Elemental assessment of vegetation via portable X-ray fluorescence (PXRF) spectrometry

Heavy metal immobilization and radish growth improvement using Ca(OH)2-treated cypress biochar in contaminated soil

Heavy metal contamination poses a significant threat to soil quality, plant growth, and food safety, and directly affects multiple UN SDGs. Addressing this issue and offering a remediation solution are vital for human health. One effective approach for immobilizing heavy metals involves impregnating cypress chips with calcium hydroxide (Ca(OH)2) to enhance the chemical adsorption capacity of the resulting woody charcoal. In the present study, un-treated cypress biochar (UCBC) and calcium-treated cypress biochar (TCBC), were introduced into pristine and contaminated soil, at rates of 3, 6, and 9% (w/w). Both BCs were alkaline (UCBC pH: 8.9, TCBC pH: 9.7) with high specific surface area, which improved the soil properties (pH, EC, and OM). Radish (Raphanus sativus) cultivated in pots revealed that both UCBC and TCBC demonstrated significant improvements in growth attributes and heavy metal immobilization compared to the control, with TCBC exhibiting superior effects. The TCBC surface showed highly active nanosized precipitated calcium carbonate particles that were active in immobilizing heavy metals. The application of TCBC at a rate of 9% resulted in a substantial reduction in Zn and Cu uptake by radish roots and shoots. In contaminated soil, Zn uptake by radish roots decreased by 55% (68.3-31.0 mg kg-1), and shoots by 37% (49.3-31.0 mg kg-1); Cu uptake decreased by 40% (38.6-23.2 mg kg-1) in roots and 39% (58.2-35.2 mg kg-1) in shoots. Uptake of Pb was undetectable after TCBC application. Principal component analysis (PCA) highlighted the potential of TCBC over UCBC in reducing heavy metal concentrations and promoting radish growth. Future research should consider the long-term effects and microbial interactions of TCBC application.

The use of a portable X-ray fluorescence spectrometer for measuring nickel in plants: sample preparation and validation

X-ray fluorescence is a fast, cost-effective, and eco-friendly method for elemental analyses. Portable X-ray fluorescence spectrometers (pXRF) have proven instrumental in detecting metals across diverse matrices, including plants. However, sample preparation and measurement procedures need to be standardized for each instrument. This study examined sample preparation methods and predictive capabilities for nickel (Ni) concentrations in various plants using pXRF, employing empirical calibration based on inductively coupled plasma optical emission spectroscopy (ICP-OES) Ni data. The evaluation involved 300 plant samples of 14 species with variable of Ni accumulation. Various dwell times (30, 60, 90, 120, 300 s) and sample masses (0.5, 1.0, 1.5, 2.0 g) were tested. Calibration models were developed through empirical and correction factor approaches. The results showed that the use of 1.0 g of sample (0.14 g cm-2) and a dwell time of 60 s for the study conditions were appropriate for detection by pXRF. Ni concentrations determined by ICP-OES were highly correlated (R2 = 0.94) with those measured by the pXRF instrument. Therefore, pXRF can provide reliable detection of Ni in plant samples, avoiding the digestion of samples and reducing the decision-making time in environmental management.

Research progress of the detection and analysis methods of heavy metals in plants

Heavy metal (HM)-induced stress can lead to the enrichment of HMs in plants thereby threatening people's lives and health via the food chain. For this reason, there is an urgent need for some reliable and practical techniques to detect and analyze the absorption, distribution, accumulation, chemical form, and transport of HMs in plants for reducing or regulating HM content. Not only does it help to explore the mechanism of plant HM response, but it also holds significant importance for cultivating plants with low levels of HMs. Even though this field has garnered significant attention recently, only minority researchers have systematically summarized the different methods of analysis. This paper outlines the detection and analysis techniques applied in recent years for determining HM concentration in plants, such as inductively coupled plasma mass spectrometry (ICP-MS), atomic absorption spectrometry (AAS), atomic fluorescence spectrometry (AFS), X-ray absorption spectroscopy (XAS), X-ray fluorescence spectrometry (XRF), laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), non-invasive micro-test technology (NMT) and omics and molecular biology approaches. They can detect the chemical forms, spatial distribution, uptake and transport of HMs in plants. For this paper, the principles behind these techniques are clarified, their advantages and disadvantages are highlighted, their applications are explored, and guidance for selecting the appropriate methods to study HMs in plants is provided for later research. It is also expected to promote the innovation and development of HM-detection technologies and offer ideas for future research concerning HM accumulation in plants.

Proximal sensing provides clean, fast, and accurate quality control of organic and mineral fertilizers

Farms use large quantities of fertilizers from many sources, making quality control a challenging task, as the traditional wet-chemistry analyses are expensive, time consuming and not environmentally-friendly. As an alternative, this work proposes the use of portable X-ray fluorescence (pXRF) spectrometry and machine learning algorithms for rapid and low-cost estimation of macro and micronutrient contents in mineral and organic fertilizers. Four machine learning algorithms were tested. Whole (i.e., as delivered by the manufacturer) (CP) and ground (AQ) samples (429 in total) were analyzed to test the effect of fertilizer granulometry in prediction performance. Model validation indicated highly accurate predictions of macro (N: R2 = 0.92; P: 0.97; K: 0.99; Ca: 0.94, Mg: 0.98; S: 0.96) and micronutrients (B: 0.99; Cu: 0.99; Fe: 0.98; Mn: 0.91; Zn: 0.94) for both organic and mineral fertilizers. RPD values ranged from 2.31 to 9.23 for AQ samples, and Random Forest and Cubist Regression were the algorithms with the best performances. Even samples analyzed as they were received from the manufacturer (i.e., no grinding) provided accurate predictions, which accelerate the confirmation of nutrient contents contained in fertilizers. Results demonstrated the potential of pXRF data coupled with machine learning algorithms to assess nutrient composition in both mineral and organic fertilizers with high accuracy, allowing for clean, fast and accurate quality control. Sensor-driven quality assessment of fertilizers improves soil and plant health, crop management efficiency and food security with a reduced environmental footprint.

Towards direct and eco-friendly analysis of plants using portable X-ray fluorescence spectrometry: A methodological approach

The assessment of the nutritional status of plants is traditionally performed by wet-digestion methods using oven-dried and ground samples. This process requires sampling, takes time, and it is non-environmentally friendly. Agricultural and environmental science have been greatly benefited by in-field, ecofriendly methods, and real-time element measurements. This work employed the portable X-ray fluorescence spectrometry (pXRF) to analyze intact and fresh leaves of crops aiming to assess the effect of water content and leaf surface (adaxial and abaxial) on pXRF results. Also, pXRF data were used to predict the real concentration of macro- and micronutrients. Eight crops (bean, castor plant, coffee, eucalyptus, guava tree, maize, mango, and soybean) with contrasting water contents were used. Intact leaf fragments (∼2 × 2 cm), fresh or oven-dried (60 °C) were obtained to be analyzed via pXRF on both adaxial and abaxial surface. Conventional wet-digestion method was also performed on powdered material to obtain the concentration of macro- and micronutrients via ICP-OES. The data were subjected to descriptive statistics, principal component analysis (PCA) and random forest (RF) algorithm regression. RF was used to predict the real concentration of macro- and micronutrients based on pXRF measurements obtained directly on intact leaves. Water content had a significant effect on pXRF results. However, a positive correlation between the concentration of macro- and micronutrients obtained via pXRF directly on intact leaves and conventional analysis performed on powdered samples was obtained. PCA analysis allowed a clear differentiation of crops based on elemental composition. The concentrations of macro- and micronutrients were very accurately predicted via RF. Even elements not detected by pXRF (N and B) were satisfactory predicted. From this pilot study, it is possible to concluded that pXRF is feasible for in-field assessment of nutritional status of plants. Further studies are needed to obtain specific and robust calibrations for each crop.

Determining the Provenance of Traded Wildlife in the Philippines

The illegal wildlife trade is a significant threat to global biodiversity, often targeting already threatened species. In combating the trade, it is critical to know the provenance of the traded animal or part to facilitate targeted conservation actions, such as education and enforcement. Here, we present and compare two methods, portable X-ray fluorescence (pXRF) and stable isotope analysis (SIA), to determine both the geographic and source provenance (captive or wild) of traded animals and their parts. Using three critically endangered, frequently illegally traded Philippine species, the Palawan forest turtle (Siebenrockiella leytensis), the Philippine cockatoo (Cacatua haematuropygia), and the Philippine pangolin (Manis culionensisis), we demonstrate that using these methods, we can more accurately assign provenance using pXRF data (x¯ = 83%) than SIA data (x¯ = 47%). Our results indicate that these methods provide a valuable forensic tool that can be used in combating the illegal wildlife trade.

Progress in quality control, detection techniques, speciation and risk assessment of heavy metals in marine traditional Chinese medicine

Marine traditional Chinese medicines (MTCMs) hold a significant place in the rich cultural heritage in China. It plays an irreplaceable role in addressing human diseases and serves as a crucial pillar for the development of China's marine economy. However, the rapid pace of industrialization has raised concerns about the safety of MTCM, particularly in relation to heavy metal pollution. Heavy metal pollution poses a significant threat to the development of MTCM and human health, necessitating the need for detection analysis and risk assessment of heavy metals in MTCM. In this paper, the current research status, pollution situation, detection and analysis technology, removal technology and risk assessment of heavy metals in MTCM are discussed, and the establishment of a pollution detection database and a comprehensive quality and safety supervision system for MTCM is proposed. These measures aim to enhance understanding of heavy metals and harmful elements in MTCM. It is expected to provide a valuable reference for the control of heavy metals and harmful elements in MTCM, as well as the sustainable development and application of MTCM.

A Proximal Sensor-Based Approach for Clean, Fast, and Accurate Assessment of the Eucalyptus spp. Nutritional Status and Differentiation of Clones

Several materials have been characterized using proximal sensors, but still incipient efforts have been driven to plant tissues. Eucalyptus spp. cultivation in Brazil covers approximately 7.47 million hectares, requiring faster methods to assess plant nutritional status. This study applies portable X-ray fluorescence (pXRF) spectrometry to (i) distinguish Eucalyptus clones using pre-processed pXRF data; and (ii) predict the contents of eleven nutrients in the leaves of Eucalyptus (B, Ca, Cu, Fe, K, Mg, Mn, N, P, S, and Zn) aiming to accelerate the diagnosis of nutrient deficiency. Nine hundred and twenty samples of Eucalyptus leaves were collected, oven-dried, ground, and analyzed using acid-digestion (conventional method) and using pXRF. Six machine learning algorithms were trained with 70% of pXRF data to model conventional results and the remaining 30% were used to validate the models using root mean square error (RMSE) and coefficient of determination (R²). The principal component analysis clearly distinguished developmental stages based on pXRF data. Nine nutrients were accurately predicted, including N (not detected using pXRF spectrometry). Results for B and Mg were less satisfactory. This method can substantially accelerate decision-making and reduce costs for Eucalyptus foliar analysis, constituting an ecofriendly approach which should be tested for other crops.

X-ray fluorescence spectrometry for environmental analysis: Basic principles, instrumentation, applications and recent trends

In recent years, the conceptual advancement on green analytical chemistry (GAC) has moved in parallel with efforts to incorporate new screening or quantitative low-cost analytical tools to solve analytical problems. In this sense, the role of solid state techniques that allow the non-invasive analysis (or with a minimum sample treatment) of solid samples cannot be neglected. This review describes the basic principles, instrumentation and advances in the application of X-ray fluorescence instrumentation to the environmental sciences research topics, published between 2006 and 2020. Obviously, and because of the enormous number of works that can be found in the literature, it is not possible to exhaustively cover all published articles and the diversity of topics related to the environment in which a solid state technique like XRF has been applied successfully. It is a question of making a compilation of the instrumentation in use, the significant advances in XRF spectrometry and sample treatment strategies to highlight the potential of its implementation for environmental assessment.

Predictive modeling of contents of potentially toxic elements using morphometric data, proximal sensing, and chemical and physical properties of soils under mining influence

Several anthropic activities, especially mining, have contributed to the exacerbation of contents of potentially toxic elements in soils around the world. Mines can release a large amount of direct sources of contaminants into the environment, and even after the mines are no longer being exploited, the environmental liabilities generated may continue to provide contamination risks. Potentially toxic elements (PTEs), when present in the environment, can enter the food chain, promoting serious risks to human health and the ecosystem. Several methods have been used to determine the contents of PTEs in soils, but most are laborious, costly and generate waste. In this study, we use a methodological framework to optimize the prediction of levels of PTEs in soils. We used a total set of 120 soil samples, collected at a depth of 0-10 cm. The covariate database is composed of variables measured by proximal sensors, physical and chemical soil characteristics, and morphometric data derived from a DEM with a spatial resolution of 30 m. Five machine learning algorithms were tested: Random Forests, Cubist, Linear Model, Support Vector Machine and K Nearest Neighbor. In general, the Cubist algorithm produced better results in predicting the contents of Pb, Zn, Ba and Fe compared to the other tested models. For the Al contents, the Support Vector Machine produced the best prediction. For the Cr contents, all models showed low predictive power. The most important covariates in predicting the contents of PTEs varied according to the studied element. However, x-ray fluorescence measurements, textural and morphometric variables stood out for all elements. The methodology structure reported in this study represents an alternative for fast, low-cost prediction of PTEs in soils, in addition to being efficient and economical for monitoring potentially contaminated areas and obtaining quality reference values for soils.

Heavy Metal-Resistant Filamentous Fungi as Potential Mercury Bioremediators

Filamentous fungi native to heavy metals (HMs) contaminated sites have great potential for bioremediation, yet are still often underexploited. This research aimed to assess the HMs resistance and Hg remediation capacity of fungi isolated from the rhizosphere of plants resident on highly Hg-contaminated substrate. Analysis of Hg, Pb, Cu, Zn, and Cd concentrations by X-ray spectrometry generated the ecological risk of the rhizosphere soil. A total of 32 HM-resistant fungal isolates were molecularly identified. Their resistance spectrum for the investigated elements was characterized by tolerance indices (TIs) and minimum inhibitory concentrations (MICs). Clustering analysis of TIs was coupled with isolates’ phylogeny to evaluate HMs resistance patterns. The bioremediation potential of five isolates’ live biomasses, in 100 mg/L Hg²⁺ aqueous solution over 48 h at 120 r/min, was quantified by atomic absorption spectrometry. New species or genera that were previously unrelated to Hg-contaminated substrates were identified. Ascomycota representatives were common, diverse, and exhibited varied HMs resistance spectra, especially towards the elements with ecological risk, in contrast to Mucoromycota-recovered isolates. HMs resistance patterns were similar within phylogenetically related clades, although isolate specific resistance occurred. Cladosporium sp., Didymella glomerata, Fusarium oxysporum, Phoma costaricensis, and Sarocladium kiliense isolates displayed very high MIC (mg/L) for Hg (140–200), in addition to Pb (1568), Cu (381), Zn (2092–2353), or Cd (337). The Hg biosorption capacity of these highly Hg-resistant species ranged from 33.8 to 54.9 mg/g dry weight, with a removal capacity from 47% to 97%. Thus, the fungi identified herein showed great potential as bioremediators for highly Hg-contaminated aqueous substrates.

A Systematic Framework for Collecting Site-Specific Sampling and Survey Data to Support Analyses of Health Impacts from Land-Based Pollution in Low- and Middle-Income Countries

The rise of small-scale and localized economic activities in low- and middle-income countries (LMICs) has led to increased exposures to contaminants associated with these processes and the potential for resulting adverse health effects in exposed communities. Risk assessment is the process of building models to predict the probability of adverse outcomes based on concentration-response functions and exposure scenarios for individual contaminants, while epidemiology uses statistical methods to explore associations between potential exposures and observed health outcomes. Neither approach by itself is practical or sufficient for evaluating the magnitude of exposures and health impacts associated with land-based pollution in LMICs. Here we propose a more pragmatic framework for designing representative studies, including uniform sampling guidelines and household surveys, that draws from both methodologies to better support community health impact analyses associated with land-based pollution sources in LMICs. Our primary goal is to explicitly link environmental contamination from land-based pollution associated with specific localized economic activities to community exposures and health outcomes at the household level. The proposed framework was applied to the following three types of industries that are now widespread in many LMICs: artisanal scale gold mining (ASGM), used lead-acid battery recycling (ULAB), and small tanning facilities. For each activity, we develop a generalized conceptual site model (CSM) that describes qualitative linkages from chemical releases or discharges, environmental fate and transport mechanisms, exposure pathways and routes, populations at risk, and health outcomes. This upfront information, which is often overlooked, is essential for delineating the contaminant zone of influence in a community and identifying relevant households for study. We also recommend cost-effective methods for use in LMICs related to environmental sampling, biological monitoring, survey questionnaires, and health outcome measurements at contaminated and unexposed reference sites. Future study designs based on this framework will facilitate consistent, comparable, and standardized community exposure, risk, and health impact assessments for land-based pollution in LMICs. The results of these studies can also support economic burden analyses and risk management decision-making around site cleanup, risk mitigation, and public health education.

X-ray fluorescence application in food, feed, and agricultural science: a critical review

Recently X-ray fluorescence techniques have been widely used in food and agricultural science areas. Minimal sample preparation, nondestructive analysis, high spatial resolution, and multiple elements measurements within a single sample are among its advantages. In this review, literature of X-ray fluorescence are extensively researched and summarized from food and agricultural science areas focusing on food safety inspection, food nutrition, plant science, soil science, and Ca-related problems in horticultural crops. In addition, the advantages and disadvantages of X-ray fluorescence comparing with traditional analytical techniques of elements are also discussed. The more advanced technology such as developments of detector, scanning system, beamline capability among others would significantly increase future application of X-ray fluorescence techniques. Combination use of XRF with other tools such as chemometrics or data analytics would greatly improve its prediction performance. These further improvements offer exciting perspectives for the application of X-ray fluorescence in the food and agricultural science areas.

Foliar Elemental Analysis of Brazilian Crops via Portable X-ray Fluorescence Spectrometry

Foliar analysis is very important for the nutritional management of crops and as a supplemental parameter for soil fertilizer recommendation. The elemental composition of plants is traditionally obtained by laboratory-based methods after acid digestion of ground and sieved leaf samples. This analysis is time-consuming and generates toxic waste. By comparison, portable X-ray fluorescence (pXRF) spectrometry is a promising technology for rapid characterization of plants, eliminating such constraints. This worked aimed to assess the pXRF performance for elemental quantification of leaf samples from important Brazilian crops. For that, 614 samples from 28 plant species were collected across different regions of Brazil. Ground and sieved samples were analyzed after acid digestion (AD), followed by quantification via inductively coupled plasma optical emission spectroscopy (ICP-OES) to determine the concentration of macronutrients (P, K, Ca, Mg, and S) and micronutrients (Fe, Zn, Mn, and Cu). The same plant nutrients were directly analyzed on ground leaf samples via pXRF. Four certified reference materials (CRMs) for plants were used for quality assurance control. Except for Mg, a very strong correlation was observed between pXRF and AD for all plant-nutrients and crops. The relationship between methods was nutrient- and crop-dependent. In particular, eucalyptus displayed optimal correlations for all elements, except for Mg. Opposite to eucalyptus, sugarcane showed the worst correlations for all the evaluated elements, except for S, which had a very strong correlation coefficient. Results demonstrate that for many crops, pXRF can reasonably quantify the concentration of macro- and micronutrients on ground and sieved leaf samples. Undoubtedly, this will contribute to enhance crop management strategies concomitant with increasing food quality and food security.

Elemental composition and moisture prediction in manure by portable X-ray fluorescence spectroscopy using random forest regression

Manure elemental composition determination is essential to develop farm nutrient budgets and assess environmental risk. Portable X-ray fluorescence (PXRF) spectrometers could facilitate hazardous waste-free, rapid, and cost-effective elemental concentration determinations. However, sample moisture is a problem for elemental concentration determination by X-ray methods. The objective of this study was to quantify the effect of sample moisture content, predict moisture content, and correct for moisture effect on elemental concentration determinations in livestock manure. Oven-dried manure samples (n = 40) were ground and adjusted to five moisture ranges of (w/w moisture) <10%, 10-20%, 20-30%, 40-50%, and 60-70%. Samples were scanned by PXRF for 180 s using a vacuum (<1,333 Pa) and without a filter. The presence of moisture negatively affected elemental determination in manure samples. Calibrations (n = 200) were prepared using random forest regression with detector channel counts as independent variables. A three-step validation was performed using all the data, random cross-validation and external validation. The back end of the spectrum (14-15 keV) had strong predictive power (r²  = .98) for moisture content. The random forest approach increased r² between PXRF and wet chemical methods from <.66 to >.90 for P, K, and Mg and from .78 to .98 for Fe, compared with linear, nonlinear, and Lucas-Tooth and Price equations. These results indicated that elemental concentration can accurately be measured in dried and moist manure samples using PXRF and expands the potential applications of PXRF to in situ elemental determinations for agricultural and environmental samples.

Quantitative Analysis of Iron and Silicon Concentrations in Iron Ore Concentrate Using Portable X-ray Fluorescence (XRF)

As a technique capable of rapid, nondestructive, and multi-elemental analysis, portable X-ray fluorescence (pXRF) has applications to mineral exploration, environmental evaluation, and archaeological analysis. However, few applications have been conducted in the smelting industry especially when analyzing the metal concentration in ore concentrate samples. This research analyzed the effectiveness of using pXRF in determining the metal concentration in Fe concentrate. For this proof of concept study, Fe ore samples dominated by Fe and Si were collected from the Northeastern University Mineral Processing Laboratory (Shenyang, China) and directly analyzed using pXRF, laboratory-based XRF, and titration methods. The compactness (density) of the ore concentrate was found to have very little effect on pXRF readings. The pXRF readings for Fe and Si were comparative to laboratory-based XRF results. Based on the strong correlations between the pXRF and XRF results (Fe: R²> 0.99, Si: R²> 0.96), linear calibrations were adopted to improve the accuracy of pXRF readings. Linear regression equations derived from the relations between XRF results and pXRF results of 21 Fe ore concentrate samples were used to calibrate the pXRF, and then validation was performed on five additional samples. Results from this preliminary study suggest that ordinary least squares (OLS) regression improves the accuracy dramatically, especially for Fe with relative errors (REs) decreasing to 0.03%-3.27% from 4.26%-8.32%. Consequently, pXRF shows strong promise for rapid, quantitative analysis of Fe concentration in Fe ore concentrate. Based on the results obtained in this study, a larger, more comprehensive study is warranted to confirm the results obtained.

Biogeochemical mapping of metal contamination from mine tailings using field-portable XRF

Trace and major element composition of selected plant species and parts may be used to map geochemical dispersion from mineral deposits and contaminated areas. This study examines the application of field-portable X-ray fluorescence spectrometry (fpXRF) in obtaining real-time biogeochemical data. In situ analysis of parts of black and silver wattle (Acacia mearnsii De Wild. and Acacia dealbata Link) was conducted to map the extent of contamination surrounding the former Woodlawn base metal mine. High levels of ore-related elements were detected in the bark of these species in a zone extending up to 1 km down-drainage from the tailing ponds. Major elements are more elevated in bark on the side of the trees facing the tailings ponds and correlations between trace and major elements indicate dust contamination. The penetration distance for X-rays is dependent on the energy of the secondary X-rays measured, with the maximum depth of penetration in bark and leaf material <30 mm. There was a close correlation for most elements between the fpXRF and laboratory-based XRF analysis but with element-dependent attenuation by the organic matrix. Providing there is consistency in sampling and analytical methodology, in situ fpXRF analysis of vegetation is an effective method in both contamination surveys and biogeochemical mineral exploration for a range of trace and major elements.

Rapid detection of cadmium and its distribution in Miscanthus sacchariflorus based on visible and near-infrared hyperspectral imaging

Monitoring the effectiveness of Miscanthus sacchariflorus to meet the basic requirements for environmental remediation projects is an important step in determining its use as a productive bioenergy crop for phytoremediation. Conventional chemical methods for the determination of cadmium (Cd) contents involve time-consuming, monotonous and destructive procedures and are not suitable for high-throughput screening. In the present study, visible and near-infrared hyperspectral imaging technology combined with chemometric methods was used to assess the Cd concentrations in M. sacchariflorus. The total Cd concentrations in different plant tissues were measured using an inductively coupled plasma-mass spectrometer. Partial least-squares regression and least-squares support vector machine were implemented to estimate Cd contents from spectral reflectance. Successive projections algorithm and competitive adaptive reweighted sampling (CARS) methodology were used for selecting optimal wavelength. The CARS-partial least-squares regression model resulted in the most accurate predictions of Cd contents in M. sacchariflorus leaves, with a determination coefficient (R²) of 0.87 and a root mean square error (RMSE) value of 97.78 for the calibration set, and an R² value of 0.91 and a RMSE value of 75.95 for the prediction set. The CARS-least-squares support vector machine model resulted in the most satisfactory predictions of Cd contents in roots, with R² values of 0.95 (RMSE, 0.92 × 10³) for the calibration set and 0.90 (RMSE, 1.64 × 10³) for the prediction set. Finally, the Cd concentrations in different plant tissues were visualized on the prediction maps by predicted spectral features on each hyperspectral image pixel. Thus, visible and near-infrared imaging combined with chemometric methods produces a promising technique to evaluate M. sacchariflorus' Cd phytoremediation capability in high-throughput metal-contaminated field applications.

Portable X-Ray Fluorescence Spectroscopy for Rapid and Cost-Effective Determination of Elemental Composition of Ground Forage

The recent development of portable X-ray fluorescence spectrometers (PXRF) has created new avenues for rapid plant elemental concentration determination at reduced cost while avoiding hazardous chemicals. A few studies have indicated the potential use of PXRF for homogenous plant tissue analysis. However, there is a lack of information for analysis of heterogeneous plant samples like livestock forage, which consists of a mixture of several species and plant parts, each varying in elemental concentration. Our objective was to evaluate PXRF for forage analysis, specifically the effect of forage particle size and scan time on important elements including P, K, Ca, and Fe determination. Hay samples (n = 42) were oven dried (60°C for 3 days) and ground into three particle sizes (≤0.5 mm, 0.25-0.5 mm and 1-2 mm). Prepared samples were scanned by PXRF using a vacuum (<10 torr) without a filter. Samples were placed in cups over thin prolene X-ray film and scanned for 180 s. A subset (n = 29) were also scanned for 60 and 120 s. PXRF counts for P, K, Ca, and Fe were compared with laboratory Inductively Coupled Plasma Optical Emission Spectroscopy (ICP) determinations, using regression models. Results indicated that these elements could potentially be determined with PXRF (r ² ≥ 0.70) in heterogeneous forage samples. Relationship strength increased with decreasing particle size, however, the relationship was still strong (r ² ≥ 0.57) at the largest particle size. Scanning time did not affect the relationship with ICP concentration for any of the particle sizes evaluated. This work demonstrated that with the right sample preparation PXRF can obtain results comparable to acid digestion and ICP regardless of sample composition, and suggests the potential for in situ determinations.

Multiple methods for the identification of heavy metal sources in cropland soils from a resource-based region

Examination of heavy metal sources in soils from a resource-based region is essential for source identification and implementation of restoration strategies regarding soil contamination. A total of 1069 samples were collected from cropland soils in the Baiyin District (Loess Plateau, Northwest China), a characteristically resource-based region to investigate the sources of arsenic (As), chromium (Cr), copper (Cu), manganese (Mn), nickel (Ni), lead (Pb), vanadium (V), and zinc (Zn). Source identification was analyzed by multiple methods including spatial deviation (SD), correlation analysis (CA), enrichment factor (EF), principal component analysis (PCA), geographic information system (GIS), and positive matrix factorization (PMF). The results showed the combined applications of PMF, GIS, and PCA were accurate, pragmatic, and effective for source apportionment. Three origins were identified and the contribution rates were calculated as follows: approximately 95% of As came from wastewater irrigation; 75, 88, 60, and 76% of Cr, Mn, Ni, and V were separately derived from natural origins; and 81, 93, and 70% of Cu, Pb, and Zn originated from industrial sources, respectively. Natural origins, industrial sources, and wastewater irrigation were the three main contributors of heavy metals to cropland soils in this region.

Application of Energy Dispersive X-ray Fluorescence Spectrometry to the Determination of Copper, Manganese, Zinc, and Sulfur in Grass ( Lolium perenne) in Grazed Agricultural Systems

Conventional methods for the determination of major nutrients and trace elements in grass rely on acid digestion followed by analysis using inductively coupled plasma optical emission spectrometry (ICP-OES), which can be both time consuming and costly. Energy dispersive X-ray fluorescence (EDXRF) spectrometry offers a rapid alternative that can determine multiple elements in a single scan. Copper, Mn, Zn, and S in grass samples were determined using EDXRF with a number of different calibration approaches using both empirical standards and the theoretical relationships between concentrations and intensities. Using an existing archive of 467 grass samples of known concentrations, a suite of 30 samples was selected as empirical grass standards to build a calibration set between sample concentrations and EDXRF intensities. The theoretical or standardless approach used the fundamental parameters method to determine element concentrations. To validate the two calibration methods, 59 samples were randomly selected from the same archive and database and analyzed by EDXRF. The measurements of Cu, Mn, Zn, and S were compared with the ICP-OES values using agreement statistics. An excellent correlation was observed between the concentrations determined by EDXRF and ICP-OES ( R > 0.90) regardless of the calibration approach. However, agreement and closeness to the true value varied and were assessed using agreement statistics. Across all elements, the empirically calibrated samples were in excellent agreement with the values determined by ICP-OES. The theoretical calibrations provided excellent agreement for Mn and Zn, but a degree of fixed and proportional bias was observed in the Cu and S values. Fixed bias was corrected by subtracting the computed bias from the EDXRF concentrations and improved the overall agreement. Similarly, proportional bias was corrected using the linear regression model to predict the corrected EDXRF values. This improved the overall agreement with the ICP-OES values for both Cu and S using corrected fundamental parameters calibrations. This study provides a practical basis for the use of EDXRF to determine Cu, Mn, Zn, and S in grass samples to monitor forage quality in grazed systems without the need for sample digestion. The observed fixed and proportional bias in the theoretical calibrations can be corrected provided that a good correlation exists between EDXRF and conventional methods.