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CN-115004012-B - Method for evaluating the spectrum of biological substances of animal origin, plant origin or mixtures thereof

CN115004012BCN 115004012 BCN115004012 BCN 115004012BCN-115004012-B

Abstract

The present invention relates to a method for evaluating the spectrum of biological substances of animal origin, plant origin or mixtures thereof, comprising the steps of a) detecting a spectrometer in a network formed by at least one spectrometer and an input/output device; b) requesting a respective status of each spectrometer in the network of step a) and displaying the detected spectrometers and their status on said input/output device, wherein the status reflects whether the spectrometers are available for recording spectra; c) receiving a selection on the input/output device from a spectrometer which can be used for recording spectra, d) recording spectra of sample materials of animal origin, plant origin or mixtures thereof on the spectrometer selected in step c), e) predicting values of at least one parameter from the spectra of step d) by means of at least one calibration function and/or calibration graph suitable for predicting values of the parameter, wherein the at least one parameter is selected from the group consisting of the content of at least one amino acid, crude protein content, ammonia content, total amino acid with ammonia, total amino acid without ammonia, crude fat content, dry matter content, crude ash content, energy content, the content of at least one biogenic amine, the content of at least one anti-nutritional factor, the content of at least one saccharide, starch content, crude fiber content, neutral washing fiber content, acid washing fiber content, total phosphorus content, phytic acid phosphorus content, reactive lysine content, total lysine content, the ratio of reactive lysine content to total lysine content, protein dispersibility index, protein solubility, trypsin inhibitor activity, urease activity and Process Condition Index (PCI), and f) displaying the predicted results of step e) on an input/output device.

Inventors

  • REIMANN ILONA
  • A .yege
  • J. Lessing
  • A. Steele
  • A. Lotts

Assignees

  • 赢创运营有限公司

Dates

Publication Date
20260508
Application Date
20210122
Priority Date
20200122

Claims (20)

  1. 1. A method for assessing the spectrum of biological material of animal origin, plant origin or mixtures thereof, comprising the steps of: a) Detecting a spectrometer in a network formed by at least one spectrometer and an input/output device, the at least one spectrometer comprising an infrared spectrometer, a raman spectrometer, an ultraviolet-visible spectrometer, or a combination thereof; b) Requesting a respective status of each spectrometer in the network of step a) and displaying the detected spectrometers and their status on the input/output device, wherein the status reflects whether a spectrometer is available for recording a spectrum; c) Receiving a selection on the input/output device from a spectrometer available for recording a spectrum; d) Recording the spectrum of sample material of animal origin, plant origin or mixtures thereof on the spectrometer selected in step c), said step d) additionally comprising the steps of: d1 Predicting the characteristics of the sample material of step d) from the spectra recorded in step d) by using a similarity analysis of a database DB1 of spectra of known materials; d2 a) displaying the predicted characteristics of the sample material obtained in step d 1) on the input/output device, and D2 b) receiving on the input/output device i) a confirmation of the predicted characteristic displayed in step d2 a) or ii) a non-confirmation of the predicted characteristic displayed in step d2 a) and an input of the characteristic of the sample material; e) Predicting a value of at least one parameter from the spectrum of step d) by means of at least one calibration function and/or calibration graph suitable for predicting the value of the at least one parameter, wherein the at least one parameter is selected from the group consisting of content of at least one amino acid, crude protein content, ammonia content, content of all amino acids with ammonia, content of all amino acids without ammonia, crude fat content, dry matter content, crude ash content, energy content, content of at least one biogenic amine, content of at least one anti-nutritional factor, content of at least one saccharide, starch content, crude fiber content, neutral washing fiber content, acid washing fiber content, total phosphorus content, phytic acid phosphorus content, reactive lysine content, total lysine content, ratio of reactive lysine content to total lysine content, protein dispersibility index, protein solubility, trypsin inhibitor activity, urease activity and Processing Condition Index (PCI); And F) Displaying the result of the prediction from step e) on the input/output device.
  2. 2. The method of claim 1, wherein the spectrometer is a portable spectrometer.
  3. 3. The method of claim 1 or 2, wherein the method is a computer-implemented method.
  4. 4. The method of claim 1 or2, wherein the spectrometer is a portable spectrometer and the input/output device is a portable device.
  5. 5. The method of claim 1 or 2, wherein the number of spectrometers is 1 to 100, 1 to 90,1 to 80, 1 to 70, 1 to 60, 1 to 50, 1 to 40, 1 to 30, 1 to 20, or 1 to 10.
  6. 6. The method of claim 1, the similarity analysis being performed according to the following procedure: d1 a) transforming the absorption intensity of wavelengths or wavenumbers in the spectrum of step d) to give a query vector; d1 b) providing a database DB1 of sets of database vectors having spectra of known materials; d1 c) analyzing the similarity between the query vector of step d1 a) and the set of database vectors of step d1 b), step d1 c) comprising the steps of: d1c 1) calculating a similarity measure and/or a distance measure between each database vector of step d1 b) and the query vector of step d1 a) to give a similarity value for each database vector and the query vector, D1c2) arranging the similarity values obtained in step d1c 1) in a descending order when the similarity measure is calculated in step d1c 1), or arranging the similarity values obtained in step d1c 1) in an ascending order when the distance measure is calculated in step d1c 1), wherein the top-ranked database vector has the largest similarity with the query vector, D1c 3) counting the number of occurrences of a material category among the top-ranked database vectors in the ranking obtained in step d1c 2), wherein the number of occurrences is represented by a variable N, D1c 4) weighting the top N similarity values of the material classes according to the positions in the ranking obtained in step d1c 2) of the similarity values of the material classes to give a weighted ranking position of the material classes, D1c 5) for the material class, forming a sum of the weighted ranking positions obtained in step d1c 4) to give a score for the material class, wherein the highest score represents the greatest similarity to the sample material of step d), and D1 d) assigning the class of materials with the largest similarity database vector to the sample material of step d).
  7. 7. The method according to claim 1, wherein said step d) additionally comprises the steps of: d3 a) extracts from the database DB2, for the material whose material properties were predicted in step d 1), which were confirmed in step d2 b), or which were entered in step d2 b), the one or more parameters whose values were predicted in step e).
  8. 8. The method according to claim 1 or 2, wherein said step d) comprises the steps of: d1 Predicting the characteristics of the sample material of step d) from the spectra recorded in step d) by using a similarity analysis of a database DB1 of spectra of known materials; d2 a) displaying the predicted properties of the sample material obtained in step d 1) on the input/output device; d2 b) receiving input on the input/output device i) confirmation of the predicted characteristic displayed in step d2 a) or ii) non-confirmation of the predicted characteristic displayed in step d2 a) and the characteristic of the sample material, and D3 a) extracts from the database DB2, for the material whose material properties were predicted in step d 1), which were confirmed in step d2 b), or which were entered in step d2 b), the one or more parameters whose values were predicted in step e).
  9. 9. The method of claim 8, wherein the similarity analysis is performed according to the following procedure: d1 a) transforming the absorption intensity of wavelengths or wavenumbers in the spectrum of step d) to give a query vector; d1 b) providing a database DB1 of sets of database vectors having spectra of known materials; d1 c) analyzing the similarity between the query vector of step d1 a) and the set of database vectors of step d1 b), step d1 c) comprising the steps of: d1c 1) calculating a similarity measure and/or a distance measure between each database vector of step d1 b) and the query vector of step d1 a) to give a similarity value for each database vector and the query vector, D1c2) arranging the similarity values obtained in step d1c 1) in a descending order when the similarity measure is calculated in step d1c 1), or arranging the similarity values obtained in step d1c 1) in an ascending order when the distance measure is calculated in step d1c 1), wherein the top-ranked database vector has the largest similarity with the query vector, D1c 3) counting the number of occurrences of a material category among the top-ranked database vectors in the ranking obtained in step d1c 2), wherein the number of occurrences is represented by a variable N, D1c 4) weighting the top N similarity values of the material classes according to the positions in the ranking obtained in step d1c 2) of the similarity values of the material classes to give a weighted ranking position of the material classes, D1c 5) for the material class, forming a sum of the weighted ranking positions obtained in step d1c 4) to give a score for the material class, wherein the highest score represents the greatest similarity to the sample material of step d), and D1 d) assigning the class of materials with the largest similarity database vector to the sample material of step d).
  10. 10. The method according to claim 1 or 2, wherein said step d) additionally comprises the steps of: d2') when in step d 1) the properties of the sample material are predicted with a probability of more than 50%, step d3 a) is performed, Wherein said d3 a) is extracting from the database DB2, for a material whose material properties are predicted in step d 1), which is confirmed in step d2 b), or which is entered in step d2 b), the one or more parameters whose values are predicted in step e).
  11. 11. The method according to claim 1 or 2, wherein said step d) additionally comprises the steps of: d1 Predicting the characteristics of the sample material of step d) from the spectra recorded in step d) by using a similarity analysis of a database DB1 of spectra of known materials; d2') performing step d3 a) when the characteristics of the sample material are predicted with a probability of more than 50% in step d 1), and D3 a) extracting, for a material whose material properties were predicted in step d 1), the one or more parameters whose values were predicted in step e).
  12. 12. The method of claim 11, wherein the similarity analysis is performed according to the following procedure: d1 a) transforming the absorption intensity of wavelengths or wavenumbers in the spectrum of step d) to give a query vector; d1 b) providing a database DB1 of sets of database vectors having spectra of known materials; d1 c) analyzing the similarity between the query vector of step d1 a) and the set of database vectors of step d1 b), step d1 c) comprising the steps of: d1c 1) calculating a similarity measure and/or a distance measure between each database vector of step d1 b) and the query vector of step d1 a) to give a similarity value for each database vector and the query vector, D1c2) arranging the similarity values obtained in step d1c 1) in a descending order when the similarity measure is calculated in step d1c 1), or arranging the similarity values obtained in step d1c 1) in an ascending order when the distance measure is calculated in step d1c 1), wherein the top-ranked database vector has the largest similarity with the query vector, D1c 3) counting the number of occurrences of a material category among the top-ranked database vectors in the ranking obtained in step d1c 2), wherein the number of occurrences is represented by a variable N, D1c 4) weighting the top N similarity values of the material classes according to the positions in the ranking obtained in step d1c 2) of the similarity values of the material classes to give a weighted ranking position of the material classes, D1c 5) for the material class, forming a sum of the weighted ranking positions obtained in step d1c 4) to give a score for the material class, wherein the highest score represents the greatest similarity to the sample material of step d), and D1 d) assigning the class of materials with the largest similarity database vector to the sample material of step d).
  13. 13. The method according to claim 7, wherein the database DB2 contains information of those parameters that are important for the material under consideration, such that only the one or more parameters that are important for the respective material are extracted from the database DB2 in step d3 a).
  14. 14. The method according to claim 10, wherein the database DB2 contains information of those parameters that are important for the material under consideration, such that only the one or more parameters that are important for the respective material are extracted from the database DB2 in step d3 a).
  15. 15. The method of claim 7, wherein said step d) additionally comprises the steps of: d3 b) displaying the one or more parameters obtained from step d3 a) on the input/output device, and D3 c) receives a selection of parameters from the one displayed in step d3 b).
  16. 16. The method according to claim 10, wherein said step d) additionally comprises the steps of: d3 b) displaying the one or more parameters obtained from step d3 a) on the input/output device, and D3 c) receives a selection of parameters from the one displayed in step d3 b).
  17. 17. The method according to claim 13, wherein said step d) additionally comprises the steps of: d3 b) displaying the one or more parameters obtained from step d3 a) on the input/output device, and D3 c) receives a selection of parameters from the one displayed in step d3 b).
  18. 18. The method according to claim 1 or 2, wherein said step d) is a multiple recording of the spectrum, said step additionally comprising the steps of i) forming the centroids of all spectra recorded in step d) and subjecting the centroids thus obtained to step e), or ii) predicting the value of said at least one parameter from each spectrum of step d) and forming an average value of said at least one parameter.
  19. 19. The method according to claim 2, wherein said step e) additionally comprises the steps of: e1.1 Extracting from the database DB 3a calibration map and/or a calibration equation for predicting the value of said at least one parameter; e1.2 Reading out the values of the at least one parameter matching the one or more spectra of step e 1.1) or the absorption in the centroid from the calibration map of step e 1.1) and/or inserting the one or more spectra of step d) or the absorption intensities at the respective wavelengths or wavenumbers in the centroid into the calibration equation of step e 1.1) to obtain the values of the at least one parameter, and E1.3 The value of the at least one parameter obtained in step e 1.2) is displayed as a result in step f).
  20. 20. The method according to claim 1 or 2, wherein said step e) additionally comprises the steps of: e2 -evaluating the predicted value for the at least one parameter, said step e 2) comprising the steps of: e2.1 Extracting from a database DB3 expected values of said parameters of said sample material of step d); e2.2 If the predicted value is at least a defined percentage lower than the expected value, the sample material of step d) is evaluated as bad, or if the predicted value is at least a defined percentage of the expected value, the sample material of step d) is evaluated as good, and E2.3 The evaluation obtained in step e 2.2) is displayed as a result in step f).

Description

Method for evaluating the spectrum of biological substances of animal origin, plant origin or mixtures thereof Technical Field The present invention relates to the field of spectrometry and to a method for assessing the spectrum of biological material of animal origin, plant origin or mixtures thereof, and a system for assessing such spectrum. Background Spectrometry is a very useful tool for obtaining information about the material of a sample. In particular, spectrometry, such as infrared spectrometry, in combination with chemometrics and calibration functions or maps, can provide information based on predictions and approximations, which can otherwise only be available via classical quantitative analysis, which is very cost-and time-consuming. However, untrained and inexperienced staff struggle with chemometric predictions of parameters by spectrometry. This also applies to the prediction of parameters by spectrometry and the evaluation or interpretation of the information obtained therefrom. These problems are even more serious when it comes to the spectrum of biological substances of animal origin, of vegetable origin or of mixtures thereof. Chinese utility model CN 208420696U discloses an on-line system for detection of wheat infection scab grade, particularly mycotoxins, based on near infrared spectrometry. Therefore, the practical use of such a system is extremely limited. Disclosure of Invention Thus, there is a need for a method for predicting a parameter of interest from a spectrum and for evaluating the spectrum that is suitable for untrained staff. According to the invention, this problem is solved by recording the spectrum of the sample material on a spectrometer, predicting at least one parameter value from said spectrum by means of at least one calibration function and/or calibration map suitable for predicting the respective parameter, and displaying the result thus obtained on the input/output device. Accordingly, one object of the present invention is a method for assessing the spectrum of biological material of animal origin, plant origin or mixtures thereof, comprising the steps of: a) Detecting a spectrometer in a network formed by at least one spectrometer and an input/output device; b) Requesting a respective status of each spectrometer in the network of step a) and displaying the detected spectrometers and their status on the input/output device, wherein the status reflects whether a spectrometer is available for recording a spectrum; c) Receiving a selection on the input/output device from a spectrometer available for recording a spectrum; d) Recording the spectrum of the sample material of animal origin, plant origin or mixtures thereof on the spectrometer selected in step c); e) Predicting a value of at least one parameter from the spectrum of step d) by means of at least one calibration function and/or calibration graph suitable for predicting the value of the at least one parameter, wherein the at least one parameter is selected from the group consisting of content of at least one amino acid, crude protein content, ammonia content, content of all amino acids with ammonia, content of all amino acids without ammonia, crude fat content, dry matter content, crude ash content, energy content, content of at least one biogenic amine, content of at least one anti-nutritional factor, content of at least one saccharide, starch content, crude fiber content, neutral washing fiber content, acid washing fiber content, total phosphorus content, phytic acid phosphorus content, reactive lysine content, total lysine content, ratio of reactive lysine content to total lysine content, protein dispersibility index, protein solubility, trypsin inhibitor activity, urease activity and Processing Condition Index (PCI); And F) Displaying the result of the prediction from step e) on the input/output device. In principle, the method according to the invention is not subject to any restrictions regarding the particular spectrometer. Thus, any conceivable type of spectrometer may be used in the method according to the invention, such as an infrared spectrometer, a raman spectrometer, an ultraviolet-visible spectrometer or even a combination of said spectrometers, provided that it is suitable for recording the spectrum of the sample material subjected to the method. It is also possible to use infrared, raman and uv-vis spectrometers in combination, which supplies the user with the widest range of applications for all possible sample materials that are generally suitable for spectrometry. However, it is preferred that the spectrometer of the method according to the invention is an infrared spectrometer, in particular a Near Infrared (NIR) spectrometer, because of its wide range of applicable applications. In this case, the method according to the invention is used to evaluate infrared spectra, in particular near infrared spectra. Near Infrared (NIR) spectra of step d) may be recorded at wavelengths between 400