Philippa Kaur

In this section, the methodology for the fluorescence spectra acquisition of the in-vivo cochineal in its natural ambient is described. The optical setup used is our own design that guarantees the detection of low levels of fluorescence present in this study. In addition, the wild cochineal reproduction model employed to ensure the existence of the samples is described. This study proposes a fluorescence standard based on a commonly used disk-shaped carmine colored pencil segment because of the non-availability of a commercial one in our laboratory.
Optical setup for fluorescence spectra measurements
The optical setup for detecting fluorescence spectra consists of (1) a power supply, (2) an excitation source, (3) a dichroic mirror, (4) a 10 × microscope objective lens, (5) a homemade sample-holder, (6) a mechanical positioning device with micrometric movements in x, y and z, (7) a 5 × microscope objective lens, (8) multi-mode optical fiber, (9) a miniature fiber optic spectrometer and (10) computer equipment. The previous elements were arranged as shown in Fig. 5.
Figure 5

Optical experimental setup for detecting fluorescence spectra: (1) power supply, (2) laser source emitting at 532 nm, (3) dichroic mirror, (4) 10 × microscope objective, (5) sample-holder with the biological specimen depicted in yellow color, (6) a mechanical positioning device with micrometric movements in x, y and z directions, (7) 5 × microscope objective, (8) optical fiber, (9) fiber optic spectrometer and (10) desktop computer showing a typical spectrum of carminic acid within a cochineal.

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The power supply (M30-TP305E, Shanghai MCP Corp.) provides a constant voltage of 3.5 V to the excitation source. The excitation source is a commercial laser pointer (KG-303-6) emitting at a wavelength of 532 nm and nominal power of 1 W. This wavelength is found within the absorption band of carminic acid diluted in methanol19, and as was demonstrated by Cárdenas23, this wavelength allows the excitation of the fluorescence of natural carminic acid from wild cochineal in-vivo. The dichroic mirror (DMLP550, Thorlabs, Inc.) is tilted at an angle of 45° with respect to the direction of laser beam propagation allowing the excitation light to be reflected towards the aperture of the 10 × microscope objective lens (M-10X, Newport, Co.) which in turn focuses this radiation on a plane where the sample is placed. The specimen to be studied is fixed on the sample holder which in turn is magnetically coupled to the mechanical positioning device (M-900, Newport, Co.), that together with a micrometric base (High-Performance Linear Stage, Newport, Co.) allows the cochineal to be placed at the focal point of the excitation beam. Both the fraction of the fluorescent light emitted isotropically by the cochineal and the fraction of the light reflected diffusely by this insect enter the 10 × objective lens and leave it as a collimated beam towards the dichroic mirror. The first fraction of light is transmitted approximately 85% to the second microscope objective lens (M-5X, Newport, Co.) while the second one is strongly blocked by the dichroic mirror. A 400 µm diameter optical fiber (QP400-2-SR, Ocean Optics, Inc.) is placed at the focal distance from this latter microscope objective to collect the fluorescence radiation and transmit it to the input port of the miniature fiber optic spectrometer (Exemplar, B&W Tek, LLC.) which allows for the spectral decomposition of the fluorescence radiation for its later processing. This spectrometer is connected through a USB port to a desktop computer where the BWSpec (B&W Tek, LLC.) software is installed, provided by the same manufacturer of this device, by means of which the acquisition of the spectra is carried out with an integration time of 200 ms. This software allows the user to save the spectra in plain text files (.txt) for further processing of the acquired information.
The use of the dichroic mirror in the optical setup allows for adequate discrimination of the excitation and emission wavelengths. It shows about a 90% reflection in the range of 380–535 nm, the range in which the wavelength of the excitation beam is found (λexc = 532 nm), whereas for wavelengths in the range of 565–800 nm it presents an 85% transmission. We expect that the in-vivo cochineal fluorescence emission should be mainly located in this last spectral region, based on the findings reported in previous research works for solutions of carminic acid in methanol19,20,23.
Carminic acid fluorescence standard
As a part of this research, we proposed the use of a segment from a carmine colored pencil (Ekuz Carmín, AZOR) as a fluorescence standard due to the absence of a commercial one in our laboratory. This homemade fluorescence standard was used to calibrate the spectra acquisition optical setup. The standard was obtained by cutting a 5 mm segment of the carmine colored pencil with a razor blade (Stainless blade, Dorco, Co.) to achieve a finer cut. Then, this segment was situated inside a plastic piece in an annulus-shape to provide protection and handling during fluorescence measurements. Finally, its face of greater diameter was polished with fine grit sandpaper (B-99 1000, Fandeli, México) to get a smooth plane surface. The opposite face was adhered to the sample-holder while the polished one was placed perpendicular to the optical axis of the 10 × objective lens so that the excitation light impinges on it allowing the optimization of the fluorescence signal recorded by the optical setup.
In order to test the temporal stability of the carminic acid fluorescence standard, a continuous acquisition of fluorescence spectra was performed by using the optical setup previously shown in Fig. 5. To do so, the excitation light source was turned on 30 min before the measurements. Then, the spectra acquisition was done one after the other by allowing the excitation light hits on the standard to detect its fluorescence, immediately blocking this radiation while its spectrum was recorded, and so on until 10 spectra were recorded.
Biological samples and reproduction model
The cochineal samples used in this research come from a cladode infected with this pest donated by farmers from the Sociedad Cooperativa Productora Agropecuaria de Nopal Tlanalapa S.C. de R. L. de C.V., from the state of Hidalgo, Mexico.
For its survival, this pest was reproduced in the Biomedical Optics Laboratory of the Polytechnic University of Tulancingo, according to the reproduction model illustrated in Fig. 6, in a small-implemented orchard of plastic pots. In these pots were planted healthy adult cladodes of approximate dimensions of 40 × 20 × 2 cm; these cladodes were intentionally infected with this pest.
Figure 6

Reproduction model of the cochineal pest in a pot with adult cladodes near an infected cladode with active wild cochineal. Observe, in the center of the pot and between the two healthy cladodes, the infected cladode.

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As observed, in a plastic pot with a diameter approximately of 20 cm, two healthy cladodes were planted with the cladode infected with the cochineal pest placed between them. The spread of the pest is carried out through the points of contact between cladodes, which in this case were the thorns of each specimen, the separation distance between them was 1.5–3 cm approximately. Since the first stage of cochineal pest is mobile, during migration to other parts of the cladode, some cochineals fell to the ground infecting it from the base.
If we consider that in literature it has been reported that during oviposition females deposit about 419 eggs4 and we note that in our reproduction model both sides of the infected cladode have about 30 colonies conformed of 1–4 adult cochineal per side, then it is assumed that the spread of the pest towards the healthy cladodes with an infected cladode is enough to contaminate. This was visually verified by observing that during the course of two days both cladodes were already contaminated. In other variants of the reproduction model it was verified that a cladode with the pest is enough to infect four to five adult cladodes approximately of similar dimensions to those used in this study. This reproduction procedure allows us to have several groups of pots where the cochineals are in different maturation stages. The reproduction model proposed in this work is similar to the previously reported reproduction methods14,15,28,29 where the planting of the cladodes is also done in pots, however, the pest infestation is carried out in a different way from the one proposed in this work.
In order for the pest to maintain an appropriate lifecycle, the pots are taken out the laboratory to expose the cladodes to sunlight during the day, taking care to avoid the rain and predators that could put the cochineals at risk. In the evening, approximately at 6 o’clock, the pots are returned to their resting place in the laboratory for their care, conservation and adequate control for this study. To be specific, in this study a photoperiod of 9 ± 1 h of direct solar radiation was stablished, to later leave the pots inside the laboratory in absolute darkness, until the next day. This photoperiod was applied for 3 months, for covering the life cycle of the cochineals (90 days approx.). A script development in MATLAB R2014a (The MathWorks, Inc.) software was performed to process the solar radiation detected by the UPT weather station (Vantage Pro2, Davis Instruments Corp.), as shown in Fig. 7, during the last five days of photoperiod from June 01st to 05th, which correspond also to the end of life cycle of the cochineal.
Figure 7

Solar radiation as a function of time (09:00–18:00 h) of the last five days of photoperiod and life cycle of the cochineal. The maximum solar radiation was between 13:00 and 14:20 h, recording a maximum solar radiation of 1037 to 1322 W/m2.

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Note that the photoperiod was established in the time range of 09:00–18:00 h, (0–540 min in graphs), corresponding to the time in which the reproduction model was exposed to direct sunlight, so that at the end of the day was placed again inside the laboratory. Figure 7 shows the following data: date, time of maximum solar radiation, also in minutes, and the value of solar radiation [W/m2], corresponding to each graph of this figure.
The values of temperature and humidity, external (blue line) and internal (red line), recorded by the UPT weather station during the fluorescence spectra acquisition period for each set presented in this work, were processed in a script developed in MATLAB R2014a (The MathWorks, Inc.) software. In Fig. 8 are shown temperature and humidity as a function of time in a temporal range of 35 min, approximately.
Figure 8

Temperature and humidity (external and internal) registered by the UPT weather station (Vantage Pro2, Davis Instruments Corp.) during the experimental fluorescence measurements for each cochineal set.

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The measurement periods of each cochineal set, comprise around 35 min, where most of the cases were performed from 18:10 to 20:15 h. In this figure, data of temperature and humidity recorded by the UPT weather station were plotted, both external and internal with respect to the CIDETyP (Centro de Investigación, Desarrollo Tecnológico, Transferencia de Tecnología y Posgrado) building where the Biomedical Optics Laboratory is located. In Table 3 the temperature and humidity related to six replicates of our experiment are shown, since the cochineal life cycle (approximately 90 days) to the spectral measurement of the cochineal sets (set 0–5). These data are arranged by stage, date, temperature and humidity. These two lasts are in turn divided into the information collected from an online weather service page (WeatherOnline Online Services, www.weatheronline.mx) and by the UPT weather station (Vantage Pro2, Davis Instruments Corp., www.davisinstruments.com). The values, corresponding to temperature and humidity recorded by the UPT weather station, are the result of an average of the data plotted in Fig. 8.
Observe that, in Table 3 exist differences between the temperature and humidity provided by the online weather service page and the UPT weather station. In this regard, since the data from the UPT weather station contain local information of the environment where both the growth of the pest in the reproduction model and the experimental fluorescence measurements were made, these values of temperature and humidity have been adopted in this work.
Selection of the samples to study spectrally
For this study 3 live-female cochineals of different sizes were selected, of around 480 µm for the small cochineal, 540 µm for the medium cochineal and 790 µm for the largest cochineal. These sizes were determined with a script developed in MATLAB R2014a (The MathWorks, Inc.) software reported in the work of Cárdenas23. We assumed that the cochineals are infesting the cladode, in other words feeding from the sap, given their morphological features and size observed and compared with those referred in the available literature4.
Sample preparation
In order to prepare the biological samples to be studied spectrally, a segment of approximately 6 mm long was cut from one of the infected cladodes planted in the laboratory. The three cochineals previously classified in different sizes were found in this portion as shown in Fig. 9a, where, according to their lifecycle, they are in the stage of nymph I (crawler) and II, that is, in their early growth stages with 23 days old. This cladode portion was fixed on the round base of a plastic cylinder with a glue stick (Lápiz adhesivo DIXON, 36 g). The other end of the round base was inserted inside of an aluminum ring to which two magnets were attached with adhesive (Kola Loka Brocha, 5 GRS) to the sides with 90° in respect to the other, which allows for the proper placement of the sample in the pinhole mount of the magnetic positioning device during fluorescence measurements. Figure 9b shows the lateral view of the sample-holder, where it can be seen that the plastic round base and the sample protrude from the aluminum ring. This device allows us to keep considerable distances and displacements between the sample and the 10 × objective microscope lens during fluorescence measurements as shown in Fig. 9c.
Figure 9

Sample. (a) Top view of the biological sample composed by a portion of cladode with presence of female cochineals classified in sizes S, M and L, indicated by yellow arrows. (b) Homemade sample-holder for the in-vivo measurement of fluorescence spectra from cochineals in their natural habitat, composed by a cylindrical plastic base where the biological sample is adhered on its top surface and an aluminum ring with two disk-shaped magnets for immobilization purposes. (c) Picture taken during the in-vivo fluorescence study of the cochineals in their natural habitat, when the cladode portion with the cochineals in nymph I and II stages is fitted on the sample-holder of the experimental setup.

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Measuring procedure of the fluorescence spectra
The in-vivo fluorescence spectra measurements from the cochineals were performed using the optical setup described at the beginning of this section under laboratory conditions of complete darkness, at a temperature range from 20 to 25 °C as well as a relative humidity range from 42 to 80%. Additionally, it was necessary to turn on the excitation laser 30 min before the measurements began in order to achieve a stable fluorescence signal. During this time the output of the laser was obstructed with a dark piece of cardboard to avoid photodegradation of the sample, which was already placed in the sample-holder. Subsequently, the sample was approached to the edge of a dark colored, 374 µm thin plastic sheet, located just at the focal distance of the objective lens. Knowing the thickness of this sheet allows for the sample to be placed at the focal distance of the microscope objective once the plastic sheet is removed by moving properly the sample with the micrometric mechanism along the z-axis. This guarantees that recorded spectra of the cochineal are carried out “correctly”, that is, when the excitation focal point is on the highest part of the surface of this insect.
Once in this location, 10 fluorescence spectra were acquired from each one of the cochineals selected and located in the cladode portion. Three displacements of the sample holder were performed on the z-axis, moving the sample away from and then back toward to the focus plane of the laser excitation beam. Finally, these fluorescence spectra were averaged using a script development in MATLAB R2014a (The MathWorks, Inc.) software, obtaining a smoothed average spectrum of each cochineal as well as the standard deviation between them.
Figure 9c shows a lateral view of the way in which the fluorescence measurements of the in-vivo cochineals were performed. This view, of the interaction area of the excitation beam with a cochineal, allow us to observe that the spot size of the excitation laser is wide enough to cover completely the body of the cochineal.
Statistical analysis
Ten fluorescence spectra were acquired from each cochineal of different size that was present in the biological samples. These spectra were processed with a script developed by the authors in MATLAB R2014a (The MathWorks, Inc.) software, obtaining a smoothed average spectrum of each cochineal as well as its standard deviation. Figures for comparative fluorescence spectra of cochineals of different sizes show average values of fluorescence intensity and its standard deviation (errors bars). Control of reference fluorescence spectra of the background medium (cladode) and a homemade fluorescence standard were taken in all the experiments. Experiments were replicated in five sets of biological samples containing cochineals of similar sizes as is shown in Fig. 10 (see Table 1, which is complementary to this figure below). Statistical analysis of the fluorescence intensity at four wavelengths namely 620, 640, 660 nm and 760 nm were carried out using ANOVA test for the three cochineal sizes (small, medium and large) in each of the five available samples. The statistical values were computed using the Minitab 16 (Minitab, LLC.) software, where the computed p values  More

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Staining of POM in presence of fine sand and coarse silt
Compared to the unstained control (unstained sample mean peak grey value ± standard deviation: 3686 ± 1557), all staining treatments caused a slight shift of the central grey value peak to the right, towards 3757 ± 1272 and 4421 ± 1646 for the PMA and PbAc treatments, respectively. This central peak represents X-ray attenuation by coarse silt and POM (Fig. 2a). However, no increased X-ray attenuation was observed in coarse silt in the reconstructed CT-sections (Fig. 3). Instead, the grey value distributions of pin-pointed POM particles (Fig. 3) confirmed that staining did cause a clear shift in POM’s grey values, viz. from 1000–4000 to 6000–18,000. This contrast enhancement of POM was more intense for AgNO3, PbAc and Pb(NO3)2 treatments compared to PMA (Fig. 3) and caused the very clear increase in the histogram’s right tail (grey values  > 7500), which was absent with PMA treatment (Fig. 2a). Staining with Pb2+ or Ag+ also resulted in broad grey value peaks of POM that overlapped those of the coarse silt (grey value range: 2500–5000) and fine sand particles (grey value range: 5000–7500), represented by the second and third peak on the bulk soil histograms (Fig. 2a). However, the histogram peak maxima of the AgNO3 (11,118), PbAc (13,829), Pb(NO3)2 (14,255) and PMA (9678) stained POM particles (Fig. 3) clearly exceeded the grey value range of both the coarse silt and fine sand, which suggests that CT grey value based discrimination of POM from these mineral fractions should be feasible. The smaller peak shift in the POM histograms for PMA (Fig. 3) shows that PMA was less efficient in increasing attenuation of POM and this could be ascribed to the lower atomic mass (AM) of Mo (95.9) compared to Pb (207.2). Likewise, the effect of Ag (AM 107.9) was closer to that of Mo. Next to atomic mass, the affinity for binding to OM likely also differs between the contrast agents. While Ag+ and Pb2+ bind with the organic functional groups of POM via ionic bonds, PMA is known to interact with conjugated unsaturated bonds37.
Figure 2

Grey scale histogram (0–16,000) for 16 bit images of (a) the fine sand + coarse silt + POM soil samples, (b) the fine sand + fine silt + POM mixtures and (c) the fine sand + clay + POM mixtures. The unstained control treatment (black) and the stained AgNO3 (red), PbAc (blue), Pb(NO3)2 (purple) and PMA (green) treatments. (d) Grey scale histogram (0–16,000) for 16 bit images of OsO4 stained fine sand + coarse silt + POM (fSa + cSi + POM; red), fine sand + fine silt + POM (fSa + fSi + POM; blue) and fine sand + clay + POM (fSa + C + POM; black) mixtures. The equivalent control treatments are indicated by dotted lines.

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Figure 3

Grey scale histograms (0–40,000) of operator-supervised pin-pointed POM particles in 16 bit images of fine sand + coarse silt + POM mixtures: the unstained control treatment (black) and the stained AgNO3 (red), PbAc (blue), Pb(NO3)2 (purple) and PMA (green) treatments. For each treatment, a two dimensional grey scale image representing a segment of a horizontal slice of the fine sand + coarse silt + POM mixtures is included (image contrast was enhanced in this figure). An unstained POM particle in the control treatment and stained POM particles in the AgNO3, PbAc, Pb(NO3)2 and PMA treatments are indicated by the red arrows.

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There was no shift towards higher grey values in the third peak in the soil core histograms, representing fine sand (Fig. 2a). In addition, we did not find traces of Pb, Ag or Mo on any SEM–EDX scanned fine sand or coarse silt particles (Supplementary Fig. S1). Visual inspection of horizontal reconstructed slices revealed a clear discrimination of the stained POM particles vs. the soil mineral phase. SEM–EDX scans confirmed substantial Mo, Pb or Ag EDX-peaks on randomly chosen POM particles, directly confirming the successful selective staining of POM by all agents (Supplementary Fig. S1). Smaller mineral patches on the surface of POM were visible in the SEM-images (Supplementary Fig. S2), but our analysis demonstrates that these were not responsible for Mo, Pb or Ag-staining of the POM. Stained POM could also be discriminated from several other high density particles such as glauconite, since the latter were not only characterized by higher grey values32 but also a different grey value pattern. The SEM–EDX spectra of some dense mineral particles moreover revealed Zr peaks, suggesting these to be ZrO2 or Zr-silicate. In conclusion, the experiment demonstrates that Pb(NO3)2, AgNO3 and PbAc are able to selectively bind with POM in fine sand + coarse silt mixtures. However the potential to raise POM contrast in such soil mixtures by treatment with PMA is smaller.
Staining of POM in presence of fine silt and clay
PMA did not appear to stain fine silt, with no shifts of peaks in the bulk soil histogram (Fig. 2B), no visible impact on contrast of the soil mineral phase (Supplementary Fig. S3) and again no discernible Mo peaks in the SEM–EDX spectra of mineral particles (Supplementary Fig. S4 and Supplementary Fig. S5). In contrast, AgNO3, PbAc and Pb(NO3)2 appeared rather non-selective for POM (Fig. 2B). Indeed, the staining increased the grey values of histogram peaks corresponding to fine silt (Fig. 2b), which can be seen from shifting fine silt histogram peaks from a grey value of 4303 to 4702–5577. The increase of the fine silt grey values following staining even resulted in a partial (Pb(NO3)2 and AgNO3) or complete (PbAc) overlap with the sand fraction histogram peak. The grey value histogram right tail was also much enlarged by Pb(NO3)2, AgNO3 and PbAc treatment and increasingly overlapped with the grey value range of stained POM (6000–18,000) (Fig. 3). This overlap was clearly an effect of staining of fine silt, as was also apparent in the CT-sections (Supplementary Fig. S3). The shift in histogram grey values following staining in the fine sand + clay mixtures was similar to that in the sand + fine silt mixtures, but with a larger shift in the right tail of the Pb2+ and Ag+-histograms to grey values between 7500 and  > 12,000 (Fig. 2c). However, we did not detect Pb or Ag EDX peaks on pinpointed fine silt or clay particles (Supplementary Fig. S4). But as inspection of CT-sections (Fig. 4) demonstrated that large patches of the mixtures were stained by AgNO3, PbAc and Pb(NO3)2, it is well possible that such discrete stained areas were coincidently not sampled in our ancillary unsystematic SEM-analysis of subsamples. Regardless, overlap in grey value-ranges of Pb2+ and Ag+ stained POM and stained clay particles clearly impedes proper segmentation of both phases. As was the case for fine silt, PMA treatment did not cause any shift in histogram peaks of mineral particles (Fig. 2c) and there was no visible impact on mineral phase attenuation in the CT-sections (Fig. 4). This also suggests that Mo has a higher potential than Pb2+ and Ag+ to bind to OM solely and not to mineral surfaces. Indeed, this may be a result of the neutral Mo(VI)O3 in PMA, while the cations Pb2+ and Ag+ may adsorb to negatively charged mineral surfaces. However, Chenu and Plante24 did not detect any sorption of both Pb and Ag on pure clay minerals (vermiculite, illite, kaolinite). Despite minerals identified in our clay fraction being kaolinite and smectite, sorption of Pb (as observed by Chenu and Plante24) or Ag onto Al and Fe oxides and hydroxides in the clay fraction could have occurred.
Figure 4

Two dimensional grey scale image representing a horizontal slice of the fine sand + clay + POM mixtures: the control treatment and the stained AgNO3, PbAc, Pb(NO3)2 and PMA treatments (image contrast was enhanced in this figure). Clusters of stained clay particles are clearly observable as brighter structures in the AgNO3, PbAc and Pb(NO3)2 treatment.

Full size image

While manual pin-pointing of the PMA stained POM was not impeded, automated POM extraction from the CT volume may still prove to be challenging due to the lack of image contrast between POM and mineral soil particles. Very recent work by Lammel et al.38 applied a machine learning segmentation tool in synchrotron-based soil CT volumes but experienced limited success. Piccoli et al.39 suggested that an operator-based ability for the selection of thresholds may still result in the most accurate segmentation of POM in soil.
Impact on sample structure
Bulk sample histogram evaluation (Fig. 2a) of the fine sand + coarse silt samples demonstrated that the grey value peak of the pore space (unstained sample mean peak grey value ± standard deviation: 1765 ± 609) had slightly shifted following staining with Pb(NO3)2 (1930 ± 386), AgNO3 (1923 ± 392) and PbAc (2204 ± 314), and to a lesser extent with PMA (1801 ± 611). There was also a reduction in the pore space peak height for the Pb(NO3)2, AgNO3 and PbAc stained treatments of 16%, 27% and 42%, respectively. In the PbAc treatment, the pore space peak disappeared nearly completely, whereas this drop was smaller for AgNO3 and much smaller for Pb(NO3)2. In contrast, no such decline in pore space peak was observed in the PMA-stained samples. A decrease in pore volume (Fig. 5) for the Pb(NO3)2, AgNO3 and PbAc treatments matched the trend in reduction of pore peak height. In addition, opposite trends of peak height increases existed for the second, coarse silt peak (+ 6%, 13%, 25%, respectively) and for the third, fine sand peak (+ 18%, 19% and 33%, respectively). Combined, these observations demonstrate that the observed changes in pore space and mineral phase peak areas are the result of the compaction of the samples by the Pb(NO3)2, AgNO3 and PbAc treatments.
Figure 5

Total X-ray µCT visible porosity in the fine sand + coarse silt + POM mixtures: the unstained control treatment (black) and the stained AgNO3 (red), PbAc (blue), Pb(NO3)2 (purple) and PMA (green) treatments.

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Given that peak height reductions were very different for Pb(NO3)2 and PbAc, the deterioration of soil structure could not be solely related to the heavy-element applied (both containing Pb2+). In addition, the decreased porosity is unlikely to have been predominantly due to the addition of NO3−, as the addition of Pb(NO3)2 would have impacted structure to a greater extent than AgNO3 which contains less (0.5×) NO3− (solutions were all at 1 M). Deterioration of the sample structure was therefore more likely to be a physical phenomenon. One possible mechanism could be that an increased underpressure, as a result of perfusion of a more viscous agent, could have more severely disrupted the structure of the soil mixtures. However, an increasing degree of structure distortion could not be related to a higher viscosity of the staining solutions (considered at 0.01 or 0.1 M40,41,42). Hence, the results in this study did not allow identification of the exact origin of this artifact. As a consequence, caution has to be taken when perfusing soil samples with liquid staining agents since deterioration of soil structure would alter spatial location of the POM as well. Thus, these results indicate that soil structure validation is still required as long as the specific cause for deterioration is not identified.
The Pb(NO3)2, AgNO3 and PbAc treatment histograms of both the fine silt (Fig. 2b) and clay (Fig. 2c) mixtures also had a smaller and broader pore space peak. It is likely that these are both derived from increased occurrence of partial volume effects (PVE) from chemical staining. It is thought that compaction following staining resulted in more voxels containing both pore space and mineral particles. With a voxel resolution of 7 µm this increased the number of pore space voxels with an intermediate grey value (2000–4000). The order in grey value increase magnitude for the fine silt and clay samples was similar as for the coarse silt mixtures: PbAc  > Pb(NO3)2  > AgNO3. Chemical staining had an increasingly stronger effect on pore space X-ray attenuation for the finer particle size mixtures, probably because of more intensive binding of Ag+ and Pb2+ on their much larger reactive surfaces.
In this study the combination of the structural degradation and the overlap in grey value ranges of POM and the mineral fractions render these staining agents unsuitable for staining natural soils. However, further development and testing of the staining method may reduce the impact on soil structure to a minimum. PMA treated silt and clay samples were not structurally degraded and no undesirable shifts of mineral particles’ grey value ranges were observed. However, the artificial soil samples are not fully representative of naturally structured soil and further testing could also rule out degradation of natural soil structure by perfusion with PMA solutions. We expect structural integrity of natural soil samples to exceed that of the ‘loose’ soil mixtures tested in this work.
Performance of PMA compared to gaseous OsO4 staining
Inspection of horizontal CT-sections suggested that OsO4 (Supplementary Fig. S6a–c) did not increase X-ray attenuation of mineral material. This is also suggested by the absence of a shift to the left of mineral fraction peaks (grey value 2500–7000), following OsO4 treatment (Fig. 2d).
The grey value distribution obtained via manual pin-pointing of POM particles demonstrated that the grey value interval of OsO4-stained POM (Fig. 6) corresponded to the right tail of the soil mixture histograms. This effect was larger but still comparable to the POM grey value shift obtained by PMA treatment. This outcome is a likely result of both staining agents targeting unsaturated bonds in e.g. hydrocarbons or proteins, and the higher atomic mass of Os (190.2) compared to Mo (95.9). Because of the significant health risks when using OsO4, PMA appears to be a suitable alternative. The increase in POM grey values following both PMA and OsO4 staining showed a sufficient differentiation of POM particles from the mineral fraction, at least for manual pin-pointing. However, single threshold-based segmentation of PMA- or OsO4-stained POM in the X-ray CT volumes obtained with this lab-scale polychromatic X-ray µCT system did not appear possible. More sophisticated segmentation algorithms are required, as was also very recently suggested by Piccoli et al.39. We propose to develop self-learning algorithms (e.g. incorporate machine learning) that consider the local grey value patterns of POM in combination with morphological characteristics for a more objective and faster segmentation of the stained POM. By using self-learning algorithms, the expert intervention to segment POM particles would be reduced to an absolute minimum and decrease further over time. In addition, technological development will very likely further enhance the X-ray CT resolution, which will strongly improve the morphological characterization of finer grained POM.
Figure 6

Grey scale histogram (0–25,000) for pin-pointed POM particles in the 16 bit images of PMA (green) or OsO4 (black) stained fine sand + clay + POM mixtures.

Full size image

Alternatively, the occurrence of a K-edge in the X-ray absorption spectrum of molybdenum could be exploited to discriminate POM in CT images. Rawlins et al.33 and Peth et al.31 have previously used the occurrence of a K-edge in the X-ray absorption spectrum of Os by scanning soil with synchrotron µCT at photon energies immediately below and beyond the K-edge successfully. However, the K-edge of Mo is situated at 20 keV, an energy level at which most X-rays may be attenuated by the soil mineral fraction, thereby probably making a dual energy approach similar to that used for Os challenging for non-synchrotron scanners and probably also for synchrotrons. Very recently, Lammel et al.38 identified gaseous iodide (I2) as a plausible candidate for selective staining of OM in soil for use with synchrotron scanners. However, they did not fully demonstrate its selectivity for OM versus silt and clay sized mineral particles, nor in X-ray µCT soil volumes obtained with non-synchrotron scanners.
The findings presented here demonstrate that laboratory based X-ray µCT scanners may also enable the segmentation of OsO4-stained POM from mineral particles, provided that better segmentation tools are developed. This opens up new possibilities for a more widespread application of the OsO4 staining technique due to much better availability of laboratory based X-ray µCT scanners and throughput time of samples. More

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