METHOD FOR CHARACTERIZING AN ANALYTE PRESENT IN A GAS SAMPLE CONTAINING AT LEAST ONE PARASITIC CHEMICAL SPECIES

Abstract

A method for characterizing an analyte A present in a gas sample using an electronic nose including M sensitive site, a parasitic chemical species P being present in the gas sample, the method include: a phase 100 of acquiring N first signatures, where N>1, of the gas samples containing the analyte A and the parasitic species P, the gas samples exhibiting deviations ?c.sub.P(n) which differ from one gas sample to the next; a phase 200 of solving an optimization problem so as to obtain N corrected signatures, characterising the analyte A present in the N gas samples, from the N first signatures, by optimizing to objective functions.

Claims

1. A method for characterising an analyte A present in a gas sample located in contact with a measuring surface of an electronic nose, the measuring surface including M sensitive sites distinct from one another, of rank m ranging from 1 to M, having receptors configured to interact by adsorption/desorption with the analyte A and with at least one parasitic chemical species P present in the gas sample, the method including the following steps: fluid injection, for contacting with the measuring surface: during a first phase Ph1, a carrier gas which may contain the parasitic species P with a concentration c.sub.P(n)i; then during a second phase Ph2, the gas sample containing the analyte A in a concentration c.sub.A(n) and the parasitic species P in a concentration c.sub.P(n)f, the value c.sub.P(n)f having a non-zero deviation ?c.sub.P(n) from the value c.sub.P(n)i; determining a measurement signal S.sub.(n,m)(t) during the fluid injection step representative of interactions of the analyte A and of the parasitic species P with receptors, for each of the sensitive sites M; determining first signatures Su.sub.(n,m)f from the measurement signal S.sub.(n,m)(t?Ph2) associated with the gas sample, which has been corrected by a reference value S.sub.(n,m)i from the measurement signal S.sub.(n,m)(t?Ph1) associated with the carrier gas; determining a difference ?c.sub.P(n) in concentration of the parasitic species P between the first phase Ph1 and the second phase Ph2; reiterating the preceding steps, by incrementing the rank n until N first signatures representative of interactions of the analyte A and the parasitic species P with the receptors are obtained, with N>1, the gas samples being such that the differences ?c.sub.P(n) are different in pairs; forming a matrix of first signatures Su.sub.A,P, of dimensions N?M, formed from N first signatures Su.sub.(n,m)f determined for the M sensitive sites; and of a relative concentration vector ?c.sub.P, of dimension N?1, from determined N differences ?c.sub.P(n); determining an estimated solution {{circumflex over (k)}.sub.P|A; ?.sub.A; {circumflex over (k)}.sub.A}; the product of which ?.sub.A{circumflex over (k)}.sub.A.sup.T forms a matrix of corrected signatures Suc.sub.A characterising the analyte A present in the N gas samples, the estimated solution: minimising the cost function f=Su.sub.A,P??c.sub.Pk.sub.P|A?c.sub.Ak.sub.A.sup.T, and maximising the cost function g=Su.sub.A,P??c.sub.Pk.sub.P|A.sup.T: where c.sub.A, k.sub.A and k.sub.P|A are variables defined as follows: c.sub.A is a concentration vector of analyte A, of dimension N, formed from N concentration values c.sub.A(n) of the gas samples, k.sub.A is an affinity vector of the analyte A, of dimension M, formed from the M values of an interaction affinity of the analyte A with the receptors of the sensitive sites, K.sub.P|A is is an affinity vector of the parasitic chemical species P, of dimension M, formed from the M values of an interaction affinity of the parasitic chemical species P with the receptors of the sensitive sites in the presence of the analyte A.

2. The charterisation method according to claim 1, wherein the step of determining the estimated solution carries out the minimisation of the objective function f and the maximisation of the objective function g at the same time, the values of the relative concentration vector ?c.sub.P all being positive.

3. The charterisation method according to claim 2, wherein the step of determining the estimated solution is performed by an iterative algorithm, of iteration indicator i.

4. The charterisation method according to claim 3, wherein the step of determining the estimated solution includes a substep of determining the value {circumflex over (k)}.sub.P|A.sup.(i+1) of the variable k.sub.P|A, given the value ?.sub.A.sup.(i) of the variable c.sub.A and of the value {circumflex over (k)}.sub.A.sup.(i) of the variable k.sub.A, by a fixed point method.

5. The charterisation method according to claim 4, wherein the step of determining the estimated solution includes a substep of determining the value ?.sub.A.sup.(i+1) of the variable c.sub.A and of the value {circumflex over (k)}.sub.A.sup.(i+1) of the variable k.sub.A, given the value {circumflex over (k)}.sub.P|A.sup.(i+1) of the variable kps having been determined by a singular value decomposition of a matrix R=Su.sub.A,P??c.sub.Pk.sub.P|A.sup.T(i+1).

6. The charterisation method according to claim 1, wherein the step of determining the estimated solution firstly includes a substep of minimising the objective function f to obtain a plurality of Q local solutions minimising the objective function f, with Q>1, followed by a substep of maximising the objective function g.

7. The charterisation method according to claim 6, wherein the minimising substep provides Q local solutions each formed by estimates {circumflex over (k)}.sub.P|A.sup.T(q), ?.sub.A.sup.(q), {circumflex over (k)}.sub.A.sup.T(q), of rank q ranging from 1 to Q, of variables k.sub.P|A, c.sub.A, and k.sub.A.

8. The charterisation method according to claim 7, wherein the maximising substep includes determining corrected signature Q matrices Suc.sub.A.sup.(q) such that: ?q?[1, Q], Suc.sub.A.sup.(q)=Su.sub.A,P??c.sub.P{circumflex over (k)}.sub.P|A.sup.T(q).

9. The charterisation method according to claim 8, including, following the determination of the corrected signature Q matrices Suc.sub.A.sup.(q), normalising each of the corrected signature matricesd Suc.sub.A.sup.(q) to obtain normalised corrected signature Q matrices Sucn.sub.A.sup.(q).

10. The charterisation method according to claim 9, including, following the determination of the corrected and normalised signature Q matrices Sucn.sub.A.sup.(q), determining variance score, for each of the Q matrices Sucn.sub.A.sup.(q), the variance score being defined as the trace of the covariance matrix for each of the Q matrices Sucn.sub.A.sup.(q), the matrix Sucn.sub.A.sup.(qf) having a minimal score characterising the analyte A present in the N gas samples.

11. The charterisation method according to claim 8, including, following the determination of the corrected signature Q matrices Suc.sub.A.sup.(q), determining a norm of each of the Q matrices Suc.sub.A.sup.(q), followed by an identification of the matrix Suc.sub.A.sup.(qf) having the maximum norm.

12. The charterisation method according to claim 11, wherein the matrix Suc.sub.A.sup.(qf) is normalised, thus providing a matrix Sucn.sub.A.sup.(qf) on characterising the analyte A present in the N gas samples.

13. The charterisation method according to claim 1, wherein the electronic nose includes a device for measuring interactions by adsorption/desorption of the surface plasmon resonance optical type or of the Mach-Zehnder interferometry type.

14. The charterisation method according to claim 1, wherein the electronic nose includes a device for measuring interactions by adsorption/desorption of the resistive, piezoelectric, mechanical or acoustic type.

15. The charterisation method according to claim 1, wherein the electronic nose includes a fluid supply device configured to perform the fluid injection step, a measurement device configured to perform the step of determining the measurement signal, a sensor for measuring and determining the relative concentration of the parasitic chemical species, and a processing unit configured for implementing the step of determining the estimated solution.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Other aspects, aims, advantages and features of the invention will become more apparent

[0047] upon reading the following detailed description of preferred embodiments, given by way of non-limiting example, and made with reference to the appended drawings wherein:

[0048] FIG. 1A, already described, is a schematic and partial view, in cross-section, of an SPR imaging electronic nose according to an example of the prior art;

[0049] FIG. 1B, already described, is a plan view, schematic and partial, of a functionalised measuring surface of the electronic nose of FIG. 1A including M distinct sensitive sites;

[0050] FIG. 1C, already described, is an example of sensorgrams Su.sub.m(t) obtained by the electronic nose of FIG. 1A, these sensorgrams corresponding here to the temporal evolution of the variation of the reflectivity ?%R.sub.m(t) associated with the sensitive sites;

[0051] FIG. 2 is a schematic and partial view of an electronic nose according to one embodiment;

[0052] FIG. 3A is an example of three signatures obtained by a characterisation method according to the prior art, showing the degradation of the characterisation of the analyte due to a variation in concentration of a parasitic chemical species (here water in the vapour phase) between the initial phase Ph1 and the characterisation phase Ph2;

[0053] FIG. 3B illustrated the temporal evolution of the concentration cp of a parasitic chemical species, of the measurement signal S.sub.m(t) and of the useful signal Su.sub.m(t), in the event where there is no variation in the concentration cp between the initial phase Ph1 and the characterisation phase Ph2 (left-hand part), and in the event that there is a non-zero variation in the concentration cp between the initial phase Ph1 and the characterisation phase Ph2 (right-hand part);

[0054] FIG. 4 is a flowchart of a characterisation method according to a first embodiment;

[0055] FIG. 5 is a flowchart of a characterisation method according to a variant of the first embodiment;

[0056] FIG. 6 is a flowchart of a characterisation method according to a second embodiment;

[0057] FIG. 7A illustrates examples of uncorrected signatures of analyte present in gas samples for which there has been a variation in concentration cp of the parasitic chemical species;

[0058] FIG. 7B illustrates the analyte signatures of FIG. 7A corrected by the characterisation method according to the second embodiment.

DETAILED DESCRIPTION OF PARTICULAR EMBODIMENTS

[0059] In the figures and in the remainder of the description, the same reference signs represent identical or similar elements. In addition, the various elements are not shown to scale in such a way as to give preference to the clarity of the figures. Moreover, the various embodiments and variants are not exclusive of one another and may be combined together. Unless otherwise specified, the terms substantially, approximately, in the order of mean to the nearest 10%, preferably to the nearest 5%. Moreover, the terms between . . . and . . . and equivalent mean that the ranges are included, unless otherwise specified.

[0060] The invention relates to the characterisation of analyte A present in a gas sample to be analysed. The characterisation is performed by means of an analysis system referred to as an electronic nose, which comprises: a measuring device; a fluid feed device; a concentration sensor for the parasitic chemical species P; and a processing unit.

[0061] As detailed below, the measuring device includes a functionalised measuring surface

[0062] defining a plurality of sensitive sites, each sensitive site including receptors capable of interacting with the analyte by adsorption/desorption, and here with at least one chemical species P, different from the analyte, and therefore qualified as a parasite insofar as it induces a measurement noise on the signature of the analyte A.

[0063] By way of illustration, the electronic nose uses optical measurement technology using

[0064] silicon interferometric technology (MZI) or surface plasmon resonance (SFR). The measuring device therefore comprises an optical source and at least one optical detector which can be an image sensor or a matrix of photodetectors. The intensity of the measurement signal detected depends on the value of the local refractive index of the sensitive site in question, which is representative of interactions between the analyte A to be characterised (and the parasitic chemical species P) and the receptors.

[0065] Alternatively, other measuring technologies may be implemented, such as electromagnetic resonators of the MEMS or NEMS type (for example described in document EP3184485). More broadly, the measuring device may be of the resistive, piezoelectric, mechanical, acoustic or optical type. In the case of techniques for measuring the resonance frequency of a NEMS or MEMS microresonator, the measurement signal can be an electrical signal representative of the vibration of a microbeam or equivalent.

[0066] In general, the term characterisation means obtaining information representative of the interactions of the analyte A contained in the gas sample with the receptors of sensitive sites of the electronic nose. The interactions in question here are adsorption and/or desorption events of the analyte A with receptors. This information thus forms an interaction pattern, in other words a signature of the analyte, this pattern being represented for example in the form of a histogram or radar diagram. In other words, in the event that the electronic nose comprises M distinct sensitive sites, the signature of the analyte A is a vector of dimension M formed by the scalar representative information, which is derived from the useful signal Su.sub.m(t) associated with the sensitive site in question.

[0067] The analyte A is at least one chemical species intended to be characterised by the electronic nose, and is present in a gas sample. They may, by way of illustration, be bacteria, viruses, proteins, lipids, volatile organic molecules, inorganic compounds, etc. Furthermore, receptors (ligands) are elements fixed to sensitive sites and which have the ability to interact with the analyte A, although the chemical and/or physical affinities denoted ka, between the analyte A and the receptors are not initially known. The receptors of different sensitive sites have different physico-chemical properties, which affect their ability to interact with the analyte A, and thus define the different sensitive sites. For example they may include amino acids, peptides, nucleotides, polypeptides, proteins, organic polymers etc. The analyte A can be a single chemical species, or a set of different chemical species whose relative proportion remains constants (for example within 5% or 10%) from one gas sample to the other.

[0068] Within the scope of the invention, the gas sample which contains the analyte A also contains at least one so-called parasitic chemical species P as it is distinct from the analyte A to be characterised and can interact with the receptors. Its interaction by adsorption/desorption with the receptors affects the measurement signal which can then include a useful part associated with the analyte A and a non-useful part associated with the chemical species P in question and forming a measurement noise. The latter can be water molecules when the relative humidity of the gas sample is not zero, an alcohol (ethanol, butanol . . . ), hydrogen sulphide, among others. However, it appears that the variation in concentration of the parasitic species P between the initial phase Ph1 and the characterisation phase Ph2 forms a measurement noise which can degrade the quality of the characterisation of the analyte A. The characterisation method according to the invention makes it possible to eliminate this measurement noise.

[0069] FIG. 2 is a schematic and partial view of an electronic nose 1 using SPR imaging according to one embodiment. The electronic nose 1 according to the invention can be similar to the one described with reference to FIG. 1A and FIG. 1B, and differs essentially in that it includes a sensor 9 for concentration of the parasitic chemical species P, located for example in the measuring chamber. If there is a variation in concentration of several parasitic species P.sub.1, P.sub.2 . . . between the fluid injection phases Ph1 and Ph2, the electronic nose can comprise a single sensor 9 or a plurality of sensors 9, adapted to measure the concentration of each parasitic species P. In the rest of the description, for the sake of clarity, it is assumed that there is only one parasitic chemical species P.

[0070] In this example, the electronic nose 1 is based on SPR technology and has in this example the features of the Kretschmann configuration, known by the person skilled in the art, without the invention being limited to this configuration. However, as indicated above, other measurement techniques can be used, such as measurements of the resonance frequency of a MEMS or functionalised NEMS type microresonator. As mentioned above, the electronic nose 1 can also be based on an optical measurement by Mach-Zehnder interferometry, for example in silicon photonics technology, as described in patent application FR2011842 filed on 18.11.2020.

[0071] The electronic nose 1 includes M sensitive sites 6.sub.m, with m ranging from 1 to M, distinct from one another and located in a measuring chamber 4 intended to receive the gas sample to be analysed, these sensitive sites 6.sub.m each being formed by receptors capable of interacting with the analyte A to be characterised (cf. FIG. 1B) and here with the parasitic species P. The sensitive sites 6.sub.m are distinct from one another in the sense that they comprise different receptors in terms of chemical and/or physical affinity for the analyte A to be characterised, and are therefore intended to provide different interaction information from one sensitive site 6.sub.m to the other. The sensitive sites 6.sub.m are distinct zones of a measuring surface 5, and can be adjacent or spaced apart from one another. The electronic nose 1 can also include a plurality of identical sensitive sites, with the aim for example of detecting any measurement drift and/or enabling the identification of a defective sensitive site.

[0072] The electronic nose includes a measuring device 3, here of the SPR imaging type, making it possible to quantify the interactions of the chemical species with the receptors, for each sensitive site 6.sub.m, here by measuring in real time the intensity of a measurement optical signal coming from the sensitive site 6.sub.m in question, this optical signal being here a reflected part of a primary optical signal emitted by a light source 7. The intensity of the optical measurement signal detected by the optical sensor 8 is directly correlated particularly with the adsorption/desorption interactions of the chemical species with the receptors. In the case of techniques for measuring the resonance frequency of a NEMS or MEMS microresonator, the measurement signal can be an electrical signal representative of the vibration of a microbeam or equivalent.

[0073] In the context of an SPR imaging measurement, the measuring device 3 is capable of acquiring in real time the optical measurement signal S.sub.m(t) coming from all of the sensitive sites 6.sub.m. Thus, the optical measurement signals S.sub.m(t) coming from the sensitive sites 6.sub.m in response to the primary optical signal are detected together and in real time, in the form of an image acquired by the same optical sensor 8.

[0074] Thus, the optical measuring device 3 includes a light source 7 capable of transmitting a primary optical signal in the direction of sensitive sites 6.sub.m, and generating surface plasmons at the level of the measurement support 5. The light source 7 can be formed by a light-emitting diode, the emission spectrum of which has an emission peak centred on a central wavelength ?.sub.c. Various optical elements (lenses, polarisers . . . ) can be arranged between the light source 7 and the measurement support 5.

[0075] The optical measuring device 3 also includes an optical sensor 8, and here an image sensor, i.e. a matrix optical sensor capable of collecting or detecting an image of the optical signal coming from the sensitive sites in response to the primary optical signal. The image sensor 8 is a matrix photodetector, for example a CMOS or CCD sensor. It therefore includes a matrix of pixels whose spatial resolution is such that preferably several pixels acquire the optical measurement signal coming from the same sensitive site 6.sub.m.

[0076] The processing unit (not shown) makes it possible to carry out the processing operations

[0077] described in the following as part of the characterisation method. This can include at least one microprocessor and at least one memory. It is connected to the optical measuring device 3, and more specifically to the image sensor 8. It includes a programmable processor capable of executing instructions recorded on an information recording medium. It further includes at least one memory containing the instructions necessary for implementing the characterisation method. The memory is also adapted to store information calculated at each instant of the measurement.

[0078] As described below, the processing unit is in particular configured to store and process a plurality of so-called elementary images acquired at a given sampling frequency f.sub.e, over a measurement period At, in order to determine a measurement signal S.sub.m(t.sub.i), at the current instant t.sub.i, associated with the sensitive site 6.sub.m. Preferably, the measurement signal S.sub.m(t.sub.i) corresponds, at a measurement instant t.sub.i, to the average intensity of the optical signal reflected and detected by the image sensor 8 on the pixels associated with the sensitive site 6.sub.m. The average optical intensity detected on the pixels can be performed for one or more images of the sensitive site 6.sub.m, as described in detail below.

[0079] The fluid feed device 2 is suitable for feeding the measuring chamber 4 with a carrier gas alone (i.e. without the analyte A) during the initial phase Ph1, and with a gas sample formed by the carrier gas and analytes during the characterisation phase Ph2. The gas sample differs from the carrier gas essentially in that it includes the analyte A to be characterised: the parasitic species P is present in the gas sample, and may, or may not, be also present in the carrier gas. One or more additional gases may be present, but they are odourless in the sense that they induce substantially no response from the electronic nose 1. An example of additional gas present in the second gas sample can be the diluent in vapour phase. The analyte A can thus be stored in a liquid diluent contained in a reservoir 10. The vapour phase of the diluent and the analyte A are added to the carrier gas (for example humid air) to form the gas sample. In any case, the parasitic species P has a concentration C.sub.P,i in the carrier gas which can be non-zero or even zero, and a concentration c.sub.P,f which is non-zero and different from c.sub.P,i in the gas sample. There is therefore a non-zero difference ?c.sub.P=c.sub.P,f?c.sub.P,i.

[0080] In the example of FIG. 2, the fluid feed device 2 can include a carrier gas inlet 11 and a reservoir 10 of analyte A. Here, the reservoir 10 contains a diluent in which the analyte A is located. It includes a plurality of fluid lines which connect the carrier gas inlet 11 and the reservoir 10 on the one hand to the inlet of the measuring chamber 4 on the other hand and includes valves and possibly mass flow regulators. It thus makes it possible to supply the measuring chamber 4 with carrier gas (e.g. humid air with a water concentration c.sub.P,i) during the initial phase Ph1 and the purge phase Ph3, and with the gas sample (e.g. humid air with a concentration of water c.sub.P,f, analyte A, and diluent in vapour phase) during the characterisation phase Ph2. It can be suitable for ensuring that the concentration c.sub.A of the analyte A in the measuring chamber remains constant over time. Furthermore, the electronic nose further comprises a sensor 9 for the concentration c.sub.P of the parasitic species P, for example here a relative humidity sensor. The concentration sensor 9 c.sub.P can be arranged in the measuring chamber or upstream or downstream thereof. It is connected to the processing unit, which is capable of calculating the variation in concentration ?c.sub.P=c.sub.P,f?c.sub.P,i (or relative concentration) between the initial phase Ph1 and the characterisation phase Ph2.

[0081] However, it appears that the variation in concentration cp of the parasitic species P present

[0082] in the measuring chamber during the fluid injection step (phases Ph1 and Ph2) causes a measurement noise which degrades the quality of the characterisation. This measurement noise is a noise linked to a non-zero difference in concentration cp within the measuring chamber between the initial phase Ph1 and the characterisation phase Ph2. It consists of a measurement noise in that it arises from a temporal variation in a parameter which characterises the environment inside the measuring chamber and should normally remain stationary over time.

[0083] This problem of measurement noise related to ?c.sub.P is particularly important when the characterisation method is carried out on the basis of useful signals Su.sub.m(t), i.e. when it includes a step of subtracting the reference value S.sub.m,i (baseline) from the corresponding measurement signal S.sub.m(t). Indeed, the aim of this step is to remove from the characterisation of the analyte A the effect associated with their environment and in particular the effect of the carrier gas. However, it appears that this reference value S.sub.m,i is representative of the carrier gas during the initial phase Ph1, but is no longer necessarily representative of the carrier gas during the characterisation phase Ph2, since the physical properties of this carrier gas in the measuring chamber may have changed (variation in the concentration cp of the parasitic species P).

[0084] FIG. 3A illustrates three interaction patterns or signatures, reflecting the characterisation of different gas samples, this characterisation being carried out by a characterisation method according to an example of the prior art. These signatures M1, M2 and M3 are here representations in the form of a radar diagram of equilibrium values (stationary) Su.sub.m,f determined by the sensorgrams Su.sub.m(t) in the steady-state equilibrium Ph2.2 (cf. FIG. 1C). They highlight the effect of the variation in concentration cp between the fluid injection phases Ph1 and Ph2, here a relative concentration ?c.sub.P, on the characterisation of the analyte A. To obtain these signatures M1, M2, M3, the carrier gas is identical for the three tests and corresponds to humid air having an initial relative humidity c.sub.P,i of about 12%.

[0085] A first signature M1 corresponds to a gas sample formed of humid air with a relative humidity c.sub.P,f equal to about 50% and for which the analyte A is butanol molecules. The implementation of the characterisation method thus has a relatively large variation in relative humidity in the measuring chamber, which passes here from c.sub.P,i equal to approximately 12% during the initial phase Ph1, to c.sub.P,f equal to approximately 50% during the characterisation phase. Also the reference value S.sub.m,i is determined for the carrier gas (humid air at a c.sub.P,i of 12%) and the equilibrium value is determined for the gas sample (humid air at c.sub.P,f of 50% with analyte A) by subtracting this reference value S.sub.m,i. This relative concentration ?c.sub.P thus forms a measurement noise, the effect of which has to be limited so that the signature M1 is effectively representative only of butanol molecules.

[0086] A second signature M2 corresponds to a gas sample formed by humid air with a relative humidity c.sub.P,f substantially equal to c.sub.P,i (i.e. 12%), and the analyte A of which is also butanol molecules. The implementation of the characterisation method makes it possible, by subtracting the reference value S.sub.m,i, associated with the carrier gas (humid air at c.sub.P,i), and insofar as the variation in relative humidity ?c.sub.P is zero, to eliminate the effect of the gaseous environment and thus characterise the interactions of the analyte A with the receptors alone. Also, the signature M2 is representative of the analyte A alone since there is no measurement noise associated with the variation in relative humidity ?c.sub.P. It is noted that the signature M1 is not superimposed on the signature M2, reflecting the presence of the measurement noise associated with ?c.sub.P in the case of M1. It is therefore important to be able to correct the signature M1 in order to move towards signature M2, which alone is representative of the analyte, even if there is a difference in relative humidity ?c.sub.P in the measuring chamber between the between the initial phase Ph1 and the characterisation phase Ph2.

[0087] The third signature M3 corresponds to a gas sample formed only by humid air with a relative humidity c.sub.P,f equal to approximately 50%. Here, the impact alone of the variation in relative concentration ?c.sub.P on the characterisation of humid air is measured by the electronic nose, in the absence of analyte. It appears that the increase in the relative concentration ?c.sub.P between the initial phase Ph1 and the characterisation phase Ph2 results in an increase in the variation of reflectivity ?%R.sub.m of sensitive sites 6.sub.m. It can be seen that the signature M1 (humid air with non-zero ?c.sub.P and presence of the analyte A) is located between the signature M2 (humid air with zero ?c.sub.P and presence of the analyte A) and the signature M3 (humid air with non-zero ?c.sub.P and absence of the analyte A), clearly showing the effect of the measurement noise associated with the non-zero relative concentration ?c.sub.P on the signature of the analyte A. It is therefore important to be able to limit or even eliminate this measurement noise in order to improve the quality of the characterisation of the analyte A.

[0088] FIG. 3B illustrates examples of the temporal evolution of the concentration c.sub.P of the parasitic species P present in the measuring chamber during the initial phase Ph1 and the characterisation phase Ph2, of the measurement signal S.sub.m(t) for the sensitive site 6.sub.m of rank m, and lastly of the useful signal Su.sub.m(t).

[0089] The left-hand side of the graphs corresponds to the situation in which the concentration c.sub.P(t) remains constant between the two fluid injection phases Ph1 and Ph2. In the initial phase Ph1, only the carrier gas is present in the measuring chamber: the parasitic species P has a concentration of value c.sub.P,i, resulting in a measurement signal S.sub.m(t) with a reference value S.sub.m,i. In phase Ph2, the gas sample to be analysed is introduced into the measuring chamber. Due to the presence of the analyte A and as the concentration c.sub.P(t) remains constant, the measurement signal S.sub.m(t) moves towards a stationary value S.sub.m,f greater than S.sub.m,i. To obtain the useful signal Su.sub.m(t), the measurement signal S.sub.m(t) is subtracted from its initial value S.sub.m,i, such that the signature is the vector Su formed from the M values Su.sub.m,f obtained.

[0090] The right-hand side of the graphs corresponds to the situation in which the concentration c.sub.P(t) varies between the two fluid injection phases Ph1 and Ph2. Thus, during the initial phase Ph1, only the carrier gas is present in the measuring chamber: the parasitic species P has a concentration of value c.sub.P,i, which results in a measurement signal S.sub.m(t) with a reference value S.sub.m,i. However, in the phase Ph2, the introduction of the gas sample causes the concentration c.sub.P(t) of the parasitic species P to vary, in this case increasing to a value c.sub.P,f, greater than ?c.sub.P relative to the reference value c.sub.P,i.

[0091] This variation in the concentration of the parasitic species P results in a variation in the measurement signal S.sub.m(t) during this phase Ph2, which moves here to a value S.sub.m,f greater than ?S.sub.m than in the case where there is no variation in the concentration c.sub.P (dotted curve). It then follows that the useful signal Su.sub.m(t) has a measurement noise of value ?S.sub.m which depends on the difference ?c.sub.P. It is then a matter, for characterising the analyte A, of evaluating the measurement noise ?S.sub.m and subtracting it from the measurement signal S.sub.m(t) with the reference value S.sub.m,i.

[0092] To subtract the measurement noise ?S.sub.m(?c.sub.P) from the measurement signal S.sub.m(t), one approach can be to perform a calibration step prior to the characterisation step. Patent application FR1913555 filed on 29 Nov. 2019 describes a characterisation method including such a calibration step. The prior calibration involves determining a correction function expressing the change in the reference value S.sub.m,i of the measurement signal S.sub.m(t) associated with the parasitic species P alone (i.e. without the analyte A) as a function of its concentration c.sub.P. Thus, in the characterisation step, instead of subtracting from the stationary value S.sub.m,f the reference value S.sub.m,i determined during the initial phase Ph1 in which the concentration c.sub.P has the c.sub.P,i, the value S.sub.m,i, from the correction function corresponding to the effective value c.sub.P,f during phase Ph2 is subtracted.

[0093] However, it appears that the affinity k.sub.P of the parasitic species P with the receptors can be affected by the presence of the analyte A. For example, when the parasitic species P is water molecules (water in vapour phase) and the analyte A is hydrophilic or hydrophobic, the analyte A adsorbed on the receptors can induce a force of attraction or repulsion to the water molecules, thereby modifying the affinity k.sub.P of the parasitic species P. The affinity k.sub.P|A is then noted as being the affinity of the parasitic species P in the presence of the analyte A. The step of calibration may then not accurately estimate the actual measurement noise during the characterisation phase Ph2 insofar as the interactions of the parasitic species P with the receptors are influenced by the presence of the analyte A.

[0094] Also, the characterisation method according to the invention is based on the idea of estimating the measurement noise from interactions of the parasitic species P with the receptors in the presence of the analyte A, and not, as in the previous calibration approach, in the absence of the analyte A. For this, the characterisation method initially involves acquiring stationary values Su.sub.(n,m)f of the useful signal Su.sub.(n,m)(t) for N different gas samples, which contain the same chemical species, i.e. the same analyte A and the same parasites species P, but for which the relative concentration ?c.sub.P(n) is different from one acquisition n to the next n+1. Then a matrix of first signatures Su.sub.A,P (i.e. uncorrected signatures) is obtained which is representative of interactions of the analyte A and the parasitic species P with the receptors, and a relative concentration vector ?c.sub.P of the chemical species P is obtained during the acquisitions N. In a second step, an optimisation problem is solved, using the matrix of first signatures Su.sub.A,P and the relative concentration vector ?c.sub.P, to obtain a matrix of corrected signatures Sucn.sub.A, which are representative only of the interactions of the analyte A with the receptors during acquisitions N. These corrected signatures then provide a characterisation of the analyte A for each of the gas samples.

[0095] In the following examples of a characterisation method, this optimisation problem can be solved in two stages: this is the first embodiment (flowcharts in FIG. 4 and the FIG. 5). Alternatively, it can be solved in one step: this is the second embodiment (flowchart in FIG. 6). Other methods of solving this optimisation problem are possible of course.

[0096] FIG. 4 is a flowchart of a method for characterising the analyte A according to a first embodiment, which makes it possible to improve the quality of characterisation of analyte A by reducing or even eliminating the measurement noise associated with the non-zero relative concentration ?c.sub.P between the injection phases Ph1 and Ph2. Analyte A is contained in gas samples, the latter also containing at least one parasitic chemical species P. In this example, the parasitic species P is water molecules, but may be one or more other parasitic species, such as ethanol and hydrogen sulphide, among others.

[0097] As indicated above, in a first phase 100, N first signatures (uncorrected signatures) of gas samples are acquired, which are therefore representative of the interactions of the analyte A and the parasitic species P with the receptors. Then, in a second phase 200, an optimisation problem is solved so as to estimate the contribution of the parasitic species P in the first signatures, in order to obtain the corrected signatures characterising the analyte A alone.

[0098] Phase 100: acquisition of first signatures N, with N>1, of gas samples containing the analyte A and the parasitic species P.

[0099] The following steps 110 to 140 are carried out N times, with N>1, each time for a different gas sample, these N gas samples containing the analyte A and the parasitic species P, and differing from one another at least by the concentration c.sub.P of the parasitic species P during the injection phase Ph2. Each acquisition phase is denoted by an indicator n, a non-zero integer ranging from 1 to N. It should be noted that the greater the value of N, the better the quality of the estimate of the water signature and therefore the correction of the first signatures.

[0100] In a first step 110, a carrier gas G.sub.(n) is injected into the measuring chamber: this is the fluid injection phase Ph1 mentioned above. The carrier gas G.sub.(n) does not contain the analyte A. It may or may not contain the parasitic species P: the concentration c.sub.P(n)i may therefore be non-zero or zero. It may also include other chemical species, but which do have adsorption/desorption type interactions with the receptors of sensitive sites 6.sub.m: so they are not so-called parasite species.

[0101] The gas sample E.sub.(n) is then injected into the measuring chamber: this is the fluid injection phase Ph2 mentioned above. The gas sample E.sub.(n) of rank n includes the analyte A in concentration c.sub.A(n) and the parasitic species P in concentration c.sub.P(n)f. Here again, the gas sample may include chemical species which do not interact with the receptors. Also, only the analyte A and the parasitic species P interact with the receptors and cause a variation in the measurement signal S.sub.(n,m)(t).

[0102] In step 120, in parallel with step 110, the measurement signals S.sub.(n,m)(t) associated with the sensitive sites 6.sub.m are determined, with m ranging from 1 to M, with M>1. These measurement signals S.sub.(n,m)(t) are therefore representative, in the fluid injection phase Ph1 (carrier gas G.sub.(n) alone), of the interactions of the parasitic species P, and then in the fluid injection phase Ph2 (gas sample E.sub.(n)), of the interactions of the analyte A and the parasitic species P.

[0103] In step 130, the reference value S.sub.(n,m)i (baseline) associated with the carrier gas is determined in the injection phase Ph1, and subtracted from the stationary value S.sub.(n,m)f of the measurement signal S.sub.(n,m)(t) determined in the injection phase Ph2. Thus a signature Su.sub.(n,m)f=S.sub.(n,m)f?S.sub.(n,m)i is obtained for each sensitive site m, associated with the gas sample E.sub.(n) for the acquisition of rank n.

[0104] In step 140, the value c.sub.P(n)i of the concentration of the species P in the injection phase Ph1 is measured, then the value c.sub.P(n)fin the injection phase Ph2, and the difference ?c.sub.P(n)=c.sub.P(n)f?c.sub.P(n)i in concentration (or relative concentration) is determined.

[0105] The preceding steps are repeated N times, so as to obtain N first signatures Su.sub.(n,m)f for N ranging from 1 to N, and m ranging from 1 to M. From one iteration to the other, therefore between n and n+1, the corresponding gas samples E.sub.(n) and E.sub.(n+1) differ from one another essentially by the difference ?c.sub.P(n)??c.sub.P(n+1): there are therefore N differences ?c.sub.P with values different from one another. It should be noted that the concentration c.sub.A of analyte A may vary, or may not vary, from one gas sample to the other.

[0106] In step 150, a matrix of first signatures Su.sub.A,P is formed which is representative of the interactions of the analyte A and the parasitic species P with the receptors of the sensitive sites. This matrix is of dimension N?M and is formed by the values Su.sub.(n,m)f. In other words, each row of rank n represents the signature associated with the gas sample E.sub.(n), i.e. the M useful measurement values Su.sub.(n,m)f of the sensitive sites 6.sub.m. A relative concentration vector ?c.sub.P is also formed from the N determined values of the concentration difference ?c.sub.P(n). This vector is here a vector column of dimension N?1.

[0107] Phase 200: solving an optimisation problem in order to obtain N corrected signatures, characterising the analyte A present in the N gas samples.

[0108] In general, an estimated solution is sought {{circumflex over (k)}.sub.P|A; ?.sub.A; {circumflex over (k)}.sub.A}; the product of which ?.sub.A{circumflex over (k)}.sub.A.sup.T forms a matrix of corrected signatures Suc.sub.A characterising the analyte A present in the N gas samples, the estimated solution minimising the cost function f=Su.sub.A,P-?c.sub.Pk.sub.P|A.sup.T-c.sub.Ak.sub.A.sup.T, and maximising the objective function g=Su.sub.A,P-?c.sub.Pk.sub.P|A.sup.T. The variables in this optimisation problem are c.sub.A, k.sub.A and k.sub.P|A, where: [0109] c.sub.A is a concentration vector of the analyte A, of dimension N?1, formed from N concentration values c.sub.A(n) of the gas samples; [0110] k.sub.A is an affinity vector of the analyte A, of dimension M, formed from the M values of an interaction affinity of the analyte A with the receptors of the sensitive sites; [0111] k.sub.P|A is an affinity vector of the parasitic chemical species P, of dimension M, formed from the M values of an interaction affinity of the parasitic chemical species P with the receptors of the sensitive sites in the presence of the analyte A.

[0112] In step 210, the following optimisation problem is solved:

[00001] $\underset{k_{P .Math. A} ? 0, c_{A} ? 0, k_{A} ? 0}{\arg \min} {.Math. {Su}_{A, P} - ? c_{P} k_{P .Math. A}^{T} - c_{A} k_{A}^{T} .Math.}_{F}^{2}$

[0113] In other words, solutions are determined, here local solutions, that minimise the cost function f such that: f=Su.sub.A,P??c.sub.Pk.sub.P|A.sup.T?c.sub.Ak.sub.A.sup.T. This involves minimising the squared error, here in the sense of the Frobenius norm, between the matrix of first signatures Su.sub.A,Pand the term ?c.sub.Pk.sub.P|A.sup.T+c.sub.Ak.sub.A.sup.T. As indicated above, the terms c.sub.A, k.sub.A and k.sub.P|A, are the variables to be optimised, and the terms Su.sub.A,P and ?c.sub.P are data that has been determined in step 150. The function f (and the function g) is referred to here as cost function, but can also be referred to as cost, objective function, objective, or even criterion. It should be noted that minimising a cost function f is equivalent to maximising the function -f.

[0114] Various known techniques for solving optimisation problems can be implemented, for

[0115] example gradient descent on all the parameters or block gradient descent where each parameter is optimised by considering the other parameters as fixed. In the two cases, it is necessary to start from a random selection of the initial conditions due to the non-convexity of the objective function which makes it difficult to find the optimum. Then a plurality of local solutions are obtained, noted {{circumflex over (k)}.sub.P|A.sup.T(q);?.sub.A.sup.(q);{circumflex over (k)}.sub.A.sup.T(q)}.sub.1?q?Q. In other words, Q local solutions are obtained, each formed by estimated vectors{circumflex over (k)}.sub.P|A.sup.T(q), ?.sub.A.sup.(q), {circumflex over (k)}.sub.A.sup.T(q).

[0116] In step 220, the following optimisation problem is solved:

[00002] $\underset{k_{P | A} ? {k_{P | A}^{T (q)}}_{1 ? q ? Q}}{\arg \max} {.Math. {Su}_{A, P} - ? c_{P} k_{P | A}^{T} .Math.}_{F}^{2}$

[0117] This consists here of determining the solution, from the local solutions {{circumflex over (k)}.sub.P|A.sup.T(q)}.sub.1?q?Q determined previously, maximising the cost function g such that g=Su.sub.A,P??c.sub.Pk.sub.P|A.sup.T.

[0118] For this, in step 221, a corrected signature matrix Suc.sub.A.sup.(q) is determined for each of the Q local solutions: ?q?[1, Q], Suc.sub.A.sup.(q)=Su.sub.A,P??c.sub.P{circumflex over (k)}.sub.P|A.sup.T(q). Thus, by subtracting the estimated contribution ?c.sub.P{circumflex over (k)}.sub.P|A.sup.T(q) of the parasitic species P in the matrix of first signatures Su.sub.A,P, only the useful contribution is retained c.sub.A{circumflex over (k)}.sub.A.sup.T(q), i.e. the one linked to the analyte A. It is then necessary to determine which solution out of the determined Q local solutions, maximises the useful contribution c.sub.A{circumflex over (k)}.sub.A.sup.T(q) (and therefore the cost function g).

[0119] Also each of the Q corrected signature matrices Suc.sub.A.sup.(q) are normalised to obtain Q corrected and normalised signature matrices Sucn.sub.A.sup.(q). Thus, it is calculated for each of the corrected signatures of rank n: Sucn.sub.A(n).sup.(q)=Suc.sub.A(n).sup.(q)/?Suc.sub.A(n).sup.(q)?.sub.2.

[0120] In step 222, it is then determined which solution qf, out of the Q local solutions, has a minimum variance score. For this, a variance score V is calculated for each of the Q matrices Sucn.sub.A.sup.(q), and the one with a minimum score is identified. The variance score is here the trace the covariance matrix for each of the local solutions, i.e. the sum of the variance of each sensor m for each local solution q. More precisely, the variance score can be to within one multiplicative factor:

V(Sucn.sub.A.sup.(q))=Trace((Sucn.sub.A.sup.(q)i ?I.sub.N?.sup.T(q)).sup.T(Sucn.sub.A.sup.(q)?I.sub.N?.sup.T(q)))

where I.sub.N is a unit vector of dimension N, and where ?.sup.T.sup.(q) is a vector of dimension M where each value m is the average of the normalised corrected signatures (Sucn.sub.A(n,m).sup.(q)).sub.1?n?N for the sensitive site 6.sub.m of rank m. The solution of rank qf, out of the Q local solutions, then corresponds to the vectors ?.sub.A.sup.(qf) and {circumflex over (k)}.sub.A.sup.T(qf) the product of which ?.sub.A.sup.(qf){circumflex over (k)}.sub.A.sup.T(qf) is equal to the corrected signature matrix Sucn.sub.A.sup.(qf).

[0121] Thus, by way of the determination method according to this first embodiment, the signatures Sucn.sub.A.sup.(qf) are determined which characterise effectively and uniquely the analyte A for the N gas samples, despite the presence of the parasitic species P, and without resorting to a prior calibration step. The characterisation method then provides more accurate signatures of analyte A since the measurement noise ?c.sub.Pk.sub.P|A.sup.T is estimated in the presence of the analyte A and not in the absence of the latter (which is the case in the prior calibration).

[0122] FIG. 5 is a flowchart of a characterisation method of the analyte A according to one variant of the first embodiment. This variant differs from the method of FIG. 4 essentially in the manner of optimising the cost function g.

[0123] The method includes a phase 100 of acquiring N first signatures. This phase 100 can be identical or similar to the one described with reference to FIG. 4. It may therefore include steps 110 to 150, and is therefore not given in detail again here.

[0124] It then includes a phase 200 of solving an optimisation problem consisting of minimising the cost function f and then maximising the cost function g. The step 210 of minimising the cost function f may be identical to that described previously and is not explained again here.

[0125] Step 230 consists of maximising the cost function g from the Q local solutions obtained after step 210. This consists here of determining the solution, from the local solutions determined previously, maximising the cost function g such that g=Su.sub.A,P??c.sub.Pk.sub.P|A.sup.T.

[0126] In step 231, the corrected signature matrix Suc.sub.A.sup.(q) is determined for each of the Q local solutions: ?q?[1, Q], Suc.sub.A.sup.(q)=Su.sub.A,P-?c.sub.Pk.sub.P|A.sup.T(q). This step is identical to step 221. However, unlike step 221, the Q corrected signature matrices Suc.sub.A.sup.(q) are not normalised.

[0127] In step 232, it is then determined which solution (of rank denoted qf), among the Q local solutions, has a maximum norm, here in the sense of the Frobenius norm. For this, the norm ?Suc.sub.A.sup.(q)?.sub.F.sup.2 is calculated for each of the Q local solutions, and the one with the maximum norm is identified. As before, the solution of rank qf, out of the Q local solutions, then corresponds to the vectors ?.sub.A.sup.(qf) and {circumflex over (k)}.sub.A.sup.T(qf) the product of which ?.sub.A.sup.(qf){circumflex over (k)}.sub.A.sup.T(qf) is equal to the corrected signature matrix Suc.sub.A.sup.(qf).

[0128] Then the corrected signature matrix Suc.sub.A.sup.(qf) is normalised: ?n?[1, N], Sucn.sub.A(n).sup.(qf)=Suc.sub.A(n).sup.(qf)/?Suc.sub.A(n).sup.(qf)?.sub.2. Thus, by way of the determination method according to this embodiment, the signatures Sucn.sub.A(n).sup.(qf) are determined which characterise effectively and uniquely the analyte A for the N gas samples without having to resort to a prior calibration step. The characterisation method then provides more accurate signatures of the analyte A.

[0129] FIG. 6 is a flowchart of a characterisation method of the analyte A according to a second embodiment. This method differs from the methods of FIG. 4 and FIG. 5 essentially in the manner of optimising the two cost functions f and g.

[0130] The method includes a phase 100 of acquiring N first signatures. This phase 100 can be identical or similar to the one described with reference to FIG. 4. It may include steps 110 to 150, and is therefore not described in detail again here.

[0131] It then includes a phase 300 of solving an optimisation problem consisting at the same time of minimising the cost function f=Su.sub.A,P-?c.sub.pk.sub.P|A.sup.T?c.sub.Ak.sub.A.sup.T and maximising the cost function g=Su.sub.A,P??c.sub.pk.sub.P|A.sup.T). This amounts to minimising the following function J:

[00003] $\underset{k_{P .Math. A} ? 0, c_{A} ? 0, k_{A} ? 0}{\arg \min} \frac{{.Math. {Su}_{A, P} - ? c_{P} k_{P | A}^{T} - c_{A} k_{A}^{T} .Math.}_{F}^{2}}{{.Math. {Su}_{A, P} - ? c_{P} k_{P | A}^{T} .Math.}_{F}^{2}} = \underset{k_{P .Math. A} ? 0, c_{A} ? 0, k_{A} ? 0}{\arg \min} J (k_{P .Math. A}, c_{A}, k_{A})$

[0132] This optimisation problem is solved here by an iterative algorithm with a convergence condition. Thus, after an initialisation step 311, steps 312 and 313 are performed iteratively, until a convergence criterion is verified. There is therefore no need to perform a large number of random initialisations, as each initialisation leads to the global solution (no local solutions). It should be noted here that for this the values of the vector Ac.sub.P are all positive (increase in concentration c.sub.P(n) between the fluid injection phase Ph1 and the fluid injection phase Ph2).

[0133] Thus, in step 311, for the iteration indicator i=0, the iterative algorithm is initialised by assigning an initial value to the three variablesk {circumflex over (k)}.sub.P|A.sup.T(i=0), ?.sub.A.sup.(i=0), {circumflex over (k)}.sub.A.sup.T(i=0). These initial values can be selected in any way.

[0134] In step 312, the value ? i+1 (here i=1) of the variable {circumflex over (k)}.sub.P|A.sup.(i+1) is then determined taking into account the values at i (here i=0) of variables ?.sub.A.sup.(i), {circumflex over (k)}.sub.A.sup.(i). The fixed point method is used here, insofar as it appears that writing that the Jacobian of the function J as a function of the variable k.sub.P|A is equal to zero is an equation of form x=h(x):

[00004] $\frac{? J (k_{P .Math. A}, c_{A}, k_{A})}{? k_{P | A}} = 0 ? k_{P | A} = \frac{S u_{A, P}^{T} ? c_{P}}{? c_{P}^{T} ? c_{P}} - \frac{k_{A} c_{A}^{T} ? c_{P}}{? c_{P}^{T} ? c_{P} (1 - J (k_{P | A}, c_{A}, k_{A}))} = h_{k_{A}, c_{A}} (k_{P | A})$

[0135] Also, the equation is solved, for example iteratively: {circumflex over (k)}.sub.P|A.sup.i+1)=h.sub.{circumflex over (k)}.sub.A.sub.i, c.sub.A.sub.i({circumflex over (k)}.sub.P|A.sup.(i)) so as to obtain the value {circumflex over (k)}.sub.P|A.sup.(i+1). The method amounts to successively applying the function h until convergence.

[0136] In step 313, the values at i+1 (here i=1) of the variables ?.sub.A.sup.(i+1), {circumflex over (k)}.sub.A.sup.(i+1) are then determined, taking into account the value{circumflex over (k)}.sub.P|A.sup.(i+1) which has just been determined. To achieve this, a Singular Value Decomposition (SVD) of the residue matrix R is performed such that R=Su.sub.A,P-?c.sub.Pk.sub.P|A.sup.T. In this example, N is greater than M: N>M. Thus, the residue matrix R can be factorised in the form: R=U?V.sup.T, where U is a matrix of dimensions N?M whose first column u.sub.m of rank m is proportional to the concentration vector c.sub.A of the analyte A, where ? is a matrix of dimensions M?M the diagonal elements of which (?.sub.m).sub.1?m?M are the singular values of the residue matrix R, and where V is a matrix of dimensions M?M the first column v.sub.m of rank m of which is proportional to the affinity vector k.sub.A.

[0137] Also the approximation ?.sub.1u.sub.1v.sub.1.sup.T of rank 1 of the singular value decomposition of the residue matrix R is equal to ?.sub.A.sup.(i+1){circumflex over (k)}.sub.A.sup.(i+1). It is therefore ?.sub.A.sup.(i+1)=?.sub.1u.sub.1 and {circumflex over (k)}.sub.A.sup.(i+1)=v.sub.1.

[0138] In step 314, it is determined whether or not a convergence criterion is verified. This may involve comparing the variation between i and i+1 of one and/or other of the variables with a predefined threshold value. When this variation is greater than this threshold, steps 312 and 313 are repeated by incrementing the indicator i by one unit. Conversely, when this variation is less than or equal to this threshold, the next step is performed.

[0139] In step 320, the corrected signature matrix Suc.sub.A is defined as follows: Suc.sub.A=Su.sub.A,P??c.sub.P{circumflex over (k)}.sub.P|A.sup.T(if), where if is the value of the indicator i when the convergence criterion is verified. Thus, the matrix of corrected signatures Suc.sub.A is defined as being equal to the matrix of first signatures Su.sub.A,P from which the estimate ?c.sub.P{circumflex over (k)}.sub.P|A.sup.T(if) of the term representative of the measurement noise (impact of parasite species) has been subtracted. The Suc.sub.A matrix can then be normalised to obtain the Sucn.sub.A matrix, as indicated above in steps 221 and 232.

[0140] Thus, by way of the determination method according to this embodiment, the signatures which effectively characterise analyte A for the N gas samples have been determined without resorting to a prior calibration step. The characterisation method therefore provides more accurate signatures Sucn.sub.A of the analyte A. It should also be noted that the method according to this second embodiment has the advantage of obtaining the optimum solution in a single step, insofar as the minimisation of the objective function f and the maximisation of the objective function g are carried out together to obtain the matrix of corrected signatures Suc.sub.A.

[0141] It should be noted that the differences ?c.sub.P(n) determined during N acquisitions of phase 100, may have a greater or lesser amplitude of variation, this amplitude of variation being defined as the difference between the maximal deviation max(?c.sub.P(n)) and the minimal deviation min(?c.sub.P(n)) among). For a small amplitude of variation, for example of in the order of 10%, it is advantageous to use the characterisation method according to the second embodiment which then gives more accurate results.

[0142] FIG. 7A illustrates three uncorrected signatures Su.sub.1, Su.sub.2 and Su.sub.3 representative of the analyte A in the presence of a parasitic species P of different concentrations. In these examples, the analyte A is butanol and the parasitic species P is water in the vapour phase (humid air). The signature Su.sub.1 (dotted line) corresponds to a deviation ?c.sub.P,1 of 36.5%, the signature Su.sub.2 (dashed line) corresponds to a deviation ?c.sub.P,2 of 33.5%, and the signature Su.sub.3 (solid line) corresponds to a deviation ?c.sub.P,3 of ?1%.

[0143] FIG. 7B illustrates two corrected signatures Suc.sub.1 and Suc.sub.2 obtained by correcting signatures Su.sub.1 and Su.sub.2 by means of the second phase 300 of the characterisation method according to the second embodiment. The uncorrected signature Su.sub.3 is reproduced here for comparison (but is not used in the optimisation). Note that a small deviation ?c.sub.P has little impact on the signature Su due to the subtraction of the reference value S.sub.m,f. It can be seen that the corrected signatures Suc.sub.1 (dotted line) and Suc.sub.2 (dashed line) are substantially merged and close to the signature Su.sub.3 (solid line), thus illustrating the fact that the impact linked to the interactions of the parasitic species P with the receptors has been corrected.

[0144] Particular embodiments have been described above. Various variants and modifications will

[0145] become apparent to the person skilled in the art.

[0146] Thus, as indicated above, the gas samples used during the acquisition phase 100 can include a plurality of parasitic chemical species: P.sub.1, P.sub.2 . . . Also, during step 140, the concentration of these different parasitic species is measured during the injection phases Ph1 and Ph2, then the deviations ?c.sub.P1, ?c.sub.P2 . . . are determined. The correction phases 200 and 300 are then similar to those described above, and are based on the optimisation of the objective functionsf=Su.sub.A,P-c.sub.Ak.sub.A.sup.T-?c.sub.P1k.sub.P1|A.sup.T-?c.sub.P2k.sub.P2|A.sup.T . . . and g=Su.sub.A,P-?c.sub.P1k.sub.P1|A.sup.T-?c.sub.P2k.sub.P2|A.sup.T . . .

METHOD FOR CHARACTERIZING AN ANALYTE PRESENT IN A GAS SAMPLE CONTAINING AT LEAST ONE PARASITIC CHEMICAL SPECIES

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G01N33/0021

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G01N21/553

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G01N33/0059

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G01N33/00

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G01N21/552

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Abstract

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