (R²CV) ranging from 0.83 to 0.92, with Root Mean Square Error of Cross-Validation (RMSECV) between 0.281 for pH and 2.085 g Trolox kg⁻¹ FW for antioxidant capacity. External validation confirmed the robustness of the models, yielding R²EV values between 0.76 and 0.88 and Root Mean Square Error of Validation (RMSEV) ranging from 0.381 for pH to 2.095% for moisture content. The highest predictive performance was obtained for total soluble solids (R²EV = 0.88; RMSEV = 1.391), followed by moisture content, pH and antioxidant capacity, whereas total phenolic content showed the lowest predictive accuracy (R²EV = 0.76; RMSEV = 1.914 mg GAE g⁻¹ FW). The Residual Prediction Deviation (RPD) and Range Error Ratio (RER) values further supported the practical applicability of the models for approximate quantitative prediction. Overall, these results demonstrate that portable NIRS is a rapid, non-destructive and reliable tool for the integrated assessment of the physicochemical and functional quality attributes of emerging red berry species. Full article

Near-infrared (NIR) spectroscopy is a rapid, non-destructive analytical tool widely used in the food and agricultural sectors. In this study, two NIR instruments were compared for classifying the addition of microplastics (MPs) to high-moisture-content samples such as vegetables and fruit. Polyethylene (PE), polypropylene (PP), and a mix of polymers (PE + PP) MP were added to mixtures of spinach and banana and scanned using benchtop (Bruker Tango) and portable (MicroNIR) instruments. Both principal component analysis (PCA) and partial least squares (PLS) were used to analyze and interpret the spectra of the samples. Quantitative models were developed to predict the addition of Mix, PP, or PE to spinach and banana samples using PLS regression. The R² _CV and the SECV obtained were 0.88 and 0.44 for the benchtop samples, and 0.54 and 0.67 for the portable instruments, respectively. Two wavenumber regions were also evaluated: 11,520–7500 cm⁻¹ (short to medium wavelengths), and 7500–4200 cm⁻¹ (long wavelengths). The R² _CV and the SECV obtained were 0.88 and 0.46, 0.86 and 0.49, respectively, for the prediction of addition in samples analyzed on the benchtop instrument using short and long wavenumbers, respectively. This study provides new insights into the comparison of two instruments for detecting the addition of MPs in high-moisture samples. The results of this study will ensure that NIR can be utilized not only to measure the quality of these samples but also to monitor MPs. Full article

Biogas upgrading and bottling represent essential processes in transforming raw biogas produced via the anaerobic digestion of organic waste into high-purity biomethane (≥95% CH₄), a renewable energy source suitable for applications in cooking, transportation, and electricity generation. Upgrading technologies, such as membrane separation, pressure swing adsorption (PSA), water and chemical scrubbing, and emerging methods, like cryogenic distillation and supersonic separation, play a pivotal role in removing impurities like CO₂, H₂S, and moisture. Membrane and hybrid systems demonstrate high methane recovery (>99.5%) with low energy consumption, whereas chemical scrubbing offers superior gas purity but is limited by high operational complexity and cost. Challenges persist around material selection, safety standards, infrastructure limitations, and environmental impacts, particularly in rural and off-grid contexts. Bottled biogas, also known as bio-compressed natural gas (CNG), presents a clean, portable alternative to fossil fuels, contributing to energy equity, greenhouse gases (GHG) reduction, and rural development. The primary aim of this research is to critically analyze and review the current state of biogas upgrading and bottling systems, assess their technological maturity, identify performance optimization challenges, and evaluate their economic and environmental viability. The research gap identified in this study demonstrates that there is no comprehensive comparison of biogas upgrading technologies in terms of energy efficiency, price, scalability, and environmental impact. Few studies directly compare these technologies across various operational contexts (e.g., rural vs. urban, small vs. large scale). Additionally, the review outlines insights into how biogas can replace fossil fuels in transport, cooking, and electricity generation, contributing to decarbonization goals. Solutions should be promoted that reduce methane emissions, lower operational costs, and optimize resource use, aligning with climate targets. This synthesis highlights the technological diversity, critical barriers to scalability, and the need for robust policy mechanisms to accelerate the deployment of biogas upgrading solutions as a central component of a low-carbon, decentralized energy future. Full article

This study presents a wireless, non-invasive sensing system for monitoring the dielectric permittivity of materials, with a particular focus on applications in cultural heritage conservation. The system integrates a passive split-ring resonator tag, electromagnetically coupled to a compact antipodal Vivaldi antenna, operating in the reactive near-field region. Both numerical simulations and experimental measurements demonstrate that shifts in the antenna’s reflection coefficient resonance frequency correlate with variations in the dielectric permittivity of the material under test. A calibration curve was established using reference materials—including low-density polyvinylchloride, polytetrafluoroethylene, polymethyl methacrylate, and polycarbonate—and validated through precise permittivity measurements. The system was subsequently applied to wood samples (fir, poplar, beech, and oak) at different humidity levels, revealing a sigmoidal relationship between moisture content and permittivity. The behavior was also confirmed using a portable and low-cost setup, consisting of a point-like coaxial sensor that could be easily moved and positioned as needed, enabling localized measurements on specific areas of interest of the sample, together with a miniaturized Vector Network Analyzer. These results underscore the potential of this portable, contactless, and scalable sensing platform for real-world monitoring of cultural heritage materials, enabling minimally invasive assessment of their structural and historical integrity. Moreover, by enabling the estimation of moisture content through dielectric permittivity, the system provides an effective method for early detection of water-induced deterioration in wood-based heritage items. This capability is particularly valuable for preventive conservation, as excessive moisture—often indicated by permittivity values above critical thresholds—can trigger biological or structural degradation. Full article

The reliability of natural gas moisture measurements is crucial in preventing corrosion in pipelines and equipment, ensuring burning efficiency and mitigating operational risks, and is often mandated by standards and regulations. An important quality parameter that aids in conformity assessment, particularly in risk assessment, is measurement uncertainty. The assessment of measurement uncertainty, based on the Law of Uncertainty Propagation, depends on the mathematical model used to calculate this physicochemical property. This study aimed to compare different algorithms for calculating moisture content in natural gas, estimate and validate the measurement uncertainty based on the algorithms implemented in the Portable Moisture Analyzer (PM880) equipment, a portable hygrometer manufactured by Panametrics in Wilmington, NC, USA, and evaluate compliance with Brazilian legislation using guard bands as a decision rule. The moisture results in natural gas varied by a maximum of 1% among the three approaches presented. Furthermore, based on the dew point and pressure results, the expanded uncertainties of moisture were about 20%, which did not compromise the risk assessment for the consumer, as the moisture results were well below the specification value. Consequently, the upper tolerance limit of 58.4 ppmv H₂O was established. Full article

Blueberries offer multiple health benefits, and their cultivation has expanded to warm tropical regions. However, references for foliar nutritional content are lacking in the literature. Proximal sensing may enhance nutritional characterization to optimize blueberry production. We aimed (i) to characterize the nutrient contents of healthy plants of three blueberry varieties adapted to warm climates (Emerald, Jewel, and Biloxi) using a reference method for foliar analysis (inductively coupled plasma (ICP)) and a portable X-ray fluorescence (pXRF) spectrometer on fresh and dry leaves and (ii) to differentiate blueberry varieties based on their nutrient composition. Nutrient content was statistically compared per leaf moisture condition (fresh or dry) with ICP results and used to differentiate the varieties via the random forest algorithm. P and Zn contents (ICP) in leaves were different among varieties. Dry leaf results (pXRF) were strongly correlated with ICP results. Most nutrients determined using ICP presented good correlation with pXRF data (R² from 0.66 to 0.93). The three varieties were accurately differentiated by pXRF results (accuracy: 87%; kappa: 0.80). Predictions of nutrient contents based on dry leaves analyzed by pXRF outperformed those based on fresh leaves. This approach can also be applied to other crops to facilitate nutrient assessment in leaves. Full article

The detection of the moisture content of wheat is an important index used to measure the quality and preservation of wheat. In order to rapidly and non-destructively detect the moisture content of wheat, in this study, we designed a stripline detection device that measures 151 frequency points in the 50–200 MHz frequency range with a vector network analyzer. Random forest (RF), extreme learning machine (ELM), and BP neural network prediction models were established, using the frequency, temperature, volume density and dielectric constant as input and the water content as output. It was shown that, in the frequency range 50–200 MHz, the permittivity of wheat decreases as the frequency increases, and that this is negatively correlated. The dielectric constant of wheat increases as the moisture content, temperature, and bulk density increase, and these are positively correlated. The random forest (RF) prediction model, which uses the frequency, temperature, effective dielectric constant ${\epsilon }_{eff}$. and volume density as inputs and the wheat moisture content as the output, demonstrates the best performance. The determination coefficient (R²) = 0.99977, the mean absolute error (MAE) = 0.044368, the mean square error (MAE) = 0.0053011, and the root mean square error (RMSE) = 0.072809. This study provides a new device and method for the detection of the moisture content of wheat. The device is small and is not easily disturbed by the external environment. It can be measured in a variety of conditions and is important for the development of low-cost, high-precision, and portable devices for the detection of the moisture content of wheat. Full article

The stem CO₂ efflux (Es) plays an important role in the carbon balance in forest ecosystems. However, a majority of studies focus on ecosystem flux, and little is known about the contribution of stem respiration to ecosystem respiration (Reco) for rubber (Hevea brasiliensis) plantations. We used a portable CO₂ analyzer to monitor the rate of Es in situ at different heights (1.5 m, 3.0 m and 4.5 m) in an even-aged rubber plantation from 2019 to 2020. Our results showed that Es exhibited a significant seasonal difference with a minimum value in April and a maximum in September. The mean annual rate of Es at 3.0 m in height (1.65 ± 0.52 μmol·m⁻²·s⁻¹) was slightly higher than Es at 4.5 m in height (1.56 ± 0.59 μmol·m⁻²·s⁻¹) and Es at 1.5 m in height (1.51 ± 0.48 μmol·m⁻²·s⁻¹). No obvious differences in vertical variations were found. An area-based method (Ea) and a volume-based method (Ev) were used to estimate stem respiration at stand levels. One-way ANOVA showed that Ea had no obvious differences in vertical variation (p = 0.62), and Ev indicated differences in vertical variation (p < 0.05). Therefore, the Ea chamber-based measurements at breast height were reasonable and practical extrapolation proxies of stem respiration in an even-aged rubber plantation. With the use of the area-based method, the stem carbon values released from a mature rubber forest were estimated to be 1.214 t C·hm⁻²·a⁻¹ in 2019 and 1.414 t C·hm⁻²·a⁻¹ in 2020. Ea/Reco and Ev/Reco showed seasonal changes, with a minimum value in April and a maximum value in December. The leaf area index (LAI) and soil volumetric moisture content (VWC) were the major impact factors of Ea/Reco in an even-aged pure rubber plantation. Full article

As one of the most popular edible fungi in the market, the quality of Agaricus bisporus will determine its sales volume. Therefore, to achieve rapid and nondestructive testing of the quality of Agaricus bisporus, this study first built a portable spectrum acquisition device for Agaricus bisporus. The Ocean Spectromeper was used to calibrate the spectral data of the device, and the linear regression analysis method was combined to analyze the two. The results showed that the Pearson correlation coefficient of significance between the two was 0.98. Then, the spectral data of Agaricus bisporus were collected, the spectral characteristic wavelength of Agaricus bisporus was extracted by the SPA and PCA algorithms, and the moisture content and whiteness prediction models based on a BP neural network and PLSR, respectively, were built. The parameters of the BP neural network model were optimized by SSA. The R² values for the final moisture content and the predicted whiteness were 0.95 and 0.99, and the RMSE values were 5.04% and 0.60, respectively. The results show that the portable spectral acquisition and analysis device can be used for the accurate and rapid quality detection of Agaricus bisporus. Full article

People have set fire to the savannas of West Africa for millennia, creating a pyrogeography. Fires render the landscape useful for many productive activities, but there is also a long history of efforts to regulate indigenous burning practices. Today, savanna fires are under scrutiny because they contribute to greenhouse gas emissions, especially methane. Policy efforts aimed at reducing emissions by shifting fire regimes earlier are untested. Most emissions estimates contain high levels of uncertainty because they are based on generalizations of diverse landscapes burned by complex fire regimes. To examine the importance of seasonality and other factors on methane emissions, we used an approach grounded in the practices of people who set fires. We conducted 107 experimental fires, collecting data for methane emissions and a suite of environmental variables. We sampled emissions using a portable gas analyzer, recording values for CO, CO₂, and CH₄. The fires were set both as head and backfires for three fire periods—the early, middle, and late dry season. We also set fires randomly to test whether the emissions differed from those set according to traditional practices. We found that methane emission factors and densities did not increase over the dry season but rather peaked mid-season due to higher winds and fuel moisture as well as green leaves on small trees. The findings demonstrate the complexity of emissions from fires and cast doubt on efforts to reduce emissions based on simplified characterizations of fire regimes and landscapes. Full article

A method of soil moisture and organic matter content detection based on hyperspectral technology is proposed. A total of 800 different soil samples and hyperspectral data were collected in the laboratory and from the field. A hyperspectral database was established. After wavelet denoising and principal component analysis (PCA) preprocessing, the convolutional neural network (CNN) module was first used to extract the wavelength features of the data. Then, the long- and short-memory neural network (LSTM) module was used to extract the feature bands and nearby hidden state vectors. At the same time, the genetic algorithm (GA) was used to optimize the hyperparametric weight and bias value of the LSTM training network. At the initial stage, the data were normalized, and all features were analyzed by grey correlation degree to extract important features and to reduce the computational complexity of the data. Then, the GA-optimized CNN-LSTM hybrid neural network (GA-CNN-LSTM) algorithm model proposed in this paper was used to predict soil moisture and organic matter. The prediction performance was compared with CNN, support vector regression (SVR), and CNN-LSTM hybrid neural network model without GA optimization. The GA-CNN-LSTM algorithm was superior to other models in all indicators. The highest accuracy rates of 94.5% and 92.9% were obtained for soil moisture and organic matter, respectively. This method can be applied to portable hyperspectrometers and unmanned aerial vehicles to realize large-scale monitoring of moisture and organic matter distribution and to provide a basis for rational irrigation and fertilization in the future. Full article

To realize precision agriculture at multiple locations in the field, a low-cost measurement system should be developed for easy collection of hydrometeorological data, such as temperature, moisture, and light. In this study, a compact and low-cost hydrometeorological measurement system with a simplified wire code, which is customizable according to the purpose of observation, was built using a circuit board that connects Arduino to the sensors, which was then implemented and analyzed. The developed system measures air and soil temperatures, soil water content, and photosynthetic photon flux density using a sensor connected to Arduino Uno and saves the continuous, high-temporal-resolution output to an SD card. The results obtained from continuous measurement showed that the data collected using this system was significantly better than those collected using commercially available equipment. Anyone can easily measure the weather environments by using this fully open, highly versatile, portable, and user-friendly system. This system can contribute to the growth and expansion of precision agriculture, field management, development of crop models, and laborsaving. It can also provide a global solution to ongoing agricultural issues and improve the efficiency of farming operations, particularly in developing and low-income countries. Full article

Mapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and cost, a combination of field proximal soil sensors was used to predict and map laboratory-measured soil properties in a 3.4-ha pasture field in southeastern Brazil. Sensor soil properties were measured in situ on a 10 × 10-m dense grid (377 samples) using apparent electrical conductivity meters, apparent magnetic susceptibility meter, gamma-ray spectrometer, water content reflectometer, cone penetrometer, and portable X-ray fluorescence spectrometer (pXRF). Soil samples were collected on a 20 × 20-m thin grid (105 samples) and analyzed in the laboratory for organic C, sum of bases, cation exchange capacity, clay content, soil volumetric moisture, and bulk density. Another 25 samples collected throughout the area were also analyzed for the same soil properties and used for independent validation of models and maps. To test whether the combination of sensors enhances soil property predictions, stepwise multiple linear regression (MLR) models of the laboratory soil properties were derived using individual sensor covariate data versus combined sensor data—except for the pXRF data, which were evaluated separately. Then, to test whether a denser grid sample boosted by sensor-based soil property predictions enhances soil property maps, ordinary kriging of the laboratory-measured soil properties from the thin grid was compared to ordinary kriging of the sensor-based predictions from the dense grid, and ordinary cokriging of the laboratory properties aided by sensor covariate data. The combination of multiple soil sensors improved the MLR predictions for all soil properties relative to single sensors. The pXRF data produced the best MLR predictions for organic C content, clay content, and bulk density, standing out as the best single sensor for soil property prediction, whereas the other sensors combined outperformed the pXRF sensor for the sum of bases, cation exchange capacity, and soil volumetric moisture, based on independent validation. Ordinary kriging of sensor-based predictions outperformed the other interpolation approaches for all soil properties, except organic C content, based on validation results. Thus, combining soil sensors, and using sensor-based soil property predictions to increase the sample size and spatial coverage, leads to more detailed and accurate soil property maps. Full article

Rice grain moisture has a great impact on th production and storage storage quality of rice. The main objective of this study was to design and develop a rapid-detection sensor for rice grain moisture based on the Near-infrared spectroscopy (NIR) characteristic band, aiming to realize its accurate and on-line measurement. In this paper, the NIR spectral information of grain samples with different moisture content was obtained using a portable NIR spectrometer. Then, the partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were applied to model and analyze the spectral data to find the rice grain moisture NIR spectroscopy. As a result, the 1450 nm band was sensitive to the rice grain moisture and a rapid-detection sensor was developed with a 1450 nm light emitting diode (LED) light source, InGaAs photodiode, lens and filter, whose basic principle is to establish the relationship between the rice grain moisture and the measured voltage signal. To evaluate the sensor performance, rice grain samples with 13–30% moisture content were detected, the coefficient of determination R² was 0.936, and the sum of squares for error (SSE) was 23.44. It is concluded that this study provides a spectroscopic measuring method, as well as developing an effective and accurate sensor for the rapid determination of rice grain moisture, which is of great significance for monitoring the quality of rice grain during its production, transportation and storage process. Full article

The improvement of water management in agriculture by exactly detecting moisture parameters of soil is crucial. To investigate this problem, a mini inside-out nuclear magnetic resonance sensor (NMR) was proposed to measure moisture parameters of model soils. This sensor combines three cylindrical magnets that are magnetized in the axial direction and three arc spiral coils of the same size in series. We calculated and optimized the magnet structure by equivalent magnetization to current density. By adjusting the radius and height between the cylinders, a circumferential symmetric constant gradient field (2.28 T/m) was obtained. The NMR sensor was set at 2.424 MHz to measure the water content of sandy soil with small particle diameter and silica sand with large particle diameter. The complete decaying, an NMR signal was analyzed through inverse Laplace transformation and averaged on a T₂ space. According to the results, moisture content of the sample is positively correlated with the integral area of T₂ spectrum peak (A_peak); T₂ of the water in small pores is shorter than that in large pores, because the movement of water molecules are limited by the inner wall of the pores. In the same volume, water in large pore sample is more than that in small pore sample, so A_peak of silica sand is larger than A_peak of sandy soil. Therefore, the sensor is capable of detecting moisture both content and pore size of the sample. This mini sensor (4.0 cm in diameter and 10 cm in length) is portable, and the lowest measurable humidity is 0.38%. Thus, this sensor will allow easy soil moisture measurements on-field in the future. Full article