# Comparing portable x-ray fluorescence spectroscopy instrumentation for metallome analysis of herbarium specimens

Imam Purwadi, Peter D Erskine, Lachlan W Casey, Antony van der Ent

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Imam Purwadi, Peter D Erskine, Lachlan W Casey, Antony van der Ent. Comparing portable x-ray fluorescence spectroscopy instrumentation for metallome analysis of herbarium specimens. Ecological Research, 2024, 39, pp.977 - 987. 10.1111/1440-1703.12501. hal-04846349

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# Comparing portable x-ray fluorescence spectroscopy instrumentation for metallome analysis of herbarium specimens

Imam Purwadi<fnref n="1" /> | Peter D. Erskine<fnref n="1" /> | Lachlan W. Casey<fnref n="2" /> |
Antony van der Ent<fnref n="1" />,<fnref n="3" />,<fnref n="4" />

## Correspondence

Antony van der Ent, Laboratory of Genetics, Wageningen University and Research, Wageningen, The Netherlands.
Email: antony.vanderent@wur.nl

## Abstract

The use of x-ray fluorescence (XRF) instruments for metallome analysis of herbarium specimens to discover hyperaccumulator plant species has gained popularity, but a growing concern arises about intercomparability from the use of different instrument makes and models. Therefore, this study aimed to assess the performance and comparability of the results generated by three different XRF instruments and three different quantification methods (empirical calibration based on XRF versus inductively coupled plasma atomic emission spectroscopy [ICP-AES] regression, in-built manufacturer algorithms, and an independent GeoPIXE software pipeline based on Fundamental Parameters). Three instruments with distinct specifications were chosen to improve the generalizability of the results, ensuring relevance to a wide range of instruments that may be used in the future for metallome analysis of herbarium specimens. Each instrument was used to scan a representative set of dried hyperaccumulator plant leaf samples, and their accuracy in quantifying elemental concentrations was then compared. The manufacturer algorithms overestimate the elemental concentrations and have the highest errors. The empirical calibrations have the closest mean concentration to the mean concentrations reported by ICP-AES, but can produce negative values. The independent pipeline performance is marginally better than the empirical calibration, but it takes substantially more time and effort to setup the Fundamental Parameters through reverse engineering the instrument hardware parameters. Using the GeoPIXE independent pipeline to extract the XRF peak intensity to use in the empirical calibration performs better than manufacturer algorithms, while avoiding the complicated setup requirements, and this should be considered for further development.

## KEYWORDS

foliar elemental concentration, herbarium, hyperaccumulator, standardization, XRF

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

© 2024 The Author(s). *Ecological Research* published by John Wiley & Sons Australia, Ltd on behalf of The Ecological Society of Japan.

<footnote n="1">¹Centre for Mined Land Rehabilitation, Sustainable Minerals Institute, The University of Queensland, St Lucia, Queensland, Australia</footnote>

<footnote n="2">²Centre for Microscopy and Microanalysis, The University of Queensland, St Lucia, Queensland, Australia</footnote>

<footnote n="3">³Laboratory of Genetics, Wageningen University and Research, Wageningen, The Netherlands</footnote>

<footnote n="4">⁴Université de Lorraine, INRAE, LSE, Nancy, France</footnote>
# 1 | INTRODUCTION

Herbarium collections are giant repositories of plant diversity holding over 397 million specimens globally (Thiers, 2022). Traditionally, herbarium specimens are used for taxonomy and systematics (Besnard et al., 2018). However, they can also be used for other purposes, including ecological investigations and environmental monitoring (Greve et al., 2016; Heberling & Isaac, 2017). Since 2019, portable x-ray fluorescence (XRF) instrumentation has been introduced to determine herbarium foliar elemental concentrations, that is, the plant metallome or elementome (van der Ent, Echevarria, et al., 2019), and XRF scanning projects have been conducted around the world, leading to numerous new hyperaccumulator plant species being discovered (e.g., Abubakari, Nkrumah, Fernando, et al., 2021; Belloeil et al., 2021; Do et al., 2020; Gei et al., 2020; McCartha et al., 2019; Nkrumah et al., 2018; van der Ent, Ocenar, et al., 2019). Prior to this, handheld XRF instruments have been commonly used for mineral exploration, archelogy and art studies, and waste management (Shugar, 2013; Vanhoof et al., 2013; Zhou et al., 2023).

To date, all herbarium XRF scanning has been conducted (coincidentally) using the same instrument model from the same manufacturer. However, this may not be the case in the future due to advances in new technology, instrument availability, and so on. Hence, a concern arose regarding comparing the results from different instrument makes and models (Purwadi et al., 2022). Handheld XRF instruments have three core components that can vary between manufacturers and models: the source and filters that produce x-rays to illuminate a sample, the detector that records fluorescent x-rays from a sample, and the algorithm that converts the recorded signals into elemental concentrations (Bosco, 2013). Different anode materials, such as silver (Ag), molybdenum (Mo), or rhodium (Rh) in the source, will produce a different polychromatic spectra, or a monochromatic spectrum if additional crystal optics are used. A monochromatized source should perform better than polychromatic sources due to the absence of the Bremsstrahlung background, thereby substantially improving the signal-to-noise/background ratio (Wang et al., 2023). Detecting light elements is challenging because these elements produce weak fluorescent radiation that is prone to absorption by the sample, by the air path, and by the detector window.

The XRF instruments usually have a manufacturer calibration based on Fundamental Parameter algorithms that assume certain sample properties to quantify the XRF spectrum into elemental concentrations present in

the sample. However, these algorithms have not been explicitly designed for measuring thin dried plant leaves, such as herbarium specimens (Purwadi et al., 2022), but for rocks or pelletized powder samples (Zhou et al., 2023). Empirical calibrations have been used to correct the raw herbarium XRF data in line with Inductively coupled plasma atomic emission spectroscopy (ICP-AES) measurements (e.g., Abubakari, Nkrumah, Fernando, et al., 2021; Belloeil et al., 2021; Do et al., 2020; Gei et al., 2020; McCartha et al., 2019; Nkrumah et al., 2018; van der Ent, Ocenar, et al., 2019). However, empirical calibration has limitations due to matrix effects (variations in sample composition, density, and thickness) between calibration standards and samples (Rousseau et al., 1996). The direct effect is underestimation or overestimation when the areal density of the sample is less or higher than the standard, respectively (Purwadi et al., 2022). A new independent data analysis pipeline based on Dynamic Analysis implemented in GeoPIXE software, hereafter called the *independent pipeline*, was developed to overcome the limitations of empirical calibrations (Purwadi et al., 2022). The independent pipeline determines the mass of a sample as its areal density (sample surface size × density) and incorporates this into the quantification process (Ryan, 2000; Ryan et al., 2005). The performance of this independent pipeline has not yet been tested across different XRF instrument make and models.

In this study, we assessed the performance of three different calibrations (manufacturer algorithms, empirical calibration, and independent pipeline) for XRF analysis of dried plant leaves. So far, herbarium XRF specimen has been conducted by using the portable XRF instrument, Thermo Fisher Scientific Niton XL3t 950 GOLDD +, and to enhance the generalizability of the findings of our study for the future instruments, two other handheld XRF instruments with distinctly different specifications were also used. Specifically, we aim to answer the following questions: (i) What are the limits of detections? (ii) What is the accuracy of the calibrations against ICP-AES analysis, and (iii) How exactly do the instruments differ in their performance?

# 2 | MATERIALS AND METHODS

## 2.1 | Plant material samples and XRF measurements

In total, 114 dried hyperaccumulator leaf samples from our archive were selected at random. Each leaf sample was cut into a 1 × 1 cm² piece and re-dried for 48 h at
<figure><img src="image_2.png" /><figcaption>**TABLE 1** Instrument settings for the three instruments.</figcaption></figure>

<table><thead><tr><td>Parameters</td><td>Bruker Tracer 5 g</td><td>Thermo Fisher Scientific Niton XL3t</td><td>XOS Rocksand</td></tr></thead><tbody><tr><td>Source anode</td><td>Rhodium (Rh)</td><td>Silver (Ag)</td><td>Molybdenum (Mo)</td></tr><tr><td>Filter</td><td>None</td><td>Propriety (Fe, Ag)</td><td>None</td></tr><tr><td>Voltage (kV)</td><td>50</td><td>50</td><td>25–50</td></tr><tr><td>Current (μA)</td><td>35</td><td>40</td><td>200</td></tr><tr><td>Duration (s)</td><td>30</td><td>30</td><td>120<sup>a</sup></td></tr><tr><td>Manufacturer algorithm</td><td>Not provided</td><td>Provided</td><td>Provided</td></tr><tr><td>Empirical calibration</td><td>Performed</td><td>Performed</td><td>Performed</td></tr><tr><td>Dynamic analysis</td><td>Performed</td><td>Performed</td><td>Performed</td></tr></tbody></table>

60°C. The leaf samples were subsequently analyzed once per sample using the three handheld XRF instruments. Each instrument was positioned with the beam facing up and nothing behind the sample apart from the instrument radiation shielding cup. The layer orders (from bottom to top) for each measurement are the portable instrument, thin film (manufacturer proprietary), sample, ±10 mm air, and safety cup (Figure 1). Compared to the herbarium measurements, where XRF scanning needs to be done on top of the cardboard and pure metal (titanium) beneath the sample, this setting aimed to attain ideal measurement conditions with no background XRF signal from the underlying material.

Three portable XRF instruments were compared, namely a Bruker Tracer 5 g, a Thermo Fisher Scientific Niton XL3t 950 GOLDD+, and a XOS HD Rocksand instrument. The settings for each instrument are given in Table 1. The three instruments have different anode sources, and only the Thermo Fisher instrument uses a permanent, propriety filter made of iron and silver. The voltage, current, and measurement durations, Bruker

and Thermo Scientific instruments have similar settings, while no adjustments could be made for the Rocksand instrument. The manufacturer algorithms for Thermo Fisher Scientific and Rocksand report elemental concentrations, whereas the Tracer 5 g instrument does not report quantitation on the device (additional software is needed). As this test is intended to mimic realistic operating conditions, the Bruker was therefore excluded from the comparison of manufacturer algorithms. In studies to date, empirical calibrations have been used by establishing a linear regression between elemental concentrations reported by the XRF instrument and ICP-AES (e.g., Abubakari, Nkrumah, Erskine, et al., 2021; Abubakari, Nkrumah, Fernando, et al., 2021; Belloeil et al., 2021; Do et al., 2020; Nkrumah et al., 2018; van der Ent, Ocenar, et al., 2019). However, in this study, the three regression lines (linear regression, linear regression forced at origin 0, 0, and power regression) were constructed by plotting the ICP-AES elemental concentrations in the y-axis against the concentrations reported by the manufacturer algorithms or independent pipelines in

<footnote>aUnchangeable.</footnote>
the x-axis. No data transformation was conducted during fitting regression models. The calibration process of the independent pipeline in GeoPIXE for each instrument followed a procedure developed previously (Purwadi et al., 2022). First, three pure thin film XRF standards (titanium, tin, and gold) were measured by the three instruments, producing the XRF spectra of the standards. The XRF spectra were then imported into GeoPIXE. All instrument settings related to sources, filters, detectors, and distances were changed iteratively, until the settings reported the concentrations for titanium, tin, and gold close to 100%. The settings are used to estimate the elemental concentration of the samples. GeoPIXE also requires sample properties, and parameters from the previous study (Purwadi et al., 2022) were used, with the sample properties set to: a density of 0.9 g cm⁻³ and chemical bulk composition of C₆H₁₀O₅, while the areal density of each sample was calculated by dividing the weight of each sample by the area of each specimen (1 × 1 cm²).

## 2.2 | ICP-AES analysis of plant samples

After all plant leaf samples were analyzed with the three XRF instruments, the samples were weighted. Each leaf sample was put into a 6-mL polypropylene tube, then 2 mL of 70% HNO₃ was added and allowed to predigest for 48 h. After pre-digestion, the tubes were inserted in a block heater (Thermo Scientific) and heated for 2 h at 125°C. Ultrapure water (18.2 MΩ·cm at 25°C) was used to make the total volume of each digested sample up to 10 mL. Finally, ICP-AES (Thermo Scientific iCAP 7400) analysis was performed to quantify the concentrations of chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), and zinc (Zn). Yttrium standard was injected during the IC-PAES analysis to compensate for matrix-based variations. A 4-point standard was prepared for the calibration lines. Certified reference material (Sigma-Aldrich Periodic Table mix 1 for ICP TraceCERT®, 33 elements, 10 mg L⁻¹ in HNO₃), internal material standard, and matrix blanks were also measured as quality controls.

## 2.3 | Statistical analysis

All statistical analysis was conducted using Microsoft Excel. The regression parameters were obtained by plotting the ICP-AES concentrations in the y-axis, and the concentrations reported by the manufacturer or independent pipeline in the x-axis. To make a fair comparison, the mean absolute errors were calculated pairwise,

meaning that the calculation was performed only on samples were detected in all algorithms. The full results (n = 114) for Mn, Fe, Ni, Cu, Co, Zn obtained using ICP-AES and XRF analysis (with manufacturer algorithms and GeoPIXE approach) are provided in the Supporting Information (Tables S1-S6 and Figure S1).

# 3 | RESULTS AND DISCUSSION

Figure 2 shows the spectra of one sample acquired using the three different instruments used in this study. The top spectrum is from the XOS instrument with an Mo-anode source with monochromatizing optics. The middle spectrum is of the Bruker instrument with an Rh-anode source, while the bottom spectrum is of the Thermo Fisher Scientific instrument with an Ag anode-source and filter. Each of the peaks represent the presence of a specific element inside the sample. All spectra varied in intensity but had similar peaks at an energy ranging from 5 to 15 keV. In contrast, different patterns are observed at energies less than ~ 5 keV, where low-atomic-number elements emit XRF. In the Ag-anode spectrum, no strong peaks are observed in the range lower than 5 keV because a filter is located in front of the anode reducing the low x-ray energy that is effective for light elements (elements with low atomic numbers). The ICP-AES results contain the elemental concentrations of macronu-trients, such as magnesium, potassium, and calcium. However, none of the XRF instruments detected the sig-nal from these elements whose atomic number was lower than Mn, as shown in Figure 2. Therefore, the perfor-mance of the three calibrations was evaluated using six elements (Co, Cu, Fe, Mn, Ni, and Zn) with atomic num-bers greater than Mn. Chromium was not included because it typically does not naturally occur inside plant leaves at concentrations greater than 10 mg kg⁻¹, and it was not detected in most of the samples.

Table 2 lists the concentrations for the six elements calculated/modeled using the different calibration methods. The average concentrations obtained from the empirical calibration based on the manufacturer algo-rithm did not show as close an agreement with the aver-age concentrations derived from ICP-AES data compared to the empirical calibration based on the independent pipeline. When comparing directly to the ICP-AES con-centrations, it is clear that the independent pipeline per-formed better compared to the manufacturer algorithms for all elements and instruments, except for Cu in the XOS instrument (Table 3). In terms of the mean absolute errors, the independent pipeline produced errors of at least 6% and at maximum 95% less than the manufac-turer algorithm. While improving the accuracy of
<figure><img src="image_3.png" /><figcaption>FIGURE 2 Spectra acquired by measuring a sample using three different instruments with different anode sources: top-Mo (XOS Rocksand), middle-Rh (Bruker Tracer 5 g), and bottom-Ag (Thermo Fisher Scientific Xlt3). The spectra are normalized to the maximum intensity per spectrum and stacked to improve clarity. The graph can be used to identify the presence of peaks not peak intensity.</figcaption></figure>

manufacturer algorithms, especially for the Thermo Fisher Scientific instrument, empirical calibrations did not always lead to improvements, especially the power regression which tends to increase the errors. The power regression is usually chosen to prevent producing negative values when making predictions, and forcing the origin of the linear regression to 0, 0 could be an alternative as it performs better than the power regression while not producing negative values. Using Geo-PIXE to determine the XRF peak intensity for empirical calibrations provides an approach with slightly lower accuracy, but it is easier to apply than then the Fundamental Parameter analysis approach because it requires instrument specifications including propriety information which the manufacturers are reluctant to share.

The manufacturer algorithms assume that samples fulfill thickness requirements, but herbarium specimens are not thick enough for elements with atomic numbers $Z < 14$ (Purwadi et al., 2022). Failing to meet the thickness requirement causes under or over-estimation of elemental concentrations. Figure 3 shows the ratio concentrations of ICP-AES to different calibrations. The samples were hyperaccumulator leaves and were measured under intact conditions. As many studies reported (Bihanic et al., 2021; Carr et al., 2003; Fernando et al., 2013; Maejima et al., 2014), hyperaccumulator plants may unevenly deposit metals, which could be primarily in the upper or lower epidermal layers. Measuring samples under such conditions could lead to underestimations and overestimations, especially when using the XRF technique, which is sensitive to the distribution of

the elements of interest. For example, 99% of the signal recorded by the detectors come from the very top of the sample surface, so an enrichment in the top epidermis could lead to overestimations. In the case of enrichment in the bottom epidermis, the primary x-ray emitted by the instrument may trigger the element of interest to produce XRF signals, but these signals may be significantly or fully absorbed by the sample matrix. Furthermore, GeoPIXE requires accurate input parameters, and there are still limitations in the software and the procedure for reverse engineering the instrument. For example, Geo-PIXE does not model the changes in x-rays emitted from the anode due to the presence of a filter. Additionally, signal artifacts at the vicinity of Ni and Zn XRF K-alpha peaks have been observed, and further investigation by triggering the instrument to another XRF instrument revealed that these artifacts did not originate from the detector, but from the XRF chamber of the Thermo Fisher Scientific instrument. No correction was performed, which may contribute to overestimations of Ni and Zn concentrations.

Although handheld XRF provides a non-destructive, fast, and easy methodology to obtain elemental concentrations of plant material, it is not a “one-stop solution” to measure and quantify all elements of the Periodic Table due to physics and hardware limitations. This study aimed to solve part of the known limitations by testing and optimizing the calibration methods, but this cannot overcome all instrument specific limitations. For example, the anode has a critical role in producing the primary x-rays, and depending on the anode material,
<table><caption>TABLE 2 Elemental concentrations (all values in mg kg⁻¹) reported by different instruments and methods of 114 samples.</caption><thead><tr><th></th><th>XOS GeoPIXE</th><th>XOS manufacturer</th><th>Thermo GeoPIXE</th><th>Thermo manufacturer</th><th>Bruker GeoPIXE</th></tr></thead><tbody><tr><td>Original</td><td>1140 [2.49-14.900]<br/>n > LOD = 114</td><td>1350 [6.8-12.900]<br/>n > LOD = 98</td><td>2410 [92.5-11.200]<br/>n > LOD = 53</td><td>13.600 [125-102.000]<br/>n > LOD = 75</td><td>1110 [6.44-11.700]<br/>n > LOD = 112</td></tr><tr><td>Linear regression</td><td>1220 [381-11.300]<br/>n > LOD = 114</td><td>1360 [268-10.700]<br/>n > LOD = 98</td><td>2550 [79.7-11.900]<br/>n > LOD = 53</td><td>1830 [-33.8-14.000]<br/>n > LOD = 75</td><td>1240 [-26-13.300]<br/>n > LOD = 112</td></tr><tr><td>Linear regression at origin</td><td>907 [1.99-11.900]<br/>n > LOD = 114</td><td>1160 [5.82-11.000]<br/>n > LOD = 98</td><td>2570 [98.3-11.900]<br/>n > LOD = 53</td><td>1860 [17.1-13.900]<br/>n > LOD = 75</td><td>1260 [7.31-13.300]<br/>n > LOD = 112</td></tr><tr><td>Power regression</td><td>686 [8.42-6350]<br/>n > LOD = 114</td><td>713 [24.9-4380]<br/>n > LOD = 98</td><td>2520 [66.1-12.900]<br/>n > LOD = 53</td><td>1590 [11.5-12.600]<br/>n > LOD = 75</td><td>1060 [8.17-10.600]<br/>n > LOD = 112</td></tr><tr><td colspan="6">Fe: ICP-AES concentration: 308 [4.06-4800] n > LOD = 114</td></tr><tr><th></th><th>XOS GeoPIXE</th><th>XOS manufacturer</th><th>Thermo GeoPIXE</th><th>Thermo manufacturer</th><th>Bruker GeoPIXE</th></tr><tr><td>Original</td><td>64.4 [4.73-375]<br/>n > LOD = 114</td><td>50.6 [9.8-239]<br/>n > LOD = 69</td><td>64.6 [18-188]<br/>n > LOD = 112</td><td>1370 [309-4520]<br/>n > LOD = 97</td><td>73.7 [9.33-296]<br/>n > LOD = 114</td></tr><tr><td>Linear regression</td><td>308 [80.3-1490]<br/>n > LOD = 114</td><td>319 [98.2-1340]<br/>n > LOD = 69</td><td>312 [-59-1300]<br/>n > LOD = 112</td><td>298 [-35.4-1290]<br/>n > LOD = 97</td><td>308 [65.6-1150]<br/>n > LOD = 114</td></tr><tr><td>Linear regression at origin</td><td>283 [20.8-1650]<br/>n > LOD = 114</td><td>21.5 [4.17-102]<br/>n > LOD = 69</td><td>81.4 [22.2-279]<br/>n > LOD = 112</td><td>38.9 [8.24-251]<br/>n > LOD = 97</td><td>36.1 [4.34-243]<br/>n > LOD = 114</td></tr><tr><td>Power regression</td><td>96 [31.7-223]<br/>n > LOD = 114</td><td>98.8 [63-171]<br/>n > LOD = 69</td><td>109 [16.2-483]<br/>n > LOD = 112</td><td>96.2 [46.4-180]<br/>n > LOD = 97</td><td>100 [22.9-285]<br/>n > LOD = 114</td></tr><tr><td colspan="6">Co: ICP-AES concentration: 106 [0.0444-3750] n > LOD = 108</td></tr><tr><th></th><th>XOS GeoPIXE</th><th>XOS manufacturer</th><th>Thermo GeoPIXE</th><th>Thermo manufacturer</th><th>Bruker GeoPIXE</th></tr><tr><td>Original</td><td>151 [1.43-3650]<br/>n > LOD = 80</td><td>303 [7.6-3780]<br/>n > LOD = 39</td><td>551 [81.9-3420]<br/>n > LOD = 19</td><td>3140 [204-25.200]<br/>n > LOD = 26</td><td>122 [3.11-1940]<br/>n > LOD = 94</td></tr><tr><td>Linear regression</td><td>143 [-7.09-3660]<br/>n > LOD = 80</td><td>282 [-6.92-3680]<br/>n > LOD = 39</td><td>552 [45.6-3650]<br/>n > LOD = 19</td><td>421 [-15-3690]<br/>n > LOD = 26</td><td>121 [-93.4-3400]<br/>n > LOD = 94</td></tr><tr><td>Linear regression at origin</td><td>151 [1.43-3650]<br/>n > LOD = 80</td><td>294 [7.36-3660]<br/>n > LOD = 39</td><td>580 [86.2-3600]<br/>n > LOD = 19</td><td>452 [29.3-3630]<br/>n > LOD = 26</td><td>199 [5.06-3160]<br/>n > LOD = 94</td></tr><tr><td>Power regression</td><td>173 [0.267-6110]<br/>n > LOD = 80</td><td>206 [5.37-2530]<br/>n > LOD = 39</td><td>547 [72.1-3610]<br/>n > LOD = 19</td><td>368 [18-3320]<br/>n > LOD = 26</td><td>162 [0.00619-10.000]<br/>n > LOD = 94</td></tr><tr><td colspan="6">Ni: ICP-AES concentration: 2080 [0.3-14.400] n > LOD = 112</td></tr><tr><th></th><th>XOS GeoPIXE</th><th>XOS manufacturer</th><th>Thermo GeoPIXE</th><th>Thermo manufacturer</th><th>Bruker GeoPIXE</th></tr><tr><td>Original</td><td>2190 [1.19-13.000]<br/>n > LOD = 97</td><td>6550 [4.2-15.000]<br/>n > LOD = 41</td><td>2260 [91.7-12.000]<br/>n > LOD = 113</td><td>52.800 [9930-96.300]<br/>n > LOD = 36</td><td>2400 [144-14.900]<br/>n > LOD = 114</td></tr><tr><td>Linear regression</td><td>2230 [48-13.000]<br/>n > LOD = 97</td><td>5270 [1900-9650]<br/>n > LOD = 41</td><td>2060 [24-11.300]<br/>n > LOD = 112</td><td>6320 [2380-10.300]<br/>n > LOD = 94</td><td>2040 [-216-14.500]<br/>n > LOD = 112</td></tr></tbody></table>
<table><caption>TABLE 2 (Continued)</caption><thead><tr><th colspan="6">Ni: ICP-AES concentration: 2080 [0.3–14,400] n > LOD = 112</th></tr><tr><th></th><th>XOS GeoPIXE</th><th>XOS manufacturer</th><th>Thermo GeoPIXE</th><th>Thermo manufacturer</th><th>Bruker GeoPIXE</th></tr></thead><tbody><tr><td></td><td>n > LOD = 97</td><td>n > LOD = 41</td><td>n > LOD = 113</td><td>n > LOD = 36</td><td>n > LOD = 114</td></tr><tr><td>Linear regression at origin</td><td>2200 [1.2–13,100]</td><td>4740 [3.04–10,900]</td><td>2110 [85.5–11,200]</td><td>6060 [1140–11,100]</td><td>2290 [137–14,100]</td></tr><tr><td></td><td>n > LOD = 97</td><td>n > LOD = 41</td><td>n > LOD = 113</td><td>n > LOD = 36</td><td>n > LOD = 114</td></tr><tr><td>Power regression</td><td>1670 [0.673–10,100]</td><td>3480 [12.4–6990]</td><td>2190 [0.228–20,000]</td><td>4790 [1780–7160]</td><td>1870 [0.122–31,200]</td></tr><tr><td></td><td>n > LOD = 97</td><td>n > LOD = 41</td><td>n > LOD = 113</td><td>n > LOD = 36</td><td>n > LOD = 114</td></tr><tr><th colspan="6">Cu: ICP-AES concentration: 5.3 [0.439–20.5] n > LOD = 107</th></tr><tr><th></th><th>XOS GeoPIXE</th><th>XOS manufacturer</th><th>Thermo GeoPIXE</th><th>Thermo manufacturer</th><th>Bruker GeoPIXE</th></tr><tr><td>Original</td><td>4.02 [0.773–14.3]</td><td></td><td>4.46 [1.1–10.1]</td><td>194 [103–357]</td><td>10.5 [1.81–29.7]</td></tr><tr><td></td><td>n > LOD = 114</td><td></td><td>n > LOD = 62</td><td>n > LOD = 55</td><td>n > LOD = 114</td></tr><tr><td>Linear regression</td><td>5.41 [4.32–8.83]</td><td></td><td>5.86 [3.26–10.3]</td><td>5.45 [4.59–7]</td><td>5.38 [3.21–10.2]</td></tr><tr><td></td><td>n > LOD = 114</td><td></td><td>n > LOD = 62</td><td>n > LOD = 55</td><td>n > LOD = 114</td></tr><tr><td>Linear regression at origin</td><td>3.85 [0.74–13.6]</td><td>5.64 [1.7–11.6]</td><td>5.49 [1.35–12.5]</td><td>5.17 [2.74–9.53]</td><td>4.89 [0.842–13.8]</td></tr><tr><td></td><td>n > LOD = 114</td><td>n > LOD = 36</td><td>n > LOD = 62</td><td>n > LOD = 55</td><td>n > LOD = 114</td></tr><tr><td>Power regression</td><td>4.08 [2.76–6.18]</td><td>4.87 [3.1–7.76]</td><td>4.71 [2.25–7.49]</td><td>4.23 [3.63–4.97]</td><td>4.07 [1.82–6.85]</td></tr><tr><td></td><td>n > LOD = 114</td><td>n > LOD = 36</td><td>n > LOD = 62</td><td>n > LOD = 55</td><td>n > LOD = 114</td></tr><tr><th colspan="6">Zn: ICP-AES concentration: 277 [1.91–4280] n > LOD = 114</th></tr><tr><th></th><th>XOS GeoPIXE</th><th>XOS manufacturer</th><th>Thermo GeoPIXE</th><th>Thermo manufacturer</th><th>Bruker GeoPIXE</th></tr><tr><td>Original</td><td>248 [2.43–4620]</td><td>377 [2.7–4600]</td><td>723 [44.2–4870]</td><td>1670 [27.3–20,500]</td><td>292 [1.88–4650]</td></tr><tr><td></td><td>n > LOD = 114</td><td>n > LOD = 85</td><td>n > LOD = 41</td><td>n > LOD = 96</td><td>n > LOD = 97</td></tr><tr><td>Linear regression</td><td>277 [24.7–4770]</td><td>368 [31.9–4170]</td><td>736 [26–5080]</td><td>327 [–10.3–4200]</td><td>324 [13.9–4990]</td></tr><tr><td></td><td>n > LOD = 114</td><td>n > LOD = 85</td><td>n > LOD = 41</td><td>n > LOD = 96</td><td>n > LOD = 97</td></tr><tr><td>Linear regression at origin</td><td>258 [2.52–4790]</td><td>343 [2.46–4190]</td><td>750 [45.8–5060]</td><td>340 [5.58–4190]</td><td>314 [2.03–5000]</td></tr><tr><td></td><td>n > LOD = 114</td><td>n > LOD = 85</td><td>n > LOD = 41</td><td>n > LOD = 96</td><td>n > LOD = 97</td></tr><tr><td>Power regression</td><td>198 [3.76–3200]</td><td>226 [5.24–2210]</td><td>728 [26–5750]</td><td>247 [2.7–3310]</td><td>296 [1.55–4910]</td></tr><tr><td></td><td>n > LOD = 114</td><td>n > LOD = 85</td><td>n > LOD = 41</td><td>n > LOD = 96</td><td>n > LOD = 97</td></tr></tbody></table>

Note: The concentrations are rounded up to three significant figures and summarized into mean [minimum–maximum] and detection limits. Tracer 5 g instrument does not include manufacturer calibration for custom settings. Original rows show the concentrations reported by the manufacturer algorithms or GeoPIXE. Regression rows show the concentration predicted based on regression models between the concentrations reported by the manufacturer algorithms or GeoPIXE and ICP-AES concentrations. The Tracer 5 g instrument does not include a manufacturer calibration.

Abbreviations: ICP-AES, inductively coupled plasma atomic emission spectroscopy; LOD, limit of detection.
<table><caption>TABLE 3 Pairwise MAE for different algorithms.</caption><thead><tr><th></th><th>XOS<br>GeoPIXE</th><th>XOS<br>manufacturer</th><th>Thermo<br>GeoPIXE</th><th>Thermo<br>manufacturer</th><th>Bruker<br>GeoPIXE</th></tr></thead><tbody><tr><td><strong>Mn</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Direct comparison to ICP-AES</td><td>500.3</td><td>574.5</td><td>497.4</td><td>11776.4</td><td>276.9</td></tr><tr><td>Linear regression</td><td>883.4</td><td>893.9</td><td>489.1</td><td>657.3</td><td>582.0</td></tr><tr><td>Linear regression at origin</td><td>899.5</td><td>864.1</td><td>491.0</td><td>654.3</td><td>585.2</td></tr><tr><td>Power regression</td><td>1307.0</td><td>1465.2</td><td>473.4</td><td>626.5</td><td>561.3</td></tr><tr><td><strong>Co</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Direct comparison to ICP-AES</td><td>62.1</td><td>137.7</td><td>61.3</td><td>3657.0</td><td>193.1</td></tr><tr><td>Linear regression</td><td>58.0</td><td>137.2</td><td>56.0</td><td>139.4</td><td>97.1</td></tr><tr><td>Linear regression at origin</td><td>62.1</td><td>137.0</td><td>64.5</td><td>155.2</td><td>135.1</td></tr><tr><td>Power regression</td><td>217.2</td><td>225.7</td><td>51.4</td><td>171.2</td><td>509.1</td></tr><tr><td><strong>Fe</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Direct comparison to ICP-AES</td><td>231.2</td><td>245.9</td><td>232.7</td><td>1087.6</td><td>231.7</td></tr><tr><td>Linear regression</td><td>373.0</td><td>338.2</td><td>342.7</td><td>333.9</td><td>350.3</td></tr><tr><td>Linear regression at origin</td><td>364.0</td><td>260.5</td><td>233.6</td><td>249.8</td><td>246.2</td></tr><tr><td>Power regression</td><td>244.6</td><td>269.1</td><td>241.3</td><td>245.6</td><td>238.6</td></tr><tr><td><strong>Ni</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Direct comparison to ICP-AES</td><td>1612.4</td><td>3139.1</td><td>1471.5</td><td>45677.3</td><td>1060.9</td></tr><tr><td>Linear regression</td><td>1607.9</td><td>3164.1</td><td>1502.0</td><td>2681.7</td><td>1086.8</td></tr><tr><td>Linear regression at origin</td><td>1612.4</td><td>3381.3</td><td>1514.7</td><td>2799.8</td><td>1128.1</td></tr><tr><td>Power regression</td><td>2320.4</td><td>3600.1</td><td>3289.0</td><td>3018.3</td><td>3120.0</td></tr><tr><td><strong>Cu</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Direct comparison to ICP-AES</td><td>8.2</td><td>5.5</td><td>7.3</td><td>209.0</td><td>5.0</td></tr><tr><td>Linear regression</td><td>6.8</td><td>4.6</td><td>6.1</td><td>6.6</td><td>7.0</td></tr><tr><td>Linear regression at origin</td><td>8.4</td><td>8.9</td><td>6.3</td><td>6.7</td><td>7.8</td></tr><tr><td>Power regression</td><td>7.6</td><td>6.2</td><td>7.1</td><td>7.6</td><td>8.0</td></tr><tr><td><strong>Zn</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Direct comparison to ICP-AES</td><td>165.2</td><td>257.6</td><td>132.3</td><td>2828.1</td><td>129.3</td></tr><tr><td>Linear regression</td><td>169.7</td><td>226.0</td><td>127.3</td><td>160.9</td><td>135.8</td></tr><tr><td>Linear regression at origin</td><td>167.7</td><td>226.5</td><td>134.8</td><td>160.3</td><td>135.0</td></tr><tr><td>Power regression</td><td>253.2</td><td>337.4</td><td>130.1</td><td>209.1</td><td>136.2</td></tr></tbody></table>

Note: In the columns are the algorithms used for concentration estimation, while the rows describe the methods for calculating MAE. “Direct comparison” calculates MAE by directly comparing the ICP-AES concentration with the estimated concentrations from both the manufacturer and the GeoPIXE algorithm. “Linear and Power regression” calculates MAE by comparing the ICP-AES concentration with the estimated concentrations obtained through linear and power regression between the manufacturer and GeoPIXE algorithms versus using ICP-AES concentration data. The Tracer 5 g instrument does not include a manufacturer calibration.

Abbreviations: ICP-AES, inductively coupled plasma atomic emission spectroscopy; LOD, limit of detection; MAE, mean absolute errors.

the efficiency to excite the electrons of atoms, leading to the emission of characteristics XRF, differs. Theoretically, with the same current, the primary x-ray generated by

the XOS Mo anode is lower than that of Bruker Rh anode, thus more suitable for light elements. However, Figure 2 reveals that the Bruker instrument is better than
<figure><img src="image_4.png" /><figcaption>**FIGURE 3** Pairwise concentrations ratio by different algorithm to inductively coupled plasma atomic emission spectroscopy (ICP-AES). A good prediction should yield a ratio close to 1 (the red dashed lines). The violin plots in the rows represent data grouped by elements, while those in the columns represent data grouped by estimation methods.</figcaption></figure>

the XOS instrument for light elements, and this is related to the choice of filter and detector. The Thermo Fisher Scientific instrument exemplifies an extreme case in which an Fe-filter is placed in front of the Ag anode, aiming to omit a portion of the primary x-rays suitable for light elements. Nevertheless, Thermo Fisher Scientific instrument is designed for first-row transition metals, and compared to Mo and Rh anodes, the Ag anode is more suitable for elements with higher atomic numbers, that is, Y, As, and Se.

## 4 | CONCLUSIONS

The use of XRF scanning of herbarium collections to obtain metallome data of herbarium specimens is anticipated to increase in the future (e.g., Abubakari,

Nkrumah, Erskine, et al., 2021; Abubakari, Nkrumah, Fernando, et al., 2021; Belloeil et al., 2021; Do et al., 2020; Nkrumah et al., 2018; van der Ent, Ocenar, et al., 2019). Therefore, ensuring comparable results regardless of the instrument used will become increasingly important. The independent pipeline approach can fulfill this need, but has several challenges as the Dynamic Analysis, or indeed any Fundamental Parameter approach, requires information on instrument settings that are often not publicly available (as they are proprietary to the manufacturer). Reverse engineering can be performed, but this is very time-consuming. Altering the independent pipeline to extract the XRF peak intensity for empirical calibrations produces better empirical calibrations, and this should be considered for further development. Much effort has been dedicated to hyperaccumulator plants, and portable XRF instruments
have proven to be particularly powerful in their discovery. When inter-comparable, the extracted data could be used for biogeographic studies, assessing phylogenetic patterns, and answering evolutionary and ecological questions about trace elemental accumulation across the globe.

## ACKNOWLEDGMENTS

We thank DKSH Australia Pty. Ltd. and XOS Inc. for loan of the Rocksand XRF instrument.

## FUNDING INFORMATION

Imam Purwadi was the recipient of The University of Queensland Graduate School Scholarships, Australia.

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

## DATA AVAILABILITY STATEMENT

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

## ORCID

Antony van der Ent https://orcid.org/0000-0003-0922-5065

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Purwadi, I., Erskine, P. D., Casey, L. W., & van der Ent, A. (2024). Comparing portable x-ray fluorescence spectroscopy instrumentation for metallome analysis of herbarium specimens. *Ecological Research*, *39*(6), 977-987.
https://doi.org/10.1111/1440-1703.12501