Method for evaluating the spectrum of biological substances of animal origin, plant origin or mixtures thereof

Abstract

This invention relates to a method for evaluating the spectra of biological material of animal, plant, or mixture thereof, the method comprising the steps of: a) detecting spectrometers in a network formed by at least one spectrometer and an input / output device; b) requesting the 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 the spectrometer is available for recording spectra; c) receiving a selection from spectrometers available for recording spectra on the input / output device; d) recording the spectrum of the sample material of animal, plant, or mixture thereof on the spectrometer selected in step c); e) predicting at least one parameter from the spectrum of step d) using at least one calibration function and / or calibration plot suitable for predicting the value of the parameter. The values ​​of the parameters, wherein the at least one parameter is selected from the content of at least one amino acid, crude protein content, ammonia content, content of all amino acids containing ammonia, content of all amino acids not containing 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 sugar, starch content, crude fiber content, neutral detergent fiber content, acid detergent 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 predicted results of step e) on the input / output device.

Description

Technical Field

[0001] This invention relates to the field of spectroscopic methods, and specifically to methods for evaluating the spectra of biological substances of animal, plant, or mixture thereof, and systems for evaluating such spectra. Background Technology

[0002] Spectroscopic methods are extremely useful tools for obtaining information about sample materials. In particular, the combination of spectroscopic methods, such as infrared spectroscopy, with chemometrics and calibration functions or calibration plots, can provide information based on predictions and approximations that would otherwise only be available through classical quantitative analysis, which is very costly and time-consuming. However, untrained and inexperienced personnel struggle with the chemometric predictions of parameters made through spectroscopic methods. This also exists in the prediction of parameters made through spectroscopic methods and in the evaluation or interpretation of the information obtained. These problems are even more pronounced when dealing with the spectra of biological materials of animal, plant, or mixtures thereof.

[0003] Chinese utility model CN ​​208420696 U discloses an online system for detecting the severity of wheat scab infection, particularly the mycotoxin levels, based on near-infrared spectroscopy. Therefore, the practical applications of such a system are extremely limited. Summary of the Invention

[0004] Therefore, there is a need for a method suitable for untrained personnel to predict parameters of interest based on spectra and to evaluate spectra. According to the present invention, this problem is solved by recording the spectrum of a sample material on a spectrometer, predicting at least one parameter value from the spectrum using at least one calibration function and / or calibration plot suitable for predicting the respective parameters, and displaying the resulting value on an input / output device.

[0005] Therefore, one object of the present invention is a method for evaluating the spectrum of biological substances of animal, plant, or mixtures thereof, comprising the following steps:

[0006] a) Detecting a spectrometer in a network formed by at least one spectrometer and input / output devices;

[0007] b) Request the respective status of each spectrometer in the network in step a), and display the detected spectrometers and their status on the input / output device, wherein the status reflects whether the spectrometer is available for recording spectra;

[0008] c) Receive selection from a spectrometer that can be used to record spectra on the input / output device;

[0009] d) Record the spectra of the sample material of animal, plant or mixture thereof on the spectrometer selected in step c);

[0010] e) Predict the value of the at least one parameter from the spectrum of step d) using at least one calibration function and / or calibration plot suitable for predicting the value of the at least one parameter, wherein the at least one parameter is selected from: the content of at least one amino acid, crude protein content, ammonia content, the content of all amino acids having ammonia, the content of all amino acids not having 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 sugar, starch content, crude fiber content, neutral detergent fiber content, acid detergent fiber content, total phosphorus content, phytate 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 processing condition index (PCI).

[0011] and

[0012] f) Display the prediction result from step e) on the input / output device.

[0013] In principle, the method according to the invention is not limited to any particular spectrometer. Therefore, any conceivable type of spectrometer, such as an infrared spectrometer, Raman spectrometer, ultraviolet-visible spectrometer, or even a combination of such spectrometers, can be used in the method according to the invention, as long as it is suitable for recording the spectra of the sample material subjected to the method. Infrared spectrometers, Raman spectrometers, and ultraviolet-visible spectrometers can also be used in combination, which provides the user with the widest range of applications generally suitable for all possible sample materials used in spectroscopic methods. However, it is preferred that the spectrometer used in the method according to the invention be an infrared spectrometer, particularly a near-infrared (NIR) spectrometer, because of its wide range of applications. In this case, infrared spectra, particularly near-infrared spectra, are evaluated using the method according to the invention.

[0014] Depending on the spectrometer used, any suitable infrared spectrometer based on either monochromatic principles or Fourier transform principles can record the near-infrared (NIR) spectrum of step d) at wavelengths between 400 nm and 2,500 nm. Preferably, the NIR spectrum is recorded between 1,000 nm and 2,500 nm. Wavelengths are easily converted to their respective wavenumbers, so the NIR spectrum can also be recorded at the corresponding wavenumbers. When the sample material in step d) is translucent, the reflectance of emitted light from the sample is measured, and the difference between the emitted and reflected light is given as absorption. Therefore, the NIR spectrometer can operate in either transmission or reflection mode.

[0015] Furthermore, it is preferable that the spectrometer in the method according to the invention is a portable spectrometer, such as a portable infrared spectrometer, a portable NIR spectrometer, or a handheld infrared spectrometer, which provides the user with the option to perform the method according to the invention at any conceivable location of interest.

[0016] In principle, the input / output device of the method according to the invention is not limited in any way and can therefore be any conceivable input / output device that can receive input from a user, a spectrometer, or a device at the periphery of the system and display any conceivable result as output. For example, the input / output device can be a computer, such as a desktop computer, a network computer, a thin client, or a portable computer, such as a laptop or notebook computer, tablet computer, or smartphone. However, it is preferred that the input / output device be a portable input / output device, such as a portable computer, tablet computer, or smartphone, preferably a tablet computer or smartphone, which provides the user with the option to perform the method according to the invention at any conceivable location of interest.

[0017] Therefore, the method according to the invention is preferably a computer-implemented method.

[0018] Therefore, it is also preferred that the spectrometer is a portable spectrometer, and the input / output device is a portable device. Furthermore, it is preferred that each spectrometer in step a) is a portable spectrometer, and the input / output device is a portable device. Detailed Implementation

[0019] The method according to the invention is not limited by the number of spectrometers in the network formed by the input / output devices of step a). Therefore, the number of spectrometers is determined by the user and can vary from 1 to any conceivable number, such as 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. From an administrative and logical perspective, the number of spectrometers is kept at a reasonable level, preferably between 1 and 10.

[0020] According to the present invention, the at least one parameter determined in step e) of the method is selected from: the content of at least one amino acid, i.e., one or more amino acids, crude protein content, ammonia content, the content of all amino acids containing ammonia, the content of all amino acids not containing ammonia, crude fat content, dry matter content, crude ash content, energy content, the content of at least one biogenic amine, i.e., one or more biogenic amines, the content of at least one antinutritional factor, the content of at least one sugar, starch content, crude fiber content, neutral detergent fiber content, acid detergent 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 processing condition index (PCI).

[0021] The at least one amino acid refers to one or more amino acids, preferably at least one essential amino acid and / or at least one non-essential amino acid. The at least one essential amino acid is selected from: methionine, cysteine, cystine, the sum of methionine and cystine, lysine, arginine, isoleucine, threonine, tryptophan, leucine, valine, histidine, phenylalanine, and / or any derivatives and / or salts thereof. The sum of methionine and cystine in the sample is synonymous with the sum of the methionine content and the cystine content. The at least one non-essential amino acid is selected from: glycine, serine, alanine, aspartic acid, glutamic acid, proline, and / or any derivatives and / or salts thereof. Preferably, the at least one amino acid includes all the mentioned essential amino acids and / or all the mentioned non-essential amino acids.

[0022] In the context of this invention, the term "ammonia content" specifically refers to the content of free ammonia found in a sample after sample preparation, i.e., unbound or uncoordinated ammonia. The presence of this ammonia may have various causes. For example, it may be produced by the hydrolysis of urea, or it may be produced by adding an ammonium compound to the material undergoing the method.

[0023] In the context of this invention, the term "content of all amino acids without ammonia" is used to represent the sum of the contents of all amino acids in a material sample minus the ammonia content in that material sample, i.e., the difference between the contents of all amino acids in the material sample and the ammonia content.

[0024] In the context of this invention, the term "content of all amino acids containing ammonia" is used to mean the sum of the contents of all amino acids in a material sample plus the ammonia content in that material sample, i.e., the sum of the contents of all amino acids and the ammonia content in that material sample.

[0025] Therefore, the determination and prediction of the content of all amino acids, with or without ammonia, always involves: the determination or prediction of the total amino acid content in a material sample, and the determination or prediction of the ammonia content in said material sample. Depending on the individual material subjected to the method according to the invention, a high ammonia content can be indicative of counterfeit products. For example, if a feedstuff or feed ingredient is mixed with urea, this will increase the value of the crude protein content, the determination of which is based on nitrogen. For example, soybeans or soybean products typically have low ammonia content, and a high ammonia content value here would indicate a manipulated or counterfeit product. On the other hand, wheat typically has a high glutamine content, which is converted to glutamic acid in its usual determination procedure, releasing ammonia in the process. Here, a high ammonia content is not a concern.

[0026] In the context of this invention, the term "crude protein" is used as is known to those skilled in the art of animal husbandry. Generally, it takes into account all nitrogen sources.

[0027] In the context of this invention, the term "crude fat" is used as is known to those skilled in the art of animal husbandry and refers to a crude mixture of fat-soluble substances present in a sample. Crude fat, also known as ether extract or free lipid content, is a conventional measure of fat in feed or feed products.

[0028] In the context of this invention, the term "dry material" is also commonly referred to as dry weight, as is known to those skilled in the art of animal husbandry, and refers to a measurement of the mass of something when it is completely dried. The dry material of plant and animal materials consists of all its components except water.

[0029] In the context of this invention, the term "crude ash" is used as is known to those skilled in the art of animal husbandry and refers to the content of inorganic materials, such as minerals, in feed.

[0030] The energy content is preferably: gross energy (GE); apparent metabolizable energy (AME), especially apparent metabolizable energy corrected for zero nitrogen storage (AMEn); digestible energy (DE), especially digestible energy of adult sows (DE_S) and / or digestible energy of growing pigs (DE_GP); metabolizable energy (ME), especially metabolizable energy of adult sows (ME_S) and / or metabolizable energy of growing pigs (ME_GP); and / or net energy (NE), especially net energy of adult sows (NE_S) and / or net energy of growing pigs (NE_GP).

[0031] In the context of this invention, the term "Gross Energy (GE)," also commonly referred to as heat of combustion, is used as is known to those skilled in the art of animal husbandry and represents the energy released by burning a feed sample in excess oxygen in an adiabatic bomb calorimeter. Therefore, in the context of this invention, it is preferable to measure the Gross Energy of processed feed ingredient raw materials and / or feed components in an adiabatic bomb calorimeter. The amount of Gross Energy depends entirely on the chemical composition of the feed, but chemical composition cannot predict energy conversion efficiency. Such Gross Energy does not take into account any energy loss during feed intake, digestion, and metabolism. In fact, 1 kg of starch has approximately the same Gross Energy value as 1 kg of straw, although pigs or poultry cannot utilize most of the energy in straw due to a lack of digestive enzymes. Gross Energy (GE) can be determined as follows:

[0032] GE[MJ / kg DM]=(4143+56×EE[%])+(15×CP[%])–(44×ASH[%]))×0.0041868,

[0033] in,

[0034] DM=dry material,

[0035] EE = Ether Extract

[0036] CP = crude protein

[0037] ASH = Coarse Ash Content.

[0038] In the context of this invention, the term "digestible energy (DE)" is used as is known to those skilled in the art of animal husbandry and represents the total energy of the feed minus the total energy of the feces, i.e., the difference between the total energy of the feed and the total energy of the feces. This energy system takes into account the digestibility of the feed and provides a useful measure of the energy that the animal may be able to use. The advantage of digestible energy is that it is easy to determine. However, the disadvantage is that it does not take into account energy losses in urine, energy losses as combustible gases, and energy losses during metabolism. These losses vary among feed components.

[0039] The digestible energy (DE_GP) of growing pigs can be determined as follows:

[0040] DE_GP[MJ / kg DM]=(4168–(91×ASH[%DM]+(19×CP[%DM])+(39×EE[%DM])–(36×NDF[%DM]))×0.0041868,

[0041] in,

[0042] ASH = coarse ash content,

[0043] CP = crude protein

[0044] DM=dry material,

[0045] EE = Ether Extract

[0046] NDF = Neutral Detergent Fiber.

[0047] The digestible energy (DE_S) of an adult sow can be determined as follows:

[0048] DE_S[MJ / kg DM]=1.041×DE_GP[MJ / kg DM]+0.0066×CF[g / kg DM],

[0049] or

[0050] DE_S[MJ / kg DM]=1.041×((4168–(91×ASH[%DM])+(19×CP[%DM])+(39×EE[%DM])–(36×NDF[%DM]))×0.0041868)+0.066×CF[%DM]

[0051] in,

[0052] DE_GP = Digestible energy of growing pigs

[0053] ASH = coarse ash content,

[0054] CP = crude protein

[0055] DM=dry material,

[0056] EE = Ether Extract

[0057] NDF = Neutral Detergent Fiber

[0058] CF = coarse fiber.

[0059] In the context of this invention, the term "metabolizable energy (ME)" is used as is known to those skilled in the art of animal husbandry and represents digestible energy minus the energy excreted in urine and as combustible gases, i.e., the difference between digestible energy and the energy excreted in urine and as combustible gases. By taking these losses into account, metabolizable energy provides a better estimate of the energy available to the animal. Metabolizable energy corrects for certain effects of protein quality and quantity on digestible energy.

[0060] The metabolizable energy (ME_GP) of growing pigs can be determined as follows:

[0061] ME_GP[MJ / kg DM]=(4194–(92×ASH[%DM])+(10×CP[%DM])+(41×EE[%DM])–(35×NDF[%DM]))×0.0041868

[0062] in,

[0063] ASH = coarse ash content,

[0064] CP = crude protein

[0065] DM=dry material,

[0066] EE = Ether Extract

[0067] NDF = Neutral Detergent Fiber.

[0068] The metabolizable energy (ME_S) of an adult sow can be determined as follows:

[0069] ME_S[MJ / kg DM]=-3.96+(1.17×ME_GP[MJ / kg DM]+(0.132×NDF[%DM])

[0070] or

[0071] ME_S[MJ / kg DM]=-3.96+(1.17×((4194–(92×ASH[%DM])+(10×CP[%DM])+(41×EE[%DM])–(35×NDF[%DM]))×0.0041868)+(0.132×NDF[%DM])

[0072] in,

[0073] ME_GP = Metabolic energy of growing pigs

[0074] ASH = coarse ash content,

[0075] CP = crude protein

[0076] DM=dry material,

[0077] EE = Ether Extract

[0078] NDF = Neutral Detergent Fiber.

[0079] In the context of this invention, the term "net energy (NE)" is used as is known to those skilled in the art of animal husbandry and represents metabolizable energy minus thermal energy expenditure, which is the heat (and energy used) generated during the digestion of feed, the metabolism of nutrients, and the excretion of waste; that is, the difference between metabolizable energy and thermal energy expenditure. The energy remaining after these losses is the energy actually used for maintenance and production, i.e., growth, pregnancy, and lactation. Net energy is the only system for describing the energy actually used by animals. Therefore, net energy is by far the most accurate and reasonable way to characterize the energy content of feed. However, net energy is more difficult to determine and more complex than digestible energy and metabolizable energy.

[0080] The net energy (NE_GP) for growing pigs can be determined as follows:

[0081] NE_GP[MJ / kg DM]=(2875+(43.8×EE[%DM])+(6.7×ST[%DM])–(55.9×ASH[%DM])–(20.1×(NDF[%DM]–ADF[%DM]))–(40.2×NDF[%DM]))×0.0041868

[0082] in,

[0083] ASH = coarse ash content,

[0084] ADF = Acid Detergent Fiber.

[0085] DM=dry material,

[0086] EE = Ether Extract

[0087] NDF = Neutral Detergent Fiber

[0088] ST = Starch.

[0089] The net energy (NE_S) of an adult sow can be determined as follows:

[0090] NE_S[MJ / kg DM]=(0.703×(DE_S[MJ / kg DM])×0.0041868+(15.8×EE[%DM])+(4.7×ST[%DM])–(9.7×CP[%DM])+(9.8×CF[%DM]))×0.0041868

[0091] or

[0092] NE_S[MJ / kg DM]=(0.703×(((4168–(91×ASH[%DM])+(19×CP[%DM])+(39×EE[%DM])–(36×NDF[%DM]))×0.0041868×1.014)+( 0.066×CF[%DM])) / 0.0041868)+(15.8×EE[%DM])+(4.7×ST[%DM])–(9.7CP[%DM])–(9.8×CF(%DM]))×0.0041868

[0093] in,

[0094] ASH = coarse ash content,

[0095] ADF = Acid Detergent Fiber.

[0096] CF = crude fiber,

[0097] CP = crude protein

[0098] DE_S = Digestible energy of an adult sow

[0099] DM=dry material,

[0100] EE = Ether Extract

[0101] NDF = Neutral Detergent Fiber

[0102] ST = Starch.

[0103] In the context of this invention, the term "apparent metabolizable energy" is used as is known to those skilled in the art of animal husbandry, and refers to a metabolic energy that takes into account the amount of nitrogen used to constitute body proteins and thus treated as if it had been excreted as uric acid. Therefore, the value of the apparent metabolizable energy of poultry refers to a value relative to zero nitrogen storage correction (AMEn). Different equations for calculating AMEn are applied depending on the feed. For the feed given in the following formula, this general formula applies:

[0104] AMEn[MJ / kg DM]=(Factor DM×DM[%]+Factor ASH×ASH[%DM]+Factor CP×CP[%DM]+Factor EE×EE[%DM]+Factor CF×CF[%DM]+Factor NFE×NFE[%DM]+Factor ST×ST[%DM]+Factor SU×SU[%DM]) / 100

[0105] in,

[0106] ASH = coarse ash content,

[0107] CF = crude fiber,

[0108] CP = crude protein

[0109] DM=dry material,

[0110] EE = Ether Extract

[0111] NFE = Nitrogen-free extract.

[0112] ST = starch,

[0113] SU = carbohydrates.

[0114] DM[%] = 100, because all data are used based on 100% dry material standardization.

[0115] In the context of this invention, the term "biogenic amine" is used as is known to those skilled in the art of animal husbandry and refers to a biogenic substance having one or more amine groups, i.e., a low molecular weight organic base produced by the metabolism of microorganisms, plants, and animals. In feed, food, and beverages, they are produced by the enzymatic formation of raw materials or by the microbial decarboxylation of amino acids.

[0116] The at least one biogenic amine is preferably tryptophan, 5-hydroxytryptamine, 1,7-diaminoheptane, phenylethylamine, histamine, putrescine, spermine, spermidine, guanidine, tyramine, phenolethanolamine, cadaverine, and / or any derivatives and / or salts thereof. Reference to any particular biogenic amine refers to its presence in the sample under consideration and / or its content in the sample under consideration.

[0117] The at least one antinutritional factor is preferably a trypsin inhibitor, glucosinolate, gossypol, and / or any derivative and / or salt thereof. Antinutritional factors are produced by plant secondary metabolism and are found only in specific plant species. They do not perform essential functions in primary metabolism. More precisely, their functions include defense against pests and diseases, regulation, and function as dyes and fragrances. The negative effects of antinutritional factors on animals include feed intake, reduced animal performance, changes in nutrient digestibility, and metabolic disorders. Although antinutritional factors can have considerable effects on animals, they are not considered toxic. Therefore, in the context of this invention, mycotoxins are not considered antinutritional factors.

[0118] In the context of this invention, the term "carbohydrates" is used as is commonly known in the art and refers to the general term for soluble carbohydrates. The term "carbohydrates" includes: simple sugars, also known as monosaccharides, which include glucose, fructose, and galactose; and oligosaccharides, also known as disaccharides or oligosaccharides, which are compounds composed of two monosaccharides linked by a glycosidic bond, such as sucrose (composed of glucose and fructose), lactose (composed of glucose and galactose), and maltose (composed of two glucose molecules), and which are hydrolyzed in vivo into monosaccharides. Carbohydrates, particularly the aforementioned monosaccharides and oligosaccharides, are found in fruits and vegetables, especially in so-called sugar plants such as sugarcane, sugar beets, sugar palms, sugar maples, sugar sorghum, silver date palms, jubaea, palmyra palms, and agave.

[0119] In the context of this invention, the term "starch," also known as corn starch (amylum), is used as commonly known in the art and refers to a polymeric carbohydrate composed of numerous glucose units linked by glycosidic bonds. It is produced by most green plants as an energy storage medium. It is abundant in potatoes, corn, rice, wheat, and cassava.

[0120] In the context of this invention, the term "crude fiber" is used as is known to those skilled in the art of animal husbandry and refers to a measurement of fiber content. Crude fiber is also known as Weende cellulose, an insoluble residue resulting from acid hydrolysis followed by alkali hydrolysis. This residue contains true cellulose and insoluble lignin. It is also used to evaluate hair, hoof, or feather residues in animal by-products. Although it has been superseded by the more precise Van Soest analysis since the 1970s, the analysis of crude fiber remains common in feed laboratories.

[0121] In the context of this invention, the term "acid detergent fiber (ADF)" is used as is known to those skilled in the art of animal husbandry and refers to a measurement of the amount of structural materials, particularly cell wall structural materials such as cellulose, lignin, and lignin-N- compounds. Acid detergent fiber is an insoluble residue left after treatment with an acid detergent solution.

[0122] In the context of this invention, the term "neutral detergent fiber (NDF)" is used as is known to those skilled in the art of animal husbandry and refers to a measurement of the amount of structural materials, particularly cell wall structural materials such as cellulose, hemicellulose, lignin, and lignin-N- compounds. Neutral detergent fiber is an insoluble residue left after treatment with a neutral detergent solution.

[0123] In the context of this invention, the term "phytic acid phosphorus" is used as known to those skilled in the art and refers to the phosphorus content in phytic acid. Phytic acid is a bioactive substance. It plays a significant nutritional role as the main storage form of phosphorus in many plant tissues, particularly bran and seeds. It is also present in many legumes, cereals, and grains. Phytic acid and phytates have a strong binding affinity for dietary minerals, calcium, iron, and zinc, inhibiting their absorption. Phytic acid and phytates are particularly relevant to ruminants, as they are the only mammals capable of breaking down phytic acid and consuming the resulting phosphates. Bacteria in their stomachs produce the enzyme phytase, which promotes the breakdown of phytates into sugars and phosphates.

[0124] Feed ingredients and / or feed ingredient raw materials typically undergo so-called processing to remove anti-nutritional factors or at least reduce their amounts in the feed ingredients and / or feed ingredient raw materials. In this processing, the feed ingredients and / or feed ingredient raw materials are subjected to heat treatment, such as cooking or baking, which causes the removal or at least partial removal of protease inhibitors and lectins, in particular, or to alkali treatment, which causes the removal or at least partial removal of sinapicin. However, this processing can also lead to damage to amino acids present in the feed ingredients or feed ingredient raw materials. For example, compounds containing amino groups, such as amino acids and proteins, undergo Maillard reactions in the presence of reducing compounds, particularly reducing sugars. This is especially true of lysine, which has an ε-amino group and reacts with a large number of components present in the feed ingredients or feed ingredient raw materials.

[0125] The processing of feed ingredients and feed ingredient raw materials also affects the following: proteins, such as urease and trypsin; and the solubility of proteins in alkali and the solubility of proteins in water, the latter also known as the protein dispersibility index (PDI).

[0126] Processing that results in damage to feed ingredient raw materials and / or feed components, particularly leading to a reduction in the amount / content of amino acids, is termed overprocessing. In contrast, processing that does not provide complete or at least acceptable removal of anti-nutritional factors from feed ingredient raw materials and / or feed components is termed underprocessing. Finally, processing that results in complete or at least acceptable removal of anti-nutritional factors without damage to amino acids and / or proteins is termed adequate processing or proper processing.

[0127] In the context of this invention, the term "reactive lysine content" is used to refer to the content of lysine actually available in an animal, particularly in the digestion of an animal.

[0128] In contrast, in the context of this invention, the term "total lysine content" is used to mean: the sum of the lysine content actually available for digestion in an animal, particularly an animal, and the lysine content that is not available for digestion in an animal, particularly an animal. The latter lysine content (not available to the animal) is generally due to lysine degradation reactions, such as the Maillard reaction.

[0129] The processing of feed ingredients and feed ingredient raw materials also affects the solubility of proteins in alkali. The solubility of proteins in alkali includes the determination of the percentage of protein dissolved in an alkaline solution. The solubility of proteins in alkali is an effective measure to distinguish over-processed materials from properly processed materials, for example, according to DIN EN ISO 14244.

[0130] Protein dispersibility index (PDI) is a measure of the solubility of a protein in water after the sample is mixed with water, for example according to AOCS Ba 10-65.

[0131] The determination of trypsin inhibitor activity is based on the ability of the considered inhibitor to form a complex with the trypsin enzyme and thus reduce trypsin activity. This analysis can be performed according to the methods of ISO 14902 (2001) and AACC 22.40-01.

[0132] Urease catalyzes the degradation of urea into ammonia and carbon dioxide. Since urease is naturally present in soybeans, urease activity is a very useful metric for assessing the quality of processed soybeans. Quantitative analysis of urease activity can be performed according to the methods of ISO 5506 (1988) or AOCS Ba 9-58.

[0133] In the context of this invention, the term "processing condition index (PCI)" is used as disclosed in EP 3361248 A1. As mentioned above, PCI is particularly suitable for processed feed ingredients and feed ingredient raw materials. The PCI of a feed ingredient and / or feed ingredient raw material is generated by obtaining a set of parameters that are complementary in importance and therefore combinable. These parameters are, in particular, trypsin inhibitor activity, urease activity, protein solubility in alkali, protein dispersibility index, and / or the ratio of reactive lysine content to total lysine content. Additional parameters are at least one amino acid selected from the following: methionine, cysteine, cystine, threonine, leucine, arginine, isoleucine, valine, histidine, phenylalanine, tyrosine, tryptophan, glycine, serine, proline, alanine, aspartic acid, and glutamic acid. These parameters are obtained by quantitative analysis of a series of samples of feed ingredient raw materials from different time points during the processing of a particular feed ingredient and / or feed ingredient raw material. For each defined parameter, a so-called Processing Condition Index (PCI) is determined, which describes all conceivable processing conditions of feed ingredients and / or feed ingredient raw materials, i.e., underprocessed, adequately processed, or overprocessed. The term "adequately processed" is equivalent to proper processing. The resulting Processing Condition Index can then be scaled to facilitate the classification of feed ingredient raw materials and / or feed ingredients into underprocessed, adequately processed, or overprocessed categories.

[0134] Preferably, based on the spectrum recorded in step d), the method of the present invention makes a prediction of the identity of the sample material in step d). To make such a prediction, the method analyzes the similarity of the spectral groups of the sample material in step d) with those of known materials in database DB1.

[0135] In one embodiment of the method according to the invention, step d) further includes the following steps:

[0136] d1) Using a similarity analysis of the spectra of known materials in database DB1, the properties of the sample material in step d) are predicted from the spectra recorded in step d).

[0137] In principle, the method according to the invention is not limited to any specific procedure for similarity analysis, provided that the respective similarity analysis used takes into account predicting the material properties with the highest possible reliability or at least acceptable accuracy. For example, the similarity analysis in step d1) can be performed according to the following procedure:

[0138] d1a) Transform the absorption intensity of the wavelength or wavenumber in the spectrum of step d) to give the query vector;

[0139] d1b) Provides a database DB1 containing a set of database vectors with the spectra of known materials;

[0140] d1c) Analyze the similarity between the query vector in step d1a) and the database vector group in step d1b), which includes the following steps:

[0141] d1c1) Calculate the similarity metric and / or distance metric between each database vector in step d1b) and the query vector in step d1a) to give a similarity value between each database vector and the query vector.

[0142] d1c2) When calculating the similarity metric in step d1c1), the similarity values ​​obtained in step d1c1) are arranged in descending order; or when calculating the distance metric in step d1c1), the similarity values ​​obtained in step d1c1) are arranged in ascending order, wherein the database vector ranked first has the greatest similarity to the query vector.

[0143] d1c3) Count the number of times a material category appears in the database vector that ranks first in the ranking obtained in step d1c2), where the number of occurrences is represented by the variable N.

[0144] d1c4) Based on the position of the material category's similarity value in the ranking obtained in step d1c2), weight the top N similarity values ​​of that material category to give the weighted ranking position of that material category.

[0145] d1c5) For the material category, sum the weighted ranking positions obtained in step d1c4) to give a score for that material category, where the highest score represents the greatest similarity to the sample material in step d); and

[0146] d1d) Assign the material category of the sample material in step d) to the database vector with the greatest similarity.

[0147] The sample material undergoing step d) is preferably a feed ingredient, feed ingredient raw material, such as soybeans, preferably whole soybeans, and / or soybean products, preferably soybean meal and soybean cake / expeller, corn, meal leftover, slaughterhouse waste, feather meal, and / or bone meal. This also applies to known materials whose spectra are located in database DB1 used for the similarity analysis in step d1).

[0148] To avoid or at least significantly reduce the risk of erroneous predictions of the properties of the sample material, the method preferably receives confirmation or non-confirmation of the predicted properties of the sample material from the user at the input / output device. When the user does not confirm the predicted properties of the sample material, the method receives input of the properties of the sample material from the user at the input / output device.

[0149] In a preferred embodiment of the method according to the invention, step d) further includes the following steps:

[0150] d2a) Display the predicted properties of the sample material obtained in step d1) on the input / output device; and

[0151] d2b) Receive on the input / output device i) confirmation of the predicted characteristics displayed in step d2a), or ii) non-confirmation of the predicted characteristics displayed in step d2a) and input of the characteristics of the sample material.

[0152] After receiving confirmation of the predicted characteristics of the sample material or input of the characteristics of the sample material, the method according to the invention can further predict the value of at least one parameter. This prediction applies to materials whose material characteristics are predicted in step d1), confirmed in step d2b), or input in step d2b), or to materials for which the user inputs the value of at least one parameter of the material, or to materials for which the value of one or more parameters of the material is predicted in step e), or to materials for which the value of one or more parameters of the material is extracted from the database DB2 according to the invention. The latter option is preferred because it best illustrates the benefits of autonomous operation of the method according to the invention. The database DB2 stores relevant parameters for each conceivable material, i.e., those parameters whose values ​​can be predicted in the method according to the invention.

[0153] In another embodiment of the method according to the invention, step d) further includes the following steps:

[0154] d3a) For materials whose material properties are predicted in step d1), confirmed in step d2b), or input in step d2b), extract the values ​​of one or more parameters of the material from the database DB2, which were predicted in step e).

[0155] Then, in this embodiment, step d) of the method according to the invention includes the following steps:

[0156] d1) Using similarity analysis of the known material spectra database DB1, predict the properties of the sample material in step d) from the spectra recorded in step d).

[0157] d2a) Display the predicted properties of the sample material obtained in step d1) on the input / output device;

[0158] d2b) Receive on the input / output device i) confirmation of the predicted characteristics displayed in step d2a), or ii) non-confirmation of the predicted characteristics displayed in step d2a) and input of the characteristics of the sample material;

[0159] and

[0160] d3a) For materials whose material properties are predicted in step d1), confirmed in step d2b), or input in step d2b), extract the values ​​of one or more parameters of the material from the database DB2, which were predicted in step e).

[0161] Preferably, step d1) of this embodiment further includes steps d1a) to d1d) described above.

[0162] In an alternative, the method according to the invention may also skip steps d2a) and d2b) and proceed to step d3). The above option is used when predicting the properties of the sample material in step d1) with a sufficiently high probability. A sufficiently high probability is preferably a probability greater than 50%, a probability of at least 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%, or even a probability of 100% in predicting the properties of the sample material in step d).

[0163] In an alternative preferred embodiment of the method according to the invention, step d) further includes the following steps:

[0164] d2') When the properties of the sample material are predicted with a greater than 50% probability in step d1), proceed to step d3a).

[0165] Then, in an alternative embodiment of the method according to the invention, the invention includes the following steps:

[0166] d1) Using similarity analysis of the known material spectra database DB1, predict the properties of the sample material in step d) from the spectra recorded in step d).

[0167] d2') When the properties of the sample material are predicted with a greater than 50% probability in step d1), proceed to step d3a); and

[0168] d3a) For materials whose material properties were predicted in step d1), extract the values ​​of one or more parameters of the material that were predicted in step e).

[0169] Preferably, step d1) of the alternative embodiment further includes steps d1a) to d1d) described above.

[0170] Preferably, database DB2 mentions every conceivable material, along with one or more parameters important to the material under consideration. For example, if the method according to the invention can be applied to feed ingredients and / or feed ingredient raw materials, and the materials under consideration are, for example, soybeans and corn, then the relevant parameters for these materials would include: amino acids, preferably the content of all naturally occurring amino acids; the content of digestible amino acids; and the content of crude protein.

[0171] In a preferred embodiment of the method of the present invention, the database DB2 contains information about those parameters that are important for the material under consideration, such that in step d3a), only one or more parameters that are important for the respective material are extracted from the database DB2.

[0172] Then, after the one or more parameters have been extracted from the database, the method according to the invention can then display the one or more parameters as a suggestion to the user, who can select from the displayed parameters one or more parameters from which the value of the one or more parameters of the material is predicted.

[0173] In another preferred embodiment of the method according to the invention, step d) further includes the following steps:

[0174] d3b) Display the one or more parameters obtained from step d3a) on the input / output device; and

[0175] d3c) Receive the selection of parameters from the parameters shown in step d3b).

[0176] According to the invention, the spectrum of the sample material is recorded in step d). Preferably, at least one spectrum is recorded in step d), particularly 1 to 10, 3 to 10, 3 to 9, 3 to 8, 3 to 7, 3 to 6, 3 to 5, for example, 3, 4, or 5 spectra. Multiple recordings of the spectrum of the sample material help reduce the effects of measurement inaccuracies or erroneous measurements in step d). When step d) involves multiple recordings of spectra, there are two options: how to process the resulting spectra, and how to transform numerous spectra into a single spectrum. The first option is to form the centroid of the spectra recorded in step d), and to subject the resulting centroid to step e). The second option is to predict the value of at least one parameter in step e) based on each spectrum recorded in step d), and to form an average of the predicted values ​​of the at least one parameter obtained thereby, wherein the average value can then be displayed as a result in step f).

[0177] In another embodiment of the method according to the invention, step d) is multiple recordings of the spectrum, further comprising: i) forming the centroid of all the spectra recorded in step d), and subjecting the centroid thus obtained to step e); or ii) predicting the value of the at least one parameter from each spectrum in step d), and forming an average value of the at least one parameter.

[0178] In mathematics and physics, the term "centroid," when used in the context of a planar figure, refers to the arithmetic mean position of all points in that figure; therefore, it is also referred to as the geometric center of the figure. Thus, it is also the point at which the cut shape can be perfectly balanced on the tip of a pin. When the figure extends to an object in multidimensional space, the term "centroid" represents the average position of all points in all coordinate directions of that object. Therefore, in the context of this invention, the term "centroid" represents the arithmetic mean position of all points in all coordinate directions of the spectrum.

[0179] According to the invention, at least one parameter is predicted from the spectrum in step e). For this purpose, respective calibration equations and / or calibration plots for predicting the parameters considered in a particular material are extracted from the database DB3. Then, the value of the at least one parameter that matches the absorption in the one or more spectra or the centroid of step d) is read from the calibration plot of step e1.1), and / or the value of the at least one parameter that matches the absorption in the one or more spectra or the centroid of step d) is inserted into the calibration equation of step e1.1) to obtain the value of the at least one parameter. The predicted value of the at least one parameter thus obtained is then displayed as a result in step f). In the foregoing steps, calibration equations and / or calibration plots for the various parameters of the material are generated by correlating the unique absorptions in the spectrum and their intensities, particularly the near-infrared absorption obtained for the sample material, with the corresponding values ​​of the same parameter obtained from the quantitative analysis of the same material. The correlation between the values ​​obtained from quantitative analysis and the absorption and their intensities obtained from spectroscopic measurements such as near-infrared spectroscopy is then depicted or plotted as a calibration graph. This facilitates matching the absorption intensity of spectroscopic measurements such as near-infrared spectroscopy of other samples with the corresponding precise values ​​of the parameters under consideration based on quantitative analysis. The calibration equations and / or calibration graphs used for predicting processing condition indices (PCIs) are preferably generated according to the technical teachings of EP 3361248A1, particularly according to claims 3 and / or 4 of EP 3361248A1. The calibration equations and / or calibration graphs used for predicting: trypsin inhibitor activity, urease activity, protein solubility in alkali, protein dispersibility index, reactive lysine content, total lysine content, the ratio of reactive lysine content to total lysine content, and the content of at least one amino acid. The calibration equations and / or calibration diagrams used for predicting energy content are preferably generated according to the technical teachings of WO 2019 / 215206 A1, and in particular according to claims 8 and / or 9 of WO 2019 / 215206 A1.

[0180] When the parameter value of the material is determined in step e) to be the content of a specific component or part, the calibration is changed to weight percentage (weight - %).

[0181] In another embodiment of the method according to the invention, step e) further includes the following steps:

[0182] e1.1) Extract calibration plots and / or calibration equations from database DB3 for predicting the values ​​of the at least one parameter;

[0183] e1.2) Read the value of at least one parameter from the calibration plot of step e1.1) that matches the absorption at one or more spectra or centroids of step d), and / or insert the absorption intensity at each wavelength or wavenumber of the one or more spectra or centroids of step d) into the calibration equation of step e1.1) to obtain the value of at least one parameter; and

[0184] e1.3) The value of at least one parameter obtained in step e1.2) is displayed as a result in step f).

[0185] Preferably, the database DB3 contains calibration plots and / or calibration equations for predicting the values ​​of all parameters, which are all parameters of the method according to the invention in all materials of animal, plant or mixture thereof to be subjected to the method, i.e., those parameters mentioned in step e).

[0186] The prediction of the Conditional Index (PCI) is preferably carried out in accordance with the technical teachings of EP 3361248 A1, and in particular according to claim 5 of EP 3361248 A1.

[0187] The prediction of energy content, particularly the following, is preferably made in accordance with the technical teachings of WO 2019 / 215206 A1, and in particular according to any one of claims 10, 11, 12 and / or 13 of WO 2019 / 215206 A1: gross energy (GE); apparent metabolizable energy (AME), particularly apparent metabolizable energy corrected for zero nitrogen storage (AMEn); digestible energy (DE), particularly digestible energy of adult sows (DE_S) and / or digestible energy of growing pigs (DE_GP); metabolizable energy (ME), particularly metabolizable energy of adult sows (ME_S) and / or metabolizable energy of growing pigs (ME_GP); and / or net energy (NE), particularly net energy of adult sows (NE_S) and / or net energy of growing pigs (NE_GP).

[0188] The method according to the invention also facilitates the evaluation of the sample material under consideration by comparing the predicted values ​​of the parameters with the corresponding expected values ​​of the parameters. Predictions of the values ​​of at least one parameter made by spectroscopic methods provide much valuable information that is otherwise only available through classical quantitative analysis, which is quite costly and time-consuming. Trained personnel are, in principle, able to use the information thus provided in the best possible way. However, even for trained personnel, evaluating the predicted values ​​of various parameters is challenging. This challenge is, of course, significantly greater for untrained personnel who may already be struggling with the massive amounts of information from the predictions. Therefore, they need relevant information about the sample material that is easy to understand and provides them with clear guidance. Therefore, preferably, the method of the invention also provides the user with a qualitative evaluation of the predicted values ​​of the at least one parameter of the sample material. This evaluation is made relative to the expected values ​​of the parameters under consideration.

[0189] In a further embodiment of the method according to the invention, step e) further includes the following steps:

[0190] e2) Evaluate the predicted value for the at least one parameter, step e2) comprising the following steps:

[0191] e2.1) Extract the expected values ​​of the parameters of the sample material from step d) from the database DB4;

[0192] e2.2) If the predicted value is at least a predetermined percentage lower than the expected value, the sample material in step d) is assessed as poor; or if the predicted value is at least a predetermined percentage of the expected value, the sample material in step d) is assessed as good.

[0193] e2.3) The evaluation obtained in step e2.2) is displayed as a result in step f).

[0194] In the context of this invention, preferably, the term "at least a defined percentage lower than the expected value" means 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 60%, 70%, or 90% lower than the expected value, preferably greater than 50%, and at least 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% lower. In the context of this invention, preferably, the term "at least a defined percentage lower than the expected value" means at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% of the expected value. Preferably, the defined percentage for the lower threshold (i.e., evaluating the sample material as poor) and the defined percentage for the upper threshold (i.e., evaluating the sample material as good) are set by the user or administrator according to the respective conditions given in their respective settings, locations, seasons, etc., for the respective parameters of the individual materials to be subjected to the method.

[0195] Preferably, expected values ​​for various parameters of the sample material are provided in database DB4. Database DB4 may differ from or be identical to database DB2. Database DB4 is based on a spectral group of all existing materials used in a specific field for which the method of the present invention is applied. Each of these spectra has been analyzed for the maximum absorption value of the respective parameter, the value of which can be predicted from the spectrum. Preferably, parameters that are important to the material under consideration and particularly useful to the user are considered. In cases where multiple parameters are important to the material under consideration, database DB4 contains a ranking of these important parameters and / or exclusion criterion categories that can be stored separately in database DB4 for various materials. For example, a specific threshold for ammonia content could be an exclusion criterion for soybeans, or a specific threshold for anti-nutritional factors could be an exclusion criterion.

[0196] Specifically, the database DB4 is preferably updated with expected values ​​of parameters for sample materials from plant sources for each season or each harvest. Therefore, when materials from plant sources around the world are subjected to the method of the present invention, the database DB4 is preferably updated with expected values ​​of parameters for sample materials from plant sources from all growing regions globally for each season or each harvest of the material under consideration.

[0197] Preferably, the method of the present invention extracts detailed information from database DB4 regarding the storage location and / or further processing of one or more materials having the same or substantially the same properties as the sample material of step d), and / or having the same or substantially the same value of at least one parameter as the sample material of step d). This prompts providing the user with specific recommendations regarding the storage and / or further processing of the sample material subjected to the method of the present invention. Such recommendations are particularly useful in agriculture, specifically in the field of animal nutrition, for example, in the preparation of feed ingredients. Here, the user needs to know the most suitable location for storing recently delivered or obtained feed ingredients or feed ingredient raw materials. Preferably, the recently delivered or obtained materials and materials with the aforementioned substances added should have the greatest common similarity, as this provides materials with consistent characteristics, which offers the best possible benefits.

[0198] In another embodiment of the method of the present invention, step f) further includes the following steps:

[0199] f1) Provide recommendations regarding the storage and / or further processing of the sample material that has undergone step d), wherein step f1) includes the following steps:

[0200] f1.1) Extract detailed information about the storage location and / or further processing of one or more materials from database DB4, wherein the materials have the same or substantially the same properties as the sample material of step d), and / or have the same or substantially the same value as the at least one parameter of the sample material of step d);

[0201] f1.2) Sort the detailed information obtained in step f1.1), wherein the material with the highest similarity in terms of the material properties and the value of the at least one parameter is ranked first; and

[0202] f1.3) Display the storage details and / or further processing details of the material that is ranked first in step f1.2) on the input / output device.

[0203] In principle, steps f1.1) and f1.2) are not subject to any restrictions regarding the specific procedures used for similarity analysis. The similarity analysis for these steps can be performed according to the routine of step d1c), unless the query vector is based on both the values ​​of the material properties and parameters predicted in the preceding steps, and the database vector is based on the values ​​of the material properties and parameters stored in database DB4.

[0204] The recommendations / details regarding further processing given in step f1.3) depend on the material under consideration and therefore on parameters important to that material. For example, if the material is soybean, the amino acid content and PCI will be important. If the PCI indicates over-processed soybean, and therefore the content of certain or all amino acids in the material is too low, the method will provide details regarding supplementing those amino acids that are insufficient in the material. For example, if the material is over-processed, the difference between the expected and actual values ​​of the amount of amino acids in the feed ingredient raw materials and / or feed ingredients can be predicted according to the technical teachings of EP 3361248 A1, particularly according to claims 8 and / or 9 of EP 3361248 A1. Similarly, when the predicted PCI for the material under consideration indicates that it is under-processed, recommendations regarding further processing of the material under consideration can preferably be given according to the technical teachings of EP 3361248 A1, particularly according to claim 14 of EP 3361248 A1. Based on the technical teachings of WO 2019 / 215206 A1, and particularly according to claim 14 of WO 2019 / 215206 A1, detailed information on further processing can be provided to compensate for energy deficiencies in the material under consideration.

[0205] In practice, after being subjected to the methods of the present invention, the materials are further processed and / or supplied to specific storage locations. Therefore, it is preferable that detailed information obtained from the materials subjected to the methods of the present invention is also stored in database DB4. This helps to keep the state of database DB4 up-to-date.

[0206] In another embodiment of the method of the present invention, the method further includes the following steps:

[0207] g) Store detailed information about the sample material from step d), wherein step g) includes the following steps:

[0208] g1) Receive detailed information on the input / output device regarding the storage location and / or further processing of the sample material from step d);

[0209] g2) If necessary, receive on the input / output device an input of the identification code of the sample material from step d) or an input of selection of the identification code; and

[0210] g3) The detailed information received in step g1), the predicted material properties in step e), and the value of the at least one parameter, if necessary, are stored on the input / output device together with the identification code received in step g2).

[0211] In principle, databases DB1, DB2, DB3, and / or DB4 can be the same or different from each other. Preferably, databases DB2 and DB4 are the same. Furthermore, databases DB1, DB2, DB3, and / or DB4 can be stored independently of each other on a single device, such as the input / output device, server, or cloud used in the method of the present invention. Alternatively, each or all of them can be stored on separate devices, which can also be servers or the cloud. In this case, in the network of the method according to the present invention, relevant information or data received on the input / output device, or relevant information or data provided / generated by any other device, preferably a spectrometer, is transmitted to the server or cloud, where it is further processed, for example, for making predictions; and then, the data thus obtained from said further processing, such as predictions, is sent to the input / output device. For example, the spectra recorded in step d) are transmitted from the spectrometer to the input / output device, which in turn transmits them to a first server having database DB1, or the spectra are transmitted directly to the first server for similarity analysis. The same server may also host database DB2, from which values ​​of one or more parameters of the material to be predicted are extracted. The same server may also host database DB3, from which calibration plots and / or calibration functions for the prediction are extracted, enabling a prediction of the considered sample material to be made and displayed as a result on the input / output device. Alternatively, when databases DB2 and DB3 are hosted on other servers, data is extracted from their respective databases, and the resulting data or information is sent to the other server where the prediction is made. The resulting prediction is then sent back to the input / output device, where it is displayed as a result. Database DB4 may be on the same or different servers, where data is again exchanged between the input / output device and different servers. The final data obtained in step g) is then stored in database DB4.

[0212] To obtain an assessment with the best possible accuracy, it is beneficial that the spectrum, as the basis for any assessment, be as reliable and representative as possible. Therefore, it is desirable to subject the sample, which should be as homogenized as possible, to step d). Preferably, homogenization of the sample material is induced by shaking. For example, the spectrometer recording the spectrum in step d) is equipped with a shaker partially filled with sample material, and then shaken for a period of time sufficient to induce satisfactory homogenization of the sample material.

[0213] Therefore, in another embodiment, the method of the present invention further includes the step of homogenizing the sample material before subjecting the sample material to step d).

[0214] The method of the present invention can be implemented on any system comprising i) an input / output device, ii) at least one spectrometer, and iii) a processing unit. The only requirement for the system is that the input / output device, the at least one spectrometer, and the processing unit must form a network such that they can communicate and share, send, and exchange data with each other, and that the processing unit must be adapted to perform the method according to the present invention.

[0215] Another object of the present invention is a system for evaluating the spectrum of biological substances of animal, plant, or mixtures thereof, said system comprising:

[0216] I) Input / output devices;

[0217] II) at least one spectrometer; and

[0218] III) A processing unit adapted to perform the method according to the invention.

[0219] The input / output device, the at least one spectrometer, and the processing unit form a network.

[0220] The processing unit may be part of the input / output device or part of different devices.

[0221] Since the processing unit, which is an essential part of the system, is adapted to perform the method according to the invention, the entire system is suitable or adapted for evaluating spectra.

[0222] In principle, the input / output device of the system according to the invention is not limited in any way and can therefore be any conceivable input / output device capable of receiving input from a user or device, such as a spectrometer, displaying information or data to the user, and transmitting information or data to the device. The input / output device can be a computer, such as a desktop computer, a network computer, or a portable computer, such as a laptop computer, tablet computer, or smartphone. In the context of this invention, and particularly considering the scope of application of the method of the invention, it is preferred that the input / output device be portable.

[0223] In principle, the at least one spectrometer in the system according to the invention is not limited in any way, and can therefore be any conceivable spectrometer, such as an infrared spectrometer, a near-infrared spectrometer (regardless of whether it operates in transmission or reflection mode), a Raman spectrometer, an ultraviolet-visible spectrometer, or a combination thereof. Furthermore, the method according to the invention can also be applied in principle to portable or stationary spectrometers. However, considering the scope of application of the method according to the invention, it is preferred that the spectrometer be portable.

[0224] In an embodiment of the system according to the invention, the input / output device and / or the at least one spectrometer is portable.

Claims

1. A method for evaluating the spectra of biological substances of animal, plant, or mixtures thereof, comprising the following steps: a) Detecting a spectrometer in a network formed by at least one spectrometer and an input / output device, said at least one spectrometer comprising an infrared spectrometer, a Raman spectrometer, an ultraviolet-visible spectrometer, or a combination thereof; b) Request the respective status of each spectrometer in the network of step a), and display the detected spectrometers and their status on the input / output device, wherein the status reflects whether the spectrometer is available for recording spectra; c) Receive selection from a spectrometer that can be used to record spectra on the input / output device; d) Record the spectra of the sample material of animal, plant, or mixture thereof on the spectrometer selected in step c), wherein step d) further includes the following steps: d1) Using similarity analysis of the known material spectra database DB1, predict the properties of the sample material in step d) from the spectra recorded in step d). d2a) Display the predicted properties of the sample material obtained in step d1) on the input / output device; and d2b) Receive on the input / output device i) confirmation of the predicted characteristics shown in step d2a), or ii) non-confirmation of the predicted characteristics shown in step d2a) and the input of the characteristics of the sample material; e) Predict the value of the at least one parameter from the spectrum of step d) using at least one calibration function and / or calibration plot suitable for predicting the value of the at least one parameter, wherein the at least one parameter is selected from: the content of at least one amino acid, crude protein content, ammonia content, the content of all amino acids having ammonia, the content of all amino acids not having 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 sugar, starch content, crude fiber content, neutral detergent fiber content, acid detergent fiber content, total phosphorus content, phytate 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 processing condition index (PCI). and f) Display the prediction result from step e) on the input / output device.

2. The method according to claim 1, wherein the spectrometer is a portable spectrometer.

3. The method according to claim 1 or 2, wherein the method is a computer-implemented method.

4. The method according to claim 1 or 2, wherein, The spectrometer is a portable spectrometer, and the input / output device is a portable device.

5. The method according to 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. The method according to claim 1, wherein the similarity analysis is performed according to the following procedure: d1a) Transform the absorption intensity of the wavelength or wavenumber in the spectrum of step d) to give the query vector; d1b) Provides a database DB1 containing a set of database vectors with the spectra of known materials; d1c) Analyze the similarity between the query vector in step d1a) and the database vector group in step d1b). Step d1c) includes the following steps: d1c1) Calculate the similarity metric and / or distance metric between each database vector in step d1b) and the query vector in step d1a) to give a similarity value between each database vector and the query vector. d1c2) When calculating the similarity metric in step d1c1), the similarity values ​​obtained in step d1c1) are arranged in descending order; or when calculating the distance metric in step d1c1), the similarity values ​​obtained in step d1c1) are arranged in ascending order, wherein the database vector ranked first has the greatest similarity to the query vector. d1c3) Count the number of times the material category appears in the top-ranked database vector of the ranking obtained in step d1c2), where the number of occurrences is represented by the variable N. d1c4) Based on the position of the material category similarity value in the ranking obtained in step d1c2), weight the top N similarity values ​​of the material category to give the weighted ranking position of the material category. d1c5) For the material category, the weighted ranking positions obtained in step d1c4) are summed to give a score for the material category, where the highest score represents the greatest similarity to the sample material in step d). and d1d) Assign the material category of the database vector with the greatest similarity to the sample material in step d).

7. The method according to claim 1, wherein, Step d) further includes the following steps: d3a) For materials whose material properties are predicted in step d1), confirmed in step d2b), or input in step d2b), extract the values ​​of one or more parameters of the material from the database DB2, which were predicted in step e).

8. The method according to claim 1 or 2, wherein, Step d) includes the following steps: d1) Using similarity analysis of the known material spectra database DB1, predict the properties of the sample material in step d) from the spectra recorded in step d). d2a) Display the predicted properties of the sample material obtained in step d1) on the input / output device; d2b) Receive on the input / output device i) confirmation of the predicted characteristics displayed in step d2a), or ii) non-confirmation of the predicted characteristics displayed in step d2a), and input of the characteristics of the sample material; and d3a) For materials whose material properties are predicted in step d1), confirmed in step d2b), or input in step d2b), extract the values ​​of one or more parameters of the material from the database DB2, which were predicted in step e).

9. The method of claim 8, wherein the similarity analysis is performed according to the following procedure: d1a) Transform the absorption intensity of the wavelength or wavenumber in the spectrum of step d) to give the query vector; d1b) Provides a database DB1 containing a set of database vectors with the spectra of known materials; d1c) Analyze the similarity between the query vector in step d1a) and the database vector group in step d1b). Step d1c) includes the following steps: d1c1) Calculate the similarity metric and / or distance metric between each database vector in step d1b) and the query vector in step d1a) to give a similarity value between each database vector and the query vector. d1c2) When calculating the similarity metric in step d1c1), the similarity values ​​obtained in step d1c1) are arranged in descending order; or when calculating the distance metric in step d1c1), the similarity values ​​obtained in step d1c1) are arranged in ascending order, wherein the database vector ranked first has the greatest similarity to the query vector. d1c3) Count the number of times the material category appears in the top-ranked database vector of the ranking obtained in step d1c2), where the number of occurrences is represented by the variable N. d1c4) Based on the position of the material category similarity value in the ranking obtained in step d1c2), weight the top N similarity values ​​of the material category to give the weighted ranking position of the material category. d1c5) For the material category, the weighted ranking positions obtained in step d1c4) are summed to give a score for the material category, where the highest score represents the greatest similarity to the sample material in step d). and d1d) Assign the material category of the database vector with the greatest similarity to the sample material in step d).

10. The method according to claim 1 or 2, wherein, Step d) further includes the following steps: d2') When the properties of the sample material are predicted with a greater than 50% probability in step d1), proceed to step d3a). Wherein d3a) refers to: for a material whose material properties are predicted in step d1), confirmed in step d2b), or input in step d2b), extracting the values ​​of one or more parameters of the material from the database DB2, which are the one or more parameters predicted in step e).

11. The method according to claim 1 or 2, wherein step d) further comprises the following steps: d1) Using similarity analysis of the known material spectra database DB1, predict the properties of the sample material in step d) from the spectra recorded in step d). d2') When the properties of the sample material are predicted with a greater than 50% probability in step d1), proceed to step d3a); and d3a) For a material whose material properties were predicted in step d1), extract the values ​​of one or more parameters of the material that were predicted in step e).

12. The method of claim 11, wherein the similarity analysis is performed according to the following procedure: d1a) Transform the absorption intensity of the wavelength or wavenumber in the spectrum of step d) to give the query vector; d1b) Provides a database DB1 containing a set of database vectors with the spectra of known materials; d1c) Analyze the similarity between the query vector in step d1a) and the database vector group in step d1b). Step d1c) includes the following steps: d1c1) Calculate the similarity metric and / or distance metric between each database vector in step d1b) and the query vector in step d1a) to give a similarity value between each database vector and the query vector. d1c2) When calculating the similarity metric in step d1c1), the similarity values ​​obtained in step d1c1) are arranged in descending order; or when calculating the distance metric in step d1c1), the similarity values ​​obtained in step d1c1) are arranged in ascending order, wherein the database vector ranked first has the greatest similarity to the query vector. d1c3) Count the number of times the material category appears in the top-ranked database vector of the ranking obtained in step d1c2), where the number of occurrences is represented by the variable N. d1c4) Based on the position of the material category similarity value in the ranking obtained in step d1c2), weight the top N similarity values ​​of the material category to give the weighted ranking position of the material category. d1c5) For the material category, the weighted ranking positions obtained in step d1c4) are summed to give a score for the material category, where the highest score represents the greatest similarity to the sample material in step d). and d1d) Assign the material category of the database vector with the greatest similarity to the sample material in step d).

13. The method according to claim 7, wherein, The database DB2 contains information about which parameters are important for the material under consideration, such that in step d3a), only one or more parameters that are important for the respective material are extracted from the database DB2.

14. The method of claim 10, wherein, The database DB2 contains information about which parameters are important for the material under consideration, such that in step d3a), only one or more parameters that are important for the respective material are extracted from the database DB2.

15. The method according to claim 7, wherein, Step d) further includes the following steps: d3b) The one or more parameters obtained from step d3a) are displayed on the input / output device; and d3c) Receive the selection of parameters shown in step d3b).

16. The method of claim 10, wherein, Step d) further includes the following steps: d3b) The one or more parameters obtained from step d3a) are displayed on the input / output device; and d3c) Receive the selection of parameters shown in step d3b).

17. The method according to claim 13, wherein, Step d) further includes the following steps: d3b) The one or more parameters obtained from step d3a) are displayed on the input / output device; and d3c) Receive the selection of parameters shown in step d3b).

18. The method according to claim 1 or 2, wherein, Step d) is the recording of the spectrum multiple times, and the step further includes the following steps: i) forming the centroid of all the spectra recorded in step d), and subjecting the centroid obtained therefrom to step e); or ii) predicting the value of the at least one parameter from each spectrum in step d), and forming the average value of the at least one parameter.

19. The method according to claim 2, wherein, Step e) further includes the following steps: e1.1) Extract calibration plots and / or calibration equations from database DB3 for predicting the values ​​of the at least one parameter; e1.2) Read the value of at least one parameter from the calibration plot of step e1.1) that matches the absorption at one or more spectra or centroids in step d), and / or insert the absorption intensity at each wavelength or wavenumber at one or more spectra or centroids in step d) into the calibration equation of step e1.1) to obtain the value of at least one parameter; and e1.3) The value of at least one parameter obtained in step e1.2) is displayed as a result in step f).

20. The method according to claim 1 or 2, wherein, Step e) further includes the following steps: e2) Evaluate the predicted value for the at least one parameter, step e2) comprising the following steps: e2.1) Extract the expected values ​​of the parameters of the sample material from step d) from the database DB3; e2.2) If the predicted value is at least a predetermined percentage lower than the expected value, the sample material in step d) is assessed as poor; or if the predicted value is at least a predetermined percentage of the expected value, the sample material in step d) is assessed as good. e2.3) The evaluation obtained in step e2.2) is displayed as a result in step f).

21. The method of claim 20, wherein the percentage lower than the expected value means 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 60%, 70%, or 90% lower than the expected value.

22. The method of claim 20, wherein the percentage lower than the expected value means more than 50%, at least 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% lower than the expected value.

23. The method of claim 20, wherein the defined percentage of the expected value refers to at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% of the expected value.

24. The method of claim 20, wherein the expected value of each parameter of the sample material is provided in the database DB4.

25. The method according to claim 1 or 2, wherein, Step f) further includes the following steps: f1) Provide recommendations regarding the storage and / or further processing of the sample material that has undergone step d), wherein step f1) includes the following steps: f1.1) Extract detailed information about the storage location and / or further processing of one or more materials from database DB4, wherein the materials have the same or substantially the same properties as the sample material of step d), and / or have the same or substantially the same value as the at least one parameter of the sample material of step d); f1.2) Sort the detailed information obtained in step f1.1), wherein the material with the highest similarity in terms of the material properties and the value of the at least one parameter is ranked first; and f1.3) Display the storage details and / or further processing details of the material that is ranked first in step f1.2) on the input / output device.

26. The method according to claim 1 or 2, comprising the following steps: g) Store detailed information about the sample material from step d), wherein step g) includes the following steps: g1) Receive detailed information on the input / output device regarding the storage location and / or further processing of the sample material from step d); g2) If necessary, receive on the input / output device an input of the identification code of the sample material from step d) or an input of selection of the identification code; and g3) The detailed information received in step g1), the predicted material properties in step e), and the value of the at least one parameter, if necessary, are stored on the input / output device together with the identification code received in step g2).

27. The method according to claim 1 or 2, the method further comprising the step of: homogenizing the sample material before subjecting the sample material to step d).

28. A system for evaluating the spectrum of biological substances of animal, plant, or mixtures thereof, said system comprising: I) An input / output device for performing steps a) to c) and f) according to claim 1; II) At least one spectrometer, said at least one spectrometer being used to perform step d) of claim 1; and III) A processing unit adapted to perform the method according to claim 1. in, The input / output device, the at least one spectrometer, and the processing unit form a network.

29. The system of claim 28, wherein the input / output device is also used to perform steps d2a) and d2b) of claim 1.

30. The system of claim 28, wherein the processing unit is part of the input / output device or part of a different device.

31. The system according to claim 28, wherein, The input / output device and / or the at least one spectrometer are portable.