Rapid Classification of the Primary Property of by Means of Mid-Infrared Spectroscopy and Subsequent Principal Component Analysis.

From Christian Göhring - Düsseldorf, Germany [email protected]

Abstract in Deutsch:

Proben von LL-, L- und H-Klasse sowie CK-, CV-, CO- und R-Klasse Meteoriten wurden mittels Infrarot-Spektroskopie im Bereich zwischen 4000 und 380 cm-1 vermessen. Die erhaltenen Daten wurden anschließend Hauptkomponentenanalysen unterworfen um auf diesem Weg Klassifikationsmerkmale der eingesetzten Proben zu finden und eine einfache Vorklassifikation innerhalb der Gruppe der gewöhnlichen Chondriten zu ermöglichen. Um die Ergebnisse der HKA zu verbessern wurde eine Methode zur Modifikation der Berechnung benutzt. Durch Einfügen von Spektrendaten von natürlichen Mineralen als auch synthetischer Spektren konnte die Trennschärfe und damit die Sicherheit einer korrekten Einstufung beträchtlich erhöht werden. Durch HKAs unter Kombination der Spektren von gewöhnlichen Chondriten mit denen der kohligen Chondriten konnte auch hier exemplarisch eine gute Abgrenzung zwischen diesen erreicht werden.

The Infrared spectroscopy in combination with the Principal Generally these minerals are organized into coarse grains [Fig. 1] or crystals Component Analysis are powerfull tools for the data analysis for inside a surrounding matrix. example in industry, agriculture or medicine. In the chemical analytics the PCA helps to correlate statistical and/or meta data with Based on their elemental compositions, the mineralogy and the petrographical the data obtained by instrumental sample measurements. appearances are grouped into several classes. The class membership Particularly samples from native materials (crops, metabolits...) are of LL, L and H chondrites is detemined by their iron contents and the ratio excellent candidates but also challenges. Now it was obvious to use between the elementary iron Fe0 and the constituents of its oxidized forms IR spectroscopy and Principal Component Analysis as a completive Fe2+/3+. method to classify new found meteorites, as they are also a „natural product“. Class Fe0 (elem.) Fe2+ (mol Fa) The classification of found meteorites is a complex and expensive process. ______Geochemical and petrological examinations are necessary and they are LL 19-22% 27-32% often not sufficent enough to take the right categorization. In these cases L 20-25% 23-26% further analytics, isotopic examinations or other, have to be done. H 25-31% 16-20%

In this paper it shall be shown that the IR spectroscopy (IR) in combination Tab.1: Pure Iron versus Fayalite content in the three ordinary classes with the mathematical Principal Component Analysis (PCA) are able to differentiate between them and this combination shows an alternative way The IR spectroscopy: for fast studies.

The IR spectroscopy is a technique to detect chemical compositions by the absorption of light energy inside the substances to be investigated. This absorption is caused by an electrical dipol field between two or more different kind of atoms assembling the sample. It is not essential which kind of bonding between these atoms persists. As a result vibrations e.g. along the bonds are perturbated. The energy of the absorbed light is determined by the mass of the atoms and the strengthness of the bonding. The result of an IR measurement is a diagramm with the wavelength (energy of light quantums) of the infrared light on the abscissa and the intensity of absorption on the ordinate axis [Fig. 2]. A material consisting of only one kind of atoms cannot absorb light energy and forms no spectrum.

The minerals contained into a meteorite form absorptions, observable as characteristical features into the IR spectrum. These minerals are essentially Olivines (Forsterite, Fayalite), Pyroxenes, Plagioclases and Iron(Fe2/3)oxides. The elementary Fe0 and either its alloys with Nickel do not form any absorptions and are not detectable by means of IR spectroscopy.

The PCA algorythm:

The Principal Component Analyis is a statistical technique to evaluate data Fig.1: A section of NWA 6080 (LL4) shows a well-formed and Fe/Ni inclusions into sets with multiple connections between sample properties and observations a grey matrix (1mm/div). made by e.g. chemical analyzing. Goal of this procedure is to reduce these complex observations to a low-dimensional representation and sort out less The composition of meteorites: important raw informations. Subsequentially these results can be used for classification, qualitative or quantitative determination of the sample The compositions of meteorites are very manyfold. The main constituents constituents. are the mineral groups of Olivines, Ortho- and Clinopyroxenes as well as Plagioclases and other minor constituents. All these minerals on their part Experimental studies: consist of several cations, primarily iron, nickel, calcium, magnesium, aluminium and further trace metals. Iron and Nickel often appear as pure From many internet supported sources meteorite samples were acquired. Most metals as well as their oxides and sulfides [Ref. 6], too. As the counterpart of them in form of slices and many as coarse fragments. All of the slices were silicates and alumosilicates are the main anions which can be found in sawed without hydrocarbon containing oils, but some of them with water or meteorites. In low concentrations carbonate, sulfate and nitrate are often ethanol. The surfaces of the water treated examples show sometimes a present (as well as a great repertory of organic constituents especially brownish rusty area around its inclusions. [Fig.1] found in carbonaceous chondrites).

Page 1 / 7 The obtained spectra show absorptions of the main components Mg/Fe- Name: Class: Fayalite Ferrosilite containing Olivines (Forsterite and Fayalite) at 885, 606, 499 and 416 cm-1 mol-% mol-% [Fig. 2]. The exact positions of the maxima are depending from the magnesium/iron ratio built into the crystal lattice of this mineral. Furthermore H5 18 16 in the range between 1000 to 1150 cm-1 the absorptions of Ortho- and DHO 2007 H5 15 14 Clinopyroxenes are visible. Their shapes and positions are also depending Gruver H4 n/a n/a from the cation ratios. In the range between 700 cm-1 and lower the Malotas H5 n/a n/a absorptions of iron and nickel oxides are overlaid. NWA 6624 H5-6 19 17

Tamdakht H4-6 18 16 Additionally to the signals of the iron minerals the formed absorptions are also

superimposed by them of these minerals which are not decisive for the DHO 1706 L6 24 20 classification, for example calcium silicate (Wollastonite) and sulfate. An JAH 055 L4-5 26 22 Mt. Tazerzait L5 extreme example shown above is the spectrum of Gruver H4. It shows further 25 21 -1 Sahara 97001 L6 24 20 absorptions between 1100 to 1200 cm of noniron and nonsilicate minerals. NWA 869 L3-6 n/a n/a Data pretreatment: Benguerir LL6 29 25 NWA 5768 LL5 30 25 After measurement the spectra were baseline corrected with a hand driven NWA 7867 LL7 n/a n/a polynominal baseline modell. Loaded into the PCA software the spectra were NWA 6042 *** LL6 n/a n/a subsequently maximum normalized and then derived by the Norris algorythm. Chelyabinsk LL5 28 23 In a pretest calculation the loadings plots [Fig. 7 appendix] were used to Bjurböle L/LL4 n/a n/a commit to a useful frequency range and number of principal components. As also seen in the IR spectra the absorptions of carbonate and matrix water from -1 NWA 5371 *** Prov. L6 n/a n/a the KBr-pellet appear into the range above 4000 to 1300 cm . This range was NWA 6080 LL4 29% 23% excluded in the next calculations. Futhermore a maximum of six Principal Estacado H6 n/a n/a Components were found to be sufficient enough to differentiate between all SaU 582 L6 26% 22% the measured meteorite samples.

Tab. 2: List of the used H/L/LL meteorite samples. Black printed individuals were used for The final parameters were now: calibration, red printed for test. Values are rounded. Meteorites with marked (*** ) abbreviations are not adequate evaluated. Items are listed in MBE with inofficial Y-Scale: Absorption denominations Normalization: Tallest band to 1 AU Derivation: Norris Derivation - Smoothing 3, Step 2 Instrumentation: Frequency Range: 1300 to 380 cm-1 No. of Principal Components: 6 The measurements were carried out by using an IR-spectrophotometer Validation: Full Cross Validation IFS66v from Bruker Optik (Karlsruhe/Germany). The spectrometer is Modell: Full equipped with a DTGS detector. For the sample spectra an effective No noise addition wavenumber range between 4000 to 380 cm-1 was choosen. For the subsequent PCA analysis the software “The Unscrambler” from All further calculations were carried out with these new parameters. CAMO AS (Oslo/Norway) was used. It uses the NIPALS algorythm (Herman World 1966) and also carries out an internal validation routine Results and Discussions: [Ref. 1].

Sample preparation and measurement:

From each sample 100 mg were in-depth pulverized in an agarte mortar. From this powder now approx. 1 to 5 mg were once more grinded and mixed with Potassium bromide (KBr, e.g. Fluka, No.: 34919) in a ball mill. The amount of the meteoritic dust depends on the maximum absorption in the later obtained IR spectrum. It was measured so that the tallest band reaches between 0.8 and 1.4 absorption units. After mixing the blend was pressed with around 10 tons in a 13 mm die. The obtained pellets are semi opaque because of the recrystallization of the KBr matrix. Often fine grained particles are visible.

PCA1a: The PC1/2 scores plot shows widely spread point groups for the three classes LL (green), L (red), H (blue) and the intermediate L/LL (light blue).

The first calculation PCA1 was performed under using the set of calibration samples [Tab.2 black]. The scores plot of the first calculation shows a widespread distribution of all samples. This result is nearly the same as also shown in the higher PC combinations [Tab. 3]. The explained variance value of the Principal Component first order (PC1) exhibits a 41,5% portion of the spectral variances used for the differentation along the PC1 axis.

For a later evaluation of the obtained results the enlargements of the colored point clouds and the gaps between them were measured. Fig. 2: Some IR spectra of several types.

Page 2 / 7 Platzhalter Platzhalter

PCA1b: The loading plots of PC1 (black) and PC2 (red) after reintegration. PCA2a: The scores plot after including the helping samples Forsterite (grey) and Fayalite (brown) . From the loadings plots of the higher PCs the property of showing higher resolved IR spectral features was used to interpret these. As a first constituent the signal maxima of the sulfate cation is visible at 1160, 1120 But in this approach the point cloud ranges are extremely stretched along the and 675 cm-1. The silicate minerals are represented between 1050 and 800 PC3 axis and these of the H and L classes are overlapping and influences a cm-1. Furthermore broad absorptions in the range below later investigation of new samples unfavorable. This behavior is unwanted and 600 cm-1 seem to be from several iron minerals. Within this range the needs to find another way to control the calculations. loadings values with the highest influences are assembled. Third approach: Optimizations: From a set of gaussian curves two full synthetic spectra were composed. The The scores plot of the first calculation [PCA1a] shows poorly separated requirements of these spectra are to represent a more perfect mask only for the point clouds for each L, LL and H meteorite class. The separation is main signals of the Olivine minerals. As seen in the second approach using complete but the individual samples are wide strewn and take a high risk to only multiple data sets of natural Olivines is not enough to fullfill these be grouped into the false class. The goal is to build a model which avoids demands. Instead of the Forsterite a synthetic spectrum with four curves with maxima at 988, 886, 510 and 417 cm-1, instead of the Fayalite a spectrum with this situation. -1 three curves and maxima at 948, 877 and 477 cm were constructed. The For the next calculation PCA2 the sample data sets of Olivine group absorption values within the wavelength ranges around the gaussian curves minerals were inserted additionally. The used Forsterite samples were were set to be zero [Fig. 9]. collected from three . The Olivine from pallasites is a mixture of approximate 95 mol-% Forsterite and 5 mol-% Fayalite. This is an attempt to adapt the chemical composition and the resulting spectra nearly to them of the natural Olivines born in space.

Because of the lack of space born Fayalite a sample of this mineral collected on Earth (India) was choosen.

The algorythm rates the differences between the loaded spectra. Distinguished spectral features affect these ratings as like as periodic variations. For the differentiation of samples with varying chemistry the highest spectral differences must not be decisive for the correct classification. In the case of meteorites also the minor constituents have a strong weight in this process. The PCA algorythm is rating all spectral features, also these of the Olivine content. It was found that a simple exclusion of the Olivine signals via the menu in the „The Unscrambler“ software is not suitable to manage the determined effects, because the positions and the shapes of the Olivine signals are also important.

PCA3a: The scores plot after including the gaussian curve assemblies (pink) . The inclusion of the Olivine rich samples turn the ratings to this mineralogy. First, the increased mineralogic (and spectral) variations become to a scale to modify the final orthogonal rotation operation of the After the third calculation [PCA3a] using the synthetic spectra all three point algorythm. The effect is a shift in the main axes determining the rating of clouds now appear well separated on the PC3 axis. The colored point clouds every spectral supporting point (loadings plots). Secondary, the calculated from the included meteorite spectra are clearly defined. With higher distance component values are more compressed within their individual classes after the synthetic sample spectra mark the limits of the synthetics dominated PC1 inserting a set of more different data. This is an inherent property of the axis. The explained variance value of PC3 amounts now 9,0%. The PCA algoryhm. intermediate LL/L meteorite sample is placed near the disjunction between the LL and L point clouds. Calculation of second approach: Both classification experiments PCA2 and PCA3 need to use the PC1 and PC3 The scores plot of PCA2a shows the benefit of addition the new spectra. to take effect. This is an indication that except of the Olivine signals of the After the data processing the point clouds of each class are more meteorite spectra the minor spectral features are also important to achieve a compressed on the PC1 axis as in the first approach and provide a better good separation. In principle the decisive PC vector can be shifted to higher classification. As a great difference the separation now takes place on the order again, through appending further reference spectra. This action would PC3 axis (rotated in the figure below). The explained variances value of possibly reduce the explained variance value anew, but this effect is not PC3 shows now a 8,3% portion of the sample variances used for the decisive for a good separation power. differentiation.

Page 3 / 7 Platzhalter: The comparation of the decisive loadings vectors shows two nearly equal curves from the first [Fig.3 black] and the third (red) calculation. The main properties of PCA1 could be transferred to the third order PC axis of PCA3. As a difference the plots show a modified weighting in the range between 410 to 440 cm-1. The curve of PCA3 shows the typical but weak signal patterns of the included synthetic spectra. Their influences were small and high selective. As a contrast the natural mineral spectra of the PCA2 (green) hide informations on a wide spectral range. Therefore the loading vector from the second approach shows a less high resolution overall, most individual spectral features are less clearly or missing. As the greatest difference an underlying signal in the range between 1300 to 700 cm-1 broaden the main features and is jointly responsible for the overlapping effects of the L and H class point clouds in the calculation PCA2.

The also gathered explained variance values for the decisive axes contributes only one information. They confirm the relatively decreasing content of spectral informations used in the calclations after inserting the mineral or synthetic spectra with their strong variations against the meteorite samples relative to itself. But the explained variances values represents no filter

PCA3b: Loadings plots of PC1 (black) and PC3 (red). functions and the separation power depends highly from the form of the loadings plot vectors.

The loadings plot of the first PC shows the dominating influence of the As a second characteristic of the model the scattering pattern of the three point synthetic spectra patterns. In the same way the influences plot [PCA3c, clouds could also be transferred into the PCA3 model. Herein the H class point appendix] depicts this high influences, too. The PC3 shows the signal cloud shows the widest spreading into its area, and shows the highest pattern which is decisive for the differentiation of the three chondrite variability of the compositions of H class meteorites, now along the PC3 axis. classes. In the wavenumber range of the silicates appears an only weak -1 influence of Forsterite at approx. 988 cm . The strong signals of the As a result the enlargements and also the areas of the three point clouds could -1 loadings plots in the range below 700 cm remain, but were slightly be minimzed and the gaps between them maximized at once [Tab.3]. Both modified. facts support the separation power in a beneficial way. The further calculations were carried out to test out the robustness of the assembled model. Evaluation of the obtained results: Tests of the new modell: Common used criteria to assess the quality of a PCA analysis fails here (e.g. Scree test). For the assessment of the obtained issues a test set of new Four samples [Tab. 2, red] were included into the calculations as a test set. All samples was choosen and the calculations were repeated with them. three classes are represented with one sample. An additionally sample has only Additionally the enlargements of the class point clouds, the gaps between a tentative recommendation for the L class. them and the explained variance values were gathered and depicted in Tab 3.

Fig 4: Scores plot of the model test calculation.

Fig. 3: Loadings plots of the decisive PCs from the three calculations above. PCA1 (black), All four test samples could be well assigned. The good separation provides a PCA2 olivine sample spectra (green), PCA3 with synthetical spectra (red). high level of certainty. In this example all four samples were classified at once. To split this task into its single parts advances the results again, because using

too many extra samples in one calculation tends to expand the point clouds

along the PC3 axis.

H Horizontal L Horizontal LL Horizontal H Vertical Gap L Vertical Gap LL Vertical Explained variance calibration spectra PC1/2 702 566 299 443 163 185 116 250 41,5 % (from PC1) PC1/3 737 370 199 … … … … … … PC1/4 403 478 176 … … … … … …

+ mineral sample spectra 430 171 262 349 overlapps 243 113 290 8,3 % (from PC3) 371 189 234 303 267 134 124 219 9,0 % (from PC3) + synthetic spectra

Tab.3:. Enlargements between the LL, L and H point clouds in scaled units *10000. Omited values are equal to the first value in the column

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Influences of preparation, measurement and baseline correction: Names: Class:

For the examination of the influences of the preparation, from the HaH 280 CK4 measurement and the baseline corrections a fresh grinded portion of the NWA 7704 CK5 Tamdakht meteorite was independently prepared four times. The amount of NWA 8597 CK6(an) the sample powder was choosen in the way to achieve individual NWA 4439 absorption maxima between 0.7 and 1.3 AU. CO3 NWA 10393 CO3 NWA 10580 CO3 To show these relations two calculations were performed. The first one was carried out under including the two synthetic spectra, used also in the NWA 1465 CV3 calculation PCA3, to ensure the comparability with it. As result the scores NWA 7454 CV3 NWA 6619 plot shows a high density point cloud with minimal variations between the CV3 individual point positions of the reproduced samples [Fig. 5]. DHO 1123 R NWA 10215 R4 NWA 7979 R5

Tab. 4: List of the used samples and their class assignments.

From each class three spectra were acquired [Tab. 4] and included to the data matrix. The scores plots show all groups well separated [Fig. 10, 11, 12, 13 appendix] from the ordinary chondrites groups. The point clouds of the ordinary chondrites can partially fuse, because of the changed loadings to the altered mineral composition contained in carbonaceous chondrites.

Limitations:

As a limitation experiment the wavenumber range covered by the IR- spectrometer was limited to 400, 420 or 450 cm-1. Restricting the wavenumer range to 400 cm-1 do not significant affects the separation power. However limiting the range to 420 or 450 cm-1 excludes important spectral information. Subsequentially the results of the PCA calculations are less well separated.

Most IR spectrometers are equiped with an DTGS detektor. The spectral range Fig 5: Scores plot of the calculation with the four Tamdakht reproduction test samples (blue). covered by them takes between 5000 to 380 cm-1 (The data sets used in this The synthetic spectra (pink) are included as a scale. work were acquired with this one). But some spectrometers uses a MCT (Mecury-Cadmium-Telluride) detektor, too. These liquid nitrogen cooled The second calculation was carried out with only the four Tamdakht devices are up to four times faster in their data collection, but the wavenumber -1 samples. Fig. 6 shows the loadings plots of the PC1 and PC2 with less range covered by them is delimited to 600 (narrow band detector) or 450 cm structured curves and permanent ascending loadings values in the lower (wide band detector). Using a wide band detector delimits the classification wavenumber range. This forms can be interpreted as variances during the resolution rapidly. A narrow band detector is totaly unfeasible for geochemical manual baseline correction process. In the range between 430 cm-1 and meteorite analysis, because most of the important spectral information is -1 lower the loadings plots possess an additional increasing random noise. represented below 600 cm . This indicates a higher noise level of sample measurements, due to the decreasing radiation flux of the infrared source and the subsequent lower In many cases there are only a few milligramms or less of the sample available. device sensitivity within this range. A pattern, typical for meteorite By only unique measuring of meteorite samples possibly misinterpretations constituents, is not visible within the whole range. can occur, because often meteorites reveal a breccial texture with more complex compositions. It is strong recommended to prepare and measure two or more samples from different sites of a meteorite to seize its complete structure. It is also recommended to use at least a massive crumb or piece of a meteorite instead of dusts from sawing, drilling or sanding. Doing this can release a collapse of the crystal lattice of the minerals and the ability to distinguish is lost.

As a methodic condition the IR spectroscopy is sensitive for the chemical compositions of the samples. Samples which are totally equal to each other or providing no IR spectrum cannot be separated by IR/PCA-coupling, because of the lack of spectral variance informations.

Conclusions:

The IR/PCA analysis is a simple way to classify several chondrite groups in a fast and simple way and with a “single shot”. Applied at the ordinary chondrite groups the H, L and LL meteorites can easily be distinguished, although they show a high similarity of their spectral data. By inserting

additional spectra, obtained from the single minerals Forsterite and Fayalite, Fig. 6: Loadings plot of the reproduction test. PC1 (black), PC2 (red). either by inserting synthetic spectra of this mineral phases, the separation results can highly be advanced. To classify a totally unknown meteorite first

the general separation between carbonaceous and ordinary chondrites is Separation from not ordinary chondrites: recommended. In a second step the fine separation of ordinary chondrites as like in PCA3 can be carried out. Samples with an extraordinary composition For the separation of other meteorites not from the LL, L or H classes it is can rapidly be recognized and then investigated on target. With high certainty essential to use at least three calibration samples of the class wanted to be another meteorite families or terristric stones can also be separated (unless the identified, because only one spectrum per class is not sufficient to raw spectra can already be differentiated by own attention). differentiate between them. As an example samples of four of the numerous found carbonaceous chondrite classes CK, CV, CO and R meteorites were used to test the modell.

Page 5 / 7 But this method cannot replace the classical methods like e.g. petrography, Appendices: elemental or geochemical analysis. The is strong oriented at meteorite prototypes, defined by analyzing new founds a first time particularly at carbonaceous chondrites [Lit.7]. Due to a continued influx of new found meteorites the previous defined classes will always be expanded more and more. Therefore new methods have to be developed again and again to overcome this challenge.

Outlook:

A closely related method is the Partial Least Squares algorythm (PLS). In contrast to PCA it can be used for the quantitative determination of the mineral content of samples. For calibration it requires spectroscopic raw data and additional the knowlegde of the mineral contents, first. After building a prediction modell the determination power is strong enough for geochemical investigations of meteorites and/or in the field of the geophysical sciences.

Of course also Raman or spectral data can be used. The Raman spectroscopy is more simply because it needs no complicate sample preparation and can be carried out during in field excursions [Lit. 8]. Fig. 7: Loadings plots of the pretest calculation A special excellence is the ability to process high quantities of input data. The potential of coupling IR spectroscopy and microscopic imaging produces large two-dimensional spectra arrays and using an up-to-date Personal Computer consumes only a short time to classificate the components of a breccial or coarse textured meteorite.

References:

[1] „The Unscrambler Tutorials“, Manual for CAMO Unscrambler Version 9.6 Camo Process AS, Nedre Vollgate 8, 0158 Oslo, Norway

[2] „Statistik und Forschungsmethoden“ Eid, Gollwitzer, Schmitt - Pages 907 - 915 Beltz Verlag, Weinheim,

[3] „Near-Infrared Spectroscopy“

Siesler, Ozaki, Kawata, Heise - Pages 204 - 211 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim PCA3c: Influences plot of samples from PCA3, H (blue), L (red), LL ( green), synthetic spectra (pink). It shows the high diversity of the included synthetic samples in contrast to the ordinary [4] „Multivariate Datenanalyse für die Pharma-, Bio- und Prozessanalytik“ chondrites. Waltraud Kessler Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

[5] „IR-Spektroskopie - Eine Einführung“ Günzler, Heise Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

[6] „Meteoritical Bulletin Database“ Lunar and Planetary Institute www.lpi.usra.edu/meteor/metbull.php

[7] „Systematics and Evaluation of Meteorite Classification“ Weisberg, McCoy, Krot Lunar and Planetary Institute www.lpi.usra.edu/books/MESSII/9014.pdf

[8] “Remote Raman Spectroscopy for Planetary Exploration: A Review” Angel, Gomer, Sharma, McKay applied spectroscopy, February 2012 – Pages 137 – 150

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Appendices:

Fig. 8: Example spectra of several carbonaceous chondrites Fig.9: Spectra of used minerals Forsterite (red), Fayalite (blue), synth. Forsterite (green), synth. Forsterite (brown)

Fig. 10: Separation test with CK chondrites Fig. 11: Separation test with CO chondrites

Fig. 12: Separation test with CV chondrites Fig. 13: Separation test with R chondrites

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