Analytical Proof of Origin for Raw Materials

minerals
Review
Analytical Proof of Origin for Raw Materials
Frank Melcher 1, Valentina Dietrich 1 and Hans-Eike Gäbler 2,*





Citation: Melcher, F.; Dietrich, V.;
Gäbler, H.-E. Analytical Proof of
Origin for Raw Materials. Minerals
2021, 11, 461. https://doi.org/
10.3390/min11050461
Academic Editor: Antonio Simonetti
Received: 11 March 2021
Accepted: 23 April 2021
Published: 27 April 2021
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1
Department of Geological Sciences and Applied Geophysics, Montanuniversität Leoben,
8700 Leoben, Austria; frank.melcher@unileoben.ac.at (F.M.); valentina.dietrich@unileoben.ac.at (V.D.)
2
Federal Institute for Geosciences and Natural Resources (BGR), 30655 Hannover, Germany
*
Correspondence: hans-eike.gaebler@bgr.de
Abstract: Growing public interest in getting information on the origin of raw materials used to man-
ufacture goods for daily life has triggered the development of concepts to increase the transparency
of raw material supply chains. Analytical proofs of origin (APOs) for raw materials may support
those transparency concepts by giving evidence about the origin of a specific raw material shipment.
For a variety of raw materials like gemstones, TTT (tantalum, tin, tungsten) minerals, and others,
APOs have been developed. The identification of features that distinguish different origins, databases
of those features from reliable reference samples, and a data evaluation strategy adopted to the
envisaged application scenario are the key aspects of APO methods. Here, an overview is given on
APO methods developed for different raw materials and application cases.
Keywords: analytical proof of origin; raw materials; review
1. Introduction
Raw material supply chains are complex. They build on the presence of economically
mineable mineral commodities that undergo several steps until they are finally used in
or as consumer products. Common steps along the supply chain include a pre-mining
stage with exploration, a mining stage with excavation and beneficiation, and a post-
mining stage with processing steps. Along the supply chain, raw materials are transported
and traded. Trustworthiness into the transparency of a supply chain is of increasing
importance, both to upstream and downstream companies. Any deviation from best-
practice and quality standards in mining, processing, and production is critically looked
at by consumers. Therefore, certification concepts have emerged within the past 20 years,
aiming at providing transparency to supply chains [1,2]. Such concepts have been and
will be developed for several raw materials [2,3]. Recently, an overarching certification
system for all raw materials called CERA4in1 was initiated [4]. Transparency in the
supply chain may be achieved at all intermediate steps using different approaches [5,6].
However, reliable and tamper-proof methods need to be available in those cases, where
illegal activities come into play. These include conflict minerals, theft, fraud, smuggling,
and others. Safety regulations (e.g., radioactive materials) may also require increased
transparency. Reliable methods need to focus on measurable material properties such as
composition. Such methods have been developed for some raw materials, e.g., platinum
ores, the conflict minerals coltan, cassiterite, wolframite, gold, or precious stones.
Forensic geoscience/geology deals with evidence used in forensic science/criminalistics
derived from the analysis of soils, rocks, minerals, and other inorganic materials [7,8]. On
the other hand, the term geochemical fingerprinting is used to trace down the origin,
formation, and/or environmental conditions under which a geological unit has formed in
geological time scales [9,10]. Methods based on material analysis supporting raw material
supply chains’ transparency are often based on both forensic geology and geochemical
fingerprinting.
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The aim of this work is to review methods and fields of application which (may) pro-
vide an additional material analysis-based tool to demonstrate or increase the transparency
of raw material supply chains.
2. The Analytical Proof of Origin (APO)
The analytical proof of origin (APO) is regarded as an appropriate term to describe
analytical methods combined with data evaluation procedures developed to trace materials
back to their source(s). An analytical proof of origin investigates the origin of a material
from a specific location (mine site, processing plant, metallurgical plant) using measurable
and quantifiable material properties. It is based on comparison of a sample in question
with reference samples of known origin stored in a database. Generally, such a comparison
is done using data plots and/or mathematical/statistical algorithms. In geology, this origin
is usually an ore body of restricted size; in processing, the “origin” is a certain step within
the processing chain.
Analytical proof of origin methods for raw materials are successfully applied under
the following assumptions: (1) minerals have measurable compositions/properties which
differ depending on their genesis; (2) minerals from an area of enrichment (orebody)
are more closely related to each other than to the same mineral from a second zone of
enrichment, e.g., a different ore body.
A large variety of analytical methods available in natural sciences are used for geo-
chemical fingerprinting and forensic geosciences. A meaningful combination of analytical
methods with proper data evaluation enable us to decipher processes and, ideally, origin.
Such parameters include:
•
Geochemistry: major and trace elements; stable, radiogenic, and cosmogenic isotopes;
•
Mineralogy: mineralogical composition of a rock, concentrate, or soil sample; quanti-
tative mineralogy, fluid inclusions, and mineral inclusions;
•
Crystallography, e.g., characteristic bands in Raman or FTIR spectroscopy;
•
Color, e.g., soil color, mineral color;
•
Grain properties, e.g., size, shape, sphericity, grain size distribution in a sample,
intergrowth with other minerals, grain boundaries;
•
Geophysical properties, e.g., magnetic susceptibility, electrical conductivity.
In geological fingerprint applications, geochemistry is the most widely used parameter,
offering a large number of different sub-parameters (element concentrations, isotope ratios,
element ratios). For later statistical treatment it is important to have in mind that some of
them may be interdependent. A good example is the perfect correlation of major elements
in whole rock samples (e.g., Si and Fe in a banded iron formations) or in minerals (e.g., Fe
and Mg in olivine). Combinations of geochemical data with crystallographic data (e.g.,
Raman bands) or grain properties including modal analysis are most promising in certain
cases like the investigation of diamonds. A sound statistical evaluation of the dataset is of
the utmost importance.
After gaining meaningful data which characterize samples obtained from different
origins, a data evaluation step has to be carried out in APO. This step depends on the
nature of the obtained data, the raw material, and the question which has to be evaluated
using APO. Possible questions which are evaluated using APO are:
•
Where does a sample in question come from?
•
Is the documented origin of a shipment credible?
•
Which of a (low number) of possible origins is the most probable?
Therefore, these data evaluation steps are manifold and include among others:
•
The detection of the presence of indicative parameters like origin-characteristic inclu-
sions in colored gem stones;
•
Binary and ternary diagrams where different areas are indicative for different origins;

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•
Multivariate statistical tools like principal component analysis (PCA) and cluster
analysis for grouping of samples and assigning a sample in question to a distinct
group (origin);
•
Comparison of populations to evaluate the similarity of a sample in question and a
reference sample;
•
Likelihood ratio concept for hypothesis testing;
•
Machine learning algorithms for provenance analysis.
Other important issues for an APO are to evaluate the variability of the discriminating
parameters in the source and to have in mind that if a sample in question and a related
reference sample originate from an inhomogeneous (e.g., zoned) ore body, special attention
should be drawn to the statistical evaluation tools in terms of being applicable in this
situation. In particular, evaluation tools which use tabulated decision criteria/critical
values (e.g., Kolmogorov–Smirnov test, Wilcoxon’s rank sum test, Kruskal–Wallis test)
assume that both samples are independent observations that are representative of a single
underlying population [11]. This assumption is often not met, e.g., if both samples are
obtained from different zones of an ore body during the progress of mining operations.
Here, both samples may contain different concentration levels or compositions which
would entail that both samples are regarded as not originating from the same ore body if
tabulated decision criteria are applied, although they do. The reason for this is that the
decision criteria have been developed/calculated under the above mentioned assumption.
This false negative result is problematic if APO is applied to evaluate whether a documented
origin of a shipment is plausible or not. Here, a false negative result would accuse a person
or a company falsely, which could induce serious consequences for the person or company
although the declaration was correct. This is different to the situation usually found in
typical forensic cases where false positive rates (e.g., a piece of glass found on the clothes
of a suspect is erroneously recognized to originate from a broken glass of a crime scene)
have to be minimized. In these kinds of APO applications, false negative rates have to
be minimized at the expense of false positive results to make a conservative statement.
Therefore, if violations of the assumptions of a given statistical tool are accepted, it has to be
checked whether this fosters a conservative decision or not. Empirically deduced decision
criteria which reflect biased sampling conditions may be applied to solve this problem.
3. APO for Different Materials
Analytical proofs of origin have been developed for a variety of raw materials using
different analytical methods and data evaluation tools (Table 1).
3.1. Coltan, Cassiterite, and Wolframite (3T)
The analytical fingerprint method (AFP) is an analytical protocol combined with
statistical data evaluation that has been developed at the Federal Institute for Geosciences
and Natural Resources (BGR, Hannover, Germany) to check a declared origin of coltan,
cassiterite, or wolframite (3T) shipments [12–17]. The regional focus of this method is
Eastern and Central Africa. Significant volumes of these commodities originate from
small-scale, artisanal mining operations [18]. Armed groups finance ongoing civil wars
by taxing miners and controlling processing and trade [19]. Mineral concentrates are
sold to processors outside of Africa, mainly to Asia (China, Malaysia, Thailand), but
also to Germany and the USA [20]. The AFP method was developed to support the
Regional Initiative against the Illegal Exploitation of Natural Resources (RINR) fostered by
the International Conference on the Great Lakes Region (ICGLR). One central analytical
laboratory, several local sample preparation laboratories, and a data evaluation unit were
set up within the ICGLR region. Here, traded materials can be analyzed to provide
additional credibility to document-based traceability systems with due diligence concepts
for raw material supply chains. In the AFP approach, samples are tested using appropriate
analytical methods together with data evaluation procedures specifically developed for 3T
ore concentrates [15–17]. The data evaluation step is based on the comparison of a sample

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in question to samples of known origins stored in a database. The AFP method includes
the following steps:
•
(Witnessed) sampling of ore concentrates;
•
Preparation of polished sections;
•
Mineral identification using scanning electron microscopy combined with mineral
liberation analysis (SEM-MLA);
•
Determination of major and trace element compositions using laser ablation-inductively
coupled plasma-mass spectrometry (LA-ICP-MS) on about 50 single grains of the
ore minerals;
•
Evaluation of chemical data using applied statistics.
A match between a sample in question and a reference sample from the mine site
declared as the origin of the sample is looked for using statistical methods. Data from
reference samples from many mine sites are stored in a database and are used to evaluate
distinct sample properties necessary for statistical testing [15–17]. For AFP, the database
of raw material from producing mines is of the utmost importance. Within a period of
about 10 years, several hundred samples from producers in Central and Eastern Africa
were collected. Additional samples from Ta, Sn, and W ore deposits all around the globe
were also included [21,22].
Case studies for the application of the AFP method are given in detail for coltan and
cassiterite in Gäbler et al. [15] and for wolframite in Martyna et al. [17]. In the coltan
case study, four coltan ore concentrates were obtained from the same mine site (a single
elongated pegmatite) at two different dates. For each sample, concentration data for a
maximum of 42 elements in about 50 coltan grains were available. If an element showed
concentrations below the respective detection limit for more than 30% of the analyzed
grains, this element was not used for data evaluation. Two different data evaluation
approaches were applied, namely the “KS-D approach” and the “likelihood ratio approach”.
Both data evaluation approaches were designed to provide evidence whether the declared
origin of a sample in question is plausible or not. In the KS-D approach, element-specific
empirical distribution functions are calculated for the sample in question and the reference
sample. For each element, the maximum distance between the two distribution functions
(Kolmogorov–Smirnov distance) is computed and the median from all element-specific
distances is calculated. This median is used to evaluate whether the sample in question and
the respective reference sample originate from the same source. For the evaluation of this
median, an empirically deduced decision criterion is applied. If more than four reference
samples from the declared mine site are available, a deposit-specific decision criterion is
calculated from all possible two-sample comparisons of reference samples from this deposit.
If the database does not contain enough reference samples from the declared mine site, all
possible two-sample comparisons of other reference samples from a common mine site are
used. In the coltan case study, all possible two sample comparisons from the studied mine
site were correctly regarded as coming from the same mine site, which corresponded to a
false negative rate of 0%. However, between 43% and 65% of the other samples from the
database would erroneously be recognized as coming from the studied mine site if they
were compared to reference samples from this mine site. This high rate of false positive
results is attributed to the fact that, in this case, a deposit-specific decision criterion was
not applied. In the cassiterite case study where a deposit-specific decision criterion could
be applied, the false positive rates were between 11% and 21%, while keeping the false
negative rates at 0%.
In the wolframite case study detailed in Martyna et al. [17] five samples from the
trading chain with reliable source documents were evaluated. In this case, nine reference
samples from the mine site declared as the origin of the samples were available. An
“area ratio approach” and a “likelihood ratio approach” were applied for statistical data
evaluation. Both approaches were developed in the framework of the likelihood ratio
concept for hypothesis testing [23,24], where two competing hypotheses (H1—two samples
come from the same source, and H2—two samples come from different sources) were

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evaluated. The two samples were the sample in question and a reference sample from the
declared mine site. The probability that H1 was true and the probability that H2 was true
were juxtaposed. To obtain these probabilities, a parameter (∆KSD, see [17]) was calculated,
which represents the similarity of the two samples. From the database, numerous sample
pairs of samples from the same source and samples from different sources were randomly
selected and used to calculate distributions of this parameter for the “same source” case
and the “different source” case. These distributions were used to evaluate a given two-
sample comparison and the probabilities of both hypotheses. These probabilities enabled
the researchers to estimate how strongly each hypothesis was supported. In the wolframite
case study, 5 * 9 = 45 comparisons of the samples in question (samples from the trading
chain) to the reference samples could be calculated, and in all cases, H1 was supported
strongly with both applied data evaluation approaches. On the other hand, 45 randomly
selected comparisons of samples from different sources were computed as well with both
approaches, and resulted in the majority of the cases in strong support of H2. If for some
comparisons of samples from different sources H1 was erroneously supported, this was
done only weakly.
Additionally, predictive methods have also been tested successfully. These tackle
those cases in which the origin of a sample can be predicted if it is not represented in the
database. In the case of coltan from Central Africa, machine learning algorithms (e.g., linear
programming boosting and support vector machines) were applied to attribute unknowns
to one of two classes, namely “Congo” and “not Congo”. A 85% probability was reached
that a sample from Congo was correctly classified [25].
Harmon et al. [26] and Hark et al. [27] tested laser induced breakdown spectroscopy
(LIBS) for APO of coltan. The method was based on the comparison of analytical spectra
reflecting chemical composition data. The LIBS method combined with partial least-square
discriminant analysis (PLSDA) offers a less expensive and less time-consuming method
of sample discrimination. Correct classifications at success levels above 90% could be
achieved for test samples from 37 locations around the world. However, it needs to be
discovered how this approach deals with the situation in which samples from shipments
along the trading chain are not independent observations that are representative of a
single underlying population, which means the heterogeneity of samples obtained from
the same mine has to be taken into account. This variability is caused by the facts that (1)
pegmatites or quartz veins are not homogeneous ore bodies per se, (2) the different skills of
artisanal miners in producing ore concentrates result in samples of variable mineralogical
composition, and (3) the mining progress over time on the same mine site may result in
additional variability.
3.2. Platinum Group Elements-Bearing Products of Mining and Metallurgical Operations
A method called “complex procedure for establishing the nature and source of origin
of precious metal-bearing products of mining and metallurgical operations” (CIP) was
developed in Russia for the source determination of stolen material containing platinum
group elements (PGE) [28]. A significant amount of PGE-bearing material from Russian
and South African production sites is illegally supplied to refineries worldwide for further
processing. These two countries produce over 80% of the intermediate PGE-bearing material.
The CIP involves a variety of chemical and mineralogical techniques (microscopy,
inductively coupled plasma-optical emission spectroscopy (ICP-OES), inductively cou-
pled plasma-mass spectrometry (ICP-MS), X-ray diffraction (XRD), and scanning electron
microscopy with energy-dispersive X-ray analysis (SEM-EDX)), and a database where ana-
lytical data for different PGE-bearing product types are stored as references. The database
is used for the comparison of the samples in question. In the first step, the bulk elemental
composition and XRD results of samples in question are compared to data from a database
using previously defined threshold values for different elements and phases to identify
“suspicious samples” and PGM-bearing products of different producers. The first idea of
the composition of a mixture of several intermediate products can be obtained by using

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an algorithm which matches the signature of a sample in question with superimposed
signatures of individual components stored in the database. However, for many mixtures
of PGM-bearing products, the bulk elemental composition is not sufficient for the identifica-
tion of the individual components. Therefore, for each product type, several characteristic
artificial phases (called pseudo-phases) are defined, and their relative abundances are
determined on the basis of a kind of automated mineralogy using SEM-EDX. With this
additional information for individual PGM-bearing products, the components of mixtures
can be identified [28]. The European Network of Forensic Science Institutes (ENFSI) vali-
dated the method in 2008 [29]. The Forensic Review Board concluded that the methodology
is regarded as fit for purpose and well-suited for the identification and source attribution
of Russian PGE-bearing materials. For worldwide use, the database should be expanded to
include information on products from all or most of the producers worldwide.
3.3. Gold
Gold is one of the commodities that has a long history in fingerprinting, mainly based
on the interest in archeology (e.g., [30,31]). An extensive review on existing methods
for gold analysis was presented by Hruschka et al. [32]. It appears that analysis of trace
elements by LA-ICP-MS combined with the determination of Cu, Ag, and probably Pb
isotopes, respectively, provides the best results (M. Brauns, Curt-Engelhorn-Zentrum
Archäometrie, oral comm.).
A pilot study on fingerprinting gold from artisanal and small-scale mining was carried
out by BRGM [33] with samples from French Guiana and Surinam. The study was based on
28 samples (gold concentrates, “doré”, and “galette” gold bars) from 12 different locations
(7 French Guiana, 5 Suriname). A combination of relatively easy-to-use analytical tools,
ranging from ore microscopy to the investigation of gold grain inclusions and the analysis
of the chemical composition of gold grains, was able to distinguish the nature and the
geographical origin of gold concentrates in a satisfactory manner. More sophisticated
techniques like Ag and Cu isotopic analysis can be used to exclude specific sources [33].
Ag concentrations in gold grains can be analyzed by a portable handheld LIBS de-
vice [34]. Gold concentrates from five alluvial gold deposits in French Guiana showed
different levels and distributions of their gold grain’s Ag concentrations [34]. If this ap-
proach is followed in the future in terms of analyzing more locations and investigating the
variability of the site-specific distributions over time, this approach could be developed to
a valuable on-site tool for gold traceability.
Australia and South Africa have established databases for reference materials of
mined and processed gold since the 1990s. The government of South Africa passed the
“Precious Metals Act, 2005” [35]. Every six months, each licensed gold producer needs
to submit a representative sample from different stages of production (native gold and
doré) to the Forensic Science Laboratory (FSL) of the South African police services. This
is analyzed by ICP-OES (major elements) and LA-ICP-MS (trace elements). According to
Dixon [36], the majority of samples stored in the South African gold database consist of
drillings from doré gold bars, which are the product of mining activity at a single shaft
or several shafts, mining the same reef and supplying a smelter on site. This unrefined
gold is representative of the mineralization, type of extraction, and original compositional
variation. A large number of prills were supplied by Gold Fields, forming an important
dataset in the gold database. Each mine needs to supply raw gold and processed gold (bar,
bullion, amalgamated button). However, as pointed out by Dixon [36], the identification
of stolen gold based on the sample analysis, in comparison with the above mentioned
database, and utilizing additional information from mine security and the police, is a
complex process. Different possible ore beneficiation processes and steps may modify the
chemical signature of seized illegal samples. Good knowledge of ore deposit mineralogy
and ore beneficiation processes is necessary to assist in criminal investigations when the
results of geochemical analysis are taken into account.

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Roberts et al. [37] investigated the chemical composition of 858 different gold samples
(jewelry alloys, jewelry, mine bullions and suspects from illegal origin). Mine bullions
contain elevated amounts of Pb and can thus be distinguished from jewelry and jewelry
alloys which do not show those Pb concentrations. The commercial alloys are much more
consistent in their composition than jewelry. Older jewelry contains elements that are no
longer commonly used. Illegal material, according to South African law, can be identified
by high levels of Sn, low but measurable levels of Pb, and the presence of Hg. Crude ore
processing and metal beneficiation like amalgamation and low-temperature smelting cause
these typical features of material that is illegal according to South African law [37].
Since 1994, element associations in gold samples analyzed by LA-ICP-MS have been
used to identify the provenance of stolen gold [38,39]. In these studies, a direct visual
comparison of raw spectral data, computerized comparison of raw count data, princi-
pal component analysis (PCA), and linear discriminant analysis (LDA) were applied to
compare the sample in question to a number (<10) of possible sources [40].
3.4. Diamonds
Due to their immense value, rarity, and associated conflict problems (“blood dia-
monds”), diamond fingerprinting is of high interest to the public. Although an interna-
tional certification system is in place (Kimberley process), it is known that the diamond
trade is still far from being transparent. On the other hand, diamond is almost pure carbon
with very little impurities; it is the hardest natural substance on Earth, and thus very diffi-
cult to handle using classical methods of sample preparation. Discrimination of diamonds
using physical and chemical methods has been undertaken since the 1970s [41,42]. So far,
cathodoluminescence microscopy, nitrogen and trace element analysis by instrumental
neutron activation analysis (INAA), and LA-ICP-MS have been applied [39,43,44]. The
chemical composition of associated or included “diamond indicator minerals” (e.g., garnet,
spinel, pyroxene) may also be used as a vector towards the source of the diamond [43].
The first paper providing statistically robust forensic data for diamond discrimination
by Dalpé et al. [45] concluded that much more work is needed to validate the results, which
were positive in general. However, all these studies focused on kimberlitic diamonds and
avoided alluvial diamonds. Coney et al. [44] proposed a two-stage approach address-
ing physical and chemical characteristics of diamonds from both kimberlitic and alluvial
sources. Morphological features were investigated using a stereomicroscope. Nitrogen con-
centrations and the aggregation state were determined using infrared spectrometry (FTIR
spectrometry). LA-ICP-MS was used for trace element analysis using a doped multielement
glucose pellet as a matrix-matched standard. The analytical results of diamond parcels
representing 10 sources in Africa showed that discrimination based on one parameter
alone does not work. Furthermore, trace element concentrations are often close to or below
the mean detection limit of the method used. Subtle differences between sources have
become evident, but discrimination is highly complex. For diamonds, the heterogeneity of
compositions encountered in the sources, and the difficulties in receiving material that can
be used to establish a large database are both regarded as major challenges. Brill et al. [46]
reported a case where diamond sets that were suspect to have origins in the Central African
Republic (CAR) were compared to diamonds previously analyzed as originating from
CAR and the Democratic Republic of the Congo (DRC). Sample morphology, FTIR, and
LA-ICP-MS combined with descriptive statistics were applied. It is recommended that
for such a task, many stones from one set (>50) should be analyzed and the need for a
comprehensive database of diamond data is highlighted.
A pilot study on the multivariate analysis of LIBS spectra obtained from natural and
synthetic diamonds suggested that diamond provenance can be determined using this
technique [47]. The study was based on 14 sets of 30 diamonds from 12 different natural
localities from all over the world and 2 sets of synthetic diamonds of different origins.
From each stone, 64 LIBS spectra were obtained and accumulated into 8 spectra per stone.
After a normalization step, two data evaluation approaches were applied: (i) unsupervised

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cluster analysis using a leave-one-out approach for validation, and (ii) a train (using 83% of
all spectra) and validate (using 17% of all spectra) decision tree approach based on a series
of partial least-square-regression (PLSR) models. Success rates of >95% were reported for
both models. Additionally, the heterogeneity within the sets of diamonds and its impact
on diamond provenance determination was studied using principal component analysis
(PCA) [47].
3.5. Colored Gemstones
The origin of high-value colored gemstones is of increasing importance for jewelry
trade, as the origin of a gemstone can be considered as a value-determining factor and
may influence the salability of a stone [48]. Analytical methods used in geographic ori-
gin determination of gemstones were reviewed by Groat et al. [48]. Various parameters
starting from basic physical properties like refractive index and specific gravity over the
characteristics of inclusions and spectroscopic characteristics up to trace element chemistry
and isotope ratios were monitored. Experienced gemologists evaluate the observations
and work out an origin call using databases of reference samples. However, sometimes the
properties are quite similar, so a definitive origin call is not always possible.
Giuliani et al. [49] analyzed 249 samples of natural ruby and sapphire from 106
deposits in 26 countries. The samples were grouped into three types: primary deposits,
secondary deposits, and heated crystals. Oxygen isotope analyses were carried out using
the laser-fluorination technique. In combination with gemological properties, it was
possible to distinguish ruby and sapphire from different deposits worldwide. Trace element
chemistry and isotope ratios are used frequently to determine the geographic origin of
ruby and sapphire using binary or ternary discriminating plots or multivariate linear
discriminant analysis (LDA) based on various analytical tools like LA-ICP-MS, portable
X-ray fluorescence (XRF), energy-dispersive XRF (EDXRF), and thermal ionization mass
spectrometry (TIMS) [50–53]. Besides trace element chemistry, UV-Vis-NIR spectroscopy,
Raman spectroscopy, and the study of inclusion scenes are additional tools to determine
the origin of blue sapphire [54]. For rubies, trace element chemistry and the inclusion
scenes are of importance for origin determination [55]. However, due to significant overlap
in the data of stones from different locations, origin determination for blue sapphires and
rubies is sometimes difficult, and thus not always possible [54,55].
Abduriyim and Kitawaki [56] studied 111 emeralds from eight locations worldwide
by LA-ICP-MS. Laser ablation was done in the shape of a logo to indicate that this stone
was already analyzed by this technique. Binary and ternary diagrams were used to
discriminate emerald origins. All localities could be separated; however, not all major
emerald sources were covered by this study. For additional confidence, Abduriyim and
Kitawaki [56] recommended to support the chemical data by optical properties and internal
characteristics. Wang and Krzemnicki [57] used a time-of-flight mass filter in LA-ICP-MS
that allowed them to analyze the whole mass spectrum from 7Li to 238U simultaneously,
without the need to establish a list of elements prior to analysis, as is the case when using a
quadrupole mass filter. Combined with multivariate cluster analysis using t-distributed
stochastic neighbor embedding (t-SNE), they were able to create clusters which coincided
with the geographic provenance of emeralds. A combination of UV-Vis-NIR spectroscopy,
the study of inclusions, and trace element chemistry data obtained by LA-ICP-MS is used
by the Gemological Institute of America (GIA) to determine the origin of emeralds from all
over the world [58]. Various trace element plots are used to evaluate the LA-ICP-MS data.
Aurisicchio et al. [59] applied electron probe micro analysis (EPMA) and secondary ion
mass spectrometry (SIMS) for the analysis of emeralds. They could carry out provenance
analysis of these gem stones from all over the world using binary/ternary diagrams
and PCA.
The oxygen isotope analysis has also been used for the determination of the origin
of emeralds since the 1990s. The δ18O-value appears to be a good criterion to indicate
the origin of exceptional-quality emeralds from Colombia, Afghanistan, Pakistan, Brazil

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(Santa Terezinha de Goiás and Quadrilatero Ferrifero deposits), and Zimbabwe, while
the δ18O-ranges from other origins like Russia, Brazil (Carnaíba and Socotó deposits),
Madagascar, and Zambia overlap [60].
The origin of gem-quality tourmaline can be determined by trace element chemistry
analyzed by LA-ICP-MS [61,62]. Binary and ternary diagrams are used to separate stones
from Nigeria, Brazil, and Mozambique.
Demantoid garnets from six countries have been analyzed by LA-ICP-MS for origin
determination [63]. Different multivariate classification models based on PCA-LDA have
been studied resulting in a separation of five out of the six countries of origin. It is pointed
out that the natural variability has to be taken into account, which makes it necessary to
analyze many gems from the same location to apply this tool successfully.
Trace element chemistry obtained by LA-ICP-MS is the most important feature to
determine the origin of alexandrite. Secondary factors are inclusions and color-change
behavior, which can be visually observed under daylight and incandescent lightning
conditions [64].
3.6. Uranium Ores and Products
Due to nuclear security issues, knowing the origin of uranium-bearing materials is
important. Švedkauskaitè-LeGore et al. [65] demonstrated the usefulness of a combined
approach determining Pb isotopes by multi-collector ICP-MS (MC-ICP-MS) and trace
elements by ICP-MS (66 variables). The results from a set of 35 uranium ore samples and
10 yellow cake samples were evaluated using cluster analysis. It was concluded that a
combination of impurity and lead isotope measurements is recommended as a highly
characteristic fingerprint for nuclear forensic purposes. In another study focusing on
uranium ore from three Australian mines, the concentrations of 40 elements and their Pb
isotopic compositions were determined and subsequently treated using PCA [66]. The
export products from the operating mines can be clearly distinguished using elemental
and isotopic profiles. Chondrite-normalized rare earth element (REE) patterns can be used
to distinguish between different uranium ore deposits [67,68]. Spano et al. [69] introduced
a forensic tool to identify the uranium ore deposit type of an unknown sample. This tool
is based on the comparison of the shapes of normalized REE patterns. The sample in
question was compared to the reference samples from different uranium ore types. The
degree of similarity was quantified using the slope and the correlation coefficient of a
linear regression approach. The formation age of uranium ore deposits differs significantly,
which allows for researchers to use the Pb isotope composition to identify or confirm the
origin of uranium ore samples [70]. Recently, Corcoran et al. [71] presented a combination
of geochemistry, isotopic composition, and mineralogy using PCA to separate different
uranium ore deposit types, which can be used as an APO if applied to an uranium ore
sample in question.
Sirven et al. [72] investigated the potential of the LIBS technique to identify the origin
of 11 yellow cake samples. PCA and soft independent modeling of class analogy (SIMCA)
were applied. The SIMCA resulted in a correct identification of all classes. However, in this
study, each class consisted of only one sample, so that the inhomogeneity of natural samples
was not taken into account, which might influence the classification results substantially.
In addition to the above mentioned methods, determination of the oxygen isotope
composition in uranium minerals offers a potential for source discrimination [73]. The
δ18O values range from −32 to +11‰, and are related to the geological setting unique to
each deposit.
3.7. Base Metals
Machault et al. [74] proposed to establish so-called identity cards for ore deposits
to increase transparency in raw material supply chains. These identity cards contain
mineralogical and microtextural parameters which record the unique signature of an ore
deposit (mineralogical composition, microtextures of target minerals, “pseudo-paragenetic

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sequence”, chemical composition and distribution of target minerals, and “memory loss”
of metals during mineral processing). At the stage of the ore concentrates, not only the
origin of the ore, but also the kind of ore processing applied determines the ore concentrate
characteristics as well. Therefore, the “memory loss” during mineral processing should
be taken into account. An example for setting up identity cards for nine different volcanic
massive sulphide ore deposits from two metallogenic provinces (Iberian Pyrite Belt, Ural)
was given by Machault et al. [74]. According to Machault et al. [74] the main challenge
which has to be addressed in establishing an ore identify card is to take ore variability into
account and obtain samples that are the most representative of a deposit.
The rapid development of LA-ICP-MS techniques in the past decade has opened
opportunities for the APO of base metal ores, because the main metal phases sphalerite,
chalcopyrite, galena, pyrite, and pyrrhotine are carriers of a large number of trace ele-
ments [75]. In addition to research devoted to problems of trace element substitution in
minerals, processes governing trace element incorporation in sulphides, and their probable
liberation from such ores, datasets, and some databases have been developed that are of
potential use for APO [76]. After having defined a question to be evaluated by APO, these
data can be used as a starting point for the development of an APO in this field.
Similar trace element analysis routines are increasingly applied to magnetite and other
iron oxides. Such methods also discriminate between genetic ore types, ore provinces, and
different ore bodies [77].
3.8. Heterogenite
Heterogenite is a trade name for Co ore concentrates produced in Katanga/DR Congo,
and also the name of a mineral with a CoOOH composition. Cobalt is regarded as a critical
raw material by the European Union [78]. About 60% of the world supply originates from
the Democratic Republic of the Congo, and an additional 2% from adjacent Zambia [79].
At present, cobalt is preferably, but not exclusively, mined from heterogenite ore.
The TRACE project financed by the Belgian Federal Public Service for the Pro Sciences
Policy investigated methods to trace heterogenite [80]. Various analytical methods (Raman
microspectroscopy, EPMA, electron backscatter diffraction, SEM, ICP-MS, and LA-ICP-MS)
have been used to characterize the mineral and to identify differences in the chemical
composition and structural state between different deposits in Katanga [81,82]. It was
demonstrated that heterogenite may be dated using U-Pb isotopes [83] and that it shows a
range of chemical compositions and structural states. However, a conclusive methodology
based on a large set of data proving that the provenance determination of heterogenite
from different mine sites in Katanga is possible has not yet been published. Application to
a real analytical proof of origin scenario could not be convincingly demonstrated.
3.9. Phosphate Raw Materials
Phosphorous, as one of the three major macronutrients, is essential for all life, and
therefore indispensable for food production. It is obtained from phosphate rocks of sedi-
mentary and igneous origin. Europe’s import dependency is almost 100% of the demand,
and supply security is a major issue [84]. Some of the raw materials, however, carry
hazardous elements such as uranium or cadmium, in concentrations higher than those
tolerated by law. These trace elements would accumulate in soil if fertilizer produced from
such raw materials were used. Sattouf et al. [85] used isotope ratio measurements of U and
Sr to identify the origin of rock phosphates and fertilizers. Four main geographic origins,
the USA, the Middle East and North Africa, the Russian Kola Peninsula, and Senegal and
Togo, can be distinguished using a combination of U and Sr isotope ratios.
3.10. Vermiculite
Vermiculite is an industrial mineral with excellent insulating and absorption proper-
ties, finding extensive use in construction, insulation, packing materials, and gardening
adjuncts. However, in some vermiculite products, asbestiform minerals have been identi-

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fied. A clear correlation was made between asbestos content in vermiculite and cases of
lung disease in Libby, Montana, where vermiculite has been mined for a long period of
time [86]. The Idaho National Laboratory used geochemical signatures to determine the
origin of commercially available vermiculite. A total of 44 bulk samples from different
origins was analyzed for 10 major elements by XRF and for 28 trace elements, including
REE, by neutron activation analysis (NAA). The analyses were carried out on homogenized
bulk samples without previous mineral separation or cleaning; no attempt was made to use
mineralogical criteria for discrimination. It was shown that the REE was especially highly
diagnostic in provenance determination. Statistical analysis of the data formed the main
part of the study. Following basic data description using probability plots and correlation
matrices, advanced statistical methods such as hierarchical cluster analysis, K-cluster anal-
ysis, and discriminant analysis using different models were carried out to identify samples
from an unknown origin. Discriminant analysis allowed for a clear differentiation between
the primary suppliers of vermiculite to the United States; therefore, this technique can be
used to determine the relative risk of asbestos contamination based on the sample origin.
3.11. Marble
A method for the provenance analysis of marble used in antique objects was developed
at Montanuniversität Leoben [87–90]. The method uses a multivariate approach to locate
the provenance of marbles used for antique sculptures. Parameters evaluated include
petrography (grain size), stable isotopes (δ13C, δ18O), trace elements, and the analysis
of fluid inclusions. Data are analyzed using multivariate discrimination methods. The
database includes analytical data of more than 3500 quarries from all regions of the Roman
Empire. The method has been successfully applied to fingerprint the origin of ancient white
marbles from artefacts, e.g., solving the Carrara–Göktepe entanglement [91], provenancing
the marbles used to construct the mausoleum of Belevi [88] and many others [90,92,93].
Table 1. Analytical proof of origin for different raw materials.
Raw Material
Method/Parameters
Regional Focus
References
3T (Ta, Sn, W) ore concentrates
AFP: combination of SEM-MLA and LA-ICP-MS
Central Africa
[12–17]
LIBS
North America
[26]
LIBS
worldwide
[27]
Gold
LA-ICP-MS
South Africa, South
America
[36,37,94]
LA-ICP-MS
Western Australia
[38,39]
SEM, EPMA, LA-ICP-MS, isotopes of Pb, Cu, and Ag
French Guiana, Surinam
[33]
LIBS
French Guiana
[34]
Gemstones
Diamond
LA-ICP-MS
Canada, South Africa
[45]
Morphology, FTIR, LA-ICP-MS
Africa
[44,46]
LIBS
worldwide
[47,95]
Sapphire
Inclusions, UV-Vis-NIR, Raman, LA-ICP-MS, δ18O, TIMS
worldwide
[49–54]
Ruby
Inclusions, LA-ICP-MS, δ18O, TIMS
worldwide
[49,50,52,53,55]
Emerald
Inclusions, UV-Vis-NIR, Raman, LA-ICP-MS, δ18O,
EPMA, SIMS
worldwide
[56–60]
Tourmaline
LA-ICP-MS
Brazil, Nigeria,
Mozambique
[61,62]
Demantoid
LA-ICP-MS
worldwide
[63]
Alexandrite
LA-ICP-MS, inclusions, color-change
worldwide
[64]
Platinum ores and intermediate
products
CIP (see text)
Russia, South Africa
[28,29]
Heterogenite
Raman spectroscopy, chemistry, U-Pb dating
Katanga, DRC
[81–83]
Base metals
Identity card for ore deposits
Iberian Peninsula, Ural
[74]
Uranium
Ore concentrates
ICP-MS (chemistry), TIMS (thermal ionization mass
spectrometry, Pb isotopes)
Australia
[65]
ICP-MS, XRF, TIMS, SIMS
Australia
[66]
Yellow cake
ICP-MS (chemistry), TIMS (Pb isotopes)
Australia
[65]
Uraninite
EPMA, LA-ICP-MS
worldwide
[67]
Uraninite
Normalized REE patterns
worldwide
[69]
Uraninite
MC-ICP-MS, geochronology
worldwide
[70]

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Table 1. Cont.
Raw Material
Method/Parameters
Regional Focus
References
Uraninite
EPMA, ICP-MS, MC-ICP-MS, powder X-ray
diffraction, PCA
worldwide
[71]
LIBS
worldwide
[72]
Minerals
δ18O
worldwide
[73]
Phosphate
U and Sr isotope ratios
worldwide
[85]
Vermiculite
Main and trace element chemistry
worldwide
[86]
Marble
Petrography, δ13C, δ18O, trace element
chemistry, inclusions
Roman Empire
[87–93]
4. Conclusions
For a variety of raw materials, analytical proofs of origin have been developed to
support transparency in raw material supply chains, trace back stolen or suspect materials
of dubious origins, increase the value of gems on the market, or exclude raw materials
carrying hazardous impurities from being widely used. All methods have in common that
in the first instance, features have to be identified which distinguish samples from different
origins. Trace element chemistry seems to be the most important feature to do this, but it is
not always the one and only solution.
In general, a successful application of the distinguishing features for APO requires a
database where information about those features from different origins are stored. There-
fore, reliable reference samples of known origins are of the utmost importance for every
APO approach. However, for some materials (e.g., gold, gemstones) the collection of
reference materials is quite challenging because of restricted access to mine sites or safety
risks in certain areas.
If distinguishing features have been identified and a suitable database is available,
data evaluation has to be done. Data evaluation methods range from experts’ experiences
(fluid inclusion scenes in gems) over the use of binary or ternary data plots up to more
sophisticated statistical data treatments, even at the raw data stage (e.g., LIBS). The rarity
and the variability of the distinguishing features in the source, and the question of whether
the used samples can be regarded as independent observations that are representative of
a single underlying population have to be considered. The latter is especially important
when statistical data evaluation methods are applied, which assume that the used samples
meet this requirement. If this assumption is violated empirically-deduced decision criteria
can be developed.
Self-learning algorithms in discriminant analysis provide criteria to evaluate whether a
sample belongs to a certain class (origin), which is used in the data evaluation of APO methods.
Nailing down the question which has to be answered is important to find a suitable
data evaluation strategy, e.g., “Where does this sample come from?” or “Is the documented
origin of a shipment credible?” or “Which of a (low number) of possible origins is the most
probable?”. All of the above-mentioned points show that there is no general recommenda-
tion for how to set up an APO for a given case. The APO must always be adopted to the
envisaged application scenario and the question which has to be answered.
Author Contributions: Conceptualization, methodology, and formal analysis F.M. and H.-E.G.;
writing—original draft preparation, F.M., V.D. and H.-E.G.; All authors have read and agreed to the
published version of the manuscript.
Funding: Parts of this research have been carried out in the framework of the CERA project (Certifi-
cation of Raw Materials) funded by EIT Raw Materials. For further information check the project
website: www.cera4in1.org (accessed on 5 March 2021).
Acknowledgments: The authors are grateful to the academic editor and two anonymous reviewers
for their valuable comments on an earlier version of the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.

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