Evaluation of the standard normal variate method for Laser-Induced Breakdown Spectroscopy data treatment applied to the discrimination of painting layers

    Evaluation of the Standard Normal Variate method for Laser-Induced Breakdown Spectroscopy data treatment applied to the discrimination of painting layers D. Syvilay, N. Wilkie-Chancellier, B. Trichereau, A. Texier, L. Martinez, S. Serfaty, V. Detalle PII: DOI: Reference:

S0584-8547(15)00235-9 doi: 10.1016/j.sab.2015.09.022 SAB 4982

To appear in:

Spectrochimica Acta Part B: Atomic Spectroscopy

Received date: Accepted date:

31 January 2015 26 September 2015

Please cite this article as: D. Syvilay, N. Wilkie-Chancellier, B. Trichereau, A. Texier, L. Martinez, S. Serfaty, V. Detalle, Evaluation of the Standard Normal Variate method for Laser-Induced Breakdown Spectroscopy data treatment applied to the discrimination of painting layers, Spectrochimica Acta Part B: Atomic Spectroscopy (2015), doi: 10.1016/j.sab.2015.09.022

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ACCEPTED MANUSCRIPT Evaluation of the Standard Normal Variate method for Laser-Induced Breakdown Spectroscopy data treatment applied to the discrimination of painting layers 1

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D. Syvilay *, N. Wilkie-Chancellier , B. Trichereau , A. Texier , L. Martinez , S. Serfaty , V. Detalle 1

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Mural paintings department, CNRS USR3224 CRC-LRMH, Champs-sur-Marne, France SATIE, UMR CNRS 8029 University of Cergy-Pontoise ENS Cachan, Cergy-Pontoise, France 3 Metal department, CNRS USR3224 CRC-LRMH, Champs-sur-Marne, France

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Abstract Nowadays, Laser-Induced Breakdown Spectroscopy (LIBS) is frequently used for in situ analyses to identify pigments from mural paintings. Nonetheless, in situ analyses require a robust instrumentation in order to face to hard experimental conditions. This may imply variation of fluencies and thus inducing variation of LIBS signal, which degrades spectra and then results. Usually, to overcome these experimental errors, LIBS signal is processed. Signal processing methods most commonly used are the baseline subtraction and the normalization by using a spectral line. However, the latter suggests that this chosen element is a constant component of the material, which may not be the case in paint layers organized in stratigraphic layers. For this reason, it is sometimes difficult to apply this normalization. In this study, another normalization will be carried out to throw off these signal variations. Standard Normal Variate (SNV) is a normalization designed for these conditions. It is sometimes implemented in Diffuse Reflectance Infrared Fourier Transform Spectroscopy and in Raman Spectroscopy but rarely in LIBS. The SNV transformation is not new applied on LIBS data, but for the first time the effect of SNV on LIBS spectra was evaluated in details (energy of laser, shot by shot, quantification).The aim of this paper is the quick visualization of the different layers of a stratigraphic painting sample by simple data representations (3D or 2D) after SNV normalization. In this investigation, we showed the potential power of SNV transformation to overcome undesired LIBS signal variations but also its limit of application. This method appears as a promising way to normalize LIBS data, which may be interesting for in-situ depth analyses.

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Keywords : LIBS ; SNV ; cultural heritage ; in-situ ; stratigraphy

1. Introduction Conservation science is the study of the composition and the alteration of work of art from cultural heritage. In this field, it is important to investigate these two last points for a better understanding of the life story of the artefact and its alteration processes. The knowledge of the material involves a quantitative or qualitative analysis, on surface or in depth of the sample. Moreover, cultural heritage is often composed of a multiplicity of materials, so several analytical methods are employed to meet these many expectations and especially techniques based on spectroscopy. However these techniques like SEMEDS, XRD, Raman spectroscopy, FTIR, XRFS…[1-8] often require sampling. Therefore, in order to minimize such removing valuable cultural heritage

matter, research for science conservation moves towards in situ analysis. This solution is necessary when works of art are immovable, but also allows an analyzing strategy to reduce the sampling. Analyzing on field involves portable instrumentation and only few measurement systems can overcome this limitation like XRFS (XRay Fluorescence Spectroscopy) [9-11] and Raman spectroscopy [12-15] for a surface analysis. For in depth analysis, Terahertz imaging is an instrument which allows a stratigraphic view and could highlight for instance the sinopia [16-18] (drawing or underpainting for frescoes). However, it cannot provide sufficient resolution for a smaller scale as the stratigraphy of a paint layer and can’t supply the chemical composition. Therefore LaserInduced Breakdown Spectroscopy (LIBS) shows

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Experimental analysis 2.1. LIBS Instrumentation Two LIBS instrumental systems were used, the one was the laboratory system which was also portable and sometimes used for field applications, and the second was a portable and lightweight system really dedicated for in-situ analysis. In the first hand, the laboratory setup for LIBS measurements is schematically shown in figure 1. A Minilite II Q-Switched Nd:YAG laser (Continuum, USA) was used as an excitation source operating at a fundamental wavelength of λ = 1064 nm, with an energy of 30 mJ/pulse. The spotsize was 500 µm. The laser pulses have 5 ns duration, and were triggered by the operator shot by shot. The laser pulses were focused onto the sample, at normal incidence, using a 100 mm focal length lens to induce a plasma plume. The laser beam diameter at the focal point was about 500 µm.

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quantitative analysis. Finally, SNV normalization was achieved on LIBS analysis on model sample as found in historic monument for a stratigraphic study. Indeed, this example would be representative of in situ LIBS analyses on mural paintings where there are both fluence variations and elemental evolution through the different layers. At last the limits of this normalization will be pointed out and especially the difficulty to understand the physical meaning of SNV transformation on LIBS spectra.

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many advantages to fulfill this purpose by its multielemental analysis capability, portability on site, and stratigraphic investigation efficiency. Nowadays, this method is often used for identification of materials from cultural heritage [19-26]. It is based on laser-material interaction. A high power pulsed laser is focused on the surface of the material to be analyzed. A tiny amount of the material is vaporized and particles are consequently created, giving rise to a plasma plume. The resulting light is directly collected and corresponds to the emission of the characteristic spectral lines of the elements. Nonetheless, in situ analyses may imply fluency variations and matrix effects thus inducing modification of LIBS signal, which deteriorates spectra and then results. Usually, to overcome these experimental errors, LIBS signal is processed. The most commonly used processing signals are the baseline subtraction and the normalization by using a spectral line [27-31]. However, the last treatment suggests that this chosen element is a constant component of the material, which may not be the case in paint layers organized in stratigraphic layers. For this reason, it is sometimes difficult to apply this normalization. The new way was to use another normalization to throw off these signal variations. Standard Normal Variate (SNV) is a normalization sometimes employed in Diffuse Reflectance Infrared Fourier Transform Spectroscopy or in Near InfraRed Spectroscopy [32-38] and in Raman Spectroscopy [39-41]. This transformation is a standard score calculation for each point of the signal. According to the literature [32, 41], this normalization seems to be encouraging. However, it has been less often used on LIBS spectra. Only a few papers [42-44] have applied SNV on LIBS spectra, where it seems to reduce the standard deviation [42] and could be used as preprocessing step for chemometric methods as multivariate analysis [43,44]. So the effect of SNV transformation on LIBS data has not been presented before in details. In this way, the aim of this study was first to test the SNV transformation ability to correct undesired variation signal observed on LIBS spectra. However, normalizing induced a change in intensity of the spectra and could result in a loss of concentration information. In order to ensure that it is not the case, we experimented in second part the SNV ability to make an elemental semi-

Laser Nd:Yag Diode

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3 spectrometers Ocean Optics (200 - 940nm) and a CCD

Figure 1 : Laboratory LIBS instrumentation

The emission was collected at a 45° angle with respect to the incident beam. A second lens (100 mm focal length) was used to focus the signal on a 7-fiber optical bundle (core

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2.2. Samples As part of this study, several kind of samples were analyzed. Firstly, calibration curves normalized by either a spectral line or by SNV were plotted in order to see the SNV ability to quantify the trace elements present in lead matrix based standards. Then model samples were stratigraphic paint and metal layers used to test SNV ability to highlight the elemental signals evolution in depth analysis. - The lead standard used were seven SYLAB references : PR1, PR2, PR3, PR4, PR5, PR7, PR8. The standards were based on about 99% of lead with different concentrations of trace elements as shows in Table 1. Five trace elements were used in this study: Ag, Bi, Cu, Sb and Sn [5]. 10 pre-shots and 30 shots were fired on each analysis area with a 10 Hz frequency and 5 analysis

PR1

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areas were designated per sample resulting in 150 spectra for each sample analyzed.

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Table 1 : Lead matrix based standards with different trace elements concentrations used for LIBS calibration curves

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diameter: 600 µm). Three of the fibers were connected to the entrance slits (5 µm width) of three spectrometers using a HR2000 (Ocean Optics, USA) which enabled the coverage of the wavelength range between 200 and 940 nm (from 200 to 340 nm and 335 to 445 nm, two 1800/mm gratings with a resolution of 0.1 nm, and from 510 to 940 nm, a 600/mm grating with a resolution of 0.31 nm) ensuring multi-elemental analysis. On can notice that using only 3 on 7 fibers, the setup discarded light The emission spectrum was recorded with an internal 2048 CCD array detector (delay: 1 µs after the laser Q-Switch with a minimum integration time of 2.1 ms). All experiments were performed under ambient atmospheric conditions. In the second hand the portable system is the EasyLIBS system (IVEA solution, FRANCE) which used a Nd:YAG laser operated at 1064 nm with an energy under 25 mJ/pulse and a frequency of 1Hz. The spotsize was 250 µm. Three of the fibers were connected to the entrance slits (5 µm width) of three spectrometers using a HR2000+ (Ocean Optics, USA) which enabled the coverage of the wavelength range between 200 and 935 nm (from 200 to 341 nm, from 330 to 467 nm, from 485 to 935 nm). The emission spectrum was recorded with an internal 2048 CCD array detector and all experiments were performed under ambient atmospheric conditions.

- The two sample models were stratigraphic metallic and paint layers deposited to reproduce as close as possible the material encountered on historic monuments field analysis. One is composed of lead metal at the bottom of the sample, where a gold layer recovers it (Figure 2). This sample was analyzed with the laboratory system, with 10 pulses on each area, and twenty areas were studied.

Figure 2 : Models of stratigraphic samples. From left to right, the sample model n°1 and the sample model n°2

The second sample is exactly the same sample with two layers added. A minium pigment (lead pigment) layer recovers the gold layer and a second layer of gold is deposited on the top. This sample was analyzed with the portable system, 75 pulses were fired on each area and 5 areas were studied. 3. Results After detection of the LIBS elemental transmitted signal by spectrometers, a spectrum is obtained (as we can see an example of it in figure 3), where raw intensity is function of wavelength. Then, the SNV normalization is applied for every raw intensity of the spectrum. The SNV normalization used is given by the following relation: (1)

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Figure 4 : Comparison of two LIBS spectra of the PR1 lead standard between shot 1 and shot 30 in similar experimental conditions after SNV transformation

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In order to simulate power laser fluctuations, five raw spectra (which are the mean of 10 spectra for each) were recorded with decreasing energy laser as we can see on figure 5. Once again, all spectra seem to be corrected at the same level of intensity (Figure 6) after SNV normalization.

Figure 5 : Comparison of six raw spectra of the same sample operated at different energies of the laser

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3.1. Evaluation of SNV efficiency in case of laser fluctuations In situ analyses often lead to matrix effects for a same sample in the equivalent conditions which are due to fluence variations, inhomogeneity of the sample, surface pollution… thereby could change the laser-matter interaction. This induces modification of LIBS signal, which deteriorates spectra and then results. Fluence fluctuations owing to experimental conditions are due to the focus of the laser on the surface sample and power fluctuations of the laser (variation of ±10% from the chosen laser power). This could lead to spectra fluctuations as we can see on the figure 3. This inconvenience is the one we can try to settle with SNV normalization. The figure 4 shows the SNV transformation efficiency to correct this variation. The two spectra are still different but much closer than before. However, one can note that for a low mean spectrum, the SNV improves the intensities of spectral lines but also increases the noise and so decreases the signal to noise ratio of the spectrum.

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where ISNV is the normalized intensity calculated for a given wavelength λ of the LIBS spectrum, is the raw intensity for a given wavelength λ of the LIBS spectrum, is the intensity of the baseline corresponding at the given wavelength λ, µ is the average of net intensities of every wavelength of the LIBS spectrum, and σ is the standard deviation of net intensities of every wavelength of the LIBS spectrum [40, 41].

Figure 3 : Example of LIBS signal variation by comparing two raw spectra of the PR1 lead standard between shot 1 and shot 30 in similar experimental conditions

Figure 6 : Comparison of six LIBS spectra of the same sample operated at different energies of the laser after SNV transformation

Taking into account the intensities of some wavelengths (Figure 7), it is logical to see that intensity decreases with laser fluence.

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SNV transformation ability was tested for semi quantitative analysis by extracting five intensities at different wavelengths on spectra of seven lead standards with different trace element concentrations. (See concentrations on Table 1). Lead standards are analyzed in order to compare both results of the calibration curves with a spectral line normalization (i.e. INorm Pb is the result of first subtracting the spectrum baseline then dividing the trace element intensity by lead intensity at 247.64 nm) and the calibration curves obtained with the SNV normalization. Similar calibrations curves (Figures 9 and 10) between both normalizations are observed. The standard deviations are also equivalent.

Figure 9 : Calibration curves of lead standards obtained with normalization by a Pb spectral line

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After SNV transformation, we can see on the figure 8 for any energy and for each element, intensities are equal, except for copper at 324.75 nm, probably due to the inhomogeneity of the sample. SNV transformation seems to further bring to light these deviations.

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Figure 7 : Comparison of five intensities of spectra from figure5 corresponding respectively from left to right to antimony, tin, bismuth, copper and silver wavelengths

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Figure 8 : Comparison of five intensities of spectra from figure 6 corresponding respectively from left to right to antimony, tin, bismuth, copper and silver wavelengths after SNV transformation

The SNV transformation allows freeing of fluencies variation from the laser. It calculates new intensities taking into account the complete spectrum. So thanks to SNV transformation, when considering a same sample, it is possible to compare intensities of two spectra which were originally different. However, such normalizing may induce a loss in the concentration information of elements present in the material. This is why the SNV transformation on semi-quantitative analysis is tested in the subsequent stage of the study. 3.2. Evaluation of SNV efficiency on semiquantification

Figure 10 : Calibration curves of lead standards obtained with SNV normalization

These results mean that the estimated concentrations are not modified by the SNV transformation. To check this assumption, concentrations and limit of detection (Table 2) were estimated by both methods on seven unknown samples using calibration curves. The limit of detection was calculated as:

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LOD (ppm)

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LIBS Spectral line normalisation

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0.9906

LIBS Spectral line normalisation

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0.9901

LIBS Inet

105

0.9436

LIBS Iraw

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Due to the calibration curve, the limit of detection for antimony is too high to detect an accurate concentration of this element. This point has ever been mentioned in a previous work [45] where further explanations are given. It leads to similar limits of detection and correlation coefficients between the LIBS spectral line normalization and the LIBS SNV transformation. LA-ICP-MS analyses were also carried out [45], considered as a reference technique, and confirm the reliability of results obtained by normalized LIBS spectra. The results were also compared to those obtain without any normalization (raw spectra) and those obtain with baseline subtraction (net spectra) in order to show the necessity to normalize spectra. Indeed, the correlation coefficients are better when normalizing and the limits of detection are similar (Cu, Sn) or much better (Ag, Bi). Only silver, copper and tin concentrations were estimated since all samples had bismuth concentration below the LOD. The trace element concentrations of raw spectra were much higher compared to the others, as well as net spectra. This is why tin concentrations were not estimated for both. However for LIBS SNV and LIBS spectral line normalization, the trace element concentrations are similar to LA-ICP-MS. The small differences with LA-ICP-MS are explained in a previous work [45]. One can notice that the normalization was essential here for quantify trace elements. However the normalization with a spectral line involves a constant concentration of the element characterized by its spectral line. In the case of stratigraphic paint layers, it is often difficult to find a constant element through the different layers. Therefore, when dealing with stratigraphic materials, SNV transformation could be used instead of normalization with a spectral line since it did not modify the concentration information in spectra.

Ag (328,07 nm)

Cu (324,75 nm) LOD (ppm)

R²

LIBS SNV

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0.9742

LIBS Spectral line normalisation

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0.9371

LIBS Inet

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0.9661

LIBS Iraw

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0.9502

Sn (286,33 nm) LOD (ppm)

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LIBS SNV

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0.9924

LIBS Spectral line normalisation

250

0.9998

LIBS Inet

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Table 2 : Comparison of trace element concentrations obtained by LIBS with normalization by a PB spectral line, with SNV normalization, and by LA-ICP-MS for an unknown sample

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Figure 12 : Evolution of Pb and Au net intensities versus the number of shots for the sample n°1

When using SNV transformation, the fluctuations of lead intensities decrease. It corrects the results as seen above and also shows a clear distinction between gold and lead when penetrating in depth (Figure 13).

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then increase at the 3 shot. However some fluctuations are visible. If this case was appeared in real in situ analysis, the question of how many layers there are would arise. According to these intensities fluctuations, it is difficult to settle between concentration fluctuations of the element (and so different layers are highlighted) and experimental conditions.

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Figure 11 - Comparison of silver, copper and tin concentrations between LA-ICP-MS analyses, LIBS analyses without any normalization, with baseline subtraction, with spectral line normalization and with SNV normalization

3.3. Evaluation of SNV efficiency on stratigraphic study Analysis on samples (see above in Experimental paragraph) as found in historic monuments, were carried out with portable LIBS instrumentation. In this way, we simulate the same conditions than in situ analysis and then the ability of SNV transformation to correct spectra of stratigraphic paint layers was tested. Firstly, a sample of lead with a single gold layer on the top was analyzed. According to the figure 12, the intensity of gold is very high on the first shots which certainly suggest the gold layer. On the contrary the intensity of the lead is very low and

Figure 13 : Evolution of Pb and Au intensities versus the number of shots after SNV transformation for the sample n°1

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Discussion 4.1. 3D representation We saw in the previous part that SNV transformation was first able to correct signal variations from fluencies fluctuations while making a semi-quantitative analysis. It was then tested on a stratigraphic sample. However, the classical 2D plot in which the intensity of a spectral line of an element is a function of the number of shots cannot allow an overview of every spectral line’s intensity fluctuation of the spectrum. The suggestion was to see these variations on a larger

ACCEPTED MANUSCRIPT The Figure 16 shows that the SNV normalization smoothed the intensity fluctuations compared to Figure 15, and decreased intensity fluctuations compared to the original signal. However it is difficult to see four layers. The first one could be discriminated with a high gold intensity which th decreases until the 20 shots. This could be the second layer (the minium layer). The third layer th th could start at this 20 shot until the 45 shots where gold intensity is still high despite some fluctuations. And finally the last layer could start at th this 45 shot since the gold intensity reach a minimum. However fluctuations cause confusion to the relevance of this hypothesis. It is visible that the 2D representation (even after SNV normalization) is difficult to be interpreted, especially because only a few spectral lines could be presented on Figure 16.

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number of wavelengths in order to extract more information. For a better understanding of this normalization, a 3D plot is suggested (figure 14). Yaxis displays the number of shots. It is like a representation of the stratigraphic layer crosssection. X-axis indicates the wavelength (in nm) and finally the color scale suggests the intensity of the signal (from blue for low intensity to red for high intensity). In figure 14, spectra were drawn in white.

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Figure 14 : 3D representation of SNV intensities evolution of LIBS peak with the number of shots on a large scale of wavelengths for the model sample n°1

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This kind of plot allows a quick overview of intensity fluctuations of many lead wavelength (261.42, 262.83, 266.29, 280.2, 283.31 nm) compared to figure 13 where it could easily become unreadable. The 3D representation allows to directly seeing which wavelength progression show similar behavior. In order to further demonstrate the usefulness of this kind of representation, a second sample of lead was analyzed. A first minium layer recovers the metal and which is subsequently recovered by a gold layer. Then a gold surface layer recovers the all.

Figure 15 - Evolution of Pb and Au net intensities versus the number of shots for the sample n°2

Figure 16 - Evolution of Pb and Au intensities versus the number of shots after SNV transformation for the sample n°2

In order to have an overview of large spectral line intensities according to the shots number, the advantage of a 3D representation is to reconcile the intensity and position of spectral lines but also to compare the fluctuations of intensities on many spectral lines. 4.2. Layers discrimination of sample 2 The previous part has shown the need of this kind of 3D representation. The following results are focused on the sample 2 in order to perform the layers discrimination. The figures 17 and 18 are in 3D, where more than two lines were presented, and summer respectively the net and SNV intensities. It is possible to see many lead spectral lines, and also the only one gold spectral line. However, after SNV normalization, one can note that the Figure 18 displays a more readable image.

ACCEPTED MANUSCRIPT After testing SNV ability for LIBS spectra correction when dealing with fluencies fluctuations, it is essential to point out the limitation of the SNV normalization. In this last part, we will try to apprehend the physical sense of this normalization coupled with the physics of the LIBS technique. First of all, for a better understanding of this transformation, SNV was realized on raw and net intensities. On net intensities, the SNV normalization throws off the signal noise which is corrected and reaches the same level for every spectrum. Therefore, it allows a better visualization of wavelength intensities and so a better recognition of the peaks. On raw signal, SNV transformation takes into account the baseline and the noise of the spectrum which is directly dependent of the continuum created during the plasma. Moreover, according to the equation 1, the SNV transformation depends on the mean and the standard deviation of the all spectrum. These latter values will change according the material. Therefore, the SNV transformation could make an important change in intensity for a given element depending on either a high global signal or a low global signal (so depending on the matrix). The intensity of the global signal is due to the laser material ablation according to the matrix. So it could be interesting to see the SNV normalization on matrix effects. To summarize, the normalization represented in 3D allows highlighting some possible layers. In this way, this kind of representation could be an easy first approach to apprehend the probably layers in a mural painting.

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Indeed, the first layer of gold is well distinguished, such as the minium layer compared to the figure 17. It is clearly visible (Figure 18) that some spectral lines are not present in the minium layer compare to the lead metal. In this way, it is possible to discriminate the minium layer. However it becomes more difficult to discriminate the second layer of gold and the lead metal. The gold signal spreads over several shots. Indeed, lead spectral lines are still present in the second gold layer. So despite on the visible signal of gold in the second layer, it is not possible to clearly discriminate a layer between the gold and the lead metal.

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Figure 17 : 3D representation of net intensities evolution of LIBS peak with the number of shots on a large scale of wavelengths for the model sample n°2

Figure 18 : 3D representation of SNV intensities evolution of LIBS peak with the number of shots on a large scale of wavelengths for the model sample n°2

However, an assumption can be done. Indeed it is known that when dealing with non-top hat spatial beam laser, it is possible to obtain a reiteration of the signal from upper layers [46] or an element fractionation [47] or finally different etching rate with depth [48]. This may explain why the gold signal was still visible even after many shots in depth. In order to settle between suggestions, penetrating depth and thickness of each layer will be evaluated.

5. Conclusion and future work We showed the potential power of SNV transformation to overcome undesired LIBS signal variations and also its influence on calibration curves and so its ability to semi quantify trace elements. Its application for stratigraphic samples as metal and paint layers seems to be interesting. However, we should pay attention to its use. We do not know yet very well its limit of application. Indeed, normalizing so much may induce a transformation of the primary information. More studies are in progress for a better understanding of its influence on LIBS spectra. Nonetheless, this work pointed out for the first time in details that

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Acknowledgements The authors wish to thank the IRAMAT laboratory, especially A. Arles and B. Gratuze who performed the LA-ICP-MS analysis on the lead unknown sample.

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this method appears as a promising way to normalize LIBS data and could be studied for other analytical methods. It could be also interesting to see the efficiency of this normalization for multivariate analyses as studied by [47, 48]. Moreover this normalization shows its full potential when using 3D representation. Thanks to that, the intensity fluctuations of all wavelengths are seen which directly highlights a change in material in a stratigraphic study and this is the notable benefit of this kind of representation.

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of protective coatings on historic metal objects using nanosecond and femtosecond laser induced breakdown spectroscopy depth profiling, Spectrochim Acta B, Volume 60, 7–8 (2005) 1163– 1171

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Highlights - Evaluation of SNV correction efficiency on LIBS spectra in case of laser fluctuations - Evaluation of SNV efficiency on semiquantification of trace elements in lead standards with LIBS calibration curves - Evaluation of SNV efficiency on stratigraphic study with metallic and paint layers as found in cultural heritage - Representation of spectra corrected with SNV for in depth analysis in 3D color images.