EP3928093A1

EP3928093A1 - Method for identifying an item by olfactory signature

Info

Publication number: EP3928093A1
Application number: EP20710239.3A
Authority: EP
Inventors: Samuel GUILAUMÉ; David Sloo
Original assignee: Aryballe SA
Current assignee: Aryballe SA
Priority date: 2019-02-18
Filing date: 2020-02-18
Publication date: 2021-12-29
Also published as: WO2020169917A1; FR3092910A1; FR3092910B1; US20220137018A1

Abstract

The present invention relates to a method implemented by a computer processing circuit (CPU) connected to an electronic nose (NN) in order to identify a given item by an olfactory signature, said method implementing (EA) the electronic nose to obtain an olfactory signature (SM), repeating (EB) the implementation of the electronic nose a first number K of times in order to acquire K olfactory signatures (S1, S2, ..., SK), implementing (EC) the computer processing circuit in order to estimate, on the basis of said K olfactory signatures, a model (SMOD) of the olfactory signature of the given item, acquiring (ED), with an electronic nose of the same type, a current measurement (SC) of the olfactory signature of a current item, of the same type as said given item (P), and comparing (EE) the current measurement with said model, in order to estimate a similarity (SIM) between the current item and the given item.

Description

Title: Method for identifying an item by scent signature

Technical area

[0001] The invention relates to a method for identifying an article by scent signature. In particular, the invention relates to a method for measuring a change in olfactory signatures over time. The invention also relates to a device configured to implement such a method.

[0002] The traceability of articles, in particular to monitor the conditions of delivery of goods intended for mass distribution, is today a major issue in supply chains. During the different phases of a supply of articles from a given producer to consumers, for example during storage and transport stages (sea, road, etc.), these goods can undergo transformations affecting their value. Thus, shocks caused during the transport of harvested fruit, or significant variations in the temperature of perfume bottles stored in a container, are damaging to their initial quality.

[0003] At the end of the supply chain, we often see that the items are damaged and cannot be distributed. Too many transactions, violent transport or poorly conditioned storage are all events that involve transformations that affect the value of these items, and therefore the reliability of their supply chains. This is especially true in the case of perishable items, the ripening and freshness of which is directly affected by such events.

[0004] For tracking articles during a supply chain, methods and devices are known for measuring and recording their condition and position at a given time. Thus, the collection of this data allows different stakeholders to control the reliability of the supply chain of these items. For example, there are NFC chips (“Near Field Communication”, or near field communication, in French) or RFID chips (“Radio Frequency Identification ”, or radio frequency identification, in French) to identify an item at a given moment, and to follow its position over time.

[0005] A disadvantage of known methods and devices is that they do not allow an accurate history of the supply chain to be established. At the end of the supply chain, and before delivery of items to a final recipient, it is therefore difficult to determine precisely when, where and under what conditions items may have been damaged based on the data collected.

[0006] Another disadvantage of known methods and devices is that they do not guarantee that the measured data has not been altered during the supply chain, either intentionally or unintentionally. While a comparison of data from different sensors, for example shock sensors placed on a bottle container and position sensors, in principle allows us to deduce at what point in the supply chain these bottles may have been damaged , a third party can delete or falsify this data. The accessibility and precision of the data collected therefore do not always allow them to be compared with each other. The origin and authenticity of these items are therefore not always verifiable.

[0007] Yet another drawback of the known methods and devices is that they do not allow the state of a perishable article to be measured and followed precisely over time. However, the maturation of a perishable article, for example a fruit or a vegetable, is likely to vary more or less rapidly depending on its environmental conditions, which current sensors do not allow reliable measurement. Although it is possible to measure the temperature, temperature or humidity inside a food container, these measurements do not provide information on the ripening of these foods which depend expressly and in a complex way on the conditions. environmental conditions among many other factors related in particular to the nature of the perishable good. [0008] There is therefore a need to propose a method improving the situation and not having at least one of the aforementioned drawbacks.

Purpose and summary of the invention

In order to respond to this or these drawbacks, the invention relates, according to a first aspect, to a method implemented by a computer processing circuit, connected to an electronic nose, to identify a given article by an olfactory signature of the donated article, said method comprising:

- implement the electronic nose comprising a plurality of sensors for the presence of fluids likely to be present in a mixture of fluids from the given article, to obtain an olfactory signature of said mixture, the olfactory signature comprising respective proportions of said fluids in the mixture;

- repeat the implementation of the electronic nose a first number K of times to acquire K olfactory signatures;

- implementing the computer processing circuit to estimate, on the basis of said K olfactory signatures, a model of the olfactory signature of the given item;

- acquire, with an electronic nose of the same type, a current measurement of the olfactory signature of a current article, of the same type as said given article; and

- compare the current measurement with said model, to estimate a similarity between the current article and the given article.

[0010] Herein, a given article comprises and exhales a mixture of fluids. In particular, it releases part of its molecular material in the form of fluids such as gases or liquids, which can interact with sensors of an electronic nose such as an electronic nose described below, and comprising sensors such as olfactory or taste sensors or detectors of particular molecules in a gas or a liquid.

[0011] In the present documents, a computer processing circuit is any type of integrated circuit, this integrated circuit comprising a storage space, for example a memory, and a processor. The storage space is for example a non-volatile memory (ROM or Flash, for example) and can constitute a medium recording, this recording medium further possibly comprising a computer program. The processor is a data processor used to implement the instructions of a computer program. These instructions can be stored in a memory of a computer device, for example a server, loaded and then executed by the processor.

In the present, the first number K is an integer strictly greater than 1.

[0013] This makes it possible to detect a deterioration of the sensors of an electronic nose which may affect the accuracy of the olfactory signatures measured by said electronic nose. In practice, the sensors of an electronic nose include detection layers with a limited lifespan and such that after a certain number of measurements, these measurements are falsified and may no longer be reproducible. The aging of the sensors of an electronic nose can thus be detected and indicate the need for repair or replacement. This also makes it possible to provide a method for calibrating such an electronic nose, for example on the basis of olfactory signatures of samples of non-perishable items such as salt, sugar, water or alcohol. .

[0014] In one embodiment of the invention, the given item is a perishable item.

In the present, a perishable article is, in general, any article likely to be transformed and not only towards the expiration. A perishable item can be any type of item that cannot be stored for a long time under normal circumstances without spoiling. A perishable article can also be a foodstuff chosen from any type of fruit, meat, fish, vegetables, dairy products, eggs, flour, cereals and / or legumes. Likewise, a perishable article can be a gas, a liquid or even a solid body not intended for food consumption and whose physicochemical properties are liable to vary during different stages of a supply chain. A perishable item can also be perfume, gasoline, glue, motor oil, etc. [0016] This makes it possible to detect modifications of an olfactory signature of a given perishable article in order to identify a precise alteration thereof, for example by comparing two olfactory signatures, a difference of which may indicate a different geographical origin or a counterfeit.

In one embodiment of the invention, the implementation of the electronic nose to obtain the first number K of olfactory signatures over time is carried out successively over time to acquire a succession of K olfactory signatures over time.

[0018] This makes it possible to build a model of change over time from several different measurements of the olfactory signatures of a mixture of fluids from a perishable article. It is thus possible to detect differences representative of a transformation of the components of this perishable article.

[0019] In one embodiment of the invention, the perishable item has several successive phases of maturation over time and the K olfactory signatures are representative of said successive phases of the perishable item.

[0020] In the present, a maturation phase of a perishable article is any phase during which this article sees its physicochemical qualities change naturally, for example by aging, or in an unnatural way, by example following exposure to bacteria. In the case of perishable foodstuffs such as fruit, we speak of physiological maturity when the fruit reaches its term of growth. This growth can be followed by a ripening process bringing it to a level of ripening that meets the standards required for distribution to a consumer, which level of ripening can be quantified using different physicochemical measurements, for example.

[0021] This makes it possible to quantify the maturation of a perishable article over time and to provide a reference from which several parameters of the article can be identified, for example its origin or the abnormal degradations which have appeared during of a supply chain of this item. In one embodiment of the invention, the computer processing circuit is implemented to estimate, on the basis of said K successive olfactory signatures, a model of the evolution over time of the olfactory signature of the article perishable according to its various qualified states or maturation phase.

[0023] This makes it possible to build models of the evolution of the maturation of a given perishable article from estimated similarities between two articles, or of the same article in successive instants. It is thus possible to precisely distinguish different perishable items according to their type, their geographical origin, differences in their ripening speed, etc.

In one embodiment of the invention, the comparison of the current measurement with the model of evolution over time gives an estimate of the similarity between the current article and the perishable article at a given phase of maturation of the perishable item.

[0025] This makes it possible to compare an olfactory signature a posteriori with the evolution model in order to deduce therefrom a deviation at a given moment from the maturation phase expected at the step of taking the measurement.

In one embodiment of the invention, the model of the olfactory signature of the given article is obtained by a multivariate analysis of the K olfactory signatures, each determined by the respective proportions of said fluids in the mixture, the model being defined in a space of L dimensions, the multivariate analysis being selected from a principal component analysis or a multidimensional positioning analysis.

[0027] Here, the application of multivariate analysis makes it possible to transform respective, measured, proportions of fluids in a mixture which are correlated with each other into new variables which are uncorrelated from each other. The number L of dimensions is less than or equal to the first number K.

[0028] As a variant, the model can be defined by means of other types of analysis such as through the use of neural networks, decision trees, random forests, etc. [0029] In one embodiment of the invention, the multivariate analysis is selected from among a principal component analysis or a multidimensional positioning analysis.

In the present, a principal component analysis makes it possible to distinguish the most important contributions among the respective proportions of the fluids in a mixture, by reducing the number of variables used. This makes the information provided by them less redundant, while allowing the synthesis of relevant information in the form of principal components, which can be defined as "odors", and resulting from a combination of the measured proportions.

A multidimensional positioning analysis makes it possible to separate the respective proportions of the fluids in a mixture according to their variances, which also makes it possible to maximize the variance in the space of dimensions L. The distances are thus contrasted and the contributions the least. weaker ones, usually due to the noise measured by the sensors, can be eliminated more easily.

[0032] In one embodiment of the invention, the model is defined by a set of phase vectors in L-dimensional space, each phase vector characterizing a maturation phase of the perishable article.

In the present documents, a phase vector is a vector with L components determined as being a representative signature, for example an average signature or a median signature, of a plurality of acquired olfactory signatures, this vector being characterized by a standard and a direction in L-dimensional space.

[0034] This makes it possible to quantify and represent a given phase of maturation of a perishable item from a plurality of olfactory signatures selected to characterize said phase of maturation.

In one embodiment of the invention, it is estimated, in the space of L dimensions, a distance between a point representing the current measurement and each phase vector, the smallest of the estimated distances characterizing a state of maturation current of the perishable item. This makes it possible, on the basis of a single current measurement, to determine which phase of maturation a current article is closest to.

[0037] In one embodiment of the invention, the distance is an absolute value, a Euclidean distance or a distance between the point representing the current measurement and several nearest neighboring phase vectors.

[0038] This makes it possible to make more precise the determination of a maturation phase to which a standard article is closest, and in particular to better discriminate between several close maturation phases.

[0039] In one embodiment of the invention, the number of sensors that the electronic nose comprises is less than or equal to 100, and preferably equal to 25.

This makes it possible to ensure the acquisition of an optimal number of fluids present in a mixture of fluids from articles given in order to estimate a sufficiently precise model of the olfactory signature of these articles while minimizing the size. electronic nose.

[0041] In one embodiment of the invention, the method further comprises:

grouping several olfactory signatures into at least two groups, each group being defined by a barycenter of the signatures in said group, the distance of each signature from said barycenter being less than or equal to a predetermined distance;

- compare the distances between the current measurement and each of said groups, to associate the current article with the group whose current article is closest.

[0042] Here, the barycenter of a group of points in a 1-dimensional space corresponds to the median or the average of these points. In a space of L dimensions where L is greater than 1, the barycenter of a group of points, each point being defined by an L-uple of coordinates, is a point whose each coordinate corresponds to the median or to the average of the coordinates of those points which are of the same rank.

This makes it possible to identify and distinguish the olfactory signatures corresponding to different maturation phases according to different groups, to represent each group by a single virtual olfactory signature, called barycenter, and to identify which phase of maturation a standard item is closest to.

Any other means of classification can be used indifferently to account for the phase of a current item vector among the learned phases.

[0045] In one embodiment of the invention, the method further comprises:

- implement an additional sensor chosen from a marking sensor, a geolocation sensor, a temperature sensor, a pressure sensor, a humidity sensor or an acceleration sensor to measure a complementary parameter of the given item ;

- acquire, with an additional sensor of the same type, a current additional parameter of the current item, of the same type as the given item; and

- associate said complementary parameter with the group whose current article is closest.

This makes it possible to improve the identification of the current article with respect to a given maturation phase from measurements made by sensors which are not olfactory sensors, by matching a tag, a spatial value. time, a temperature value, a pressure value, a humidity value or an acceleration value at this maturation phase.

[0047] In one embodiment of the invention, the method further comprises processing data of a result of said estimation of similarity between the current article and the given article, to protect said data against tampering.

In the present, and in a nonlimiting manner, an example of a data processing system implements a transmission-coding device comprising means for encoding an olfactory signature and / or a result of an estimate of a similarity between a standard article and a given article. This transmission-coding device is for example included in the electronic nose or in the computer processing circuit, which further comprises means for transmitting the coded information. This example of a processing system further comprises a reception-decoding device comprising means to receive this coded information and means for decoding it. This reception-decoding device is for example included in a man-machine or machine-machine interface.

[0049] This prohibits a third party from altering the olfactory signatures acquired by the sensors of the electronic nose, as well as the result of an estimate of similarity between a current article and the given article.

[0050] In one embodiment of the invention, said result data is processed by a chain of blocks.

In the present, a blockchain is any type of distributed computing environment such as a client-server system networked with a user interface, in particular in combination with a data processing system .

[0052] This allows information to be stored and transmitted with enhanced security, through a consensus process within a network formed by several stakeholders in a supply chain. The use of a blockchain also preserves the reliability of scent signature measurements taken during a supply chain, which further prevents possible tampering attempts. The processing of an olfactory signature and / or outcome data within a blockchain provides reliable assurance of the measurements made within a given supply chain.

According to another aspect, the invention relates to a device for identifying a given item by its olfactory signature, the device comprising: - an electronic nose, comprising a plurality of sensors for the presence of fluids likely to be present in a mixture of fluids resulting from the given article, - a computer processing circuit, connected to the electronic nose to obtain an olfactory signature of said mixture, the olfactory signature comprising respective proportions of said fluids in the mixture, and the computer processing circuit being configured to implement the method according to one of the preceding claims. [0054] According to another aspect, the invention relates to a computer program comprising instructions for all or part of a method as defined herein when said instructions are executed by a processor of a processing circuit. Brief description of the drawings

[0055] Other features, details and advantages of the invention will become apparent on reading the detailed description below, and on analyzing the accompanying drawings, in which:

[0056] [Fig. 1], Figure 1 shows a view of an electronic nose for implementing a method according to the invention;

[0057] [Fig. 2], Figure 2, shows a view of sensors of an electronic nose for implementing a method according to the invention;

[0058] [Fig. 3], Figure 3, shows an example of representation of an olfactory signature acquired by an electronic nose;

[0059] [Fig. 4], Figure 4, shows a schematic view of a method according to an exemplary implementation of the invention;

[0060] [Fig. 5], FIG. 5 represents an example of a model of the evolution over time of an olfactory signature of a perishable article;

[0061] [Fig. 6], Figure 6 shows an example of implementation of a method according to the invention in the context of a supply chain;

[0062] [Fig. 7], Figure 7, shows an example of a model defined by the application of a multivariate analysis with several olfactory signatures to define a model in a 2-dimensional space; and

[0063] [Fig. 8], Figure 8, shows an example of a comparison between a standard item and given items by means of a set of phase vectors in a 2-dimensional space.

Unless otherwise indicated, the elements common or similar to several figures bear the same reference signs and have identical or similar characteristics, so that these common elements are generally not described again for the sake of simplicity.

Description of embodiments

[0065] Herein, an electronic nose is a physical device configured to acquire an odor signature of a given object, such as a perishable item, from odors exhaled by that item. An electronic nose typically includes a plurality of sensors configured to recognize the presence of a target compound, such as a chemical or biological analyte, in a fluid such as a gas or liquid sample.

FIG. 1 represents an example of an electronic nose NN configured to implement a method in accordance with the invention. FIG. 2 is a view of a metallic layer CM that comprises the electronic nose NN and of its sensors Ci, C2, .... CN, this metallic layer being provided to promote the detection, and in particular the adsorption, of fluids in a mixture in contact with the CM layer.

In an example presented here, the electronic nose NN comprises a metallic layer CM, preferably flat, and comprising for example gold. The CM layer of the electronic nose NN further comprises a number N of sensors Ci, C2, .... C _N formed on a first face F1 of the metallic layer CM so that the first face F1 of the metallic layer CM and said sensors are in contact with a mixture of a fluid, in particular a fluid of a dielectric nature, for example a liquid or a gas which is exhaled by an article to be analyzed by means of the electronic nose NN.

In an example presented here, the number N of sensors that the NN nose comprises can vary between 1 and several hundred, preferably between 20 and 100. In a non-limiting manner, the examples presented here relate to an NN nose comprising 10 or 25 sensors to optimize the size of the NN nose while allowing it to maximize its sensitivity. In the present, the plurality of sensors comprises at least two sensors of different sensitivity to obtain an olfactory signature of said mixture. In an example presented here, the electronic nose NN also comprises a support SS for said metallic layer CM. The support SS is arranged against a second face F2 of the metallic layer CM, this second face F2 being opposite to the first face F1. In general, the support SS is formed from a dielectric material and has a refractive index greater than the refractive index of the mixture to be analyzed. This SS support is for example a glass prism.

In an example presented here, another metal layer (not shown) of low thickness, for example made of Chromium (Cr), is provided between the second face F2 and the support SS to ensure stable grip of the metal layer CM on the SS bracket.

In an example presented here, the electronic nose NN further comprises a suction system for capturing a volume sample of a fluid. The metallic layer CM and the sensors Ci, C2, .... C _N are housed in a chamber CC, and this chamber CC comprises an input NI and an output NO. The NO output is for example connected to an external pump (not shown) which allows the CC chamber to be supplied with a perfectly controlled fluid flow between the NI input and the NO output.

The electronic nose NN further comprises computer means such as a microprocessor, an input / output communication stage and means for connection and communication with other electronic devices, in particular with a processing circuit. computer or server. These connection and communication means can be wired or wireless.

In an example presented here, the sensors Ci, C2, .... C _N of the electronic nose NN are transducers sensitive to plasmon resonances ("surface plasmon resonance", or SPR, in English) generated at the level of the first face F1 of the metal layer CM in contact with the fluid in the chamber CC. According to the principles of SPR measurement, a plasmon resonance generated at the first face F1, by polarization of the incident light, makes it possible to measure variations in the refractive index of the fluid by the sensors by means of detection of corresponding gray levels, by means of CCD cameras, for example. Local variations in the refractive index can thus be measured by the sensors Ci, C2, .... C _N when different molecules present in the analyzed fluid are adsorbed, for example when organo-volatile compounds present in a gas exhaled by an article placed near the electronic nose NN are adsorbed by several of the sensors Ci, C2, .... CN. The adsorbed molecules are then imaged in order to deduce gray levels representative of their concentration.

For example, these sensors Ci, C2, .... C _N are configured to adsorb various compounds such as heptanes, octanes, nonanes, ethanol or even beta-pinene. Each sensor of the electronic nose NN thus corresponds to a measured intensity h, I2, .... IN respective of the respective proportions of these compounds or fluids in this mixture. These proportions can optionally be normalized during the measurement or after it.

Measurement results obtained by 10 sensors Ci, C2, .... C10 of an electronic nose NN are represented in FIG. 3 in the form of a “radar” graph, these results forming an olfactory signature d 'a mixture of fluids resulting from a given article P, for example from a perfume produced by a counterfeiter or from a banana just picked in Brazil.

[0077] As part of a measurement of an olfactory signature of this article, this article exhales a set of organo-volatile compounds and each sensor adsorbs one or more of these compounds. Measuring refractive index variations in the gas then makes it possible to identify that different compounds react with different intensities depending on each sensor.

In the example presented here, the intensities measured by the electronic nose NN are clearly separated and quantified according to each sensor, for example an intensity \ ^ equal to 40 of an ethanol compound is measured by the sensor Ci, an intensity I2 equal to 60 of an octane compound is measured by the sensor C2, ... and an intensity ho equal to 35 of a nonane compound is measured by the sensor C10. These intensities can also be represented by a vector with 10 components, equal to (40, 60, ..., 35). In the example presented here, the signal supplied by each sensor corresponds to the intensities measured and standardized with respect to all the sensors. In particular, the corresponding normalized intensity can be defined as being the intensity measured by a given sensor divided by the standard, said standard being equal to the square root of the sum of the squares of the intensities measured by each sensor. This normalization makes it possible to separate the intensity of the signature itself, which is advantageous for example when the concentration of fluid present in the mixture, or the odor, of the article is too strong, which results in saturation. of the corresponding sensor. Since the relative proportions are respected during standardization, it is possible to compare different signatures acquired over time by the same type of electronic nose. For each acquired olfactory signature, a normalization of the intensities corresponding to the respective proportions of the fluids also makes it possible, subsequently, to estimate a model of olfactory signatures by means of a multivariate analysis applied to normalized data.

Thus, a given intensity reflects the respective proportion of fluids adsorbed by the various sensors of the NN electronic nose, and therefore the concentration of these compounds in the fluid mixture of the article. For 10 sensors we therefore obtain, for a given item, 10 rays each corresponding to the response of a sensor. An olfactory signature is thus represented on the radar graph by a surface whose shape varies depending on the odor exhaled by the item at a given time.

We can therefore compare different surfaces to distinguish the olfactory signatures of a given article at different times, the differences between these surfaces or signatures may be the consequence of internal transformations of the article, for example due to a counterfeit or maturation, or aging of the electronic nose NN due to the degradation of its sensors.

[0082] FIG. 4 illustrates a flowchart representing a method according to an example of implementation of the invention.

During a step EA, an electronic nose NN is implemented to acquire, as input, an olfactory signature SM of a mixture of fluids obtained from a given article P. The signature is acquired by all the sensors that the electronic nose NN comprises, for example 25 sensors Ci, C2, .... C25 ·

[0084] During a step EB, the implementation step EA is repeated a first number K of times to acquire as many corresponding olfactory signatures. According to different variants, these K olfactory signatures can be acquired for the same given article P at times T1, T2, TK very close to each other to provide a more precise measurement by calculating averages, which makes it possible in particular to calibrate a nose imperfect NN electronics. These K olfactory signatures can also be acquired from several articles of the same type, for example from several bananas in the same container, to provide an overall measurement. These K olfactory signatures can also be acquired from the same perishable article in successive instants T1, T2, ..., TK distant from each other to provide olfactory signatures representative of successive phases of maturation over time of this perishable item, or to define a model of the evolution over time of its olfactory signature as explained below.

In the example presented here, the K acquired signatures are recorded in a memory, for example in the form of a first number K of pairs (S1, T1), (S2, T2), .... ( SJ, TJ), ... (SK, TK) where the “J-th” olfactory signature SJ is measured at a corresponding instant TJ, J being a number less than the first number K. As a variant, the K signatures are recorded in a memory in the form of a number K of triplets (S1, T1, P1), (S2, T1, P2), ..., (SK, TY, PZ) where "Y" is the number of times of measures and "Z" the number of items, the sum of the numbers "Y" and "Z" being equal to the first number K.

According to different variants, it is thus possible to measure the olfactory signatures of a single perishable item transported during a supply chain, or the olfactory signatures of a plurality of perishable items belonging to the same batch, by example ten bananas from the same bunch stored in a transport container. In this case, a single olfactory signature can be used to represent all the items in the batch, or to allow subsequent calibration of the sensors of the NN electronic nose. FIG. 5 represents the simplified example of 10 olfactory signatures acquired successively over time in 5 instants Ti, T ₂ , .... T ₅ , these olfactory signatures being defined by the intensity I as measured by a single sensor Ci. The case of a first perishable article P1 corresponding to the 5 black dots and of a second perishable article P2, corresponding to the 5 white dots. In this example, we see that the proportion of fluid measured by this sensor in a mixture of fluids resulting from P1 decreases over time while the proportion of the same fluid measured by this same sensor in a mixture of fluids resulting from P2 decreases overall. over time, which results in two very different olfactory signatures.

[0088] Figure 6 is a flowchart schematically showing an example of implementing a method according to the invention as part of an article supply chain.

We consider here the case of a given perishable article P, in particular bananas of the same type, which is supplied by a producer during the same supply chain to a distributor. It is assumed that these bananas are harvested during phase T1, packaged and stored during phase T2, transported by vehicle during phase T3 and distributed in stores during phase T4.

At any time during one of these 4 phases T1, T2, T3 and T4, an electronic nose NN can be used to acquire respective olfactory signatures S1, S2, S3 and S4 from samples of bananas representative of one or other of these phases.

Depending on the state of the bananas in each of these phases, different olfactory signatures are acquired over time. In the case where the electronic nose NN comprises 10 sensors, the olfactory signature S1 corresponds to 10 respective proportions I ¹ 1, ..., I ¹ 10 of the fluids in the mixture exhaled by these bananas during phase T1. Depending on the environmental conditions, the supply conditions and the proper ripening of these bananas changing over time, the olfactory signature S2 corresponding to the 10 proportions \ ² , ..., l ² io of the mixture exhaled by the bananas during phase T2 is different, and so on for the 10 proportions measured by the sensors during of phase T3 and for the 10 proportions measured by the sensors during phase T4. The representations of the 4 signatures S1, S2, S3 and S4 in the form of a “radar” graph are therefore different.

Each of these signatures is then transmitted to a computer processing circuit CPU, to estimate a SMOD model of the olfactory signature of the bananas for all of the phases T1, T2, T3 and T4, and giving an account of its evolution at over time. This SMOD model is estimated using statistical analysis, as described below.

At any time, the electronic nose NN can be used to measure, and compare with the SMOD model, a current measurement SC of an olfactory signature of a common item PC, for example a sample of bananas dropped during a intermediate instant T5 between the two phases T2 and T3. The result of this comparison makes it possible to identify and estimate a similarity SM between the current measurement SC and the model SMOD.

Returning to Figure 4, steps EA and EB are implemented by an electronic nose NN. After step EA, following steps EA, EB or simultaneously with one of the two steps EA and EB, an electronic nose of the same type as NN, and preferably identical, is implemented to perform a current measurement SC of olfactory signature of a current item PC, of the same type as the given item P. This subsequently makes it possible to compare the current measurement SC with one or more of the other olfactory signatures, and in particular, with a model of the olfactory signature of the given article P established during another step.

The K olfactory signatures acquired during step EB are then transmitted to a computer processing circuit CPU which applies a statistical analysis to them in order to estimate a SMOD model of the olfactory signature of the given article P. The how this model is estimated will be detailed in connection with the following figures.

In the example presented here, the electronic nose NN is connected to the computer processing circuit CPU and are both included in a device DD to identify the given article P by its olfactory signature in accordance with the present. The CPU processing circuit is connected to the nose electronics are configured to implement the method according to one of the preceding claims.

After step EB, the K olfactory signatures are transmitted to an output module SOUT of the electronic nose NN which is in communication with an input module CIN of the computer processing circuit CPU, these two modules forming an interface of communication between the electronic nose NN and the computer processing circuit CPU.

The DD device further comprises a communication module (not shown) for connecting said device to an external network R, and for exchanging data with other devices via said network. For example, the communication module can be a Wifi or Ethernet network interface, or else a Bluetooth communication module. Preferably, the communication module also includes a data reception module and a data transmission module.

Once the SMOD model has been established, it is compared with a current measurement SC of the olfactory signature of a common article PC, for example from a sample of bananas. The SC measurement is acquired during step ED. For example, the SC measurement is acquired in any of the phases T1, T2, T3, T4, SC pertaining to the same type of bananas as that used to establish the SMOD model. As with the K olfactory signatures, the SC measurement is transmitted to the SOUT output module of the electronic nose which communicates it to the CIN input module of the computer processing circuit CPU. The CIN input module then compares the SC measurement to the SMOD model to estimate a SIM similarity with the PC article. This SIM similarity estimate is then transmitted to an output module COUT of the computer processing circuit CPU, which communicates it to an external network R, for example a server or any other device making it possible to process the outgoing data securely, for example by means of of data processing of a result of said similarity estimate, in particular to protect these data against falsification, for example via a blockchain data processing circuit (not shown).

[0100] In another example (not shown), the method is also applicable to the case of a non-perishable article P, for example sea salt collected during a phase T1, routed during phases T2 and T3 and finally distributed during phase T4. In this case, the calibration, the precision and / or the reliability of the same electronic nose NN can be checked at any time by comparing at least two olfactory signatures of a mixture of fluids from article P. Precise control of the electronic nose NN can therefore be carried out by estimating a SIM similarity between a current measurement SC of the olfactory signature of a salt sample of a common article PC during any one of the phases T1, T2, T3 and T4 and the established model of the olfactory signature of salt P on the basis of the olfactory signatures of the salt during all of the phases T1, T2, T3 and T4 or of some of them.

FIG. 7 represents an example of results obtained by applying a statistical analysis to 40 olfactory signatures S1, S2, ..., S40 which were acquired from the same type of given article P .

[0102] In the present case, these 40 olfactory signatures are acquired for the same perishable article, for example bananas originating in Brazil, during 4 successive maturation phases. According to one variant, these 40 olfactory signatures can also relate to those corresponding to 10 bananas taken from 4 different samples.

[0103] In the example presented here, an estimation of an SMOD model of the olfactory signature of the given article P is implemented via the application of a multivariate analysis. A multivariate analysis consists of performing a dimensional reduction of the acquired data.

In particular, the multivariate analysis which is applied is a principal component analysis, which makes it possible to determine a plurality, here a second number L of parameters defining a respective state of the article in a space of L dimensions, L defining two dimensions Dim1 and Dim2 in Figures 7 and 8.

In the present case, each olfactory signature represents the respective proportions of fluids resulting from P as measured by 25 sensors C i, C2, .... C25 of an electronic nose NN. Prior to the application of multivariate analysis, the given item P is identified by 40 ^* 25 values of proportions, and therefore can be represented by 40 points in 25-dimensional space. After application of multivariate analysis, the article P can be represented by 40 points each comprising two coordinates, in a space of 2 dimensions. In a nonlimiting manner, the number L of dimensions is between 1 and 10 and preferably between 1 and 5, and in all cases less than the number of sensors.

[0106] Other analytical methods can be used. Mention may be made, by way of nonlimiting example, of the analysis by principal components, or PCA; a multidimensional positioning analysis, or MDS; principal component regression, or PCR; a partial least squares regression, or PLS; a discriminant analysis by partial least squares, or PLS-DA. Most of these methods, in particular PLS-DA, have the advantage of allowing for prior learning of different groups of samples having undergone similar processing, which optimizes the separation of points into different groups.

[0107] A multivariate analysis thus makes it possible to reduce the information corresponding to 25 measured proportions of an olfactory signature and to reduce them to 2 main components. A limited number of principal components, ideally the most significant, are chosen to explain the variability of the olfactory signatures in an optimal way.

[0108] In the example presented here, several olfactory signatures are therefore grouped into different groups after the application of the multivariate analysis. The 40 olfactory signatures of the perishable item form 4 clouds each comprising 10 points grouped into 4 groups G1, G2, G3 and G4 in this space of reduced dimensions. These point clouds, or centroids, thus determine groups having in particular the shape of an interval (in 1 dimension), circle (in 2 dimensions), sphere (in 3 dimensions) or hyper-sphere (in more than 3 dimensions) . Multivariate analysis therefore makes it possible to group together the olfactory signatures represented in the space of L dimensions, and to estimate variabilities with respect to the centroids, these variabilities possibly being due, for example, to the maturation of the perishable article that l 'we seek to identify or aging sensors of the electronic nose over time.

[0109] In the case where the number of dimensions is equal to 2, it is possible to define a virtual olfactory signature representing the set of the most close in a given group, here in this case 4 centers SG1, SG2, SG3 and SG4 corresponding to groups G1, G2, G3 and G4, respectively. These virtual olfactory signatures define a centroid or barycenter of the corresponding cloud of points (or, in the case of a single dimension, its median) as well as a corresponding radius (not shown). Such a radius is for example defined by the confidence interval of the reference points. Centroids can for example frame 95% or 99% of the variability of each group, etc. A radius can also be defined by a calculated distance between the center and the furthest point, as detailed below.

[0110] In connection with FIGS. 7 and 8, and for each point corresponding to an olfactory signature of an article to be identified, it is possible to determine a distance D between several points and / or several phase vectors. A distance defined in a space of L dimensions can be of different types, for example an absolute value, a Euclidean distance or a distance between a given number of nearest neighbors. Taking for reference point the origin of the frame of reference of the L-dimensional vector space, a distance D is generally a norm "L In a 1-dimensional space, the distance can be the norm" 1 "given by the sum of the values absolute. In a 2-dimensional space, the distance can be the square root of the sum of the squares. In a space of L dimensions, the norm L is the “1 / L ^th ” root of the sum of the elements to the L ^th power, and so on.

In both cases, these 40 signatures were acquired using the same electronic nose NN comprising 25 sensors, each sensor being sensitive to a different fluid which may have been present in a mixture of fluids obtained from P

[0112] A SMOD model can then be estimated on the basis of the interpolation of a trajectory passing through each group of points, and in particular, through each of the barycenters of these groups. As a variant, the application of a regression on the principal components makes it possible to obtain an expected trajectory, which may or may not be linear. FIG. 8 represents an example of comparison between a current article and given articles by means of a set of phase vectors in a space of 2 dimensions.

[0114] In the case of a space of L dimensions, the definition of a distance makes it possible to quantify and compare the stages of maturation of a perishable article. An indicator phase vector in an L-dimensional space is typically defined by a set of coordinates X1, X2, ..., XL in this space, said coordinates being used to calculate distances between the phase vectors.

In the present example, a first vector VG1 with two components designates the position of a first barycenter SG1, a second vector VG2 with two components designates the position of a second barycenter SG2 and a third vector VG3 with two components designates the position of a third barycenter VG3.

[0116] In this example, a point SGC corresponding to the current measurement SC is represented in the space of 2 dimensions Dim1 and Dim2 following the application of a multivariate analysis. We then determine which barycenter the point SGC is closest to by relative comparison of the distances separating this point from the different barycenters, which makes it possible to estimate from which group and therefore from which phase of maturation the current measurement SC of the current article PC is the closest. In practice, a VGC virtual phase vector (not shown) can be determined to correspond to the SGC point. Based on the comparison of the distance D1 defined between the vector-phase VG1 and VGC, the distance D2 defined between VG2 and VGC, the distance D3 defined between VG3 and VGC, we can deduce that SGC is the closest to SG2 in the space of dimensions Dim1 and Dim2. Indeed, the distance D2 being smaller than the distances D1 and D3, we deduce from this comparison that the current article PC more probably belongs to the group G2, and that this one is probably closer to the corresponding maturation phase.

In the example presented here, at least one of the distances determined between the current measurement represented in the space of L dimensions and one of the barycenters makes it possible to define a SIM similarity, moreover quantified, between the current article PC the given article thanks to the estimation of the SMOD model.

[0118] Advantageously, the probability that a current item is in a given maturation phase can also be quantified by virtue of the distance separating the VGC vector from the phase vector associated with the barycenter of the group corresponding to this maturation phase.

In general, data resulting from an estimation of SIM similarity between the current article and the given article comprises data selected from any olfactory signature, any phase vector, any olfactory signature model and / or any distance established as explained previously.

[0120] In the example presented here, this result data is processed to be protected against falsification. In particular, this result data is processed by a chain of blocks.

[0121] For example, this processing is implemented by the computer processing unit CPU, which is configurable to encode the result data by placing them in a checksum ("hash") of a string of blocks ("blockchain" in English). As a variant, this processing is implemented by a blockchain data processing circuit, CBC (not shown), which is in communication with the computer processing circuit CPU (for example a succession of communicating servers or the like). This communication can be permanent or occasional. As a variant, the computer processing circuit CPU comprises this circuit CBC or itself constitutes this circuit for processing data by block chain.

[0122] In particular, all of these data are included in one or more checksums defining coded data, for example in hexadecimal checksums. In a nonlimiting manner, these checksums can be checksums determined by algorithms of MD5 or SHA type, which have the advantage of securing the format of the data included such that any unauthorized reading attempt automatically results in a modification of the data. this format, therefore directly identifiable. Advantageously, in addition, such signatures can be protected with a encryption encryption (RSA or other) so that the decryption may require at least one secret key.

A chain of blocks using these checksums makes it possible to store the data in a secure database, and / or to validate other data stored in the same database. This database can include one or more servers, in communication with the computer processing circuit CPU and possibly with one or more local or remote terminals, via a network such as the Internet.

[0124] During a first step in the processing of the data of the example presented here, CBC can receive data from the electronic nose NN, from the computer processing circuit CPU or from a secure database and possibly record them. locally in a CBC memory. A CBC processor is configured to extract the data from the secure database.

[0125] In a next step, the CBC processor can generate metadata including blockchain data. This metadata includes additional protections, in particular via the storage of a checksum which makes it possible to encrypt each data, this storage forming an "A" block of the chain of blocks. This metadata may contain information about other blocks in the blockchain and / or the value of a checksum, for example a checksum of another block. In a next step, and for each block, "A" the CBC processor generates a checksum of the previous "A - 1" block.

For example, to determine a checksum of a block "A", all the data of this block "A" are concatenated with the value of the checksum of the block "A - 1" which precedes A, which provides a new checksum, and so on. Gradually, a plurality of blocks comprising the encrypted data are thus generated. Since the encryption of the data of a given block depends on checksums constructed from each preceding block, the security of the data is thus increased. [0127] The security of the data is thus improved against falsification, starting from the acquisition of an olfactory signature of the article P by an electronic nose NN, a chain of blocks, and in particular a chain of blocks using sums control among those described above.

[0128] Advantageously, it is thus possible to securely record data resulting from an estimation of SIM similarity between the current article and the given article, and to compare it subsequently with a current measurement to authentication purposes.

Claims

[Claim 1] A method implemented by a computer processing circuit (CPU), connected to an electronic nose (NN), to identify a given item (P) by an olfactory signature of the given item, said method comprising:

- implementing (EA) the electronic nose comprising a plurality of sensors (C1, C2, ..., CN) for the presence of fluids likely to be present in a mixture (M) of fluids from the given article, to obtain an olfactory signature (SM) of said mixture, the olfactory signature comprising respective proportions of said fluids in the mixture;

- repeat (EB) the implementation of the electronic nose a first number K of times to acquire K olfactory signatures (S1, S2, ..., SK);

- implement (EC) the computer processing circuit to estimate, on the basis of said K olfactory signatures, a model (SMOD) of the olfactory signature of the given item (P);

- acquire (ED), with an electronic nose of the same type, a current measurement (SC) of the olfactory signature of a current article (PC), of the same type as said given article (P); and

- compare (EE) the current measurement to said model, to estimate a similarity (SIM) between the current article and the given article.

[Claim 2] The method of claim 1, wherein, the given article being a perishable article, the operation of the electronic nose to obtain the first number K of olfactory signatures over time is carried out successively over time to acquire a succession of K olfactory signatures over time.

[Claim 3] The method of claim 2, wherein, the perishable article having several successive phases of maturation over time, the K olfactory signatures are representative of said successive phases of the perishable article.

[Claim 4] Method according to one of claims 2 and 3, in which the computer processing circuit is implemented to estimate, on the basis of said K successive olfactory signatures, a model of the evolution over time of the olfactory signature of the perishable item.

[Claim 5] The method of claim 4, taken in combination with claim 3, wherein the comparison of the current measurement to the pattern of change over time gives an estimate of similarity between the current article and the perishable article at a given phase of maturation of the perishable article.

[Claim 6] Method according to one of the preceding claims, in which the model of the olfactory signature of the given article is obtained by a multivariate analysis of the K olfactory signatures, each determined by respective proportions of said fluids in the mixture, the model being defined in a space of L dimensions, the multivariate analysis being selected from a principal component analysis or a multidimensional positioning analysis.

[Claim 7] The method of claim 6, taken in combination with claim 5, wherein the model is defined by a set of phase vectors in L-dimensional space, each phase vector characterizing a maturation phase of l. perishable item.

[Claim 8] A method according to claim 7, in which a distance between a point representing the current measure and each phase vector is estimated in the space of L dimensions, the smallest of the estimated distances characterizing a current maturation state. of the perishable item.

[Claim 9] A method according to claim 8, wherein the distance is an absolute value, a Euclidean distance or a distance between the point representing the current measurement and several nearest neighbor phase vectors.

[Claim 10] Method according to one of the preceding claims, in which the number of sensors that the electronic nose comprises is less than or equal to 100, and preferably equal to 25.

[Claim 11] Method according to one of the preceding claims, further comprising:

[Claim 12] The method according to one of the preceding claims, further comprising processing data of a result of said estimate of similarity between the current article and the given article, to protect said data against tampering.

[Claim 13] A method according to claim 12, wherein said result data is processed by a blockchain.

[Claim 14] Device for identifying a given article (P) by its olfactory signature, the device comprising:

- an electronic nose, comprising a plurality of sensors for the presence of fluids likely to be present in a mixture of fluids from the given article,

- a computer processing circuit, connected to the electronic nose to obtain an olfactory signature of said mixture, the olfactory signature comprising respective proportions of said fluids in the mixture, and the computer processing circuit being configured to implement the method according to one of the preceding claims.

[Claim 15] A computer program comprising instructions for implementing the method according to one of claims 1 to 13, when said instructions are executed by a processor of a processing circuit.