# LIBS and Raman image fusion: An original approach based on the use of chemometric methodologies

Alessandro Nardecchia, Anna de Juan, Vincent Motto-Ros, Cécile Fabre, Ludovic Duponchel

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## To cite this version:

Alessandro Nardecchia, Anna de Juan, Vincent Motto-Ros, Cécile Fabre, Ludovic Duponchel. LIBS and Raman image fusion: An original approach based on the use of chemometric methodologies. Spectrochimica Acta Part B: Atomic Spectroscopy, 2022, Spectrochimica Acta Part B: Atomic Spectroscopy, 198, pp.106571. 10.1016/j.sab.2022.106571. hal-04475112

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# LIBS and Raman image fusion: an original approach based on the use of chemometric methodologies

**Authors:** Alessandro Nardecchia¹, Anna de Juan², Vincent Motto-Ros³, Cécile Fabre⁴, Ludovic Duponchel¹,.*

¹ Univ. Lille, CNRS, UMR 8516 – LASIRE – Laboratoire de Spectroscopie pour Les Interactions, La Réactivité et L'Environnement, Lille, F-59000, France

² Chemometrics Group. Department of Chemical Engineering and Analytical Chemistry, Universitat de Barcelona, Diagonal 645, 08028, Barcelona, Spain

³ Institut Lumière Matière, UMR 5306, Université Lyon 1 – CNRS, Université de Lyon, Villeurbanne, 69622, France

⁴ Université de Lorraine, Laboratoire GeoRessources, UMR CNRS, 7395, France

* corresponding author: ludovic.duponchel@univ-lille.fr

**Keywords:** Hyperspectral imaging; chemometrics; MCR-ALS; LIBS; Raman; data fusion.

## ABSTRACT

Laser-induced breakdown spectroscopy (LIBS) imaging is a powerful and innovative technique with a constant increasing success and interest in many scientific fields. Using LIBS imaging, it is possible to highlight the presence of atoms in complex samples of different nature to achieve important spectral and spatial information. Simple preparation of the sample, an acquisition rate that can reach a speed of 1 kHz, a high spatial resolution (in the order of µm scale) and a sensitivity in the order of ppm are among the assets of this technique. An additional valuable aspect in the current LIBS setups is the possibility to acquire with the same LIBS platform spectroscopic responses resulting from another radiation-matter interaction, such as Raman measurements. The most common data treatment
approach to LIBS imaging data is still univariate, i.e., the observation of maps at certain representative LIBS wavelength, but this prevents extracting all the useful information contained in the acquired spectra and obtaining an integral understanding of the correlation between the spatial and spectral information. Chemometrics and multivariate analysis in the framework of spectral unmixing can lead to these outcomes. Therefore, the aim of this work is to show the potential of investigating simultaneously LIBS and Raman imaging spectral data acquired on the same sample with the assistance of the unmixing method Multivariate Curve resolution – Alternating Least Squares (MCR-ALS). To illustrate the value of the thorough interpretation of fused LIBS and Raman images by unmixing analysis and the steps to take place in this kind of study, a real sample of a complex polymetallic mineral formed by several mineral phases incorporating carbonates, silicates and **sulphides** has been used. In this paper we will show that using a pipeline analysis already validated in another work of our group, it is possible to **extract pure chemical contributions** of the heterogeneous aforementioned **minerals**. The data analysis protocol presented is valid for the investigation of LIBS and Raman spectroscopies separately, but becomes much more valuable when the two acquired data sets for the same sample are simultaneously examined, leading to new aspects that would be hindered if not investigated at the same time with a suitable fusion approach.

# 1. INTRODUCTION

Nowadays, Laser-Induced Breakdown Spectroscopy (LIBS) is one of the most relevant spectroscopic techniques in the framework of multi-element hyperspectral imaging [1–4], comparable to techniques such as micro-XRF mapping [5,6]. The principle of this technique is the use of a **pulsed** laser beam that hitting the surface of the sample generates a plasma that atomizes and excites the ablated matter. In this way, an excess of energy is released from the excited atoms, ions and molecules that generate emission spectra, with one or several
characteristic emission lines, representative of each element present in the sample. In the last
years, LIBS has been used in many research areas [7–9] due to the multi-elemental
capabilities of the technique, the acquisition rate up to the order of 1000 spectra/s, the
compatibility with optical microscopy and the possibility of observing from major
components to traces (very high sensitivity for various elements) [10]. An asset of the LIBS
imaging platforms is the ease of integrating other spectroscopic techniques using the same
microscope to provide multimodal images [11]. In the latter work, we have already
demonstrated the potential of data fusion between LIBS and Plasma Induced Luminescence
for a better interpretation of complex spectral signatures. In the present work, the same
sample was analyzed with a multimodal image platform that allowed sequentially acquiring
the Raman and the LIBS image of the same sample in order to obtain both molecular and
elemental information. The challenge of this kind of multimodal measurement is fusing the
different spectral responses and maximizing the extraction of information coming from the
spatial and spectral dimensions of the acquired data cubes. Chemometrics, and particularly
multivariate data analysis, can represent a very suitable strategy to solve this task. However, a
clear data analysis pipeline including a compression step of the large data set (containing for
example more than 50,000 spectra) and a subsequent unmixing procedure needs to be
followed to characterize the identity and the distribution of the sample constituents based on
the multimodal LIBS/Raman measurement [11]. The method proposed by the research team is
based on a double compression of the LIBS/Raman data set, with the main intention of
selecting only the most important variables from both the spectral- and pixel- dimensions (i.e.
along the columns and the rows of the dataset respectively). For the spectroscopic signal
unmixing task, Multivariate Curve Resolution – Alternating Least Squares (MCR-ALS)
analysis [12–14] will be applied on the compressed data set coming from the previous step.
The results of this analysis will be the seed to recover the full distribution maps and spectral signatures of the sample constituents.

In the context of the LIBS/Raman fusion, the LIBS and Raman images will be first investigated separately, in order to achieve a better comprehension of the elemental and molecular nature of the sample, respectively. Then, using the proposed data fusion strategy, we will deepen the knowledge on the complexity of the observed sample to find new chemical aspects and correlations that cannot be encountered unless different spectroscopic modalities are simultaneously exploited. This data fusion will be carried out by a data augmentation procedure, well known in chemometrics, which consists in extending the spectral range of a first spectroscopy by gluing the spectral range of the second one.

The proposed procedure will be tested to study a complex geological sample, showing the presence of various mineral phases, a perfect example to prove the interest in the use of this innovative method. To the best of our knowledge, this is the first time that the proposed procedure and spectral unmixing is used for the fusion of hyperspectral images obtained with LIBS and Raman spectroscopies to exploit the correlation between the elemental and molecular information of samples.

## 2. MATERIAL AND METHODS

The data analysis pipeline used in this work is a dedicated adaptation to the LIBS/Raman fusion of a previous methodology described in a recent research for the fusion of LIBS/Plasma Induced Luminescence (PIL spectra [11]. More details about the PIL technique can be found in previous works) [15–17]. The characteristics shared in the two image fusions mentioned are the large size of the data set images, with hundred thousand pixel spectra per image, which required a smart compression in the pixel and spectral dimension, and the need to characterize the sample constituents; hence, the unmixing step. This section will mainly
describe the specific aspects linked to the LIBS/Raman fusion. For further detail in some of
the general data analysis pipeline steps, the reader is addressed to [11].

**2.1. Acquisition setup**

LIBS and Raman imaging experiments were conducted with the same instrument (Figure 1), described in detail elsewhere [18,19]. Briefly, the LIBS ablation was performed using a Nd:YAG laser emitting at the fundamental wavelength 1064 nm with a pulse duration of 5 ns and a 10 Hz repetition rate. A 500-mW laser source (785 nm) has been used for Raman measurements. Both laser beams were focused onto the sample surface using the same microscope objective (LMM-15X-P01, Thorlabs). Special attention was paid to the shaping of the two laser beams with the objective to obtain an equivalent spot size on the sample for both measurements. As mentioned in the paper, the same focusing lens were used for both LIBS and Raman lasers. Furthermore, beam splitters were also used for both lasers to perfectly cover the entrance pupil of the objective, so one can expect to have an equivalent beam diameter for both measurements. In addition, and as shown in figure 1, the system includes a monitoring camera to image the sample surface. A fine adjustment is made with this camera to superimpose the two laser spots. This adjustment is realized on the Raman beam injection thanks to a micrometric XY support (not shown on the figure). This spot size was about 5 µm diameter. The step size of both imaging experiments was set to 25 µm.

Raman acquisition was performed prior to LIBS experiment in order to conduct the
measurement on a fresh surface. The integration time for Raman was fixed to 0.5 s/spectrum.
Considering the imaging structure, formed by 500 × 316 pixels (i.e., 158000 pixels), the
Raman scanning lasts about 24 hours. LIBS imaging was conducted in the same location of
Raman analysis. The total time for LIBS analysis (operating here at 10 Hz) was about 5 hours.
Considering the positioning repeatability of the motorized stages (i.e., 0.5 µm) we can assume
a good superposition of both measurements. LIBS detection was performed using a 500 mm
focal Czerny-Turner spectrometer coupled to an ICCD camera (Istar, Andor Technology).
Measurements were conducted with 1 µs and 5 µs for the delay and gate, respectively. The
spectrometer was configured to cover the range from 250 to 330 nm, including intense lines
of a large number of elements, such as Si, Al, Mg, Pb, Mn, Ca, Cu, Ag, among other. The
Raman detection was performed using a QePro spectrometer (Ocean Insight).

**2.2. Data set description**

The sample selected for this work is a mineral from the district of Tighza (Central
Morocco), related to the Sidi Ahmed hydrothermal event [10]. The thin rock section analyzed
has been sampled in the complex polymetallic W-Au-Pb-Zn-Ag(Sb-Ba) mine, but the
sample may not display all of the parageneses. Due to its heterogeneity, it can be easily
expected the simultaneous presence of major and minor compounds, but also traces in various
mineral phases (silicate, carbonate, sulfur, oxide, etc.) [7]. Spectra were collected by means of
a platform suitable for the simultaneous acquisition of both LIBS and Raman spectra. In this
work, the dimensions of the image related to the sample analyzed are equal to 500 x 316
pixels x 2048 spectral channels (for LIBS) and 500 x 316 pixels x 1044 spectral channels (for
Raman), meaning a total of 158000 spectra for each spectroscopic technique. Figure 2 shows
the very heterogeneous nature of the mineral. From left to right, the optical image of the
corresponding mineral used for the analysis can be seen, together with different integration
images linked to LIBS spectra at various wavelengths specific for different elements. In the
last row, Raman contributions selecting wavenumber regions related to the molecular
distribution of different components present in the rock are also displayed. These first
outcomes have been obtained using a conventional univariate procedure, i.e., each element
map in the images is associated with a specific wavelength (for LIBS) or mineral phase maps
are linked to a potentially specific wavenumber (for Raman). Although this first investigation
of the sample provides a first idea of the complex nature of the specimen, it has to be
necessarily complemented by a multivariate analysis analysis that can help to fully define and
confirm the identity and location of the sample components.

## 2.3. Spectral data correction

A fundamental aspect to be considered in this work is that the collection of measurements
by two different spectroscopies using different lasers at the same time can lead to instrumental
issues (e.g., due to different technical settings) which have to be corrected before data
analysis. A description of the procedure is reported below. A great detail on the LIBS
pretreatment can be found in [11] and will be briefly described. Conversely, the preprocessing
of Raman data, specific of the present work will be reported in detail. First, the whole data set
was divided into different subimages (precisely ten subsets of spectra), with the main
intention of working with reduced matrices and speed up the computational steps. Every
single reported operation was applied on each of the blocks and then, after the spectral
unmixing, merged newly together to obtain the final results for the whole reconstructed
sample.

### 2.3.1. LIBS preprocessing

LIBS devices usually provide very good spectra and only a soft pretreatment is required
for this kind of spectroscopy. Nevertheless, correcting the presence of saturated signals
[20,21] is always necessary. As previously described [11], the saturated signals (identified
applying a threshold on the intensities of the spectral peaks for each pixel) were corrected
replacing them with the average obtained from the signal of non-saturated neighborhood
pixels. Then, a mild baseline correction using the asymmetric least squares (AsLS) algorithm
based on the Whittaker smoother [22] was applied.

### 2.3.2. Raman preprocessing
Since Raman images are acquired using the same excitation source as in LIBS images, very extreme raw signals are obtained that need to be investigated and corrected before any further data analysis (Figure 3a). The spectra of this data set show pixels with clearly saturated signals. These pixels are detected and located setting a threshold defining the maximum signal intensity allowed and the spectra showing signals above it are removed and replaced by the average of correct neighbouring pixels for further analysis. Another problem in the data set is the presence of a negative peak around 1606 cm⁻¹, probably due to instrumental issues. To correct this spectral imperfection, an interpolation with the neighboring signal points of this spectral zone was applied. Finally, a baseline correction by means of the AsLS algorithm (λ = 10⁴ and asymmetry parameter of 0.001) was used [23]. As final step, spectra cosmic spikes were easily detected by using derivatives, and then replaced by the average spectrum of correct neighboring pixels. As a proof of the significant difference between the raw and the final data, the pretreated spectra are displayed in Figure 3b.

## 2.4. Compression method in the framework of signal unmixing

As in the previous work, a double compression (on the pixels and the spectral channels) was applied [11]. The use of this strategy in the data set analyzed is particularly focused on the selection of the purest information in the spatial and spectral direction, very helpful to get a faster and better spectral unmixing. By definition, a pure spectral information is in fact data that cannot be retrieved by linear combination of others in the considered dataset. The double compression procedure was first applied separately on the two data sets (i.e. LIBS and Raman). The selection was based on the use of a SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA)-based method [24–26]. This is an approach that has been used in chemometrics for early work on signal unmixing. The algorithm is based on a simple calculation of mean and standard deviation to select the purest information contained in a dataset. It should be noted that SIMPLISMA is also an excellent tool for generating initial
estimates in the MCR-ALS approach. Regarding the L
IBS data set, the selection

was applied first on the spectral channels to take advantage of the fine spectral features of this
technique with the purpose of obtaining a reduced matrix containing only the purest spectral
variables (for the LIBS image, the most 772 purest variables were selected, out of an initial
number of 2048 and for the Raman image, the most 401 purest variables were selected, out of
an initial number of 1044). Then, once obtained a reduced spectral channel list formed by the
most relevant variables found in the different sub-images analyzed, the same approach was
applied on the spectral direction in order to select the pixels containing the purest information
related to the different elements in the mineral, which were around 21200, meaning a %13.3
of the initial pixels in the data set. A different approach was used to select only some of the
channel variables of the Raman data set. In fact, it is rather impossible to find very fine
Raman peaks, as those encountered in LIBS spectra. Therefore, the compression procedure
consisted of selecting one channel every three to reduce the amount of considered variables to
a third without risking the loss of important information. Then, the SIMPLISMA-based
approach was used for the selection of the purest pixels, as in the LIBS image. As final step,
after merging together the selected spectra from each block subimage, the SIMPLISMA-
based method was newly applied to compress even more the pixels to be analyzed by
unmixing analysis.

2.5. Unmixing MCR-ALS analysis on the double compressed image data and
reconstruction of full distribution maps and spectral signatures

Finally, the unmixing approach was applied on the double compressed image data using
the MCR-ALS algorithm, with the goal of obtaining the pure contribution of the different
components present in the LIBS and Raman matrices, separately. MCR-ALS is an unmixing
algorithm used to decompose the initial set of pixel spectra (ordered in a matrix D) into pure
contributions following a bilinear model that mimics the Beer-Lambert law, as described in equation 1,

$$D = CS^T + E \quad [1]$$

where $S^T$ is the matrix that contains the pure spectral signatures of the image constituents and $C$ their related pixel concentration profiles. When the profiles of $C$ are correctly refolded recovering the 2D spatial structure of the initial image, the pure distribution maps can be obtained. MCR-ALS is an iterative algorithm that works alternatingly optimizing the profiles in $C$ and $S^T$ under constraints, such as non-negativity. Previous steps to the algorithm optimization are the determination of the number of components in the model and the generation of initial spectral estimates of the $S^T$ matrix.

The selection of the optimal number of components is often a challenge in this kind of procedure, particularly for complex images where minor compounds, located in few pixels, can be present [27]. In this work, Singular Value Decomposition (SVD) has been used in order to estimate the number of pure contributions as already done in a previous work [11]. Once the number of components has been defined, initial estimates of the $S^T$ matrix were obtained by a SIMPLISMA-based method.

As already mentioned, the MCR-ALS results obtained in this work correspond to the double compressed data. So, as the last part of the pipeline, two suitable single least-squares steps were used to recover the full spectral signatures and complete distribution maps of the original image, as described in [11].

## 2.6. Data fusion strategy

Despite the interest in observing the results related to LIBS and Raman spectral ranges separately, the LIBS/Raman fused image provides more complete information. In fact, while
LIBS is particularly related to the elemental composition or the trace element distribution in a mineral phase, Raman provides straightforward information about the molecular structure and mineral phases, an important aspect for complex geological samples. Therefore, a fusion procedure perfectly fits the goal of obtaining a better interpretation regarding the general composition of the sample. It is important to highlight that the image fusion step is trivial, since the LIBS/Raman instrumental setup ensures a perfect spatial congruency among the pixel spectra of the two techniques. Nevertheless, a block normalization of the spectra of the two spectroscopies is essential to resize their signal intensities to comparable values. To be more precise, this was done by setting the maximum intensity to a value of 1 in each of the blocks. Different fusion approaches are nowadays available, as reported in literature [28,29]. A low-level data fusion was applied in the present work. This means that the LIBS and Raman spectra related to each image pixel were concatenated in order to form a multiset configuration. It is essential to mention the fact that, in this stage of the investigation, only the previous double compressed data obtained with the SIMPLISMA-based method were considered in the fusion, since all the essential information provided by the two techniques is taken into consideration. So, the final compressed data matrix will be represented by all the pixels that were selected only in the LIBS, the Raman or in both images. After this fusion step, MCR-ALS is applied to the fused multiset configuration as described in section 2.4.

### 3. RESULTS AND DISCUSSION

This section reports the interpretations of the outcomes obtained using the MCR-ALS unmixing approach. First, the LIBS data set was explored separately in order to extract its pure contributions to obtain specific details regarding the composition of the studied sample from an elemental point of view. Then, the separate investigation of the Raman data set was carried out to obtain components that could be associated with molecular information or mineral phases, although the signal-to-noise ratio of the Raman signal and some remaining
fluorescence contributions may hinder the straightforward identification of every unmixed
spectral contribution. Therefore, a direct comparison between Raman and LIBS pure
contribution maps has been used as additional action to facilitate the interpretation of the
results obtained in this second step. Lastly, the LIBS and Raman data sets were used
simultaneously in a fusion framework to confirm relationships among unmixed contributions
by the separate analysis of LIBS and Raman images and to offer new findings that cannot be
obtained unless the two kinds of information are analyzed simultaneously.

**3.1. LIBS spectral unmixing results**

The LIBS results are related to spectra that offer very characteristic elemental information,
an important aspect to obtain a preliminary interpretation of the chemical nature of the
investigated sample. Thus, applying MCR-ALS, it is possible **to detect** the presence of some
specific mineral phases based on their major element contributions. The LIBS image spectral
unmixing resulted in ten pure contributions reported in Figure 4. The first pure contribution is
mainly related to silicon, leading to the hypothesis that the corresponding mineral phase may
be quartz (SiO2). The second and the fourth components show **emission lines** linked to lead
and traces of other elements such as copper, silver and antimony. Comparing these outcomes
with specific mineral libraries and considering the location of the sample collected, it is
conceivable that the corresponding mineral is galena (PbS), represented by different elemental
contribution intensities. The third component shows typical silicon, iron, aluminum and traces
of magnesium LIBS **contributions**. The corresponding mineral could be related to
aluminosilicates or clays. Fifth and ninth components are related to very specific **emission
lines** of titanium around 323.5 nm (probably the second image is related to saturated signals
of this element) and silicon. They can correspond to an anatase phase, a metastable mineral
containing TiO2, probably scattered in the aluminosilicate phase. More specifically, the ninth
pure contribution seems to be also linked to typical iron signals. Sixth to eighth pure
contributions are related to different elements, mainly iron, manganese, calcium, magnesium and traces of silicon, which are usually found in ankerite, a class of carbonate mineral. Finally, the tenth component is particularly interesting. In fact, its corresponding map shows a very distinct distribution of various trace elements diluted on sulfur phases. Observing the spectra of this pure contribution, it is possible to identify elements such as copper, silver, iron and zinc. Copper seems to be present in the upper part of the galena as trace elements in a growth band, and a sphalerite (ZnS) mineral could also be present in this thin rock section. Undoubtedly, it can be confirmed that the preliminary observation of the LIBS spectral unmixing outcomes is fundamental to obtain a better knowledge of the heterogeneous nature of the investigated sample, as previously stated.

## 3.2. Raman spectral unmixing results

Raman image spectral unmixing provided eight pure contributions, as shown in Figure 5. It is here important to highlight again the fact that potential acquisition problems arisen from using the same instrumentation for two distinct spectroscopies may have resulted in an increase of complexity to interpret the Raman signals. This is the reason why the interpretation of the pure Raman contribution maps was assisted by the comparison with related LIBS maps, particularly when the quality of the corresponding Raman spectra hinders their independent investigation. The first unmixed Raman contribution corresponds to the quartz phase. In addition, comparing the pure maps from both Raman and LIBS, the same mineral distribution is observed, which confirms this initial interpretation. On the contrary, the second and fourth pure contributions are represented by very noisy signals, hard to be evaluated. Therefore, a direct comparison with LIBS outcomes is here mandatory. In this way, it is possible to appreciate some similarities with the second and fourth LIBS pure contribution maps, related to galena. Raman spectra are then coherent since the Raman signal of galena is known to be very weak. The fifth Raman contribution shows very specific
emission lines that can be associated to the titanium oxide. Furthermore, the simultaneous
observation of LIBS and Raman maps has been fundamental to obtain more precise
information regarding this particular mineral phase. Using this approach, it has been possible
to confirm that this pure contribution corresponds to the presence of anatase, as previously
suggested in the LIBS interpretation. The sixth component points clearly to the presence of a
specific mineral: ankerite. Furthermore, the distribution map shows that this mineral is present
in a very vast area of the sample, but its concentration intensity widely changes in the
different zones of the rock. An important aspect here is that comparing the distribution maps
extracted from the two spectroscopies alone and the information of this component (extracted
when LIBS and Raman are fused), it seems to be mainly related to the Raman response and
less to the LIBS one. Lastly, third, seventh and partially eighth components are linked to very
specific spectral signals. In addition, observing the maps of these contributions, it is possible
to notice that they are showing peculiar areas that are not highlighted by LIBS, and are only
related to Raman spectroscopy. Nevertheless, it has been impossible to give a chemical
interpretation to these contributions and, hence, to identify the corresponding mineral phases.
This is a perfect scenario in which a data fusion is mandatory, in order to retrieve the missing
information about the correlation between the two imaging methodologies to obtain a more
complete interpretation of the nature of the mineral sample studied.

3.3. Data fusion spectral unmixing results

As described in the previous paragraphs, it is not always possible to identify a particular
compound using only one or the other technique. This is the main reason why a fusion
approach is mandatory, to finally obtain a more complete interpretation of the heterogeneity
of the given sample. Ten pure contributions were found from the fused LIBS/Raman data set,
as shown in Figure 6. The first component is again related to quartz, as previously described.
An interesting aspect here is that applying the fusion strategy is possible to obtain a more
contrasted map of this mineral and then less ambiguity about it. Third and eighth components
are related to galena. This interpretation comes mainly from LIBS signals, typical of this
mineral. In fact, agreeing with section 3.2, the contribution provided by Raman spectra is very
weak and so, not really interpretable if not coupled with the other spectroscopic technique.
The spectra of the second pure contribution (both for LIBS and Raman) are again related to
the typical signals of anatase, confirming its presence in the rock. The sixth component is also
related to the same mineral, except for the fact that Raman spectra show new bands. The fifth,
the seventh and the tenth components seem to be related to ankerite. However, the
corresponding images show some distribution differences. Furthermore, while the seventh
contribution has very fine and well defined spectral signal for both the spectroscopic
responses, the other two components are related to some broadened bands regarding the
Raman spectra. Ideally, and for our future research, we should add another spectroscopic
modality to this fusion framework to try to resolve this complexity. Another interesting aspect
is that applying the fusion strategy and observing the maps, it is noticeable a better
distribution of the concentration intensity of this compound in the different areas of the
mineral. Lastly, the fourth and ninth components deserve a more detailed description. They
are related to the fine Raman spectral bands that were not identified previously (i.e., third and
seventh pure Raman contributions in section 3.2). Finally, using the fusion strategy, and
exploiting the LIBS signals, it is possible to give a first interpretation to these mineral phases.
The fourth contribution seems to be related to the typical LIBS signals of lead (for more
details, refer to the second pure contribution of LIBS outcomes before the data fusion).
Therefore, this component (and the corresponding Raman spectral bands) can be related again
to galena. Considering the ninth contribution, LIBS emission lines can be linked to the ones
typical of the aluminosilicate class (refer to the third pure LIBS contribution before the data
fusion), which interpretation allows identifying also these Raman spectral signatures. The
most interesting aspect of these two last components is that finally, using the fusion strategy,
has been possible to recognize the corresponding related mineral phases. In conclusion, the
analysis of the joint elemental and molecular information provided by the LIBS/Raman fusion
is recommended to obtain a much more solid description and interpretation of the general
characteristics of the mineral analyzed. Such an outcome points to the use of the combination
of these two methodologies for a general improvement in the investigation of any
heterogeneous mineral sample.

4. CONCLUSIONS

LIBS imaging is obtaining in the last years an increasing interest in many research areas
because of the intrinsic value of the elemental information provided by this technique and the
ease to couple this imaging modality with others using the same experimental setup.
LIBS/Raman fusion appears as an attractive option because of the rich spectral features of the
two techniques and the complementary elemental and molecular information that they offer.

Nevertheless, the use of chemometric strategies to fully exploit the potential of this fusion is
still limited as these methods are often poorly adapted to the specificity of LIBS. In this work,
a full analysis pipeline is offered that covers relevant aspects such as an unmixing-oriented
compression, crucial to preserve the information of minor and trace constituents in the
mineral, and specific steps to remove artifacts related to the two techniques, such as the
presence of saturated signals in both techniques or the removal of strong fluorescence
baselines in Raman, essential to allow an efficient fusion and interpretation of the joint
components unmixed. To the best of our knowledge, this is the first time that a full protocol
for the study of fused LIBS/Raman images has been proposed. As shown in this work, such a
fusion can be of invaluable help in the study of complex heterogeneous mineral samples,
where understanding and correlating the elemental information of LIBS with the molecular
information from Raman can greatly contribute to the characterization of the location and
identity of the mineral phases of this kind of samples. Although the data analysis protocol
proposed has been focused on the LIBS/Raman fusion, it can be easily extended to any
multimodal measurement that could be obtained with a LIBS platform, e.g, with a
LIBS/Raman/PIL setup, and the final information retrieved would benefit from the
complementary knowledge and the correlation existing among the fused signals.
1 FIGURE CAPTIONS

2 Figure 1: A schematic view of the LIBS-Raman imaging system.

3 Figure 2: A first observation of the investigated mineral sample (optical image, LIBS and

4 Raman images generated by signal integration at given spectral variables).

5 Figure 3: Comparison between a) raw Raman spectra and b) final pretreated ones.

6 Figure 4: MCR-ALS results on the LIBS data set of the studied mineral sample.

7 Figure 5 – MCR-ALS results on the Raman data set of the studied mineral sample.

8 Figure 6: MCR-ALS results on the LIBS and Raman fused data sets of the studied mineral

9 sample.

10

11
<figure><img src="image_3.png" /><figcaption>Figure 1</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 4</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 5</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 6</figcaption></figure>
REFERENCES

[1] V. Motto-Ros, S. Moncayo, C. Fabre, B. Busser, LIBS imaging applications, in: Laser-Induced Breakdown Spectroscopy, Elsevier, 2020: pp. 329–346.
https://doi.org/10.1016/B978-0-12-818829-3.00014-9.

[2] V. Motto-Ros, S. Moncayo, F. Trichard, F. Pelascini, Investigation of signal extraction in the frame of laser induced breakdown spectroscopy imaging, Spectrochimica Acta Part B: Atomic Spectroscopy. 155 (2019) 127–133.
https://doi.org/10.1016/j.sab.2019.04.004.

[3] R.R.V. Carvalho, J.A.O. Coelho, J.M. Santos, F.W.B. Aquino, R.L. Carneiro, E.R. Pereira-Filho, Laser-induced breakdown spectroscopy (LIBS) combined with hyperspectral imaging for the evaluation of printed circuit board composition, Talanta. 134 (2015) 278–283.
https://doi.org/10.1016/j.talanta.2014.11.019.

[4] S. Moncayo, L. Duponchel, N. Mousavipak, G. Panczer, F. Trichard, B. Bousquet, F. Pelascini, V. Motto-Ros, Exploration of megapixel hyperspectral LIBS images using principal component analysis, J. Anal. At. Spectrom. 33 (2018) 210–220.
https://doi.org/10.1039/C7JA00398F.

[5] C. Fabre, Advances in Laser-Induced Breakdown Spectroscopy analysis for geology: A critical review, Spectrochimica Acta Part B: Atomic Spectroscopy. 166 (2020) 105799.
https://doi.org/10.1016/j.sab.2020.105799.

[6] A.M. Ollila, H.E. Newsom, B. Clark, R.C. Wiens, A. Cousin, J.G. Blank, N. Mangold, V. Sautter, S. Maurice, S.M. Clegg, O. Gasnault, O. Forni, R. Tokar, E. Lewin, M.D. Dyar, J. Lasue, R. Anderson, S.M. McLennan, J. Bridges, D. Vaniman, N. Lanza, C. Fabre, N. Melikechi, G.M. Perrett, J.L. Campbell, P.L. King, B. Barraclough, D. Delapp, S. Johnstone, P.-Y. Meslin, A. Rosen-Gooding, J. Williams, The MSL Science Team, Trace element geochemistry (Li, Ba, Sr, and Rb) using Curiosity's ChemCam: Early results for Gale crater from Bradbury Landing Site to Rocknest: trace element results for Gale crater, J. Geophys. Res. Planets. 119 (2014) 255–285.
https://doi.org/10.1002/2013JE004517.

[7] C. Fabre, D. Devismes, S. Moncayo, F. Pelascini, F. Trichard, A. Lecomte, B. Bousquet, J. Cauzid, V. Motto-Ros, Elemental imaging by laser-induced breakdown spectroscopy for the geological characterization of minerals, J. Anal. At. Spectrom. 33 (2018) 1345–1353.
https://doi.org/10.1039/C8JA00048D.

[8] R. Noll, Laser-induced breakdown spectroscopy: fundamentals and applications, Springer, Berlin, 2012.

[9] L. Jolivet, M. Leprince, S. Moncayo, L. Sorbier, C.-P. Lienemann, V. Motto-Ros, Review of the recent advances and applications of LIBS-based imaging, Spectrochimica Acta Part B: Atomic Spectroscopy. 151 (2019) 41–53.
https://doi.org/10.1016/j.sab.2018.11.008.

[10] A. Nardecchia, C. Fabre, J. Cauzid, F. Pelascini, V. Motto-Ros, L. Duponchel, Detection of minor compounds in complex mineral samples from millions of spectra: A new data analysis strategy in LIBS imaging, Analytica Chimica Acta. 1114 (2020) 66–73.
https://doi.org/10.1016/j.aca.2020.04.005.

[11] A. Nardecchia, A. de Juan, V. Motto-Ros, M. Gaft, L. Duponchel, Data fusion of LIBS and PIL hyperspectral imaging: Understanding the luminescence phenomenon of a complex mineral sample, Analytica Chimica Acta. 1192 (2022) 339368.
https://doi.org/10.1016/j.aca.2021.339368.

[12] J. Jaumot, A. de Juan, R. Tauler, MCR-ALS GUI 2.0: New features and applications, Chemometrics and Intelligent Laboratory Systems. 140 (2015) 1–12.
https://doi.org/10.1016/j.chemolab.2014.10.003.
[13] A. de Juan, R. Tauler, Multivariate Curve Resolution: 50 years addressing the mixture analysis problem – A review, Analytica Chimica Acta. 1145 (2021) 59–78.
https://doi.org/10.1016/j.aca.2020.10.051.

[14] A. de Juan, J. Jaumot, R. Tauler, Multivariate Curve Resolution (MCR). Solving the mixture analysis problem, Anal. Methods. 6 (2014) 4964–4976.
https://doi.org/10.1039/C4AY00571F.

[15] M. Gaft, L. Nagli, Y. Groisman, Plasma induced luminescence (PIL), Optical Materials. 34 (2011) 368–375.
https://doi.org/10.1016/j.optmat.2011.05.024.

[16] M. Gaft, Y. Raichlin, F. Pelascini, G. Panzer, V. Motto Ros, Imaging rare-earth elements in minerals by laser-induced plasma spectroscopy: Molecular emission and plasma-induced luminescence, Spectrochimica Acta Part B: Atomic Spectroscopy. 151 (2019) 12–19.
https://doi.org/10.1016/j.sab.2018.11.003.

[17] M. Gaft, R. Reisfeld, G. Panczer, Modern luminescence spectroscopy of minerals and materials, Springer, Berlin; New York, 2005.

[18] T.A. Labutin, V.N. Lednev, A.A. Ilyin, A.M. Popov, Evaluation of compact VUV spectrometer for elemental imaging by laser-induced breakdown spectroscopy: application to mine core characterization, J. Anal. At. Spectrom. 31 (2016) 90–118.
https://doi.org/10.1039/C5JA00301F.

[19] V. Motto-Ros, E. Negre, F. Pelascini, G. Panczer, J. Yu, Precise alignment of the collection fiber assisted by real-time plasma imaging in laser-induced breakdown spectroscopy, Spectrochimica Acta Part B: Atomic Spectroscopy. 92 (2014) 60–69.
https://doi.org/10.1016/j.sab.2013.12.008.

[20] C. D'Angelo, D. Diaz Pace, D. Bertuccelli, G. Bertuccelli, Spectroscopic analysis of signals from LIBS experiments, in: A. Marcano O., J.L. Paz (Eds.), 2004: pp. 1037–1042.
https://doi.org/10.1117/12.591209.

[21] A. Nardecchia, V. Motto-Ros, L. Duponchel, Saturated signals in spectroscopic imaging: why and how should we deal with this regularly observed phenomenon?, Analytica Chimica Acta. 1157 (2021) 338389.
https://doi.org/10.1016/j.aca.2021.338389.

[22] E.T. Whittaker, On a New Method of Graduation, Proceedings of the Edinburgh Mathematical Society. 41 (1922) 63–75.
https://doi.org/10.1017/S0013091500077853.

[23] P.H.C. Eilers, A Perfect Smoother, Anal. Chem. 75 (2003) 3631–3636.
https://doi.org/10.1021/ac034173t.

[24] Willem. Windig, Jean. Guilment, Interactive self-modeling mixture analysis, Analytical Chemistry. 63 (1991) 1425–1432.
https://doi.org/10.1021/ac00014a016.

[25] W. Windig, J.L. Lippert, M.J. Robbins, K.R. Kresinske, J.P. Twist, A.P. Snyder, Interactive self-modeling multivariate analysis, Chemometrics and Intelligent Laboratory Systems. 9 (1990) 7–30.
https://doi.org/10.1016/0169-7439(90)80050-G.

[26] A. Nardecchia, L. Duponchel, Randomised SIMPLISMA: Using a dictionary of initial estimates for spectral unmixing in the framework of chemical imaging, Talanta. 217 (2020) 121024.
https://doi.org/10.1016/j.talanta.2020.121024.

[27] M. Boiret, A. de Juan, N. Gorretta, Y.-M. Ginot, J.-M. Roger, Distribution of a low dose compound within pharmaceutical tablet by using multivariate curve resolution on Raman hyperspectral images, Journal of Pharmaceutical and Biomedical Analysis. 103 (2015) 35–43.
https://doi.org/10.1016/j.jpba.2014.10.024.

[28] A. de Juan, A. Gowen, L. Duponchel, C. Ruckebusch, Image Fusion, in: Data Handling in Science and Technology, Elsevier, 2019: pp. 311–344.
https://doi.org/10.1016/B978-0-444-63984-4.00011-9.

[29] S. Piqueras, C. Bedia, C. Beleites, C. Krafft, J. Popp, M. Maeder, R. Tauler, A. de Juan, Handling Different Spatial Resolutions in Image Fusion by Multivariate Curve
Resolution-Alternating Least Squares for Incomplete Image Multisets, Anal. Chem. 90 (2018) 6757–6765. https://doi.org/10.1021/acs.analchem.8b00630.
LIBS

Raman

<figure><img src="image_9.png" /></figure>

Data Fusion

Spectral and
spatial
compression

Signal
unmixing