A method for transcribing an odor or an aroma into a colorimetric description comprises: physico-chemical analysis of said odor or of said aroma in order to associate with it a physico-chemical description comprising a physico-chemical vector, comprising proportions associated with a predetermined set of respective volatile molecules; physico-chemical analysis of a set of so-called test odors and/or aromas in order to assign to each test odor or aroma of said set a so-called test physico-chemical description comprising a so-called test physico-chemical vector comprising test proportions associated with said predetermined set of respective volatile molecules; assigning to the test odors and/or aromas respective test colorimetric descriptions comprising so-called test proportions associated with a set of respective colors; assigning to the odor or to the aroma a colorimetric description comprising proportions associated with said respective colors.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for transcribing an odor or an aroma into a colorimetric description comprising the following steps: a first step of physico-chemical analysis of said odor or of said aroma in order to associate with it a physico-chemical description comprising a physico-chemical vector, comprising proportions associated with a predetermined set of respective volatile molecules, a second step of physico-chemical analysis of a set of so-called test odors and/or aromas in order to assign to each test odor or aroma of said set a so-called test physico-chemical description comprising a so-called test physico-chemical vector comprising test proportions associated with said predetermined set of respective volatile molecules, a step of assigning to the test odors and/or aromas respective test colorimetric descriptions comprising so-called test proportions associated with a set of respective colors, a step of assigning to the odor or to the aroma a colorimetric description comprising proportions associated with said respective colors, said assignment step comprising a first step of assigning to the odor or to the aroma a theoretical colorimetric description comprising theoretical colorimetric proportions associated with said respective colors, this first assignment step being performed by means of a first artificial neural network exhibiting a layered architecture, by presenting to it, as input, the physico-chemical vector, said first neural network, as output, said theoretical colorimetric description, said first neural network being previously subjected to a first learning step during which there are supplied to it, as input, physico-chemical vectors associated with the set of test odors and/or aromas, said first learning step being performed in a supervised manner by taking into account the test colorimetric descriptions associated with the respective set of test odors and/or aromas. 2. The transcription method as claimed in claim 1, comprising, prior to the first learning step, a fixing step during which weights of first synaptic links linking neurons of said neural network two by two are set such that the weights associated with the first synaptic links are not modifiable during the learning step. 3. The transcription method as claimed in claim 2, in which the fixing step is performed from first correlation coefficients, each first correlation coefficient being computed between a molecule of the set of molecules and a color of the set of colors, computed in a preliminary step from test physico-chemical vectors and colorimetric descriptions associated with the test odors and/or aromas to which said physico-chemical vectors are assigned. 4. The transcription method as claimed in claim 3, in which the preliminary step comprises for at least one color of index j: a step of selecting, from the set of test odors and/or aromas, a subset of index j of test odors and/or aromas which are associated with test colorimetric descriptions exhibiting non-zero proportions for said color of index j, a first step of grouping together test odors and/or aromas of the subset in a set of groups of test odors and/or aromas, such that the physico-chemical vectors associated with the test odors and/or aromas present in a group exhibit, between them, greater first correlation coefficients than with the physico-chemical vectors associated with the test odors and/or aromas present in the other groups of said set of groups, and for at least one of said groups, a step of preparing a list of common molecules corresponding to the molecules for which the test proportion is non-zero in all the test physico-chemical vectors associated with the test odors and/or aromas grouped together in said group, and, for at least one molecule taken from the list of common molecules, a step during which a first series is established, in which the test odors and/or aromas grouped together in said group are arranged in ascending order of the proportion of said molecule in the test physico-chemical vectors associated with said test odors and/or aromas, a step during which a second series is established, in which the test odors and/or aromas grouped together in said group are arranged in ascending order of the proportion of said color in the colorimetric descriptions associated with said test odors and/or aromas, a step of computing another correlation coefficient between the first series and the second series, a step of computing at least one first correlation coefficient between a molecule of the set of molecules and a color of the set of colors from the other correlation coefficients computed for said molecule and said color. 5. The transcription method as claimed in claim 1, in which the colorimetric description is the theoretical colorimetric description. 6. The transcription method as claimed in claim 1, in which the step of assigning the colorimetric description comprises a step of enhancing the theoretical colorimetric description from values of a set of sensory descriptors assigned to the odor or to the aroma and to respective test odors and/or aromas so as to obtain said colorimetric description, the set of sensory descriptors comprising at least one sensory descriptor taken from a source of an odor or of an aroma, a wholesomeness index, an agreeability index, a familiarity index, an olfactory note, and an intensity index. 7. The transcription method as claimed in claim 6, in which the enhancement step comprises: a step of submitting at least one second artificial neural network of index b to a second learning step, a second neural network exhibiting a layered architecture comprising an input layer suitable for receiving values associated with the set of sensory descriptors and supplying as output first Lm, second cm and third Hpm mean coordinates, in which p is an integer ranging from 1 to v an integer, in a colorimetric reference frame called L, C, H, said second neural network receiving, during the second learning step, values of the set of sensory descriptors associated with the test odors and/or aromas present in a color series made up of at least a part of the test odors and/or aromas of the set of test odors and/or aromas, a second step of analysis of the odor or of the aroma by supplying the values of the set of sensory descriptors as input for said second neural network of index b which supplies, as output, first Lmbutb, second cmbutb and third Hpmbutb target mean coordinates of index b in reference frame L, H, C, of a target colorimetric description associated with the odor or with the aroma concerned, a step computing a new colorimetric description comprising new proportions qcnbj which observe, regardless of the value of j between 1 and J, the following equations: Lmbutb = ∑ j = 1 J  Lj * qcnb j cmbutb=Σj=1jcj*qcnbj, Hpmbutb=Σj=1jPHpj*qcnbj with p an integer ranging from 1 to v and v an integer, in which Lj, cj and PHpj are the respective coordinates of the color Cj of index j in the reference frame L, H, C, and which minimize, for at least one color Cj, the absolute value |qcnbj−qcTbj| of the difference between the new proportion of order b and the theoretical proportion of order b. 8. The transcription method as claimed in claim 6, comprising, prior to the enhancement step, a second step of grouping together test odors and/or aromas belonging to the set of test odors and/or aromas in a set of series, called color series, by similarity of the test colorimetric descriptions which are assigned to them. 9. The transcription method as claimed in claim 8, comprising: a step of computing coefficients of similarity between the odor and the respective color series, from values of the set of sensory descriptors assigned to the odor or to the aroma and to the test odors and/or aromas grouped together in respective color series and/or from the theoretical colorimetric description and from the test colorimetric descriptions assigned to the test odors grouped together in the respective series of odors, a verification step during which there are identified, from the color series, similar color series with which the odor or the aroma satisfies a first predetermined similarity criterion, and during which the question of whether the odor or the aroma satisfies a second criterion of similarity with a color series taken from the similar color series is verified, and, when the odor or the aroma satisfies the first and the second similarity criteria, the values of the set of sensory descriptors assigned to the odor or to the aroma are subjected, during the second analysis step, to a single second neural network previously subjected to the learning step during which the values of the set of sensory descriptors assigned to the test odors and/or aromas grouped together in said color series with which the odor or the aroma satisfies the second similarity criterion are supplied to it as input, and, when the odor or the aroma satisfies the first similarity criterion but not the second similarity criterion, the values of the set of sensory descriptors assigned to the odor or to the aroma are subjected, during the second analysis step, to a plurality of second neural networks previously subjected to the learning step during which there are supplied to it, as input, the values of the set of sensory descriptors assigned to the test odors and/or aromas grouped together in the color series with which the odor or the aroma satisfies the first similarity criterion, the colorimetric proportions associated with said respective colors being linear combinations of new colorimetric proportions associated with the respective colors. 10. The transcription method as claimed in claim 6, comprising, prior to the enhancement step, a correction step consisting in updating the theoretical colorimetric description from the values of the set of descriptors associated with the odor or with the aroma and from a global rule making it possible to compute, for at least one color, and from values of the set of sensory descriptors assigned to the aroma or to the odor, a probability of presence, in a colorimetric description associated with these values, of a colorimetric proportion associated with said color, said correction step being followed by a return to the step of computing similarity coefficients. 11. The transcription method as claimed in claim 1, in which the step of assigning to the test odors and/or aromas a colorimetric description comprising test proportions associated with a set of respective colors, comprises: a step of assigning colors, called judged colors, to test odors and/or aromas by a set of human judges, a step of preparing a so-called test colorimetric description of each test odor or aroma comprising test proportions associated with a set of respective colors from the proportion of judges who, out of the judges belonging to another set of judges comprising all or part of said set of human judges, have each assigned judged colors to said test odor or aroma. 12. The transcription method as claimed in claim 11, in which the test proportions relating to the different colors are determined from the proportion of judges who, out of the judges belonging to the other set of judges, have each assigned judged colors to said aroma or to said odor and from proximity coefficients computed between the judged color and the colors of the set of colors. 13. The transcription method as claimed in claim 1, comprising a step of representing the new odor or of the new aroma by means of a chromatic map in which the percentage of the surface of the chromatic map occupied by a color of the set of colors corresponds to the ratio between the proportion associated with said color in the colorimetric description and the sum of the proportions associated with a subset of the set of colors in the colorimetric description, the subset being chosen in such a way that the sum of the proportions associated with the colors that it contains, in the colorimetric description, is at least equal to a predetermined threshold. 14. A method for transcribing an initial colorimetric description comprising initial colorimetric proportions relating to a set of respective colors into a result physico-chemical description comprising a list of molecules comprising the following steps: a second step of physico-chemical analysis of a set of so-called test odors and/or aromas in order to assign, to each test odor or aroma of said set, a so-called test physico-chemical description comprising a so-called test physico-chemical vector comprising test proportions associated with said predetermined set of respective volatile molecules, a first step of assigning to the test odors and/or aromas respective test colorimetric descriptions comprising so-called test proportions associated with a set of respective colors, a second step of assigning of a hypothetical colorimetric description comprising hypothetical colorimetric proportions to the set of respective colors, to a hypothetical physico-chemical vector comprising hypothetical proportions associated with said set of respective volatile molecules, this second assignment step being performed by means of a first artificial neural network exhibiting a layered architecture, by presenting to it, as input, a hypothetical physico-chemical vector comprising hypothetical proportions associated with said set of respective volatile molecules, said first neural network generating, as output, said hypothetical colorimetric description, said first neural network being previously subjected to a first learning step during which there are supplied to it, as input, physico-chemical vectors associated with the set of test odors and/or aromas, said first learning step being performed in a supervised manner by taking into account the test colorimetric descriptions associated with the set of respective test odors and/or aromas, an error computation step in which an error representative of a deviation between the hypothetical colorimetric description and the initial colorimetric description is computed, said error computation step being followed by a step of updating the hypothetical physico-chemical vector, from the error and the hypothetical physico-chemical vector, and of return to the assignment step as long as the error is greater than a predetermined error threshold, the result physico-chemical description being determined from the hypothetical physico-chemical vector which generates a computation error less than or equal to the error. 15. The transcription method as claimed in claim 14, in which the initial description is associated with so-called initial values of a set of sensory descriptors and the test odors and/or aromas are associated with values of said set of sensory descriptors, said set of sensory descriptors comprises at least one sensory descriptor taken from a source of an odor or of an aroma, a wholesomeness index, an agreeability index, a familiarity index, an olfactory note and an intensity index, and comprising a preliminary step of determining the hypothetical physico-chemical vector from so-called initial values of the set of sensory descriptors and from values of said set of sensory descriptors associated with the test odor or aroma. 16. The transcription method as claimed in claim 15, comprising, prior to the step of determining the hypothetical physico-chemical vector: a step of constructing another global law making it possible to determine, from the values of a set of sensory descriptors associated with an odor or an aroma, the probabilities of presence of the set of respective molecules in the odor or the aroma being non-zero, this step being performed from test physico-chemical vectors and from the values of the set of sensory descriptors associated with the test odors and/or aromas, a step of grouping together the test odors and/or aromas in a set of color series by similarity of their respective test colorimetric descriptions, the step of determining the hypothetical physico-chemical vector comprising: a step of determining probabilities of presence, in the result odor or aroma, of the set of the respective molecules, this step being performed from the initial values of the set of sensory descriptors and from the other global law, a step of establishing a list of possible molecules taken from the set of molecules, this list of possible molecules corresponding to the molecules which, out of the molecules of the set of molecules, exhibit a non-zero probability of presence, a step of computing correlation coefficients between the initial description and the respective color series from initial values of the set of sensory descriptors and from the values of the set of sensory descriptors associated with the respective test odors and/or aromas as well as from the test and initial colorimetric descriptions, a verification step during which there are identified, out of the color series, the color series with which the initial description satisfies a predetermined correlation criterion, this step is performed from similarity coefficients cited in the preceding paragraph, a step of preparing the hypothetical physico-chemical vector from the list of possible molecules and from test physico-chemical vectors associated with the test odors and/or aromas included in the color series with which the initial description satisfies the correlation criterion, the hypothetical physico-chemical vector comprising hypothetical proportions associated with the respective possible molecules Mi. 17. The method as claimed in claim 14, in which the following steps are implemented by a computer: the second step of assigning a colorimetric description, the first learning step, the error computation step, the step of updating the physico-chemical vector.