Pierre Aumond
email: pierre.aumond@ifsttar.fr

Arnaud Can

Guillermo Rey Gozalo

Nicolas Fortin

Enrique Suarez

Method for in situ acoustic calibration of smartphone-based sound measurement applications

Keywords: Environnemental sound levels, low-cost calibration, participatory measurements

Participatory measurement of environmental sound levels has gained interest in recent years. The calibration of measuring instruments is currently the main technical limitation. It is often the responsibility of the users and can be a potential source of error or add costs to the measurement protocol. In this article, a calibration protocol is proposed, based on the low variability of the average noise emission of individual vehicles. The advantage of this protocol for the user is that it does not require specific equipment, i.e. reference sound source or device, or special knowledge in acoustics. The method consists in measuring the noise level of a few vehicles as they pass through at different measuring points. The measured levels are compared to the levels expected by a numerical model, the difference serving as an offset for subsequent measurements. The robustness of the protocol is first tested over a large experimental campaign, and it turns out that measuring the passage of 15 vehicles at 3 different locations limits the error to -1.8 +/-1.0 dB(A). Then, the protocol is tested in real conditions with a set of 8 smartphones. The comparison with a class 1 sound level meter on 6 control points shows an average error on all phones of -0.6 +/-1.2 dB(A).

Introduction

The characterization of sound environments has been enriched in the last years by an unprecedented expansion and diversification of measurement methods. The emergence of low-cost sensors, and more recently the possibility of making measurements via smartphones, are changing the way in which sound environments are characterized. Participatory measurement gained interest as a measurement protocol in which each citizen can perform geo-localized measurements via his smartphone, sent to a server where postprocessing is performed [START_REF] Guillaume | Noise mapping based on participative measurements[END_REF]. The user thus becomes both a producer and a consumer of noise data: the data he communicates is used to calculate noise maps, and his smartphone sends him back information on his individual exposure.

Many smartphone applications have recently been developed to acquire acoustic data, such as Noise-Tube [START_REF] D'hondt | Participatory noise mapping works! An evaluation of participatory sensing as an alternative to standard techniques for environmental monitoring[END_REF], [START_REF] Maisonneuve | NoiseTube: Measuring and mapping noise pollution with mobile phones[END_REF], Ambiciti [START_REF] Ventura | Estimation of urban noise with the assimilation of observations crowdsensed by the mobile application Ambiciti[END_REF] and NoiseCapture [START_REF] Picaut | An open-science crowdsourcing approach for producing community noise maps using smartphones[END_REF], Hush city [START_REF]From Crowdsourced Data to Open Source Planning: the Implementatio[END_REF], Niosh SLM [START_REF]The potential use of a NIOSH sound level meter smart device appli[END_REF], etc. Thanks to the measurements resulting from these applications, noise maps have been proposed, either by aggregating the measurements produced [START_REF] D'hondt | Participatory noise mapping works! An evaluation of participatory sensing as an alternative to standard techniques for environmental monitoring[END_REF], [START_REF] Murphy | Smartphone-based noise mapping: Integrating sound level meter app data into the strategic noise mapping process[END_REF] or by correcting modelled noise maps through data assimilation algorithms [START_REF] Ventura | Assimilation of mobile phone measurements for noise mapping of a neighborhood[END_REF].

Researches have investigated in parallel the ability of smartphones to measure environmental noise. Kardous & Shaw have shown that while many applications provide erroneous results, some meet the criteria for environmental measurement [START_REF] Kardous | Evaluation of smartphone sound measurement applications[END_REF], [START_REF] Kardous | Evaluation of smartphone sound measurement applications (apps) using external microphones-A follow-up study[END_REF]. Ventura et al. [START_REF] Ventura | Evaluation and calibration of mobile phones for noise monitoring application[END_REF] observed on a selection of mobile phones from the market that responses are linear for levels in the 45 to 75 dB(A) range. Aumond et al. [START_REF] Aumond | A study of the accuracy of mobile technology for measuring urban noise pollution in large scale participatory sensing campaigns[END_REF] showed that used in similar conditions the instantaneous sound levels measured with mobile phones correlate very well (r > 0.9, p < 0.05) with sound levels measured with a class 1 reference sound level meter with a root mean square error smaller than 3 dB(A).

The calibration phase therefore remains the main technical lock of the measurement protocol, as it can be costly and time-consuming, although essential. A cross calibration procedure has been proposed in Can et al. [START_REF] Can | Cross-calibration of participatory sensor networks for environmental noise mapping[END_REF], in which measurements made by a smartphone are compared with those made by the fleet of devices, in order to identify and filter operator/device pairs giving imprecise measurements, and to propose corrections for precise but biased measurements. The method requires nevertheless that a high density of measurements has already been collected on the server. In Picaut et al. [START_REF] Picaut | Calibrage Acoustique de Smartphones pour la Mesure du Bruit dans l'Environnement[END_REF], an individual calibration procedure is developed: the principle is based on the use of a reference smartphone, previously calibrated, communicating automatically with other smartphones that one wishes to calibrate, by means of an acoustic communication protocol.

Although valuable, these two methods do not respond to the specific case of scattered individual participatory measures, carried out individually. To fill this gap, some suggested using everyday objects (using a box full of coins or tearing a sheet of paper) [16], [START_REF] Ziegler | Bedienungsanleitung. Spaichinger. Schallanalysator. Version 2.2. Freeware[END_REF]. The standard deviations announced for these protocols may seem acceptable, on the order of 2 dB, but no document has yet been published rigorously presenting the repeatability of the experiment. Another source of noise in our daily lives that is well known and modelled because it has been studied for many years is road traffic. Thus, this article proposes a low-cost in-situ calibration based on traffic measurements that any user can follow without a previously calibrated reference (a sound level meter or another smartphone). The method is based on the hypothesis, which is tested in the article, that although individual vehicle sound power levels can be highly variable, their average varies little as long as: (i) traffic conditions are controlled (constant speed, conventional pavement), (ii) measurements are performed near the sound source and thus propagation effects are limited. The method therefore consists in measuring continuously sound levels on the side of several roads, letting each time several vehicles pass by. The measured sound levels are compared to the levels expected by the CNOSSOS-EU model, the difference being an estimate of the offset to apply for subsequent measurements which is consistent with the results of Schreurs et al. who showed that the median values of traffic noise maxima measured in situ on Brisbane roads are comparable to those predicted by the European IMAGINE/HARMONOISE models [START_REF] Schreurs | Maximum pass-by noise levels from vehicles in real road traffic streams: comparison to modeled levels and measurement protocol issues[END_REF].

The method is tested in this article on an experimental campaign of 83 usable sound measurements (out of 302) of 15 minutes in the city of Talca, Chile.

The calibration protocol and the experimental campaign is first detailed in Section 2. Section 3 presents the different uncertainty items associated with this protocol. Section 3.1 presents the difference between the sound power level estimated by a numerical model and its estimated value from measurements and using the calibration protocol. Section 3.2 presents the uncertainty related to the input parameters of the protocol left to the user. Section 3.3 shows the analysis of the standard deviation of the measured estimates depending on the number of pass-by and location to consider keeping this uncertainty item below a targeted value. Section 3.4 presents an estimate of the overall uncertainty associated with this method. Section 4 presents the protocol assessment under real conditions. Discussion is presented in Section 5.

Method

Calibration protocol

Results from the literature show that the individual emissions of vehicles measured at a site are highly variable [START_REF] Brown | Distribution of the noise level maxima from the pass-by of vehicles in urban road traffic streams[END_REF]. However, the proposed protocol relies on the hypothesis that, although the sound power levels of individual vehicles may vary widely, the average sound power levels of vehicles passing by a receiver during a measurement period vary little as long as :

• traffic conditions are controlled (constant speed, mainstream asphalt concrete or concrete pavement),

• measurements are made close to the sound source and the effects of propagation are therefore limited. Thus, it becomes possible to compare the measurements made at the roadside with the expected results, provided by a numerical model. If the number of performed measurements is sufficient, the measurements will converge in theory towards the results provided by the model. In this article, the CNOSSOS-EU model is used, for light vehicles and assuming the reference road pavement [20].

The calibration protocol consists of four steps described below, and illustrated in Fig. 1.

• Step 1: The user performs continuous measurements of roadside sound levels, with a 1-second time resolution (LFmax,1s) and a height of 1.5-m. The user is asked: (i) to wait the passage of a sufficient number of vehicles, (ii) to inform on the distance to the center of the road as well as the estimated mean speed of the vehicles, and (iii) to repeat the procedure at several locations. These numbers of vehicles and locations will be determined in section 3.3. • Step 2: An algorithm detects the noise peaks in the signal that correspond to the individual pass-by of vehicles. The difficulty here stands in designing a noise-peak selection algorithm that captures vehicle pass-byes, knowing that the absolute noise levels measured might be biased. The algorithm used for this article is findpeaks from the R package pracma v.1.9.9. It allows to detect peaks in the measured time series LFmax,1s. A set of criteria is set up to keep only the peaks related to the passage of cars. Firstly, only peaks at least 3 seconds apart are retained, to avoid, for example, the measurement of a very close passage of two cars (probably from two different traffic lanes). Secondly, the sound level during the two seconds preceding the peak must be increasing and decreasing during the two seconds following it. Finally, only peaks that correspond to a sound level higher than L95 + 15 dB are retained because it is considered that for the protocol to be valid, the signal-to-noise ratio must be greater than this value. Fig. 1b gives several examples of the peaks detection algorithm. • Step 3: The sound power level LW that corresponds to each peak detected at the Step 2 is calculated based on the measured Lmax and the following equation:

LW=Lmax+20 log(r)+10log2π (1)
where r is the distance between the source and the microphone. It accounts for the divergence of the sound energy from the source (center of the roadway) to the receiver (microphone), as described in previous studies [START_REF] Brown | Distribution of the noise level maxima from the pass-by of vehicles in urban road traffic streams[END_REF], [START_REF] Hamet | New vehicle noise emission for French traffic noise prediction[END_REF].

• Step 4: The bias between the median of the LW values and the given by the numerical model for light vehicles and reference conditions is calculated for each of the n sampled site. Then, the average of the n calculated bias is calculated. This value corresponds to the offset that will be stored. The interest of taking the median in there is that it statistically removes heavy or abnormally noisy vehicles, in order to enable the comparison with the numerical model for light vehicles. The advantage of calculating the offset in two steps, calculating an average of the bias calculated for each site, is that it avoids giving too much weight to a site if it has been measured during the passage of more vehicles than the other locations.

(a) Step 1 -Perform measurement with app or soundmeter (b) Step 2 -Detect peak levels (c) Step 3 -Calculate the sound power level from the peak level and measurement conditions (d) Step 4 -Get the median sound power level Lw

Fig. 1 The four steps of the calibration protocol. Note that the user is only concerned by Step 1, the three following ones consisting on post-treatment that need to be implemented on the sensor. The calibration protocol as presented in this paper has been implemented in the NoiseCapture mobile application [START_REF] Picaut | An open-science crowdsourcing approach for producing community noise maps using smartphones[END_REF]. Figure 2 shows the user interface for this step within this application. First the user chooses the calibration mode (Figure 2

Experimental campaign

A total of 302 sound measurements of 15 minutes each were carried out in different locations in the city of Talca (Chile) between 2015 and 2016 [START_REF] Gozalo | Analysis of Sampling Methodologies for Noise Pollution Assessment and the Impact on the Population[END_REF]. Measurements were carried out on working days. A type-I sound level meter (2250 Brüel & Kjaer) was used with tripod and windshield and it was placed at a height of 1.5 m in free-field conditions following the ISO 1996-2 guidelines [START_REF] Barrigón Morillas | A review of the measurement procedure of the ISO 1996 standard. Relationship with the European Noise Directive[END_REF], [START_REF]Acoustics --Description, measurement and assessment of environmental noise --Part 2: Determination of environmental noise level)[END_REF]. The sound-level meter was located 1 m from the curb. Calibration of the equipment was checked at the beginning and end of each measurement using a 4231 Brüel & Kjaer calibrator. The noise parameters Leq and LFmax for each 1/3 octave band were recorded at 1-second intervals. Flow rates and the average vehicle speed over the 15 minutes were registered together with the sound measurements. Relevant urban features (street dimensions, road surface type, state of the road surface) were also noted. Road traffic was the main source of noise during the sound measurements. When other sound sources or anomalous noise events (e.g., horns and sirens) were detected, the measurements were deleted. In addition, the sampling points that encountered the following conditions were selected:

• measured roads with no or very slight slope;

• measured flow free and with constant speed. The selected average speed were between 57 and 63 km/h (The urban speed limit in Chile was 60 km/h); • measured roads with standard pavement. Pavements in very poor condition or with specific acoustic performance (as cobbled roads) were discarded; • measured roads with one or two lanes.

As a result of this filtering process, a total of 83 sound measurements of 15-min taken at different sampling points were used for the analysis.

Results

Bias on the estimation of the sound power level between a numerical model and measurements

Fig. 2 presents the estimated sound power levels at each of the 83 locations, using the protocol presented in section 3 over the 15-min measurements (boxplots). The results are reported by octave band and for global values in dB and dBA, and are compared to the sound power levels estimated with the CNOSSOS model, cars driving at 60 km/h on reference pavement (red curve).

Although the measurements were carried out in Chile, on a different vehicle fleet from the one on which the relationships of the CNOSSOS emission model were built, the average sound power values estimated based on measurements are close to the ones estimated with the CNOSSOS model. The average bias in the global sound power levels is low (0.5 dBA or -1.8 dB), and the interquartile range is moderately low (2.1 dBA or 1.4 dB). Concerning frequency bands, the average bias and its interquartile ranges are lowest for 500 Hz and 1000 Hz octave bands. Sound power levels in the octave bands from 125 Hz to 250 Hz are slightly overestimated, whereas sound power levels in the octave bands from 2kHz to 8kHz are underestimated. This might be due to differences in the vehicle car fleet or road pavement between Chile and the reference conditions used in CNOSSOS but also to the close proximity of the sound source to the measuring point (r < 5m). For example, effects related to the vertical directivity of the source for some frequency bands may not be negligible. In addition, the interquartile range is higher for the octave bands 63 Hz, 4 kHz and 8 kHz. This suggests that the calibration protocol is more robust for global and mid-frequency values. However, since the response of the microphones embedded within smartphones is flat between 250 and 4000 kHz [START_REF] Heutschi | Schallpegelmess-Apps[END_REF], and in view of the results obtained, we suggest applying the protocol at the global level (in dB or dBA), applying the same equivalent offset for each octave band. Finally, the estimated global sound power levels show a relatively low interquartile range between the measured locations (2.1 dBA or 1.4 dB). Paragraph 3.3 shows that offering the user to reproduce the measurements at different locations and using the resulting average noise level can further reduce this range.

Uncertainty related to the input parameters of the numerical model

The calibration protocol proposed relies on the comparison between an estimated value of the mean sound power level relying on measurements and its numerical estimation. In this study, the numerical model is composed of CNOSSOS-EU emission part linked to a simple law for acoustic propagation (Eq. 1). For the application of the protocol, three input parameters have to be estimated by the user, distance from the road, height of the measuring point and average vehicle speed. User estimation of these parameters leads to uncertainty in the estimation of the calibration offset. A study of the sensitivity of the model to these parameters is presented in this section.

The height h and the distance to the road d are reference values mentioned in the protocol, which are recommended to be set at h = 1.5 m and d = 1m. A distance to the roadside d = 1m corresponds to a distance dtotal = d + 1.5m = 2.5m suggesting that the width of the road is 3m. Eq.2 allows to set the source-receiver distance r in the Eq. 1.

= + (ℎ -0.05) (2) with 0.05 m corresponding to the source height as suggested in the CNOSSOS model. The user can change these input values if needed. These values can also shift around these references of a few tens of centimeters due to a wrong appreciation of the user, what can lead to uncertainty in the estimate.

The vehicles mean speed can vary within the range of validity of the CNOSSOS emission model, namely from 20 to 130 km/h and must be estimated by the user, who can alternately use the limit speed on the road segment. The literature reports that the estimation of vehicle speeds by pedestrians is subject to error. Strauss et al. show that the standard deviation in the percentage of error on the task of estimating the vehicle speed is about 20% [START_REF] Strauss | The Accuracy of Pedestrians in Estimating the Speed of a Moving Vehicle[END_REF].

This section evaluates the influence of errors in the estimation of these parameters, based on Eq. 1 and the emission model used in the reference conditions. Fig. 4 shows the sensitivity to each input parameter. Errors in the height of the measurement point have a low impact on the numerical estimation of the mean sound power level, which stands below 0.5 dB(A) in the tested range (Fig. 3.a). Errors in the distance to the road are slightly more influential, and can exceed 1 dB(A) (Fig. 3.b); however it can be assumed that this parameter is estimated with little error by the user. Errors in the estimated vehicle speed are more influential. For a reference speed of 50 km/h, an underestimation (resp. overestimation) of 10 km/h of the estimated vehicle speed leads to an underestimation of 2.3 dB(A) (resp. overestimation of 2 dB(A)) of LW. The estimated offset would be biased accordingly. Fig. 4. Influence of the height, distance to the road, and vehicle mean speed on the numerical estimation of the average sound power level estimated at a site. The offset determined following the calibration protocol would be biased accordingly. In addition, the protocol leaves the responsibility to the user to sample locations with classical road pavements, the errors on which might also affect the LW value calculated following the CNOSSOS emission model and thus the calibration offset. The user could possibly be asked to choose a corresponding pavement from the database available for his country [START_REF] Shilton | Conversion of existing road source data to use CNOSSOS-EU[END_REF].

Influence of the number of locations and vehicles per site on the variability of the average LW estimates

To estimate the average sound power level from measurements with the lowest possible uncertainty, it is necessary to measure a representative number of vehicles, ideally at different locations, in order to obtain a reliable convergence towards the actual average sound power levels of the vehicles fleet. Under an individual calibration protocol context, the proposed method must also be short enough not to discourage the user. Therefore, a sensitivity analysis is conducted to determine the influence of the number of considered locations and vehicles per site on the estimated variability. For each couple "number of locations / number of pass-by per site", a loop of 100 repetitions in which passages are taken randomly within the complete dataset is achieved, on which statistics are calculated. For instance, if the number of locations is set at 7 and the number of vehicle passages is set at 15, for each of the 100 repetitions 7 locations and 15 vehicle passages are randomly selected for each location. Thus, 100 average sound emission levels values are estimated according to the procedure presented in Section 2.1. In rare cases, the method may deviate widely from the standard due to sensitivity to parameters related to peak detection. As these cases are rare, they may be considered outliers. In this case, values that deviate from the mean by two or more times the standard deviation are eliminated from the statistical analysis. A user implementing the protocol would probably also detect these outliers by observing an exaggerated calibration in one measurement relative to the others (> 15 dB). In this case, he would eliminate it himself. Fig. 6 shows the standard deviation of the estimated average sound power level over the 100 repetitions as a function of the number of locations and the number of passages per site. The results suggest that it is possible to limit the standard deviation under a low value with an acceptable number of vehicle passage and number of locations. For instance, 3 locations and 15 vehicles per location result in a standard deviation of 1.0 dB(A).

A multiple linear regression is calculated and proposed in Table 1 to estimate the standard deviation of the estimated average sound power level from these two variables. The explained variance is 81%. On this basis, reverse modeling can allow the user to choose the best compromise to achieve a targeted uncertainty. For example, using Table 1, for a target uncertainty of less than 1.25 dB, the user can measure 15 vehicle passages at 3 different locations.

Table 1. Estimation the standard deviation from the logarithm of the considered number of pass-by and locations.

Estimation of the overall uncertainty related to the calibration protocol

Three principal items of uncertainty linked to the proposed protocol are:

• The uncertainty um linked to the bias of using the emission and propagation model under different conditions than the reference ones, evaluated within the section 3.1. This bias was less than 2 dB(A) in this case study, although it presents the unfavorable case of using the European model CNOSSOS on a Chilean experiment. This bias should be relatively small if local models are used; • The uncertainty up related to the input parameters of the calibration protocol that the user must specify.

In view of the results presented in section 3.2, this uncertainty should be in most cases smaller than 2.5 dB; • The uncertainty us related to the sampling strategy, that is the number of locations and pass-byes considered by the user when applying the protocol, evaluated in section 3.3. Applying the protocol with 3 locations and 15 pass-byes per location seems an acceptable compromise, which limits the uncertainty to less than 1.25 dB. Assuming that all these uncertainties are Gaussian, the overall uncertainty U can be obtained according to Equation 2:

" = #( $ % ) + &$ ' ( + ($ ) ) (2)
The overall uncertainty associated to this calibration protocol is therefore smaller than (2) + (2.5) + (1.25) = 3.4 dB. Moreover, considering that um and up are fixed to 2 and 2.5 dB respectively alleviates the sampling strategy. Indeed, applying the protocol in a constraining case with 10 locations and 20 pass-byes will yield an uncertainty us of 0.5 dB and consequently to an overall uncertainty slightly reduced to 3.2 dB. On the other extreme, the protocol is sensitive to a degradation of the sampling strategy: an unfavorable case with 2 locations and 5 pass-byes would yield an uncertainty us of 2.2 dB and consequently to an overall uncertainty of 3.8 dB.

Protocol assessment under real conditions

4.1. Method 8 smartphones were selected to evaluate the calibration protocol in real conditions. 8 participants, owners of the phones, used the NoiseCapture application in a Beta version in which the protocol was implemented. All phones were different (brand and model) and use the android operating system. For the calibration procedure, participants went to 3 locations and waited while 15 vehicles passed by (automatically detected in real time by the app) following the instruction given by the app. As shown in Figure 1, after filling in the measuring distance and evaluating the speed of passage of the vehicles, a calibration value was provided to them.

In a second step, 6 measurements of 3 minutes each were taken. These measurements concern 6 urban sound environments (park, boulevard, quiet street, busy street, courtyard and ring road). These sound environments were measured simultaneously between a Class 1 sound level meter and the 8 phones. The quality of the protocol is assessed on the basis of this set of measures, by applying or not the determined offset.

The L50 indicator is used for the comparison because it is less sensitive than the Leq to measurement conditions as short-term noise close to a specific smartphone.

Results

Figure 7 shows the sound levels measured by the sound level meter and phones before and after applying the calibration protocol.

For seven of the eight phones, the average error decreases from 4.2 dB(A) before calibration to -0.6 dB(A) after calibration. Analysis of the results also shows that there is a large average standard deviation between the phones prior to calibration 4.8 dB(A) which is strongly reduced after calibration 1.2 dB(A). The eighth phone is considered an outlier and is discarded from the analysis. A saturation of the phone for the highest sound levels may have induce this unexpected behavior. However, the calibration protocol still reduces its bias from 10.4 dB(A) to 4.2 dB(A).

The standard deviation of the bias between sound level meters and smartphones averages 0.7 dB(A). This corresponds to the part of the total standard deviation due to the measurement conditions (e.g. directivity, distance between the sound level meter and the phones, etc.). The resulting standard deviation, only due to the proposed calibration protocol is about 1 dB(A).

Figure 4 Sound levels measured by the sound level meter and phones before and after applying the calibration protocol.

Discussion

In this article, an in situ and low-cost calibration protocol is proposed, based on roadside measurements of vehicle passages at different locations. The measured levels are compared to the levels expected by a numerical model, the difference serving as an offset for subsequent measurements.

The uncertainties linked to the protocol are evaluated in section 3. It appears that the two main uncertainty items are the speed evaluated by the user and the fact of using the model under conditions different from those of reference, i.e. 2 dB and 2.5 dB respectively.

As concerning the use of the model under different conditions than the reference one, the consequence is a bias in the estimation, which is supposed to be constant under similar conditions: for instance a bias of +0.5 dB in the case of this Chilean experiment. Thus, the consequence would be that all the smartphones calibrated on this study area would have an excessive offset of 0.5 dB, which can be evaluated and corrected if fixed sensors are deployed in parallel.

Beyond the listed uncertainties, the method is dependent on the proposed algorithm for finding peaks. If it is too loose, then events that cannot correspond to vehicle pass byes will be selected, hence adding a bias to the method. The proposed algorithm to find peaks algorithm relies on the threshold L95+15 that was shown to remove the low levels peaks. Such a threshold remains however inefficient in the case of high background noise. Therefore, the user is asked to only sample relatively quiet roads.

The robustness of the model should be tested on a wider number of locations, taken in a large variety of countries. Here, the use of the European CNOSSOS model on a data set recorded in Chile leaded to a small and acceptable bias. The optimal approach would be to use locally recommended noise emission models for each territory, where they exist, or to adapt a model according to the vehicle fleet, the type of surfacing most commonly used, etc. A review on the existing noise emission models can be found for instance in [START_REF] Can | Estimation of road traffic noise emissions: The influence of speed and acceleration[END_REF].

Finally, Section 3.4 shows that the overall uncertainty associated with this calibration protocol remains lower than 3.2 dB. This uncertainty may seem important but these results have to be contrasted:

• The range of sound levels in environmental acoustics can vary from 40 to 95 dB(A). The error is therefore quite low front of the total variability. • The biases of smartphones without prior calibration can be very important. Mallet, 2017 shows that numbers of phone models bias range between -20 dB(A) and 7 dB(A) [START_REF] Ventura | Evaluation and calibration of mobile phones for noise monitoring application[END_REF]; • The measurement uncertainty of a properly calibrated smartphone can reach 2 to 3 dB [START_REF] Aumond | A study of the accuracy of mobile technology for measuring urban noise pollution in large scale participatory sensing campaigns[END_REF], [START_REF] Zamora | Accurate Ambient Noise Assessment Using Smartphones[END_REF].

• The short experiment carried out under in situ conditions shows that this error is probably overestimated. In addition the proposed protocol has the advantage of being simple and can be applied by any user without the need to have a calibrated reference sensor nearby. It is therefore particularly suitable for participatory measures of the noise environment. In view of the above considerations, this protocol seems to offer a valid and simple alternative to the calibration of phones.

Finally, the height of 1.50 m chosen in the experiment to hold the measuring tool is a little high (at eye level because it is more suitable for the sound level meter). For an application of the protocol with mobile phones, the height can be reduced to chest height without changing the protocol.

Conclusion

This article proposes a calibration protocol based on a noise level measurement of a few vehicles as they pass through different measuring points. The measured levels are compared with the levels expected by a numerical model, with the difference serving as an offset for subsequent measurements. The robustness of the protocol is tested over a large experimental campaign and it turns out that measuring the passage of 15 vehicles at 3 different locations limits the error to -1.8 +/-1.0 dB(A). Then, the same protocol was applied in real conditions using 8 different phones. 6 control points were used to compare measurements made by a Class 1 sound level meter and the phones. The resulting error after the application of the calibration protocol is -0.6 dB(A) +-1.2 dB(A).

The present protocol proposes a calibration that does not require special knowledge of the user and a limited time (a few minutes per measuring point). The participatory measurement of environmental noise levels has gained interest in recent years and the main technical limitation is currently the calibration of measuring instruments. Although the uncertainty associated with the protocol is not negligible, it could allow a significant increase in the quality of the measurements collected.

Figure 2

 2 Figure 2 User interface of the calibration protocol in the NoiseCapture mobile application. (a) Selection of the calibration record. (b) Adding a set of pass-by measurement. (c) While the measurements are in progress, the user can inform the distance to the road centerline, the estimated speed of the vehicle flow and see how many pass-by were validated.

Fig. 1 .

 1 Fig.1. Estimated sound power levels calculated for each of the 83 selected 15-min measurements using the protocol (boxplots) compared to the estimated sound power levels using the CNOSSOS model (red line)

Fig. 3 Fig. 2

 32 Fig. 3 Standard deviation of the estimated average sound power level calculated over the 100 repetitions as a function of the number of locations and the number of passages per locations.

Acknowledgements

This research was funded by the Chilean National Commission for Scientific and Technological Research (CONICYT) through the project FONDECYT No. 1180547. This work was also supported by IFSTTAR (France), a Juan de la Cierva-Incorporación contract from the Spanish Ministry of Economy, Industry and Competitiveness (IJCI-2016-28923), and by the Consejería de Economía, Ciencia y Agenda Digital of Junta de Extremadura, through grants for attracting and returning research talent to R&D&I centres belonging to the Extremadura Science, Technology and Innovation System (TA18019), where University of Extremadura was the beneficiary entity.


Léna Jaravel

Baptiste Schindler

Jérôme Randon

Isabelle Compagnon

Claire Demesmay

Vincent Dugas
email: vincent.dugas@univ-lyon1.fr

Off-line coupling of capillary isotachophoresis separation to IRMPD spectroscopy for glycosaminoglycans analysis: Application to the chondroitin sulfate disaccharides model solutes

Keywords: Isotachophoresis, glycosaminoglycans, capillary electrophoresis, off-line coupling, microscale separation

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

Introduction

A general trend in analytical chemistry concerns the development of original approaches opening new perspectives in the context of complex molecules analysis and/or characterization. Such analyses are driven, for example, by the pressing demand of biologists investigating biomolecules and bio-molecular interactions as well as pharmacists qualifying new biologic drugs that enter the pharma market, to mention just a few. Dealing with complex samples, oligosaccharides analysis (also referred to as carbohydrates or glycans) are very exciting due to their inherent structural complexity and importance in biological and physiological processes through the regulation of their various protein partners. [START_REF] Gandhi | The Structure of Glycosaminoglycans and their Interactions with Proteins[END_REF][START_REF] Varki | Biological roles of oligosaccharides: all of the theories are correct[END_REF] Glycosaminoglycans (GAGs) are of particular importance in this context. Their presence on the cell membranes and in the extracellular matrix plays a critical role in the intercellular communication in organisms [START_REF] Townley | Deciphering functional glycosaminoglycan motifs in development[END_REF][START_REF] Richter | Glycosaminoglycans in extracellular matrix organisation: are concepts from soft matter physics key to understanding the formation of perineuronal nets?[END_REF]. The GAG family, composed of 5 members: hyaluronan (HA), heparan sulfate (HS; including the highly sulfated form heparin), chondroitin sulfate (CS), dermatan sulfate (DS) and keratin sulfate (KS), are linear, highly charged, polysaccharides, composed of repeating disaccharides units. Understanding the structure of GAGs via building blocks composition, blocks connectivity, stereochemical configuration and sulfation patterns, represents a major analytical challenge. [START_REF] Compagnon | Lasers and ion mobility: new additions to the glycosaminoglycanomics toolkit[END_REF] Standard approaches for GAGs characterization, rely on depolymerization reactions (chemical or enzymatic reactions) followed by fractionation for their further structural identification by sensitive mass spectrometry and nuclear magnetic resonance experiments [START_REF] Jones | Heparin Characterization: Challenges and Solutions[END_REF][START_REF] Sisu | Modern developments in mass spectrometry of chondroitin and dermatan sulfate glycosaminoglycans[END_REF].

Such bottom-up approaches benefit from optimal fractionation methods. Chromatographic methods such as size-exclusion chromatography (SEC) and anion exchange chromatography (AEX) as well as capillary electrophoresis are widely used and even coupled to one another for the separation of heterogeneous mixtures before their characterization. Each have their pros and cons and there is still great way of improvement in the sample preparation [START_REF] Jones | Heparin Characterization: Challenges and Solutions[END_REF]. Underivatized oligosaccharides are hydrophilic species that can be separated in ion exchange (IEX), ion-pair chromatography (IPC) and HILIC modes. With respect to mass spectrometric detection, HILIC is usually preferred as this chromatographic mode, compared to IEX and IPC, does not suffer from ionization suppression effects due to mobile phase components. But this separation mode has not always been effective in resolving isomeric oligosaccharides [START_REF] Jones | Heparin Characterization: Challenges and Solutions[END_REF][START_REF] Gill | Disaccharide Analysis of Glycosaminoglycans Using Hydrophilic Interaction Chromatography and Mass Spectrometry[END_REF]. With regard to sample volume, standard chromatographic autosamplers require often few tens to hundreds microliters of sample volume even when low volume of sample (i.e. 1 µL) is injected. Capillary electrophoresis is an alternative and powerful separation technique for and ionized compounds. Its separation mechanism depends on the ratio of the ionic charge to the hydrodynamic radius that determines the effective mobility of ionic species in a given background electrolyte. The high peak efficiencies in CE lead to very narrow peaks (a few seconds) requiring high sampling rate detection systems. Isotachophoresis (ITP) is a powerful but often overlooked electrokinetic separation technique because its unique plateau-like signal signature may be disturbing for chromatographers accustomed to work with peak signals. This technique is amenable to work with small volume of crude samples [START_REF] Eldridge | Separation and Analysis of Trace Degradants in a Pharmaceutical Formulation Using On-Line Capillary Isotachophoresis-NMR[END_REF] (no additional sample preparation needed before analysis) and produces pure solute bands (without impurities like salts or organic modifiers that impede subsequent MS characterization). Capillary ITP was successfully applied to the preconcentration and microseparation of heparin and heparan sulfate samples on-line coupled to NMR detection via a 1-mm microcoil probes wrapped around the fused silica capillary [START_REF] Korir | Separation and Analysis of Nanomole Quantities of Heparin Oligosaccharides Using On-Line Capillary Isotachophoresis Coupled with NMR Detection[END_REF][START_REF] Korir | On-line NMR detection of microgram quantities of heparin-derived oligosaccharides and their structure elucidation by microcoil NMR[END_REF]. The plateau-like signals of ITP was well-adapted to such specific microcoil NMR probes and the relatively long acquisition time required for NMR (1.4 s). IRMPD spectroscopy benefits from the sensitivity of Mass Spectrometry and the structural resolution of IR spectroscopy: it is a powerful tool to deduce detailed structural information on the monosaccharide content and stereochemistry of the glycosidic linkages [START_REF] Korir | Separation and Analysis of Nanomole Quantities of Heparin Oligosaccharides Using On-Line Capillary Isotachophoresis Coupled with NMR Detection[END_REF][START_REF] Korir | On-line NMR detection of microgram quantities of heparin-derived oligosaccharides and their structure elucidation by microcoil NMR[END_REF][START_REF] Mucha | In-depth structural analysis of glycans in the gas phase[END_REF]. However, the acquisition time required to obtain an IR spectrum using this technique is several minutes, which is very long compared to standard in-line detectors of separation methods. Therefore, the online coupling of IRMPD with peak signal analytical separation methods is a tremendous challenge. [START_REF] Schindler | Online Separation and Identification of Isomers Using Infrared Multiple Photon Dissociation Ion Spectroscopy Coupled to Liquid Chromatography: Application to the Analysis of Disaccharides Regio-Isomers and Monosaccharide Anomers[END_REF] In this context, we propose capillary ITP as microseparation technique prior to IRMPD characterization. Capillary ITP was investigated due to its plateau-like signals that allow more time for relatively long acquisition time for the detection system or for the collection of pure bands of solutes for off-line coupling with sophisticated characterization methods at the microscale. The proof of feasibility was demonstrated with the separation of two chondroitin sulfate disaccharide isomers, namely CSA (∆U(1-3)GalNAc,4S) and CSC (∆U(1-3)GalNAc,6S). Chondroitin Sulfates like other GAGs display substantial diversity within tissues and molecular diversity across different tissues and over developmental time [START_REF] Townley | Deciphering functional glycosaminoglycan motifs in development[END_REF] and are involved in controlling neurogenesis, neuronal plasticity and regeneration after injury to the central nervous system for example. In addition, it was demonstrated that the specificity of interaction of CS with proteins depends on the level and distribution of sulfation [START_REF] Richter | Glycosaminoglycans in extracellular matrix organisation: are concepts from soft matter physics key to understanding the formation of perineuronal nets?[END_REF]. The ITP conditions were optimized for the separation of these two CS disaccharides isomers. Collected fractions were then characterized by CZE to determine the purity yield. Finally, the collected fractions were analyzed by IRMPD by nanospray infusion, demonstrating the effectiveness of such coupling at the microscale.

Materials and methods

Reagents and chemicals

Methanol (HPLC grade), triethylamine (TEA), dipotassium hydrogen phosphate (K2HPO4), Ophosphoric acid, sodium hydroxide (NaOH), acetic acid, hydroxypropylcellulose (HPC, Mw ~100,000), hydrochloric acid, glycine, formic acid were purchased from Sigma Aldrich (France). Chondroitin sulfate disaccharide standards ∆U(1-3)GalNAc,4S (CSA) and ∆U(1-3)GalNAc,6S (CSC) were purchased from Iduron (ALderley Edge, UK). Stock solutions of each chondroitin sulfate disaccharides (CSA and CSC) were prepared in ultra-pure water at 100 mM. Acetic acid (AcOH) was purchased from Riedel-de Haënn (Riedel-de Haënn, Seelze, Germany). All aqueous solutions were prepared using >18 MΩ-cm water (Millipore, Molsheim, France). Polyimide (TSP)-coated fused-silica capillaries were purchased from Polymicro Technologies (Polymicro Technologies, Arizona, USA).

HPC Coating protocols

Capillary liquid manipulations were controlled by pressure using the microfluidic flow controller MFCS-100 system (Fluigent, France). This system is useful to fill, wash and dry capillaries.

The HPC coating procedure follows the protocol proposed by Koval et al [START_REF] Koval | Analysis of glycated hemoglobin A1c by capillary electrophoresis and capillary isoelectric focusing[END_REF]. Briefly, a 5 % (w/v) HPC aqueous solution was flushed through 1 m of polyimide coated fused silica capillary (75 µm ID and 375 OD µm) at 5 bars pressure. The capillary was placed in a gas chromatography oven (HP 5890 series II GC system) heated at 60°C, and flushed with nitrogen at 1 bar pressure, for 10 minutes. The temperature was increased up to 140°C with a 5°C/min ramp and kept at 140°C for 20 min.

ITP experiments

ITP experiments were carried out on an Agilent HP3DCE instrument (Agilent Technologies, Waldbronn, Germany) equipped with a diode array detector (detection at 230 nm).

Separations were done in a 35-cm long HPC coated fused silica capillary (75-µm id / 365 µm od).

Otherwise stated in the text, ITP experiments were performed using a pH 3.2 leading electrolyte composed of HCl 2.5 mM, glycine 10mM and a pH 3.5 terminating electrolyte composed of formic acid 2.5 mM and glycine 10 mM. Mixtures of CSA and CSC at 1 mM each were prepared in the terminal electrolyte. Injections were done under hydrodynamic pressure mode.

CZE experiments

Capillary zone electrophoresis experiments were carried out on an Agilent 7100 CE instrument (Agilent Technologies, Waldbronn, Germany) equipped with a diode array detector. Separations were done in a 35-cm length HPC coated fused silica capillary (75-µm id / 365 µm od). Prior to each injection, the capillary was equilibrated by flushing with the background electrolyte (formic acid 50 mM and glycine 100 mM pH 3.24) for 30 sec. Samples were injected under hydrodynamic mode by applying a 5 mbar pressure for 1s. Separation was done at -20 kV.

Mass Spectrometry and IRMPD spectroscopy

Off-line coupling experiments were realized by injecting 22 nl of the 1 mM chondroitin sulfate disaccharides mixture (corresponding to 44 pmol of each chondroitin sulfate disaccharides). Bands collection of CSA and CSC after ITP separation were done in 10 µL H2O and diluted in 10µL MeOH. MS analysis was carried out in a commercial mass spectrometer (Thermofinnigan LTQ XL) equipped with a nanospray ionization source set in negative mode (production of deprotonated ions) and a linear ion trap analyzer. Tandem MS experiments are performed via collision induced dissociation (CID) in a helium collision gas. A similar setup used for IRMPD analysis was previously described by Schindler et al. [START_REF] Schindler | FAIMS-MS-IR spectroscopy workflow: a multidimensional platform for the analysis of molecular isoforms[END_REF]. In brief, the mass spectrometer is modified to allow irradiation of the trapped ions by a laser beam produced by a YAG-pumped tunable IR OPO/OPA. This enables in situ gas-phase IRMPD (InfraRed Multiple Photon Dissociation) spectroscopy of the trapped ions. Excitation of a vibrational mode by the laser induces photofragmentation, which is detected by mass spectrometry. The photofragmentation yield is calculated using the formula:

IRMPD yield = -log[Iparent ÷ (Iparent + Ifragment)],
where Iparent is the intensity of the parent ion and Ifragment is the total intensity of the fragments detected after laser excitation. The fragmentation yield is then monitored as a function of the wavenumber to retrieve the absorption spectrum of the analyte. The spectral range 2700-3700 cm -1 was scanned at 1 cm -1 /s, which correspond to an acquisition time of 17 minutes. In these conditions mass-selected ions were isolated and irradiated for 500 ms per photofragmentation MS spectrum and MS spectra were not averaged. The reference IRMPD spectra of pure CSC and CSA standards were measured on a different setup, as described in [START_REF] Schindler | IRMPD Spectroscopy Sheds New (Infrared) Light on the Sulfate Pattern of Carbohydrates[END_REF].

Results

Determination of the optimal ITP separation conditions -Choice of the electrolytes (pH and nature)

As for other differential migration methods, the condition for ITP separation is that the ionized solutes in the sample will separate whenever their migration rates in the mixed state are different.

Figure 1 The two chondroitin sulfate disaccharides will better separate if the pH in the mixing zone increases the difference in their respective effective mobility. The electrophoretic mobility of the two chondroitin sulfate disaccharides was thus first investigated in Capillary Zone Electrophoresis in acidic conditions due to the weak acidic properties of the CS disaccharides (carboxylic acid and sulfate groups) [START_REF] Larsson | The binding of inorganic and organic cations and H+ to cartilage in vitro[END_REF]. Figure 1 presents the relative electrophoretic mobility difference between the two CS disaccharides at a pH ranging from 2.18 to 4.06. The largest relative electrophoretic mobility difference was observed between pH 3.21 to 3.45. This pH range was thus considered optimal for the ITP separation and was fixed for the preparation of the leading electrolyte and terminal electrolyte.

A high effective mobility constituent such as chloride (strong ionic species commonly used in ITP) must be used as leader. The pH of the leading electrolyte was adjusted using glycine to pH 3.2. To cope with the objective to form long "plateau-like" bands of pure solute, low chloride leading electrolyte concentrations were investigated (ranging from 1 to 10 mM). The lowest concentration that can be used is however limited by the minimal current that can be carried by the electrolyte and controlled accurately (down to 1µA) by the CE instrument. The best compromise for chloride concentration versus applied current was 2.5 mM.

Considering the mobility of the chondroitin sulfate disaccharides in this pH range, formate anions were used as constituent of the terminating electrolyte. Formic acid (pKa 3.75, formate electrophoretic mobility 5.66 10 -4 cm² V -1 s -1 ) [START_REF] Beckers | Steady-state models in electrophoresis: From isotachophoresis to capillary zone electrophoresis[END_REF] has an effective mobility below pH 3.5 lower than 2.18 10 -4 cm² V -1 s -1 , which is a lower mobility value than the mobility of CS disaccharides.

The terminating electrolyte was thus prepared by adjusting the pH of the formic acid solution with glycine as counter buffering and counter constituent.

-Sample zone optimization ITP separation of CS disaccharides injected individually. In first approach, each chondroitin was injected separately (Figure 2A, blue and orange plot) after conditioning the capillary with the leading electrolyte (HCl 2.5 mM, Glycine (10 mM) pH 3.5). ITP separation was initiated by applying a constant current between the inlet side of the capillary immersed in the terminating electrolyte and its outlet sideimmersed in the leading electrolyte. While contactless conductivity was also tested, the difference in conductivity of the two CS disaccharide zones is quite low. Hence it is preferred to monitor the separation using UV detection. A plateau was detected at the optimal wavelength of 230 nm for the two CS disaccharides. The two CS disaccharides present a relatively different absorbance that is useful to attribute the signal obtained to the chondroitin passing through the detector. The two CS disaccharides were then injected successively (CSC was injected in first position). Two plateaus were obtained and the UV signal for each chondroitin (Fig2A, purple plot) was comparable to the signal observed for the individual ITP separated CS disaccharides. UV detection is useful for monitoring electrokinetic separation using commercial instrument as the UV detection is done incapillary, the two CS disaccharides are detected at 230 nm and can be differentiated. Figure 2 ITP separation of CS disaccharides in mixture. A mix of the two CS disaccharides solubilized in water was injected according to the previous experimental conditions. Only one plateau was observed (Figure 2B, grey plot). The two CS disaccharides were not separated in these experimental conditions.

The pH of the sample zone is also an important experimental parameter for the separation of ionic species in ITP [START_REF] Mikkers | Isotachophoresis: The concepts of resolution, load capacity and separation efficiency I. Theory[END_REF]. In order to trigger the separation at the early stage of the ITP separation, the pH of the sample zone was adjusted to 3.

Fractionation/collection of the ITP separated CS disaccharides -Optimization of the injection volume

For in-line or off-line coupling, preparation of long band lengths of pure solute would be obviously highly valuable. This is ever true at the miniaturized scale where collecting fraction is not easy. In order to maximize the band width, a 1-m long HPC coated capillary (75 µm ID) was used. Indeed, the length of capillary needed to separate efficiently the two CS disaccharides increases with the quantity injected. A 1-m column is the longest capillary compatible with our instrumental set-up. This is due to the low leading electrolyte concentration used. Indeed, it is difficult to maintain constant current during the separation using longer capillary. Figure 3 The sample volume was increased linearly by adjusting the injection pressure during the injection step (Figure 3). Up to 30 mbar, the separation led to the formation of two plateaus. At 40 mbar, two plateaus were still detected but the boundary between the two plateaus seemed less pronounced and at higher injection pressure conditions (50 mbar) 3 plateaus were observed. This intermediate plateau corresponds to a mixing zone of CS A and CS S between the two pure bands in formation. For the following experiments, the injection conditions were set to 30 mbar for 10 sec. The linear velocity of mobile phase at 30 mbar was estimated to be 0.05 cm/sec using leading electrolyte. This corresponds to an injection plug length of 5 mm. After separation, the band lengths (plateau) were about 12 mm (this was estimated by measuring the duration of plateaus under pressure after their formation by ITP).

In the context of miniaturized separation methods, the optimization of the ITP separation conditions led to increase the injection volume up to about 22 nL and the formation of individual bands of solutes of about 12 mm highlighting the potential interest of such methods for sample preparation at the miniaturized scale.

-Fractionation of the two chondroitins and analysis of each fraction by CZE

Under the optimized separation conditions, the two CS disaccharides were well-separated and formed two plateaus of more than 1 cm length. Fractionation of the sample after separation was conducted by collection of a fraction of each plateau in 10 µL of water. From the experimental point of view, the ITP separation was stopped before complete elution of the CS disaccharides from the capillary. The outlet vial was then replaced by a vial containing a low volume conical insert filled with 10 µL of water. The fraction of pure solute selected was eluted under hydrodynamic flow mode (50 mbar). The two fractions were recovered using two different ITP separations. The hydrodynamic flow is convenient to accurately fractionate a part of the band, however it was supposed that during this step, all interfaces (CSC/CSA, CSA or CSC/electrolyte) undergo band diffusion. This is expected to mix the two separated chondroitins and thus affect their purity. Purity of each collected fraction was assessed by capillary zone electrophoresis (Figure 4 B). Regarding the electrophoregram obtained using a chondroitins standard solution, two pairs of chondroitin peaks were detected. Each pair was attributed to chondroitin sulfate anomer forms. The first peak pattern corresponds to the CSC chondroitin sulfate and the second one to the CSA. Each collected fraction presents a major chondroitin sulfate isomer. Fraction 1 correspond to the CSC isoform with a purity of 89.6% and fraction 2 corresponds to the CSA isoform with a purity of 94.6 %. The isolation of oligosaccharide components for structural investigations must have a purity greater that 80% to 90 %. Moreover, organic impurities or salts that can impede the characterization must also be minimized [START_REF] Jones | Heparin Characterization: Challenges and Solutions[END_REF]. These are generally observed in HPLC separations for example. ITP seems well-adapted for the separation and purification of CS samples.

IRMPD analysis

Each collected fraction was characterized by IRMPD spectroscopy. The two disaccharides (CSA and CSC) derived from chondroitin sulfate are challenging to characterize as they possess two neighboring sites of sulfation on the N-acetyl galactosamine that is O6 and O4. Infrared multiple photon dissociation spectroscopy (IRMPD) was recently applied to the characterization of isomeric glycosaminoglycan disaccharides from heparins and chondroitin sources and has shown great potential as a novel structural tool for carbohydrates [START_REF] Schindler | IRMPD Spectroscopy Sheds New (Infrared) Light on the Sulfate Pattern of Carbohydrates[END_REF]. However, this powerful technique requires relatively long time of acquisition, which is not convenient for coupling with most separation methods [START_REF] Schindler | Online Separation and Identification of Isomers Using Infrared Multiple Photon Dissociation Ion Spectroscopy Coupled to Liquid Chromatography: Application to the Analysis of Disaccharides Regio-Isomers and Monosaccharide Anomers[END_REF][START_REF] Renois-Predelus | Analysis of Sulfate Patterns in Glycosaminoglycan Oligosaccharides by MSn Coupled to Infrared Ion Spectroscopy: the Case of GalNAc4S and GalNAc6S[END_REF] Figure 5 The MS/MS spectra of CSA and CSC are shown in the top layer of Figure 5. The singly deprotonated precursor ions are observed at m/z 458. CSA features a main fragment at m/z 300, which corresponds to the sulfated GalNAc moiety and is generally considered as its diagnostic fragment ion; and CSC features a main fragment at m/z 282, which is generally considered as its diagnostic fragment ion. [START_REF] Renois-Predelus | Analysis of Sulfate Patterns in Glycosaminoglycan Oligosaccharides by MSn Coupled to Infrared Ion Spectroscopy: the Case of GalNAc4S and GalNAc6S[END_REF]. [START_REF] Desaire | Detection and quantification of the sulfated disaccharides in chondroitin sulfate by electrospray tandem mass spectrometry[END_REF][START_REF] Zhang | Detection and Quantification of Sulfated Disaccharides from Keratan Sulfate and Chondroitin/Dermatan Sulfate during Chick Corneal Development by ESI-MS/MS[END_REF]. Several authors reported that the fragmentation patterns are highly dependent on the experimental conditions and MS/MS analysis must be carried out with extreme caution. Yet, LC coupled to MS and/or tandem MS analysis is the most widespread approach for the analysis of glycosaminoglycan composition [START_REF] Staples | Analysis of Glycosaminoglycans Using Mass Spectrometry[END_REF].

The use of the IR vibrational spectrum of molecular ions(sensitive towards structural details) enables the investigation of functional groups, intra-or intermolecular interactions and molecular conformations bringing additional information compared to the MS/MS analysis. The IR spectrum presents intrinsic features, which do not depend on the MS conditions, that benefits to the saccharides analysis by recording IR fingerprints of the CS disaccharides for unambiguous characterization. The IRMPD spectra of the two collected fractions show an apex at 3410 cm -1 and 3375 cm -1 attributed to the CSA and CSC disaccharides respectively, which is in good agreement with reference spectra of CS disaccharides standards [START_REF] Schindler | IRMPD Spectroscopy Sheds New (Infrared) Light on the Sulfate Pattern of Carbohydrates[END_REF]. In the specific case of CSA and CSC, the MS/MS fingerprint is sufficient to distinguish between the isomers, and the differences in the IRMPD signatures are relatively subtle. However, IRMPD has proven useful to distinguish between a variety of isomers when tandem MS is ambiguous. [START_REF] Schindler | Anomeric memory of the glycosidic bond upon fragmentation and its consequences for carbohydrate sequencing[END_REF] Therefore, the presented example supports that the fractionation of tens of nL of isobaric saccharides mixtures using ITP can be applied to the off-line collection of high purity compounds for further IR spectroscopic characterization. This demonstrates the pertinence of such coupling strategy at the microscale separation.

Conclusion

Electrokinetic separation of isomeric chondroitin sulfate disaccharides (CSA and CSC) at the microscale was achieved by isotachophoresis and fractions of high purity were collected for further structural analysis. ITP generates plateau-like bands of purified compounds which is particularly advantageous for collecting sample at the microscale or for coupling with long acquisition time detection methods. In contrast with ion exchange chromatography, ITP does not require the use of high salt concentration that can interfere severely with Mass Spectrometry. Such proof of feasibility aims at bringing an original coupling strategy with sophisticated spectroscopic characterization method of oligosaccharides. The molecular characterization of such complex molecules requires combined approaches of separation and characterization. Optimization of the ITP conditions allows the separation of two isobaric CS disaccharides from few tens of nL sample solution and their collection in 10 µL. The purity of the collected fraction, more than 90 %, is well-adapted to off-line coupling with gas phase IR action spectroscopy. This case study validates this proof of feasibility study using two similar CS disaccharides GAGs precursor. In order to fully respond to biological demand, the in-line coupling ITP-IRMPD spectroscopy would be further investigated allowing miniaturized and automated analysis of complex samples.

2 - 3 . 5 .

 235 The CS were diluted in the terminating electrolyte to adjust the pH. The buffering of the sample zone separation allowed to successfully separate the CS disaccharides (Fig 2B, dark plot). The ITP separation leads to the formation of two plateaus of increasing UV absorbance signals. The more mobile CSC solute is detected first and CSA second.

Figure Captions:Figure 1 .

 1 Figure Captions:

Figure 2 .

 2 Figure 2. Isotachophoregrams of each individual chondroitin (CSA and CSC at 1 mM) injected separately and injected in stacked configuration (CSC + CSA) (A) and ITP separation of CS disaccharides in mixture in water or in the terminating electrolyte (B). Conditions for (A): HPC-coated capillary length: 50 cm, Injection 50 mbar for 3 sec. Applied Current = -4 µA and in mixture (CSA and CSC at 1 mM each in water (grey) and solubilized in the terminating electrolyte (black)). Conditions for (B) : ITP of CS disaccharides solubilized in water was realized in a 45.9 cm-length HPC-coated capillary, injection 5 mbar for 8 sec. Applied Current = -5µA. ITP of CS disaccharides solubilized in terminating electrolyte was realized in a 58.7 cm length HPC-coated capillary, injection 20 mbar for 4sec). Applied Current = -5µA.

Figure 3 .

 3 Figure 3. Influence of the injection pressure of CS disaccharides mixture at 1 mM solubilized in the terminating electrolyte, separation at -3 µA, HPC-coated fused silica capillary (75 µm id, total length 1m), UV detection at 230 nm, injection time (10 sec), corresponding injection volumes 7.8 to 39 nL.

Figure 4 .

 4 Figure 4. ITP separation of chondroitin sulfate disaccharides (CSA and CSC) in a 1-m long HPC-coated capillary and fractionation of the separated solutes. Injection of chondroitin sulfate disaccharides at 1 mM each in terminating electrolyte, separation under -3 µA, detection at 230 nm, injection 30 mbar/ 10 sec (A). CZE separation of the two collected fractions and of the mixture of the two chondroitin sulfate disaccharides. HPC Fused silica capillary (75 µm id, 30-cm total length, 21.5 cm-length to detection), UV detection at 230 nm, BGE: Formate 50 mM, glycine 10 mM, pH 3.5, injection CS standard mixture (20 µM in terminating electrolyte) (-10kV, 5 sec) injection of the CS fraction (-10kV, 10 sec), response time 0.063 sec (40 Hz).

Figure 5 .

 5 Figure 5. IRMPD analysis of fraction 2 (A) and fraction 1 (B). Top panels: CID spectra of singly deprotonated disaccharides (m/z 458). Center panels: IRMPD signatures of the m/z 458 ions. Lower panels: reference IRMPD spectra of pure CSA (A) and CSC (B) disaccharide standards. Strips corresponding to the maximum of absorption of CSA (light red) and CSC (light blue) standards are overlaid for facile structural identification.