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CN-122029447-A - Method for generating a representation of a property of a substance

CN122029447ACN 122029447 ACN122029447 ACN 122029447ACN-122029447-A

Abstract

A method for generating a representation of a property of a substance is described. The method includes obtaining one or more portions of Nuclear Magnetic Resonance (NMR) data of the substance, wherein each NMR data portion includes one or more NMR data sets, each NMR data set including intensity data and relaxation data, processing each NMR data portion into a multi-dimensional distribution of fit data sets by performing a plurality of fits to a function, wherein the function includes at least 3 fit parameters, and generating a characteristic representation of the substance from the fit data sets. The processing includes applying at least one initial guess value. Each fitting dataset comprises data embedded in at least one fitting data subset comprising an apparent longitudinal relaxation time (T1 x), an associated apparent transverse relaxation time (T2 x) and an associated intensity (M0), preferably the function is a function of a model representing the obtained NMR data fraction.

Inventors

  • N c nielsen
  • O. Jason
  • B. H. Harvey

Assignees

  • 纳诺努德股份公司

Dates

Publication Date
20260512
Application Date
20240722
Priority Date
20230720

Claims (20)

  1. 1. A method for generating a representation of a property of a substance, comprising: recording Nuclear Magnetic Resonance (NMR) data of at least one sample of said substance, Generating one or more portions of Nuclear Magnetic Resonance (NMR) data of the recorded NMR data, wherein each NMR data portion is generated to include one or more NMR data sets, -Processing each NMR data portion into a multidimensional distribution of fitted data sets, the processing comprising selecting a function and performing a plurality of fits on the selected function, and Generating a characteristic representation of the substance from the fitting dataset, Wherein each dataset comprises associated intensity data and relaxation data, Wherein the selected function comprises at least 3 fitting parameters, Wherein the processing of each of said NMR data portions comprises selecting and applying at least one initial guess value, and Wherein each fitting dataset comprises data embedded in at least one fitting data subset comprising an apparent longitudinal relaxation time (T1 x), an associated apparent transverse relaxation time (T2 x) and an associated intensity (M0), preferably the function is a function of a model representing the obtained NMR data fraction.
  2. 2. The method of claim 1, wherein each NMR dataset is generated to include frequency data, such as at least one of resonant frequency, chemical shift, quadrupole coupling, J-coupling, or dipole-dipole coupling.
  3. 3. Method according to claim 1 or 2, wherein each NMR dataset is generated to comprise at least one of inversion recovery data or echo NMR data, preferably each NMR dataset comprises data representing at least one Inversion Recovery Delay (IRD) and/or data representing at least one Echo Delay (ED).
  4. 4. The method of claim 3, wherein the echo data comprises at least one of spin echo data, fast spin echo data, stimulated echo data, or gradient echo data.
  5. 5. The method of any preceding claim, wherein each NMR dataset comprises data from the recorded NMR data, wherein the NMR data is recorded using a pulse sequence comprising at least one period of time during which the NMR data is affected by T1 (longitudinal) relaxation and/or at least one period of time during which the NMR data is affected by T2 (transverse) relaxation, e.g. each NMR dataset comprises data from the recorded NMR data, the NMR data is recorded using a pulse sequence comprising at least two periods of time during which the NMR data is affected by T1 relaxation or at least two periods of time during which the NMR data is affected by T2 relaxation.
  6. 6. A method according to any one of the preceding claims, wherein each NMR dataset comprises data from recorded NMR data, the NMR data being recorded using a pulse sequence comprising at least one period of time during which the recorded data is affected by chemical exchange.
  7. 7. A method according to any one of the preceding claims, wherein each NMR dataset comprises data from recorded NMR data, the NMR data being recorded using a pulse sequence comprising at least one period of time during which the recorded data is affected by diffusion, such as molecular diffusion, such as ion diffusion, such as spin diffusion.
  8. 8. A method according to any one of the preceding claims, wherein the one or more NMR data portions comprise at least 2 NMR data sets, such as at least 3 NMR data sets, such as at least 4 NMR data sets, such as at least 8 NMR data sets, such as 16 or more NMR data sets.
  9. 9. A method according to any one of the preceding claims, wherein the data of at least one NMR dataset is data from a common or identical pulse sequence of the recorded NMR data, optionally the NMR dataset of at least one NMR dataset part comprises data from a common or identical pulse sequence of the recorded NMR data.
  10. 10. The method according to any of the preceding claims, wherein the data of at least one NMR dataset is data from a common or identical pulse sequence set of recorded NMR data, the pulse sequence set comprising one or more of a reverse recovery pulse sequence, a Carr-Purcell-meiboost-Gill (CPMG) sequence and/or a spin echo sequence with one or more echoes, optionally the NMR dataset of at least one NMR dataset part comprising data from the common or identical pulse sequence set.
  11. 11. A method according to any one of the preceding claims, wherein the data of at least one NMR dataset is based on data of recorded NMR data from a common isotope, preferably the NMR dataset of at least one NMR data portion is based on data of recorded NMR data from a common NMR readable isotope.
  12. 12. The method of any preceding claim, wherein processing each of the NMR data portions comprises performing the multiple fits of the NMR data portions separately to the formula.
  13. 13. The method of any preceding claim, wherein processing each of the NMR data portions comprises performing the multiple fits of the one or more NMR data sets of the NMR data portions to the formula in a common fitting process.
  14. 14. The method of any preceding claim, wherein performing the multiple fits on the NMR data portion comprises applying the at least one initial guess value for at least one of the multiple fits, preferably performing the multiple fits on the NMR data portion comprises applying the at least one initial guess value for multiple of the multiple fits, e.g., all of the multiple fits, wherein the at least one initial guess value may be the same or different.
  15. 15. The method of claim 14, wherein the initial guess comprises an initial guess for at least one of the at least 3 fitting parameters for at least one of the multiple fits, preferably the initial guess comprises an initial guess for at least one of the at least 3 fitting parameters for multiple of the multiple fits, e.g. for all of the multiple fits, wherein the initial guess can be the same or different from one fit to a subsequent fit depending on the selected pattern.
  16. 16. The method of any of the preceding claims, wherein the at least one initial guess comprises at least one of a random initial guess or an initial guess generated by applying a self-help method.
  17. 17. The method according to any of the preceding claims, wherein the at least one initial guess is applied to at least one fitting parameter of a plurality of fits, for example to each of the plurality of fits to the function, for example comprising in at least two or more of the plurality of fits to the function, the initial guess applied to the at least one fitting parameter being the same initial guess, for example being the same initial guess in each of the plurality of fits to the function.
  18. 18. A method according to any preceding claim, wherein the processing of respective NMR data portions of a plurality of NMR data portions comprises applying a randomly selected initial guess for at least one fitting parameter of each of the NMR data portions.
  19. 19. The method according to any of the preceding claims, wherein the at least one initial guess is characterized in that it is a random initial guess of the monte carlo type.
  20. 20. A method according to any of the preceding claims, wherein the at least one initial guess value is selected to be within a starting range, preferably the at least one initial guess value is an initial guess value for the fitting parameters, and the initial guess value is within a starting range represented by a span of selected values, wherein the span of selected values preferably comprises or consists of one or more typical expected values.

Description

Method for generating a representation of a property of a substance Technical Field The present invention relates to a method and system for generating a characteristic representation (e.g. a multidimensional profile, such as a T1-T2 profile) of a substance (e.g. a complex substance, such as a food product, a substance containing chemical contaminants or a manure slurry). Background Substances, in particular complex substances comprising a plurality of elements, such as fat and/or water and/or proteins and/or nucleic acids and/or carbohydrates and/or vitamins and/or minerals and/or chemical contaminants and/or nutrients, wherein the chemical species of each element interact with other chemical species in the substance, are typically characterized in a laboratory environment using expensive equipment and time consuming methods (e.g. chromatography). In many applications, substance characterization is used and is often required. One example is the food industry, where food companies are required to label the nutrients in a product, and therefore must frequently characterize their product in terms of ingredients, or monitor changes in a continuous stream. Another example is the conversion of biomass to a circulating energy source, where it is desirable to characterize the slurry during the conversion from biomass to energy source to identify element distribution to control the process. A third example is the chemical industry, where companies use chemical processes to convert organic and inorganic raw materials into chemicals, and where the characterization of chemicals and intermediates is critical to control the process and ensure product quality. In general, nuclear Magnetic Resonance (NMR) has proven to be an excellent non-invasive technique for studying microscopic molecular interactions, where laplace transforms are commonly applied to transform NMR echo data into relaxation time distributions. WO2018/163188A1 discloses a method of characterizing chemical and/or morphological characteristics of a material comprising obtaining energy relaxation data from 1H low field nuclear magnetic resonance (1H LF-NMR) measurements of the material, converting the relaxation signals into a multi-dimensional distribution of longitudinal and transverse relaxation times by solving the inverse problem under L1 and L2 regularization and further applying a non-negative constraint, and identifying one or more characteristics of the material by means of the multi-dimensional T1-T2 distribution. There is a substantial need for alternative or improved methods for characterizing substances, in particular very efficient methods and/or methods which are suitable for generating valuable information of substances even for complex substances having a complex composition. Disclosure of Invention It is an object of the present invention to provide a method for characterizing a substance to provide valuable information about the chemical, physical, biological and/or morphological characteristics of the substance. In one embodiment, it is an object to provide a method for generating a property representation of a substance, wherein the property representation describes one or more property features of the substance, preferably related to interactions between the content of the substance and/or its components and/or chemical and/or physical and/or biological influences and/or components or component fragments of the substance. In one embodiment, it is an object to provide a method for generating a representation of a property of a substance, wherein the representation of the property describes the substance in the form of a depiction of the substance or data representing a depiction of the substance. In one embodiment, it is an object to provide a method for generating a characteristic representation of a substance, wherein the characteristic representation describes at least one quality feature of the substance, preferably wherein the characteristic representation comprises data of a depiction of the substance or of a characterization of a quality parameter of the substance, or a marking relating to a change in a process of the substance. In one embodiment, it is an object to provide a method for generating a representation of a property of a substance, wherein the representation of the property. In one embodiment, it is an object to provide an NMR system for performing a method of characterizing a substance to provide valuable information about chemical, physical, biological and/or morphological characteristics of the substance. In one embodiment, it is an object to provide a method for generating a trained computer for performing processing of a representation of a property of a generated or produced substance in the method of the invention. These and other objects have been solved by the invention or by embodiments thereof as defined in the claims and/or as described hereinafter. The invention or embodiments thereof have been found to h