domain: sdv sde

text:
Julian Guézénoc

Anne Gallet-Budynek

Bruno Bousquet

Julian Guezenoc

Critical review and advices on spectral-based normalization methods for LIBS quantitative analysis

Keywords: LIBS, normalization, assessment, good practices, figures of merit

scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

As it is the case for any spectroscopic technique, laser-induced breakdown spectroscopy (LIBS) is strongly influenced by signal fluctuations [START_REF] Hahn | Laser-induced breakdown spectroscopy (LIBS), part I: review of basic diagnostics and plasma-particle interactions: still-challenging issues within the analytical plasma community[END_REF]. Indeed, the LIBS spectra are correlated to the plasma properties, themselves correlated to: i) the measurement conditions such as laser energy, lens-to-sample distance, and spectrometer calibration, and ii) the physical properties of the sample such as roughness, hardness, and porosity.

Reducing the signal fluctuations allows increasing the quality in LIBS, which is of major importance for the development of this technique. The first strategy in order to reduce fluctuations is to build LIBS experiments with a high degree of control and stabilization. In this case, stabilizing the laser energy, the lens-to-sample distance as well as the spectrometer calibration allows for significantly reducing the signal fluctuations [START_REF] Motto-Ros | Precise alignment of the collection fiber assisted by real-time plasma imaging in laser-induced breakdown spectroscopy[END_REF]. However, all the laboratory setups don't share the same level of control and thus don't allow for reaching the same reduction of signal fluctuations. In addition, in the case of on-site analyses, involving compact LIBS systems [START_REF] Galbacs | A critical review of recent progress in analytical laser-induced breakdown spectroscopy[END_REF], for either stand-off or handheld operation, the degree of control and stabilization is expected to be much lower, and the related level of fluctuations much higher compared to laboratory instruments. In this context, normalization approaches have been proposed in order to reduce the fluctuations observed in the raw LIBS data. A first type of normalization consists in monitoring other data in addition to the LIBS spectrum, either simultaneously, like the acoustic signal induced by the shock-wave [START_REF] Hahn | Laser-induced breakdown spectroscopy (LIBS), part II: review of instrumental and methodological approaches to material analysis and applications to different fields[END_REF][START_REF] Zorov | A review of normalization techniques in analytical atomic spectrometry with laser sampling: From single to multivariate correction[END_REF][START_REF] Tognoni | Signal and noise in laser induced breakdown spectroscopy: an introductory review[END_REF], or after, like the ablated mass [START_REF] Gornushkin | Effective normalization technique for correction of matrix effects in Laser-Induced breakdown spectroscopy detection of magnesium in powdered samples[END_REF]. Then, the LIBS signal is corrected thanks to this additional information, namely divided by a factor related to this external parameter. Then, if the variation of the monitored parameter is correlated with those of the LIBS signal, this normalization process is expected to efficiently reduce the signal fluctuations. However, most of the LIBS instruments don't allow to monitor additional parameters during or after the measurement and the analyst has to treat the raw LIBS signals, without any extra information. In this last case, normalization simply consists in a mathematical transformation of the raw data.

Data normalization is expected to have a positive impact on both quantification and sorting models. Nevertheless, we decided to focus the present discussion only on quantification since this is, by principle, the top objective of any analytical method. In addition, quantification in LIBS can be addressed by building either a calibration model from a series of samples with known concentration values, or a physical model from the plasma properties and spectroscopic data, without any calibration, and well-known as calibration-free (CF-LIBS) [START_REF] Tognoni | Calibration-free Laser-induced breakdown spectroscopy: State of the art[END_REF]. It is interesting to note that signal fluctuations due to a change in plasma properties are naturally taken into account by CF-LIBS. Thus, any change in electron density and temperature induces complex modifications of the calculated LIBS spectra, which cannot be described by a simple multiplicative coefficient. More precisely, the components of the LIBS spectra, namely the continuum as well as the ionic, neutral, and molecular emission lines are changing in non-proportional ways according to the changes in the plasma properties. The CF-LIBS approach thus includes a kind of self-normalization process that enables to correctly interpret each single LIBS spectrum individually. Consequently, in the present review, we will focus our discussion only on the case of calibration models. In this case, the transformations to be applied to the raw LIBS spectra for normalization purposes are quite basic, but to date there is no normalization approach considered as being more efficient than the others to treat the LIBS data. Even more, the advantage of normalization in LIBS is sometimes questionable, making this topic still an open question. In the critical review proposed here, a state-of-the-art about normalization in LIBS is presented in the first section, with a particular emphasis on the four methods of normalization the most often adopted in LIBS, specifically normalization by: i) background, ii) total area, iii) internal standard, and iv) standard normal variate (SNV). Other factors were also tested to normalize the LIBS spectra such as the Euclidian norm, the maximum and the minimum intensity values of each individual spectrum [START_REF] Sarkar | Evaluation of the prediction precision capability of partial least squares regression approach for analysis of high alloy steel by laser induced breakdown spectroscopy[END_REF][START_REF] Castro | Twelve different types of data normalization for the proposition of classification, univariate and multivariate regression models for the direct analyses of alloys by Laser-induced breakdown spectroscopy (LIBS)[END_REF][START_REF] Dos Santos Augusto | Calibration strategies for the direct determination of Ca, K, and Mg in commercial samples of powdered milk and solid dietary supplements using laser-induced breakdown spectroscopy(LIBS)[END_REF]. However, the related normalization approaches can be considered as quite similar to the four methods previously mentioned, and they are finally rarely used and not detailed in this review. Then, in the second section of this review, in order to properly compare the performance of the different normalization approaches between them, good practices and significance of selected figures of merit, namely the coefficient of determination (R 2 ); the root mean square error (RMSE) declined as the root mean square error of calibration (RMSEC) when strictly applied to the calibration set, the root mean square error of prediction (RMSEP) when strictly applied to the validation set not involved in the construction of the model, and the root mean square error of cross-validation (RMSECV) after a cross-validation process; the limit of detection (LOD), and the limit of quantification (LOQ) are presented as a key-step to make accurate decisions. Finally, we have decided to illustrate the present discussion through the quantitative analysis of a selected dataset after different normalization approaches, and we have accurately compared the related figures of merit to conclude if a given normalization method can be finally recommended in a general context.

State of the art

The need for normalization concerns not only LIBS but all spectroscopic techniques. And the most efficient strategies of spectra normalization in general are well documented in the scientific literature and routinely applied, especially for Near-Infrared Spectroscopy (NIR), and inductively-coupled plasma atomic emission spectroscopy (ICP-AES), selected here as two emblematic examples of the spectroscopic techniques, and detailed below.

In the case of NIR, the spectra can significantly be influenced by light scattering through a baseline change, known as the multiplicative effect, and non-linear variations of intensity. Therefore, the most used normalization techniques in NIR are divided into two groups [START_REF] Rinnan | Review of the most common preprocessing techniques for near-infrared spectra[END_REF]: i) the scatter-correction methods, including Standard Normal Variate (SNV) and Multiplicative Scatter Correction, and ii) the signal derivative based on algorithms such as Savitzky-Golay or Norris-Williams. The review made by Rinnan et al. [START_REF] Rinnan | Review of the most common preprocessing techniques for near-infrared spectra[END_REF] about the preprocessing methods in NIR, theoretically describes each of these normalization techniques as well as their advantages and limitations. Nowadays, all the NIR spectra are systematically normalized and the normalization process is well-established [START_REF] Li | Application of near infrared spectroscopy for rapid analysis of intermediates of Tanreqing injection[END_REF][START_REF] Rom Án-Ospino | Near infrared spectroscopic calibration models for real time monitoring of powder density[END_REF].

In the case of ICP-AES, the plasma fluctuations are eliminated by the use of the internal standard method [START_REF] Pilon | La validation de méthode en spectrométrie d'émission optique à source plasma (ICP-OES) : de l'échantillon au résultat[END_REF]. It means that a chemical element is chosen for its compatibility with the matrix of interest, and by the presence of an atomic emission line exhibiting good signal-to-noise ratio and without any risk of spectral interference with emission lines related to other elements. The selected element is then used as a dopant and introduced under the same concentration into each solution prepared for quantification. In this context, the elements Yttrium (Y) and Scandium (Sc) are often selected as internal standards for these reasons and also because they are absent from almost all the real-life samples. Then, the emission line selected for the normalization process is exploited as follows: its peak intensity is measured for each sample, including the blank; and then, the ratio to the intensity of the blank is calculated for this emission line for each sample. Finally, the peak intensities related to all the other elements are multiplied by this ratio. This method is well-established and has been routinely applied to ICP-AES for many years [START_REF] Gazulla | High precision measurement of silicon in naphthas by ICP-OES using isooctane as diluent[END_REF].

Regarding LIBS, it is worth noticing that, despite many attempts of normalization during these last years, there is no clear preference for one type of normalization, the analysts adopting the method considered as the most efficient for their specific application. In 2010, Zorov et al. [START_REF] Zorov | A review of normalization techniques in analytical atomic spectrometry with laser sampling: From single to multivariate correction[END_REF] described the strategies of normalization to be applied to LIBS and other spectroscopic techniques based on laser sampling, such as ICP-AES, atomic absorption spectroscopy and laser-enhanced ionization spectroscopy. They presented examples of LIBS data successfully normalized [START_REF] Body | Optimization of the spectral data processing in a LIBS simultaneous elemental analysis system[END_REF][START_REF] Kurniawan | Application of primary plasma standardization to Nd-YAG laser-induced shock wave plasma spectrometry for quantitative analysis of high concentration Au-Ag-Cu alloy[END_REF] as well as cases for which the normalization was not efficient [START_REF] Gornushkin | Some considerations on the correlation between signal and background in laser-induced breakdown spectroscopy using single-shot analysis[END_REF][START_REF] Castle | Variables influencing the precision of laser-induced breakdown spectroscopy measurements[END_REF], confirming that determining the right normalization method for LIBS is not an easy task. In 2012, Hahn and Omenetto [START_REF] Hahn | Laser-induced breakdown spectroscopy (LIBS), part II: review of instrumental and methodological approaches to material analysis and applications to different fields[END_REF] emphasized that it was almost impossible to apply the internal standard method to normalize the LIBS data since many important criteria couldn't be satisfied. Furthermore, Tognoni and Cristoforetti [START_REF] Tognoni | Signal and noise in laser induced breakdown spectroscopy: an introductory review[END_REF] proposed three types of normalization approaches, namely the internal standard method, the normalization by the background and finally a normalization based on the calculation of the plasma properties, similar to the CF-LIBS method. The authors finally recommended to select the most relevant normalization strategy among the three, based on each single experimental context.

To go further into details, an extended selection of articles dealing with normalization in LIBS is presented in Table 1. The aim of this list is to: i) show which normalization methods have been investigated and finally selected, ii) point out that some authors have demonstrated the advantage of treating normalized compared to uncorrected data, while some others haven't, and iii) present the figures of merit that have been used to determine the prediction ability of each model, and thus the advantage of applying normalization. Seven normalization methods are reported in Table 1, namely normalization to the maximum, minimum, background, total area, Euclidian norm, internal standard and SNV. Details about the calculations related to these methods can be found in the corresponding references. Table 1 also reveals that the comparison between normalized and uncorrected data has not been systematically presented, some of the authors assuming that normalization was expected to provide a priori better analytical results than uncorrected data, but without verifying if this statement is always true. The figures of merit that have been used to assess the normalization methods are also given in this table. First, the reduction of the variations of the LIBS signals was presented by some authors [START_REF] Juvé | Space-resolved analysis of trace elements in fresh vegetables using ultraviolet nanosecond laser-induced breakdown spectroscopy[END_REF][START_REF] Fabre | In situ calibration using univariate analyses based on the onboard ChemCam targets: first prediction of Martian rock and soil compositions[END_REF][START_REF] Ismael | In situ semi-quantitative analysis of polluted soils by laser-induced breakdown spectroscopy[END_REF][START_REF] Body | Optimization of the spectral data processing in a LIBS simultaneous elemental analysis system[END_REF][START_REF] Karki | Comparison of spectrum normalization techniques for univariate of stainless steel by laserinduced breakdown spectroscopy[END_REF]. The coefficient of determination R 2 has been very often used, either alone, or in complement to other figures of merit such as the root-mean square error (RMSE) declined as RMSEC (calibration set), RMSEP (validation set), and RMSECV (cross-validation), as well as the limits of detection (LOD) and quantification (LOQ). It is worth noticing that these figures of merit should be calculated with an extreme thoroughness since they allow not only for demonstrating if the normalization enables to reach better analytical performances than by uncorrected data, but also to determine if a normalization method provides a better correction than the others. In addition, it is worth pointing out that very recent publications [START_REF] Takahashi | Partial least squares regression calculation for quantitative analysis of metals submerged in water measured using laser-induced breakdown spectroscopy[END_REF][START_REF] Thomas | Characterization of hydrogen in basaltic materials with Laser-induced breakdown spectroscopy (LIBS) for application to MSL ChemCam data[END_REF][START_REF] Andrade | Calibration strategies for determination of the In content in discarded liquid crystal displays (LCD) from mobile phones using laser-induced breakdown spectroscopy (LIBS)[END_REF] were partly dedicated to the selection of the best normalization approach, revealing it is still an open question. From Table 1, it is also interesting to notice that the best normalization method seems to depend on the element and the sample. However, in order to push further the discussion, the principles and advantages of the four most common normalization methods applied to LIBS are presented hereafter.

Normalization to the background

The background emission is theoretically considered to be a relevant indicator to monitor the plasma properties. In 2008, De Giacomo et al. [START_REF] De Giacomo | Laser-induced breakdown spectroscopy methodology for the analysis of copper based alloys used in ancient artworks[END_REF] demonstrated the existence of a correlation between the intensity of the continuum radiation, i.e the background intensity, and the plasma density, itself correlated to the amount of emitters in the plasma. In the case of normalization to the background, the peak intensity value of the emission line of interest is divided by the value of the background emission. This one is recorded at the vicinity of the emission line of interest, either from the intensity value at a single wavelength, or from the average value over a selected spectral window [START_REF] Tognoni | Signal and noise in laser induced breakdown spectroscopy: an introductory review[END_REF]. It is worth pointing out that the background emission must be carefully recorded. In practice, it means that the detector dark current must be separately recorded and then subtracted prior to such signal processing. This point has been deeply discussed in ref. [START_REF] Tognoni | Signal and noise in laser induced breakdown spectroscopy: an introductory review[END_REF][START_REF] Schröder | Hydrogen detection with ChemCam at Gale crater[END_REF] and is crucial to take into account the actual background signal. Zorov et al. [START_REF] Zorov | A review of normalization techniques in analytical atomic spectrometry with laser sampling: From single to multivariate correction[END_REF] attributed the first results about the use of normalization to the background to Xu et al. [START_REF] Xu | Absolute analysis of particulate materials by laser-induced breakdown spectroscopy[END_REF], but the corresponding work was quickly disapproved by other authors [START_REF] Gornushkin | Some considerations on the correlation between signal and background in laser-induced breakdown spectroscopy using single-shot analysis[END_REF], which clearly revealed a lack of generalization ability. However, this type of normalization has been successfully applied to several cases [START_REF] Zorov | A review of normalization techniques in analytical atomic spectrometry with laser sampling: From single to multivariate correction[END_REF][START_REF] Dell'aglio | Monitoring of Cr, Cu, Pb, V and Zn in polluted soils by laser induced breakdown spectroscopy (LIBS)[END_REF][START_REF] Senesi | Heavy metal concentrations in soils as determined by laser-induced breakdown spectroscopy (LIBS), with special emphasis on chromium[END_REF]. In the frame of analysis of soils polluted with heavy metals, Dell'Aglio et al. [START_REF] Dell'aglio | Monitoring of Cr, Cu, Pb, V and Zn in polluted soils by laser induced breakdown spectroscopy (LIBS)[END_REF] adopted the normalization to the background for emission lines related to several elements (Cr, Cu, Pb, V, Zn) in order to reduce matrix effects. They concluded that the correlation between the data derived from ICP-AES and the normalized LIBS signals was quite satisfactory. Senesi et al. [START_REF] Senesi | Heavy metal concentrations in soils as determined by laser-induced breakdown spectroscopy (LIBS), with special emphasis on chromium[END_REF] also adopted the normalization to the background to quantify the concentrations of heavy metals in soils, with a particular focus on chromium. However, despite significant reduction of the fluctuations and high correlation between the normalized LIBS and ICP-AES data, it is very important to notice that only 3 to 4 points were taken into account to build the quantitative models in this study. The conclusions are thus likely to be non-robust.

Normalization to the total area

In the case of normalization to the total area, for each LIBS spectrum, the peak intensity value of the emission line related to the analyte is divided by the value of area of the spectrum over the whole spectral range. Here again, the detector dark current must be separately measured and subtracted prior to such signal processing, to deal with real LIBS spectra. The total area is calculated as the sum of all the intensity levels describing the LIBS spectrum, once the detector dark current has been removed, and is considered to be a good indicator of the plasma properties. Thus, Body and Chadwick [START_REF] Body | Optimization of the spectral data processing in a LIBS simultaneous elemental analysis system[END_REF] demonstrated the existence of a strong correlation between this area and the laser energy. Then, Fabre et al. [START_REF] Fabre | In situ calibration using univariate analyses based on the onboard ChemCam targets: first prediction of Martian rock and soil compositions[END_REF] applied this normalization method to the analysis of Martian rocks and soils by ChemCam onboard the MSL Curiosity rover. In this case, three separate spectrometers were operated and the normalization to the total area was implemented by considering only the spectrometer for which the emission line of the analyte was detected. Therefore, all the results of quantification obtained from the ChemCam data, and based on Partial Least Squares regression (PLS) for the major elements and univariate analysis for the minor and traces, were entirely based on normalized data. In 2010, Zorov et al. [START_REF] Zorov | A review of normalization techniques in analytical atomic spectrometry with laser sampling: From single to multivariate correction[END_REF] reported that J.A. Bolger [START_REF] Bolger | Semi-quantitative laser-induced breakdown spectroscopy for analysis of mineral drill core[END_REF] proposed the normalization method to the total area for the first time in LIBS, which allowed to improve the linearity of the calibration curves of Cu, Fe, Ni, Mn and Cr in mineral rocks. However, it is interesting to notice again that the calibration curves were built from a very small number of points, making the conclusion of this study potentially non-robust. Finally, Yu et al. [START_REF] Yu | Laser-induced breakdown spectroscopy coupled with multivariate chemometrics for variety discrimination of soil[END_REF], in the context of sorting certified soils by chemometric tools, examined the compensation of matrix effects and changes in experimental conditions, mainly temperature, through the normalization to the total area. They presented this normalization as a standard step of preprocessing but without comparing the corresponding analytical results to those obtained from uncorrected data. More generally, most of the articles about the normalization to the total area have concluded that this pre-processing step provides a benefit.

Normalization to an internal standard

The internal standard (IS) method is frequently adopted by the LIBS analysts. It consists in dividing the peak intensity (or area) of the emission line related to the analyte by the peak intensity (or area) of a selected emission line related to the internal standard. The concentration of the internal standard is supposed to remain constant for the whole set of samples, or at least to be known by the analyst. The underlying hypothesis is that the variations of the peak intensity related to the internal standard are only due to changes in the plasma properties. This is expected to be true when all the samples considered to build a calibration model are characterized by a single matrix in order to avoid any risk of bias linked to matrix effects. But it is interesting to notice also that this normalization method can be indifferently applied to any kind of matrix [START_REF] Juvé | Space-resolved analysis of trace elements in fresh vegetables using ultraviolet nanosecond laser-induced breakdown spectroscopy[END_REF][START_REF] Sarkar | Analysis of barium borosilicate glass matrix for uranium determination by using ns-IR-LIBS in air and Ar atmosphere[END_REF][START_REF] Gupta | Quantitative elemental analysis of nickel alloys using calibration-based laser-induced breakdown spectroscopy[END_REF][START_REF] Moncayo | Classification of red wine based on its protected designation of origin (PDO) using Laserinduced Breakdown Spectroscopy (LIBS)[END_REF][START_REF] Ismail | LIBS limit of detection and plasma parameters of some elements in two different metallic matrices[END_REF][START_REF] Vrenegor | Investigation of matrix effects in laser-induced breakdown spectroscopy plasmas of high-alloy steel for matrix and minor elements[END_REF]. Moreover, it requires the use of another analytical technique to measure the concentration values of the internal standard inside each sample.

The list of criteria defined by Barnett et al. [START_REF] Barnett | Theoretical principles of internal standardization in analytical emission spectroscopy[END_REF] to be verified in order to identify a reliable internal standard, have been applied to LIBS [START_REF] Hahn | Laser-induced breakdown spectroscopy (LIBS), part II: review of instrumental and methodological approaches to material analysis and applications to different fields[END_REF]. Three of these criteria are related to the choice of the element playing the role of internal standard, since the analyte and the internal standard are expected to share similar: i) volatilization rates; ii) ionization energies; and iii) atomic weights. Then, three other criteria are focused on the selection of the spectral line, which is expected to: i) have the same excitation energy than the one of the analyte; ii) be not affected by self-absorption; iii) have a similar intensity as the one of the analyte. In practice, this list of criteria seems to be almost impossible to fulfill, especially in the case of natural and complex matrices, driving to conclude that the internal standard method is generally not ideal to LIBS analysis. Despite this statement, several studies have concluded on the advantages of applying the internal standard method to LIBS, even if the choice of the internal standard according to the criteria previously mentioned was not always clearly presented. Thus, Juvé et al. [START_REF] Juvé | Space-resolved analysis of trace elements in fresh vegetables using ultraviolet nanosecond laser-induced breakdown spectroscopy[END_REF], selected the spectral line of carbon at 247.86 nm considered as an internal standard, to process the space-resolved analysis of trace elements (Mg, Al, Ca, Ti, Mn, Fe) in fresh vegetables. The choice of carbon was based on the fact that it was found under constant concentration in the studied samples but no reference to the selection criteria neither for the elements nor for the spectral lines can be found in their article. Thus, the advantage of adopting internal standard normalization reported in this article might be due to chance. Similarly, Kwak et al. [START_REF] Kwak | Quantitative analysis of arsenic in mine tailing soils using double pulse-laser induced breakdown spectroscopy[END_REF] did not consider the criteria related to the selection of internal standard. In the case of quantification of arsenic in soils, they normalized the intensity of the As I line at 228.812 nm to the one of the Fe I line at 248.8 nm and also to the Mg I line at 285.2 nm, then the Al I line at 309.3 nm, and also the Si I line at 288.2 nm. The only criterion to select these lines was the increase of the coefficient of determination. Indeed, starting from 0.22 without normalization, R 2 =0.96 after a normalization to Fe, 0.91 with Mg, 0.75 with Al, and 0.58 with Si, demonstrating the advantage of normalizing the LIBS signal to the Fe I line at 248.8 to reach the best performance for the calibration model. Moreover, Sarkar et al. [START_REF] Sarkar | Analysis of barium borosilicate glass matrix for uranium determination by using ns-IR-LIBS in air and Ar atmosphere[END_REF], in the context of quantification of uranium in a barium borosilicate glass matrix, selected barium as internal standard, after comparison with other elements, such as silicon and boron. Then, they selected the emission line of Ba I at 649.786 nm, which was satisfying to all the given criteria. Finally, by comparing the figures of merit (R², RMSECV and the slope of the calibration curve), obtained after this normalization to the selected Ba I line, to the ones derived from other elements and spectral lines, they concluded that the best normalization according to theoretical criteria was also the best one from the experimental results.

To conclude, normalization to an internal standard has been demonstrated to reduce the LIBS signal fluctuations and thus increase the analytical performance of the related quantitative models. However, in addition to the cases exhibiting a successful effect of this type of normalization on the analytical performance, it is worth noticing that LIBS is also often applied to analysis of samples for which it is impossible to find a proper internal standard, according to the list of criteria previously discussed. This normalization approach can't thus be considered as a general strategy for LIBS analysis, and should only be applied to specific cases.

Standard Normal Variate -SNV

Standard Normal Variate (SNV) is one of the most used normalization techniques in near-infrared spectroscopy (NIR) [START_REF] Bi | A local preprocessing method for near-infrared spectra, combined with spectral segmentation and standard normal variate transformation[END_REF][START_REF] Buratti | Discrimination between washed Arabica, natural Arabica and Robusta coffees by using near infrared spectroscopy, electronic nose and electronic tongue analysis[END_REF][START_REF] Guo | Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics[END_REF] and Raman spectroscopy [START_REF] Heraud | Effects of pre-processing of Raman spectra on in vivo classification of nutrient status of microalgal cells[END_REF][START_REF] Romero-Torres | Raman spectroscopy for tablet coating thickness quantification and coating characterization in the presence of strong fluorescent interference[END_REF]. SNV calculation is processed on each spectrum individually by centering the spectrum on the mean value and then dividing the corrected spectrum by the standard deviation of the original spectrum (Equation 1):

I = Equation 1
where represents the intensity value at the wavelength k of the original spectrum; I , the corresponding SNV normalized intensity value; , the mean of the intensity values of the original spectrum; and , the standard deviation of the original spectrum.

In the context of in-situ quantitative LIBS analyses of polluted soils involving Pb, Cu and Fe, Ismaël et al. [START_REF] Ismael | In situ semi-quantitative analysis of polluted soils by laser-induced breakdown spectroscopy[END_REF] obtained good correlations between the SNV-corrected LIBS data and the reference concentration values derived from ICP-AES, for Pb (405.78nm), Cu (327.39nm) and Fe (344.08nm) and observed that SNV allowed to reduce the standard deviation after a series of repetitions. Then, Syvilay et al. [START_REF] Syvilay | Evaluation of the Standard Normal Variate method for Laser-Induced Breakdown Spectroscopy data treatment applied to the discrimination of painting layers[END_REF] demonstrated in the context of quantitative analysis of Ag, Bi, Cu and Sn in lead samples, that the univariate models built from SNVcorrected data provided better figures of merit (R 2 and LOD) than those built from either raw data or data normalized to a selected emission line of lead, considered as internal standard. To conclude, the examples of LIBS studies reported in Table 1 indicate that SNV might be an efficient normalization technique for processing the LIBS data and we point out that the advantages of the SNV normalization is worth being accurately assessed in future works.

As a general conclusion, the four normalization methods presented in the present review have been successfully applied to LIBS data, meaning they have permitted to reduce the signal fluctuations and by consequence, improve the calibration performances.

Recommendations

To support the advantages of normalizing, we recommend to systematically compare the figures of merit related to the models built from either normalized or uncorrected data. Indeed, from Table 1, one can notice that this simple comparison was not made in all the cases. In addition, it is worth pointing out that, in some cases, normalization might negatively impact the analytical performance of the models [START_REF] Gornushkin | Some considerations on the correlation between signal and background in laser-induced breakdown spectroscopy using single-shot analysis[END_REF][START_REF] Sarkar | Analysis of barium borosilicate glass matrix for uranium determination by using ns-IR-LIBS in air and Ar atmosphere[END_REF][START_REF] Dos Santos Augusto | Calibration strategies for the direct determination of Ca, K, and Mg in commercial samples of powdered milk and solid dietary supplements using laser-induced breakdown spectroscopy(LIBS)[END_REF]. Based on the works reported in Table 1, it is not possible to conclude if one of the normalization methods might be advantageously adopted whatever the element and application. Thus, we recommend the LIBS analyst to test different normalization methods and then adopt the one offering the best analytical performance. This strategy of comparing different normalization methods has been adopted by some authors [START_REF] Sarkar | Evaluation of the prediction precision capability of partial least squares regression approach for analysis of high alloy steel by laser induced breakdown spectroscopy[END_REF][START_REF] Castro | Twelve different types of data normalization for the proposition of classification, univariate and multivariate regression models for the direct analyses of alloys by Laser-induced breakdown spectroscopy (LIBS)[END_REF][START_REF] Karki | Comparison of spectrum normalization techniques for univariate of stainless steel by laserinduced breakdown spectroscopy[END_REF][START_REF] Dos Santos Augusto | Calibration strategies for the direct determination of Ca, K, and Mg in commercial samples of powdered milk and solid dietary supplements using laser-induced breakdown spectroscopy(LIBS)[END_REF][START_REF] Andrade | Direct determination of contaminants, major and minor nutrients in solid fertilizers using Laser-induced breakdown spectroscopy (LIBS)[END_REF], who concluded that, in some cases, uncorrected data provide better analytical performances than normalized ones, and also that the normalization method should be selected on a case-to-case basis. Regarding the figures of merit calculated to assess the performance of each model, the coefficient of determination R² has been widely used. However, in some cases, R² was the only indicator considered for the assessment [START_REF] Kwak | Quantitative analysis of arsenic in mine tailing soils using double pulse-laser induced breakdown spectroscopy[END_REF][START_REF] Sallé | Comparative study of different methodologies for quantitative rock analysis by Laser-Induced Breakdown Spectroscopy in a simulated Martian atmosphere[END_REF][START_REF] Gupta | Quantitative elemental analysis of nickel alloys using calibration-based laser-induced breakdown spectroscopy[END_REF], which is clearly insufficient to establish robust conclusions, as it will be further discussed in this section.

Minimum number of points to build a reliable calibration model

In order to determine what is the best normalization approach for LIBS, we recommend to strictly apply the basic rules allowing to calculate correct values for the figures of merit dedicated to assess the analytical performances of the models. Our first advice concerns the minimum number of points (concentration values) to build a robust linear calibration model. This number is not defined in a unique way by the organizations in charge of standardization and traceability of chemical measurements. The ISO standard 11095:1996 [START_REF]Linear calibration using reference materials[END_REF], namely the standard dedicated to linear calibration using reference materials published by the International Organization for Standardization (ISO), advises to include more than three points to build a reliable model. Moreover, through a guide named "The Fitness for Purpose of Analytical Methods" [START_REF]Eurachem Guide: The Fitness for Purpose of Analytical Methods -A Laboratory Guide to Method Validation and Related Topics[END_REF], the European network of organizations EURACHEM, promoting good quality practices about chemical measurements, recommends to include a minimum of 6 concentration levels plus blank. In the same way, a commission decision (2002/657/EC), edited by the commission of the European communities [51], about the performance of the analytical methods and the interpretation of results advises to include at least 5 points, i.e. concentration levels, including zero. Based on these references, the minimum number of points to build a reliable calibration model should be around 5 or 6. Furthermore, these points should be, ideally, equally distributed over the concentration range related to the calibration to obtain the most representative calibration model related to the analytical context. But, despite of its major importance for quality purpose, this last criterion is generally under-estimated or simply not considered by the LIBS analysts. We have thus decided to illustrate how the use of an unappropriated dataset to build the calibration models might drive to non-robust conclusions.

Coefficient of determination, R 2

The coefficient of determination, R 2 , is the figure of merit the most commonly used (cf. Table 1) to assess the quality of a calibration model. However, this indicator is reliable only when the points are equally distributed over the concentration range and when there is no extreme point, very far from the other ones. At the opposite, if one of these conditions is not fulfilled, the decision based only on the parameter R 2 might be non-robust and this statement seems not always be taken into account in the LIBS literature [START_REF] Senesi | Heavy metal concentrations in soils as determined by laser-induced breakdown spectroscopy (LIBS), with special emphasis on chromium[END_REF][START_REF] Bolger | Semi-quantitative laser-induced breakdown spectroscopy for analysis of mineral drill core[END_REF], still nowadays [START_REF] Bennett | Calibration curves for commercial copper and aluminum alloys using handheld laser-induced breakdown spectroscopy[END_REF]. Figure 1 illustrates the case of univariate calibration models built from a set of 36 LIBS spectra. This dataset has been specially selected to illustrate the case of non-satisfying conditions to build a robust model. Thus, all the details about the samples, the analyte, and the experimental setup are voluntary not provided here, to highlight the general conclusions of this discussion and not consider it as a specific case-study. For each model displayed in Fig. 1, the baseline-subtracted signal (in arbitrary units, a.u.) was plotted on the vertical axis as a function of the concentration of the analyte (µg/g) on the horizontal axis in three cases: a) without normalization; b) after normalization to the total area of the spectrum; c) after normalization by SNV. In agreement with the a priori expectation, the R 2 values are higher after normalization. However, it is worth noticing that, considering a range of calibration from 0 to 8000 µg/g, 89% of the points of the calibration set correspond to concentration values lower than 1500 µg/g. Moreover, for these points, whatever the normalization process, there is no correlation between the LIBS signal and the concentration values. In such conditions, the increase of the R 2 value from 0.52 (without normalization) to 0.84 (SNV) should be considered with caution, since the regression models over the full concentration range are, in this case, not robust. Indeed, none of the points from the calibration set is between 1500 and 2500 µg/g and between 4500 and 7500 µg/g. Moreover, in such conditions, the coefficient of determination value is highly sensitive to extreme points, considered as outliers [START_REF] Lewis Publishers | Quality Assurance of Chemical Measurements[END_REF]. For instance, in this case, the last point of the calibration curve corresponds to a concentration value of 7500 µg/g, quite far from the previous one, which corresponds to 4500 µg/g. Indeed, after removing this last point from the calibration set, the new R² values become 0.38, 0.53 and 0.59, for the non-normalized data, data normalized to the total area, and data normalized by SNV, respectively. Finally, the example given here aims at warning the LIBS analyst about the danger of looking only at R² [START_REF] Golbraikh | Beware of q²![END_REF] to characterize the calibration performances of a model, whatever the method of normalization. It also means that outliers should induce additional investigation through either new measurements, or new steps in data processing.

Root Mean Squared Error of prediction (RMSE)

In addition to the R 2 parameter, the prediction ability has to be determined. It consists in calculating the common Root Mean Squared Error (RMSE) [START_REF] Haddad | Good practices in LIBS analysis: Review and advices[END_REF] resulting from the predicted concentration values from a series of samples. This indicator is expected to be as low as possible for a reliable model, and is mathematically written as in Eq.2.

= ∑ ! " # $% & Equation 2
Where ' ( is the predicted concentration value, ' ( ) the reference concentration value of the sample p, and N the number of samples taken into account. It is worth noticing that, similarly to the case of R 2 , this figure of merit should be considered with extreme attention, as discussed below.

When all the samples belonging to the calibration (C) set are simultaneously taken into account, the resulting value is referred to as *. However, * might not be a reliable indicator of the calibration model in case of the presence of extreme points, namely points exhibiting very high concentration values compared to the other ones [START_REF] Haddad | Good practices in LIBS analysis: Review and advices[END_REF]. To overcome this drawback, the value should be calculated on the basis of a crossvalidation (CV) process, and referred to as *+. Among the different techniques of cross-validation, the Leave-One-Out (LOO) method is the most common. It consists in removing one point from the dataset and then calculate the * value from the regression built from the N-1 remaining points. Then the excluded point is reintroduced, another point is excluded, and a new * value is calculated, and so on, until all the points of the calibration set have been excluded. Finally, *+ is the average value of the N values of * calculated during the cross-validation process.

*+ is expected to better reveal the limitations of the calibration model than *. When the calibration set is big, another method of cross-validation, known as bootstrapping, is generally preferred to the LOO, and consists in excluding not only one single point at a time but many. Finally, to fully assess a calibration model, it is necessary to predict the concentration values of a set of known samples different from the calibration set. Thus, it has been suggested [START_REF] Westad | Validation of chemometric models -a tutorial[END_REF] to split the original dataset into the calibration set (2/3) and the validation set (1/3), both of them equally spread over the concentration range. Then, the RMSE value is calculated over the validation set, and referred as ,, with (P) related to the prediction of the concentration values of the samples belonging to the validation set. Comparing * and , allows to evaluate if there is a risk of overfitting (if * ≪ ,), before comparing different strategies of normalization. Note also that *+ and , are commonly used in the frame of multivariate regression, not only to evaluate the model performances but also to select the best number of components, which can easily become user dependent. Finally, the validation step in multivariate analysis is not the aim of this present review, but many information can be found in the following studies [START_REF] Haddad | Good practices in LIBS analysis: Review and advices[END_REF][START_REF] Westad | Validation of chemometric models -a tutorial[END_REF][START_REF] Zhang | Chemometrics in laser-induced breakdown spectroscopy[END_REF].

Limits of detection and quantification, LOD and LOQ

The limits of detection (LOD) and quantification (LOQ) have been discussed by Mermet in review papers [START_REF] Mermet | Calibration in atomic spectrometry: a tutorial review dealing with quality criteria, weighting procedures and possible curvatures[END_REF][START_REF] Mermet | Limit of quantification in atomic spectrometry: an unambiguous concept?[END_REF] dedicated to atomic spectrometry. In LIBS, the LOD is the most frequently reported, and defined by Eq.3 [START_REF] Mermet | Limit of quantification in atomic spectrometry: an unambiguous concept?[END_REF], for a regression model written as: . = / + 1. 3 456 = where is the standard deviation of the background signal measured close to the peak of interest. Similarly, the limit of quantification is defined by Eq. 4 [START_REF] Mermet | Calibration in atomic spectrometry: a tutorial review dealing with quality criteria, weighting procedures and possible curvatures[END_REF][START_REF] Mermet | Limit of quantification in atomic spectrometry: an unambiguous concept?[END_REF]:

459 = :; 8 = :; 7 456
Equation 4

The limits of detection and quantification are calculated from the regression model. And, as already stated for the calculation of R 2 and RMSE, the calibration set needs to fulfill basic requirements to obtain reliable results for the values of these limits.

Finally, the figures of merit R 2 , RMSE, LOD (or LOQ) are perfect to assess the analytical performance of the models as well as the advantage of normalization [START_REF] Motto-Ros | Critical aspects of data analysis for quantification in laser-induced breakdown spectroscopy[END_REF]. In the next section, we exploit these figures of merit to compare, for a dataset recorded in our laboratory with a handheld LIBS instrument, the analytical performance obtained by i) non-normalized spectra, ii) spectra normalized to the total area, and iii) spectra normalized by SNV.

Example of normalization

The general discussion about normalization in LIBS has been presented in the state-ofthe-art of this review, and then important recommendations that deserve to be completed by a case-study. It aims at illustrating the question of normalization and allows us to propose a general guideline to assess and then report the analytical performance of the quantitative models based on different normalization methods. Let's mention that this guideline could efficiently be applied to any spectroscopic technique and normalization method but it appears to be of major importance for LIBS. We consider a first set of 14 samples of plants (calibration set), sampled from field crops in France, and stored in a sample bank managed by the French National Institute for Agricultural Research (INRA), in the frame of the French national observatory network Quasaprove [START_REF]RMT Quasaprove[END_REF]. More precisely, these samples were analyzed by a portable LIBS instrument and the analyses presented here were focused on the quantification of magnesium. Each sample was dried and then ground by using a planetary ball mill (PM 400, Retsch), allowing to obtain a grain size smaller than 250 µm. Then, each sample was prepared as a 12-mm diameter pressed pellet after 8 tons load during 3 minutes without using any additional binder. 10 locations were randomly chosen at the surface of the sample. And at each location, 3 laser shots were considered as pre-shots and then the LIBS spectra related to the following 7 shots were recorded. The 10 locations and the 7 spectra per location gives access to 70 LIBS spectra per sample. The average spectrum over the 70 spectra was considered for the analyses. At each wavelength, the RSD value from the 70 spectra has also been calculated and reveals that each sample can be considered as homogeneous, which confirms the observation one can make with the naked eyes.

Considering the quantification of magnesium as an example for the present guideline, the concentration values of the 14 samples composing the calibration set ranged from 0.7 to 8 µg/g, according to the reference ICP-AES analyses. In addition, 4 other samples of plant, from the same bank, and not used for the calibration, were selected to validate the calibration models, based on their availability and their concentration values measured by ICP-AES. one should apply to be able to objectively compare the different methods of normalization between them. Thus, even if the models based on normalization to the background and internal standard could have also been presented in Figure 2, we have decided to select only two methods of normalization to illustrate our purpose. These two normalization methods were expected to reduce the fluctuations and thus to improve the analytical performance of the models, according to previous works [START_REF] Fabre | In situ calibration using univariate analyses based on the onboard ChemCam targets: first prediction of Martian rock and soil compositions[END_REF][START_REF] Yu | Laser-induced breakdown spectroscopy coupled with multivariate chemometrics for variety discrimination of soil[END_REF][START_REF] Payré | Alkali trace elements in Gale crater, Mars, with ChemCam: Calibration update and geological implications[END_REF][START_REF] Takahashi | Partial least squares regression calculation for quantitative analysis of metals submerged in water measured using laser-induced breakdown spectroscopy[END_REF][START_REF] Ismael | In situ semi-quantitative analysis of polluted soils by laser-induced breakdown spectroscopy[END_REF][START_REF] Karki | Comparison of spectrum normalization techniques for univariate of stainless steel by laserinduced breakdown spectroscopy[END_REF], and this result is again verified in the case of plant samples displayed in Figure 2. Indeed, the points related to the concentrations of magnesium larger than 3 µg/g are much closer to the linear regression curve after normalization. At the opposite, two of the points related to concentration values close to 1 µg/g and normalized by SNV are displayed far from the regression line and the other points.

Table 2 displays the values of the figures of merit dedicated to the assessment of the quantitative models, specifically R 2 , RMSEC, RMSECV, RMSEP, LOD (reminding that LOD and LOQ are closely linked, no need to report also the values of LOQ), calculated for the three regression models displayed in Figure 2. As already discussed, even if the R 2 values are higher after normalization, it might be risky to draw a conclusion based on this single parameter. Indeed, one can observe in Table 2 that while R 2 is rising from 0.84 (without normalization) to 0.88 (SNV), RMSEP is unexpectedly increasing from 0.74 to 1.42, indicating that the regression model built after SNV normalization exhibits a lower predictive ability than the model built from non-normalized data. The SNV model might suffer from over-fitting since both the RMSEC and RMSECV values are half the RMSEP value. At the opposite, the values reported in Table 2 related to the normalization to the total area reveal for this model stronger correlation (higher R 2 ) and better prediction ability (lower errors of prediction). Finally, even if the values of R 2 , RMSEC, RMSECV, and RMSEP are better after normalization to the total area, the values of LOD are not significantly different from data without normalization and after normalization. In the case of the models reported in Table 2, the normalization to the total area allows for building a calibration model with a better coefficient of determination and smaller prediction errors. This type of normalization might thus be adopted here even if it doesn't allow for lowering the limits of detection and quantification. At the opposite, the data normalized by SNV drive to worse prediction ability, namely a higher RMSEP value, than non-normalized data.

More generally, the normalization to the total area has been considered as valuable not only in the case reported in Table 2 but also in many previous studies. It is thus a priori expected that this type of normalization should improve the quantification ability of any LIBS analysis. However, we recommend to demonstrate its advantage a posteriori, from a comparison of the values taken by the figures of merit reported in Table 2. At the opposite, the normalization by SNV appears to be more questionable. Some previous studies have demonstrated its advantage but it was not the case in the frame of the case-study reported in Table 2.

We finally recommend, whatever the types of samples and elements to be quantified, to always compare the figures of merit related to the models calculated from not-normalized and normalized spectra, as displayed in Table 2. Let's point out that, in addition to the normalization to the total area and by SNV, the normalization to the background and to an internal standard should also be considered. Based on the methodology presented here, not only the best normalization method can be objectively selected, but the calculated values of the figures of merit can also be directly exploited to report the analytical performance of the selected model.

Conclusion

LIBS spectra are often normalized in order to take into account signal fluctuations and thus obtain enhanced analytical performance. However, normalization in LIBS remains an open question and, in the present review, we have identified and discussed the normalization methods commonly applied to LIBS. We have more specifically detailed and discussed four of them, that we consider as the most relevant, namely i) background, ii) total area, iii) internal standard, and iv) SNV. We have also highlighted that the gain resulting from normalizing LIBS spectra was often not assessed and that the rare comparisons between different normalization methods were sometimes questionable. Indeed, this review has also revealed that basic requirements to calculate robust indicators were sometimes not fulfilled. A first case-study, based on the selection of LIBS spectra and concentration values unsuitable to build a robust calibration, has been discussed to emphasize the risk of taking a decision about the advantage of normalization from only the R 2 value. Finally, advices and good practices have been proposed in order to correctly calculate the relevant figures of merit dedicated to the assessment of the performance of the quantitative models. Obviously, an efficient normalization method is expected to provide higher R 2 values, and lower RMSE and LOD or LOQ values than those obtained without normalization. However, through a second casestudy, related to the quantification of magnesium in plant samples, we have demonstrated that these three figures of merit were not always simultaneously improved. Then, considering that the analytical context naturally introduces some hierarchy within the figures of merit, in the context of quantification of major or abundant elements, reducing the RMSE value becomes the top priority and reducing the LOD or LOQ value can be of secondary importance. At the opposite, for the quantification of minor or trace elements, the normalization method should be able to reduce the LOD or LOQ value while keeping a good predictive ability, i.e. a small RMSE value. Based on this methodology, it becomes possible to objectively compare different normalization methods between each other and finally select the best one according to the analytical context. Future works may adopt this methodology in order to take the best advantage from normalization in LIBS, and thus increase the analytical performance of this technique.

Figure 1 :

 1 Figure 1: Linear calibration models (red lines) obtained (after baseline-subtraction) for: a) not normalized data, b) data normalized to the total area, and c) data normalized by SNV, with the corresponding R 2 values.

Figure 2 :

 2 Figure 2: Linear calibration models (red lines) obtained (after baseline-subtraction) for: a) not normalized data, b) data normalized to the total area, and c) data normalized by SNV, with the corresponding R 2 values. In this case-study, the analyte was magnesium and the samples were plants.

Figure 2

 2 Figure 2 displays calibration models built from; a) the baseline subtracted peak intensity value of Mg II at 280.27 nm, b) normalization by the total area of the spectrum, c) normalization by SNV. It should be emphasized that the present discussion is focused on the

  methodology

Table 1 :

 1 Selection of articles dealing with normalization in LIBS. The investigated and selected normalization methods are presented as well as the figures of merit used to assess the prediction ability of each model. IS: internal standard; SNV: standard normal variate; Imax: maximum intensity; Imin: minimum intensity.

	Investigated	Selected	Comparison
	normalization	normalization	normalized/	Figures of merit	References
	method	method	uncorrected data
	IS, Background, Total area	IS	yes	RMSECV, LOD	2018, Thomas et al. [27]
	IS, Total area, Imax, Norm	IS	yes	R², RMSEC	2019, Andrade et al. [28]
	Background	Background	no	R², LOD	2011, Dell'Aglio et al. [30]
	Background	Background	no	R², RMSEC	2009, Senesi et al. [31]
	Total area	Total area	no	R², RSD	2014, Fabre et al. [22]
	Total area	Total area	no	R², RMSE	2017, Payré et al. [61]
	Total area	Total area	yes	R², RMSECV	2018, Takahashi et al. [26]
	IS	IS	no	RSD	2008, Juvé et al. [21]
	IS	IS	yes	R²	2011, Gupta et al. [48]
	IS applied to Fe, Mg, Al, Si	IS (Fe line)	yes	R²	2009, Kwak et al. [35]
	IS applied to Ba, Si, B	IS (Ba line)	no	R², RMSECV	2014, Sarkar et al. [36]
	SNV	SNV	no	RSD	2011, Ismael et al. [23]
	SNV, IS	SNV	yes	R², LOD	2015, Syvilay et al. [42]
	Total area, IS, Imax, norm	Element dependent	yes	RMSEC, RMSECV, RMSEP, LOD	2016, Castro et al. [45]
	Total area, Background	Total area	yes	RSD, RMSEP	2001, Body and Chadwick [17]
	Total area, IS, Calibration-free	Element dependent	yes	R²	2006, Sallé et al. [47]
	Total area, IS, Imax, norm	Element dependent	yes	R², RMSEC, RMSEP	2017, Augusto et al. [43]
	IS, Imax, norm	Element dependent	yes	RMSEC, RMSECV, RMSEP, LOD, LOQ	2016, Andrade et al. [46]
	Total area, Background, IS	IS, total area	no	R², RMSEP, RSD	2016, Karki et al. [24]
	Imin, Imax, IS, norm	Norm	no	REP, RMSEP	2015, Sarkar et al. [44]

Table 2 :

 2 Figures of merit calculated to assess the performance of regression models involving different normalization strategies.

		R²	RMSEC(µg/g) RMSECV(µg/g) RMSEP(µg/g)	LOD(µg/g)
	Not-normalized	0.84	0.90	0.89	0.74	0.20
	Total area	0.95	0.45	0.45	0.57	0.24
	SNV	0.88	0.76	0.75	1.42	0.32

Acknowledgements

The authors acknowledge the regional council of Region Nouvelle Aquitaine for the funding of J. Guezenoc's doctoral position in the frame of the project AGROLIBS, and Dr. Laurence Denaix for having given access of samples from the Quasaprove network.


Pierre Gallian

Elodie Pouchol

Rachid Djoudi

Sébastien Lhomme

Lina Mouna

Sylvie Gross

Philippe Bierling

Azzedine Assal

Nassim Kamar

Vincent Mallet

Anne-Marie Roque-Afonso

Jacques Izopet

Pierre Tiberghien
email: pierre.tiberghien@efs.sante.fr

Transfusion-Transmitted Hepatitis E Virus Infection in France

Keywords: Hepatitis E, Hepatitis E virus, Transfusion-transmitted infection

INTRODUCTION

Hepatitis E Virus (HEV) is a small non-enveloped RNA virus belonging to the Hepeviridae family, genus Orthohepevirus. HEV is a major cause of acute viral hepatitis worldwide [START_REF] Kamar | Hepatitis E virus infection[END_REF][START_REF] Kamar | Hepatitis E virus infection[END_REF] . At present, multiple HEV genotypes have been described, including four major HEV genotypes (genotypes 1-4) involved in human pathology. Genotypes 1 and 2 infect only humans and are transmitted by orofecal route. They are prevalent in developing countries with poor sanitation where they may cause large outbreaks.

Genotypes 3 and 4 (HEV-3 and HEV-4) are ubiquitous and zoonotic, with domestic swine, wild boar, and deer as main reservoirs. HEV infections in Western Europe are autochthonous cases mainly caused by HEV-3 [START_REF] Mansuy | Acute hepatitis E in southwest France over a 5-year period[END_REF] . Transmission to humans occurs by consumption of contaminated undercooked meat or pork liver sausage, or by environmental sources such as contaminated water [START_REF] Mansuy | A nationwide survey of hepatitis E viral infection in French blood donors[END_REF] . In developed countries, HEV-3 and 4 are most often responsible for self-limiting asymptomatic acute hepatitis E, with the notable exception of acute liver failure with preexisting chronic liver disease and chronic hepatitis E occurring in immunocompromised patients [START_REF] Kamar | Hepatitis E virus and chronic hepatitis in organ-transplant recipients[END_REF][START_REF] Abravanel | Hepatitis E virus reinfections in solid-organ-transplant recipients can evolve into chronic infections[END_REF] .

Seroprevalence studies indicated that HEV infection is present in Europe with significant geographic heterogeneity between countries as well as within countries. In blood donors, reported anti-HEV IgG rates measured by the same serological test have ranges from 4.7% in Scotland [START_REF] Smith | Hepatitis E virus in Scottish blood donors[END_REF] to 52% in South-Western France [START_REF] Mansuy | Seroprevalence in blood donors reveals widespread, multi-source exposure to hepatitis E virus, southern France[END_REF] . A similar heterogeneity has been observed in Europe regarding HEV-RNA prevalence ranging from 1/14 520 in Scotland to 1/658 in Netherlands 9 Hepatitis E has emerged as a transfusion-transmitted (TT) infection, with a first case of TT hepatitis E formally described in 2002 in Hokkaido, Japan [START_REF] Matsubayashi | Transfusion-transmitted hepatitis E caused by apparently indigenous hepatitis E virus strain in Hokkaido, Japan[END_REF] . Since the first TT-HEV infection reported in 2006 in the UK [START_REF] Boxall | Transfusion-transmitted hepatitis E in a 'nonhyperendemic' country[END_REF] , several cases have been documented in European countries including Germany [START_REF] Domanović | Hepatitis E and blood donation safety in selected European countries: a shift to screening?[END_REF][START_REF] Huzly | Transfusion-transmitted hepatitis E in Germany[END_REF] , Spain [START_REF] Riveiro-Barciela | Red blood cell transfusiontransmitted acute hepatitis E in an immunocompetent subject in Europe: a case report[END_REF] , and France (first case reported in 2006 [START_REF] Colson | Transfusion-associated hepatitis E, France[END_REF] ) raising the attention about this emerging concern for blood safety. To assess further the characteristics of TT hepatitis E and to identify factors associated with transmission and/or severity, we describe here the 23 TT HEV cases with high imputability reported in France between 2006 and 2016.

MATERIALS AND METHODS

Blood products in France

In France, collection, qualification, preparation and distribution of labile blood products for civilians are under the exclusive responsibility of a national transfusion public service: Etablissement Français du Sang (EFS).

During the study period (2006-2016), approximately three million labile blood products were transfused annually in France including 2.4 million of red blood cells concentrates (RBC), ~300 000 platelets concentrates including ~ 50 to 60% whole blood-derived pooled platelets concentrates (WBPC) and ~ 40 to 50% apheresis platelets concentrates (APC) and ~ 300 000 fresh frozen plasma units (FFP).

Plasma units underwent a > 60 day quarantine or pathogen reduction by means of amotosalen treatment and UVA illumination (Intercept™ -Cerus®), methylene blue treatment and visible light illumination (Theraflex™ -Macopharma®) (until 2011) or solvent-detergent (SD) -treatment after pooling (100 apheresis plasma donations / pool, produced by EFS until 2014).

Platelets concentrates, from apheresis or from whole blood (5 pooled buffy-coats) (50/50 ratio on average during the study period) were mostly prepared in additive suspension SSP + (Macopharma®) or Intersol (Fresenius®) (on average 63% additive solution and 37% plasma) while a small minority remained in plasma. A fraction (approximately 7%) of platelets concentrates underwent pathogen reduction (Intercept™).

Hepatitis E virus screening of blood donations

HEV nucleic acid testing (NAT) was first implemented in France for plasma donations intended for solvent-detergent (SD)-plasma production in December 2012. Implementation resulted from the documentation of the first SD-plasma associated hepatitis E cases (Table 1, #3 and 4). HEV NAT was performed in pools of 96 apheresis plasma donations using a RT-PCR assay (Real Star HEV RT-PCR kit 1.0 (Altona diagnostic, Hamburg, Germany with an estimated 95% limit of detection (LOD) of 23 IU/ml). After interruption of SD-plasma production by EFS by end of 2014, HEV screening was pursued for approximately 30% of individual plasma units (from apheresis or from whole blood) using the same assay but with pools of 6 plasma donations. Since 2015, plasma units screened for HEV are preferably oriented towards patients at risk for severe HEV infections: transplant recipients and other severely immunocompromised patients as well as patients with preexisting chronic liver disease.

Reporting and investigation of hepatitis E in blood products' recipients

As for all infections potentially resulting from a transfusion, cases of hepatitis E occurring in transfusion recipients underwent mandatory reporting to the French national haemovigilance database ("e-FIT"= web-based secured reporting system) managed by the French National Agency for Medicine and Health Products Safety (ANSM). Hepatitis E was considered chronic when the estimated HEV viremia was longer than 6 months. HEV NAT screening of all potentially involved blood donations were performed according to established procedures and relied on the availability of archived cryopreserved plasma samples (for 3 to 5 years) of all blood collections in France. When a blood donation was found to harbor HEV, recipients from all blood products stemming from the involved donation were screened for HEV. Furthermore, prior and subsequent blood donations occurring within 4 months of the index donation were screened as well. Transfusion imputability was defined as described below.

Laboratory investigations

Hepatitis E diagnosis and HEV RNA detection in transfusion recipients were mostly performed by the virology laboratories of the involved hospitals and were all further investigated by the National reference laboratories for hepatitis A and E (Virology laboratory, Purpan University Hospital, Toulouse and Virology laboratory, Paul Brousse University Hospital, Villejuif). All archived samples from suspected blood donations were tested by RT-PCR as previously described [START_REF] Abravanel | Genotype 3 diversity and quantification of hepatitis E virus RNA[END_REF] . When positive, viral titer measurement and genotyping were systematically performed [START_REF] Legrand-Abravanel | Hepatitis E virus genotype 3 diversity in France[END_REF] . Total HEV RNA dose infused per patient (with the exception of SD-plasma) was calculated based on donor viral titer x the estimated volume of plasma in the transfused blood product(s).

When TT HEV infection was suspected, sequencing and phylogenetic analysis of partial sequences of blood donor and recipient viral strains in ORF-1 (RNA dependent RNA polymerase) and/or ORF-2 genomic regions was performed as previously described [START_REF] Haïm-Boukobza | Transfusion-transmitted hepatitis E in a misleading context of autoimmunity and drug-induced toxicity[END_REF] .

Transfusion imputability was classified as very likely if a 100% sequence identity was observed on analyzed genomic fragments between donor and recipients' strains, as possible if genomic comparison was suggestive of strain identity but not conclusive or if comparison between donor and recipient viral strains was not possible. Imputability was deemed non-evaluable in case of incomplete testing of all involved blood donations, and excluded in the absence of positive blood donation or if, in the presence of a HEV positive blood donation, comparison of donor and recipient viral strains was concluded to be phylogenetically distinct.

RESULTS

Patients

In 2006, the first case of TT HEV was reported in France in a 8 year-old child treated for a kidney tumor (#1, Table 1) [START_REF] Colson | Transfusion-associated hepatitis E, France[END_REF] . Up to December 2016, 78 suspected cases of possible HEV-TT were reported to the French hemovigilance network.

In 22 of these 78 cases, TT HEV was excluded based on negative HEV NAT of all blood products transfused to the patients. Imputability was deemed non-evaluable in 4 cases. In 29 cases, absence of evaluable recipient viral genome or inconclusive phylogenetic analysis did not allow to firmly establish TT HEV infection.

A total of 23 cases of TT hepatitis with high imputability (i.e. 100% sequence identity between donor and recipient) were reported during the study period and are presented here (Table 1). Some of these cases have been previously reported, in part or as case report [START_REF] Colson | Transfusion-associated hepatitis E, France[END_REF][START_REF] Haïm-Boukobza | Transfusion-transmitted hepatitis E in a misleading context of autoimmunity and drug-induced toxicity[END_REF][START_REF] Coilly | Posttransplantation hepatitis E: transfusion-transmitted hepatitis rising from the ashes[END_REF][START_REF] Belliere | Transfusion-acquired hepatitis E infection misdiagnosed as severe critical illness polyneuromyopathy in a heart transplant patient[END_REF][START_REF] Loyrion | Hepatitis E Virus Infection after platelet Transfusion in an immunocompetent trauma patient[END_REF][START_REF] Hauser | Hepatitis E transmission by transfusion of Intercept blood system-treated plasma[END_REF][START_REF] Mallet | Transmission of Hepatitis E Virus by Plasma Exchange: A Case Report[END_REF][START_REF] Mallet | Transmission of Hepatitis E Virus With Plasma Exchange in Kidney Transplant Recipients: A Retrospective Cohort Study[END_REF] .

Involved patients included 17 men and 6 women with an age range of 8 to 88 years old (median age: 55) (Table 1). With the exception of 3, all patients were immunosuppressed (87%), including 13 (57% of total) transplant patients: kidney (n=5), liver (n=3), heart (n=1), allogeneic hematopoietic stem cells (n=4). All graft donors were tested negative for HEV RNA. HEV contamination in 4 out of 5 of the kidney transplant recipients occurred during plasma exchanges performed for the prevention or treatment of graft rejection. Ten patients were treated for malignant hematological diseases (including the 4 hematopoietic allograft recipients).

Circumstances of hepatitis E diagnosis

In 20 of the 23 cases, hepatitis E diagnosis resulted from diagnostic work-up in the patient because of abnormal liver tests, mainly elevated liver enzymes. For the last 3 cases (#9, 11, 14), hepatitis E diagnosis resulted from look-back investigations in recipients of blood products to which had contributed a donor identified as harboring HEV. The case #9 involved a FFP from an Intercepttreated plasma donation implicated in another TT hepatitis E (#7). In case #11, the blood product was a RBC from a donation involved in case #10 (whole blood platelet concentrate), Lastly, the involved blood product in case #14 was an SD plasma unit from a lot found to be HEV positive (no other TT hepatitis E infection associated with SD plasma units from the same lot were identified).

Clinical course

Fourteen patients, all immunosuppressed, experienced chronic hepatitis E (Table 1). Eight of these patients were organ transplant recipients. The remaining 6 patients with chronic hepatitis E had undergone allogeneic hematopoietic stem cell transplantation (n=3) or were receiving chemotherapy for malignant hemopathy (n=2) or immunosuppression for auto-immune disease (n=1). Notably, 2 organ transplant recipients with chronic hepatitis E, had advanced (Ishak stage 5) liver fibrosis at diagnosis (patients #2 and 4). Ten of these 14 patients were treated with ribavirin for 1.5 to 15 months (median: 5.5 months) resulting in hepatitis E resolution. However, several patients required prolonged and/or repeated courses to achieve HEV clearance (Table 1). One patient with advanced liver fibrosis (#2; Ishak liver fibrosis score stage 5), underwent repeat liver biopsy 40 months after sustained virological response to ribavirin treatment which revealed fibrosis regression (Ishak stage 1). The second patient with advanced liver fibrosis (#4) did not have a repeat biopsy and was lost to follow-up. Four patients with TT chronic hepatitis E cleared the HEV infection upon reduced immunosuppression without ribavirin treatment (#5, 12, 16 and 17).

One organ transplant recipient with acute hepatitis E associated co-morbidities did not clear HEV infection despite ribavirin treatment and died of multiple organ failure 5 months after transplantation (#21). Hepatitis E contributed probably to the fatal outcome. However, formal imputability was deemed not assessable.

The last 8 patients experienced acute hepatitis E among which resolved spontaneously (n=4), with ribavirin treatment (n=3) or with reduced immunosuppression (n=1). Five of these 8 patients were immunosuppressed (Table 1).

Involved products

Involved blood products included 7 FFP, 7 RBC, 3 APC and 6 WBPC (Table 1). Seven blood products had undergone pathogen reduction by means of SD treatment (4 plasma) or amotosalen / UVA treatment (2 plasma units from a single plasma donation and 1 WBPC).

Viral genotype and titer in infected donors, viral HEV RNA dose in involved blood products

An HEV-3 genotype was identified in all but one case (#17, genotype 4d), including 3f subtypes in 16, 3c in 4, and 3a in one case (Table 1). In all 23 cases, a 100% sequence identity was observed between recipient and blood product strains.

Viral titers in contaminated blood donations varied from 1.14 x 10 3 IU/ ml (3.06 logIU/ml) to 31 x 62.10 6 IU/mL (7.50 logIU/ml) with a median of 2.72 x 10 4 IU/ml (4.43 logIU/ml) (Table 1, Figure 1A). 10 Total HEV RNA dose per contaminated blood product(s) infused to each patient varied from 22.88 x 10 3 IU (4.36 logIU) to 12.65 x 10 9 IU (10.10 logIU) with a median of 6.16 x 10 5 lU (5.79 logIU) (Figure 1B).

Of note, the 4 patients infected with SD-plasma had received several plasma units from the same lot of contaminated SD plasma (Table 1). An additional patient received 2 plasma units from a contaminated apheresis plasma donation (#2).

Lastly, to approach a "worst-case scenario" regarding the minimal donor viral titer associated with TT hepatitis E, we calculated the theoretical donor viral titer had the implicated RBC or PC blood product (with plasma volume ranging from 20 to 173 ml) had been in fact a 200 ml plasma unit (Figure 1C). In such case, the (inferred) lowest "infectious" donor viral titer would have been of 1.14 x 10 2 IU/ml (2.06 logIU/ml).

DISCUSSION

With 23 reported cases of transfusion-transmitted infections of high imputability between 2006 and 2016 in France, including 14 with chronic hepatitis, HEV has emerged as a significant infectious transfusion risk. These cases highlight the clinical burden associated with TT-HEV, while most probably only representing a fraction of the current occurrences. Indeed, and despite recent progress, hepatitis E may be misdiagnosed. HEV infection is often asymptomatic in immunocompetent patients. Chronic hepatitis E may also remain clinically asymptomatic even in immunosuppressed patients. Lastly, fulfilling the criteria necessary to adequately assess transfusion imputability may be challenging.

Immunosuppressed individuals, notably transplant recipients, may develop chronic infection and fibrosis that can rapidly progress to cirrhosis, end-stage liver disease, liver graft loss [START_REF] Kamar | Factors associated with chronic hepatitis in patients with hepatitis E virus infection who have received solid organ transplants[END_REF] and hepatocellular carcinoma [START_REF] Borentain | Hepatocellular carcinoma complicating hepatitis E virus-related cirrhosis[END_REF] . Furthermore, HEV infection in patients with chronic liver disease are at risk of acute liver failure and death [START_REF] Kamar | Hepatitis E virus infection[END_REF][START_REF] Peron | Fulminant liver failure from acute autochthonous hepatitis E in France: description of seven patients with acute hepatitis E and encephalopathy[END_REF] . HEV infection can also result in extra-hepatic manifestations, notably neurological disorders [START_REF] Wedemeyer | The hepatitis E virus: a likely cause of extrahepatic diseases![END_REF] . Hepatitis E treatment, when required, comprises reduction of iatrogenic immunosuppression and, if not feasible or unsuccessful, ribavirin treatment [START_REF] Kamar | Ribavirin for chronic hepatitis E virus infection in transplant recipients[END_REF] . Ribavirin is reportedly associated with sustained virologic response in 85% of chronically infected organ transplant recipients 30 .

In France, autochthonous HEV infection is frequent with regional disparities [START_REF] Mansuy | A nationwide survey of hepatitis E viral infection in French blood donors[END_REF][START_REF] Van Cauteren | Estimated Annual Numbers of Foodborne Pathogen-Associated Illnesses, Hospitalizations, and Deaths, France, 2008-2013[END_REF] . A recent nation-wide study found a 22% anti-HEV IgG prevalence [START_REF] Mansuy | A nationwide survey of hepatitis E viral infection in French blood donors[END_REF] . Approximately 68 000 HEV infections occur annually in France, most often asymptomatic and self-limited [START_REF] Kamar | Ribavirin for chronic hepatitis E virus infection in transplant recipients[END_REF] . Over 2200 symptomatic hepatitis E were reported yearly in 2016 and 2017 [START_REF] Couturier | Surveillance de l'hépatite E en France, 2002-2016 // Hepatitis E surveillance in France, 2002-2016[END_REF] . HEV donor screening has revealed a frequency of HEV-RNA positive blood donations ranging from 1/2218 33 to 1/1000 [START_REF] Gallian | Comparison of hepatitis E virus nucleic acid test screening platforms and RNA prevalence in French blood donors[END_REF] (and unpublished data, EFS 2017-2018).

Similar frequencies have been observed in blood donors in England [START_REF] Hewitt | Hepatitis E virus in blood components: a prevalence and transmission study in southeast England[END_REF] and in the Netherlands [START_REF] Hogema | Incidence and duration of hepatitis E virus infection in Dutch blood donors[END_REF] .

Foreign visitors may be exposed as well to HEV, as exemplified by the first case of TT hepatitis E in Australia and involving a blood donor most probably infected in France [START_REF] Hoad | First confirmed case of transfusiontransmitted hepatitis E in Australia[END_REF] .

Chronic hepatitis E, all in immunosuppressed patients, was observed in a majority of our cases (n=14, 61%), with advanced liver fibrosis in 2 cases. Ribavirin administration allowed for viral clearance in close to all treated patients. However, the prolonged treatment needed in most patients, as well as the death of one patient in the setting of an ongoing hepatitis E despite ribavirin treatment, highlight current limitations regarding hepatitis E treatment in immunocompromised patients [START_REF] Hoad | First confirmed case of transfusiontransmitted hepatitis E in Australia[END_REF] . Notably, chronic hepatitis E resolved spontaneously in 3 recipients of allogeneic hematopoietic stem cell grafts, probably in relation with reduced immunosuppression later after transplantation.

Hepatitis E virus transmission and disease were observed with RBC and additive-solution containing platelet concentrates despite the limited amount of donor plasma in such products. Pooled whole blood platelet concentrates are associated with an increased probability of a contributing infected donor. The observation of 6 TT-HEV cases with WBPC versus 3 cases with APC during a time period where only 50% of transfused platelets were WBPC might suggest an increased risk associated with WBPC. On the other hand, the probability of an anti-HEV IgG positive donor contributing the pooled product increases as well. However, the protective role of anti-HEV antibodies on the transmission risk of an infected blood product is debated. Blood donations containing anti-HEV antibodies have been found to be less likely to transmit and, when they did, there was a trend for lower levels of antibodies [START_REF] Gallian | Hepatitis E virus infections in blood donors, France[END_REF] . On the other hand, HEV particles circulating in the blood are associated with host lipids and are protected from the humoral response [START_REF] Chapuy-Regaud | Characterization of the lipid envelope of exosome encapsulated HEV particles protected from the immune response[END_REF] .

Current pathogen inactivation technologies for blood products are unable to prevent TT-HEV as exemplified by the occurrence of hepatitis E after transfusion of SD-plasma in 4 patients as well as after transfusion of amotosalen +UVA -treated blood products in 3 other patients. Efficacy of SD treatment relies on the disruption of lipid-enveloped pathogens [START_REF] Solheim | Viral safety of solvent/detergent-treated plasma[END_REF] . The resistance of non-enveloped virus such as HEV to SD treatment is therefore expected and transmission may occur despite the dilution-mediated reduction of the viral titer. As mentioned earlier, HEV is a quasi-enveloped virus in the blood, but remains infectious after lipid removal [START_REF] Solheim | Viral safety of solvent/detergent-treated plasma[END_REF] . The mechanism of HEV resistance to Intercept technology is not well established and may involve sub-optimal amotosalen concentration inside the tight viral capsid of such a non-enveloped virus [START_REF] Hanson | Photochemical inactivation of viruses with psoralens: an overview[END_REF] .

Most of the cases (20 out of 23) were diagnosed based on clinical findings in infected patients, and not a diagnosis resulting from the screening of patients having received blood products from HEV RNA positive donors. Therefore, the frequency and clinical profile of the reported cases cannot be considered as representative of the actual TT hepatitis E risk. Patients with TT chronic hepatitis E are most certainly overrepresented, while TT acute self-limited hepatitis E in non-immunosuppressed recipients may remain undetected. On the other hand, such bottom-up approach allows for the assessment of the HEV RNA dose required for transmission and occurrence of clinical hepatitis E in the most susceptible patients without the potential bias introduced by the limited sensitivity of the blood donation screening tests, most often performed on pools of up to 96 [START_REF] Domanović | Hepatitis E and blood donation safety in selected European countries: a shift to screening?[END_REF] .

In a study performed in southeast England [START_REF] Gallian | Hepatitis E virus infections in blood donors, France[END_REF]41 , Tedder and colleagues reported that the HEV-infected donations associated with transmission exhibited a median viral titer of 4.53log IU/ml and a lowest titer leading to infection of 2.61log IU/ml, in line with our findings (median 4.43 log IU/ml and lowest titer at 3.06 log IU/ml). Similarly, the lowest HEV RNA dose in a blood product associated with transmission (1.9 x 10 4 IU, 4.3 log IU) in their series is comparable to the lowest HEV RNA dose observed in our cases (2.29 x 10 4 IU, 4.36 log IU). Of note, lower donor viral titers resulting in infection have be described, although rarely (see reference 42 for review). Indeed, an infected donor with a viral titer of 120 IU/ml (2.1 log IU/ml) resulting an APC containing an HEV RNA dose of 7056 IU (3.85 logIU) has been associated with an HEV transmission to an immunosuppressed patient who subsequently developed a chronic hepatitis E [START_REF] Huzly | Transfusion-transmitted hepatitis E in Germany[END_REF] . Nevertheless, if one considers the viral titer threshold associated with TT hepatitis E observed in our recipient cohort (3.06 log IU/ml) in comparison to the median HEV viral titer found in our blood donor screening program (2.84 log IU/ml, data not shown), one can extrapolate that approximately 50% for HEV contaminated blood donation may result in HEV infection in the recipient. Interestingly, a similar 50% infectivity of HEV contaminated blood components has been reported in England [START_REF] Gallian | Hepatitis E virus infections in blood donors, France[END_REF] and more recently in Japan [START_REF] Satake | Unique clinical courses of transfusiontransmitted hepatitis E in patients with immunosuppression[END_REF] .

Systematic or partial HEV screening has been introduced or is being actively considered in a number of countries, in particular in Europe and Asia, which exhibit higher HEV endemicity than in North or South America [START_REF] Domanović | Hepatitis E and blood donation safety in selected European countries: a shift to screening?[END_REF] . Notably, hepatitis E screening of a fraction (27%) of blood products (as well as all apheresis platelet donations) for specific high-risk patient groups was introduced in the United Kingdom in 2015. Logistic issues as well as difficulty to insure the transfusion of HEV-tested products to all high-risk patient resulted in the decision to implement screening of all blood products in 2017 44 .

Other European countries and regions such as Ireland, the Netherlands, Catalonia, and more recently Switzerland, have implemented testing of all blood products [START_REF] Domanović | Hepatitis E and blood donation safety in selected European countries: a shift to screening?[END_REF] . A recent cost-effectiveness study in the Netherlands on blood donation HEV screening reported a cost of 310 000 euros per prevented chronic case, a cost that appeared not excessively expensive compared to other blood-screening measures in the Netherlands [START_REF] De Vos | Cost-effectiveness of the screening of blood donations for hepatitis E virus in the Netherlands[END_REF] .

In France, a fraction of plasma products to be transfused to high-risk patients (transplant recipients as well as patients with chronic liver disease or severe immunodeficiency) is tested for hepatitis E RNA since late 2012. Interestingly, despite being the most infectious blood product [START_REF] Gallian | Hepatitis E virus infections in blood donors, France[END_REF] , the last reported case of TT-HEV involving a plasma occurred in 2013. This finding suggests that the partial plasma strategy implemented in France may have indeed reduced the risk of plasma-associated symptomatic hepatitis E. Increasing awareness of hepatitis E among physicians may also be contributing to more timely diagnosis and treatment when warranted.

An important issue when considering HEV testing is to determine the donor samples pooling size, if any. The observed donor viral titers combined with the sensitivity of current NAT screening tests (LOD 95% of 12 IU/ml) [START_REF] Couturier | Surveillance de l'hépatite E en France, 2002-2016 // Hepatitis E surveillance in France, 2002-2016[END_REF] suggest that a pool of 24 donations, as implemented in the UK [START_REF] Domanović | Hepatitis E and blood donation safety in selected European countries: a shift to screening?[END_REF] and the Netherlands (Zaaijer H, personal communication), would have detected all the contaminated blood donations associated with TT hepatitis E described in this cohort (fig 1A). However, in a worst-case scenario, infused HEV RNA dose provided in a 200 ml plasma), 3 out the 23 involved blood donations may have gone undetected with a pooling of 24 donations strategy while remaining detectable with a pooling of up to 12 donations (fig 1C). A recent simulation study in Germany reported that using a screening test with 95% LOD of 20 IU/ml and a pooling of 24 would reduce by 90.7 % (1488 to 139 / year) the expected TT HEV infections and by 92% (93 to 10 / year) the expected TT chronic HEV infections [START_REF] Kamp | Impact of hepatitis E virus testing on the safety of blood components in Germany -results of a simulation study[END_REF] .

Lastly, but importantly, efforts to control of the zoonosis and therefore limit the exposure to contaminated food or water is an important requirement that may supersede specific transfusion mitigation strategies. In England, it was found that the TT hepatitis E risk only exceeds the annual dietary risk when more than 13 individual donor components are transfused 41 . Furthermore, vaccination [START_REF] Zhang | Long-term efficacy of a hepatitis E vaccine[END_REF] as well as specific surveillance and management of high risk populations [START_REF] Kamar | Hepatitis E virus and chronic hepatitis in organ-transplant recipients[END_REF][START_REF] Bettinger | Chronic hepatitis E virus infection following allogeneic hematopoietic stem cell transplantation: an important differential diagnosis for graft versus host disease[END_REF][START_REF]European Association for the Study of the Liver. Clinical Practice Guidelines on hepatitis E virus infection[END_REF] could also contribute to reducing the overall HEV infectious risk. A recent study in France reported that among a cohort of 60 HEV-positive organ transplant recipients, 7 had been transfused within 6 months of diagnosis, and of those 3 had received HEV-positive blood products [START_REF] Lhomme | Hepatitis E Virus Infection in solid organ transplant recipients, France[END_REF] . On the other hand, while only one in 700 hepatitis E in the Netherlands has been estimated to be due to blood transfusion, a much higher ratio (one in 3.5) was reported when considering cases of chronic hepatitis E [START_REF] De Vos | Cost-effectiveness of the screening of blood donations for hepatitis E virus in the Netherlands[END_REF] .

Overall, hepatitis E virus has emerged in France as well as elsewhere in Europe as a significant transfusion risk. Such transfusion-transmitted hepatitis E infections, resistant to current pathogen reduction technologies, occur with red cells, platelets, and fresh-frozen plasma blood products and can result in chronic severe hepatitis E in immunosuppressed patient. Where HEV is endemic, enhanced hepatitis E clinical awareness, particularly in immunosuppressed patients, reduced environmental or dietary exposure as well as screening of blood donations will reduce the hepatitis E risk associated with transfusion.

Figure 1.

Measured HEV viral titers (HEV RNA IU/ml) in the blood donors (1A), calculated HEV RNA dose in the blood products (1B) and inferred HEV viral titer in the blood donors had the RBC or platelet recipient received a 200 ml plasma unit with the same HEV RNA dose than in the involved blood product (1C), red lines report the expected 95% LOD with current HEV screening tests (12 IU/ml) [START_REF] Couturier | Surveillance de l'hépatite E en France, 2002-2016 // Hepatitis E surveillance in France, 2002-2016[END_REF] depending on the pooling strategy. Inferred HEV viral titer in the blood donor had the recipient received a 200 ml plasma with the same viral dose than in the involved blood product 1 C

Acknowledgments

The authors express their thanks to the French hemovigilance network members for the careful reporting of these cases.

Author's contributions PG, RD and PT designed the study; all authors contributed data; PG, EP, RD and PT wrote the first draft; JI, AMRA, NK and VM further contributed to the writing of the manuscript; all authors reviewed and approved the final version of the manuscript.

Conflict of interest

PG, EP, RD, SG, PB, AA and PT are employed by the French transfusion public service (Etablissement Français du Sang) in charge of blood products manufacturing and issuing in France.