Vegetable fibrin molecule conformation change evaluation method

Abstract

The invention relates to the technical field of protein molecule conformational change evaluation, in particular to a plant fibrin molecule conformational change evaluation method which comprises the following steps: acquiring plant fibrin map data and animal protein map data; respectively acquiring a plurality of characteristic peaks of the plant fiber protein map data and the animal protein map data and corresponding data pairs by adopting a Gaussian fitting algorithm; performing feature analysis on feature peaks in the two pieces of map data in different secondary structure ranges, and training a conformation change evaluation decision function; based on the content difference between the to-be-detected plant fibrin molecules and the target imitated animal protein samples under all secondary structures, the evaluation value of the conformational change of the to-be-detected plant fibrin molecules is obtained by adopting the conformational change evaluation decision function. The invention aims to solve the problem that the conformation evaluation of plant fibrin molecules is difficult to accurately carry out due to the fact that the artificial plant fibrin molecular weight is too large and the spectrum is too complex.

Description

Technical Field

[0001] The present application relates to the technical field of protein molecule conformational change evaluation, and in particular to a method for evaluating plant fiber protein molecule conformational change. Background Art

[0002] Plant fiber protein molecular processing is a technology that processes plant protein into a fiber structure to simulate animal protein, thereby giving plant protein food the taste of animal protein. The animal protein content in existing meat products is mainly in lean meat, but the price of lean meat faces unstable cyclical price increases, and lean meat loses water and shrinks during the aging process, reducing the yield. Accordingly, in order to meet the needs of corporate development and different consumers, this application processes plant fiber protein molecules to give them the taste of meat protein, aiming to provide a wider variety of protein sources and optimize the nutritional content and taste characteristics of food through technical means.

[0003] The properties of chemical substances come from their molecular conformation. As a biological macromolecule, the structural feature of protein that determines its physical properties (such as taste and oil absorption) at the molecular conformation level is its secondary structure. The secondary structure is formed by the polypeptide chains of the protein coiled together through hydrogen bonds. Proteins with similar secondary structures often have similar physical properties. Currently, the most sensitive method for detecting hydrogen bonds is Fourier infrared spectroscopy. When this method is applied to the determination of protein secondary structure, it can determine the proportion of different types of secondary structures of proteins. The proportion of protein secondary structures can be used to compare the similarity of the secondary structures of two proteins. However, in the process of evaluating the molecular conformation of plant fiber proteins, due to the large molecular weight and complex structure of plant fiber proteins, the infrared spectrum corresponding to its secondary structure is too complex, making it difficult to extract features by traditional secondary structure infrared spectroscopy processing methods, and thus the problem of difficult to accurately evaluate the conformation of plant protein fiber molecules. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a method for evaluating conformational changes of plant fiber protein molecules to solve the existing problems.

[0005] The present invention provides a method for evaluating conformational changes of plant fiber protein molecules using the following technical solution:

[0006] One embodiment of the present application provides a method for evaluating conformational changes of plant fiber protein molecules, the method comprising the following steps:

[0007] S1, adding potassium bromide to the processed protein mixture for grinding, mixing and tableting, and scanning with an infrared spectrometer to obtain an FT IR spectrum of plant fiber protein, from which the wavelength band of amide I is intercepted to obtain plant fiber protein spectrum data; accordingly, obtaining animal protein spectrum data;

[0008] S2, using Gaussian fitting algorithm to obtain several characteristic peaks of plant fiber protein map data and animal protein map data and their corresponding data pairs; by performing characteristic analysis on the characteristic peaks in the two map data within different secondary structure ranges, training the conformational change evaluation decision function, specifically:

[0009] A1, based on the distribution area and central wave number difference of any characteristic peak under any secondary structure, determine the characteristic peak ambiguity weight of any characteristic peak for any secondary structure;

[0010] A2, determining the characteristic peak confidence of any characteristic peak based on the difference between the first-order derivatives of the Gaussian function of any characteristic peak and the spectrum data, and the fluctuation of the characteristic peak ambiguity weight of any characteristic peak in all secondary structures;

[0011] A3, based on the characteristic peak confidence, overlapping area ratio, and characteristic peak fuzziness weight of any two characteristic peaks between two spectral data, determine the content difference of any secondary structure;

[0012] A4, constructing an input data set based on the content differences of all secondary structures and manually evaluated values ​​in any two atlas data under all types of combinations, and training the input data set to obtain a conformational change evaluation decision function;

[0013] S3, based on the content difference of all secondary structures between the plant fiber protein molecule to be detected and the animal protein sample it imitates, a conformational change evaluation decision function is used to obtain the evaluation value of the conformational change of the plant fiber protein molecule to be detected.

[0014] Preferably, the data pair is related information of the corresponding characteristic peak, and the related information of the corresponding characteristic peak contained in the data pair includes: the central wave number, half-height width, peak height and peak area of ​​the characteristic peak.

[0015] Preferably, the method for determining the characteristic peak ambiguity weight includes:

[0016] Obtain the corresponding sequence of each secondary structure corresponding to the wave number range expanded under the preset step size;

[0017] Accumulate and sum the values ​​corresponding to the wavelength of any characteristic peak in the corresponding sequence of any secondary structure to obtain the area of ​​any characteristic peak in any secondary structure band;

[0018] Based on the central wavenumber difference between any characteristic peak and any secondary structure, determining the central wavenumber conformity of any characteristic peak in any secondary structure band;

[0019] The normalized value of the product of the area and the central wave number conformity is used as the characteristic peak ambiguity weight.

[0020] Preferably, the method for determining the central wavenumber conformity includes: calculating the difference between the central wavenumber of the data pair of any characteristic peak and the central wavenumber of any secondary structure, and taking the normalized value of the reciprocal of the difference as the central wavenumber conformity of any characteristic peak in any secondary structure band.

[0021] Preferably, the method for determining the characteristic peak confidence comprises:

[0022] Determine the fitting confidence of any characteristic peak based on the numerical difference between the first-order derivative of the Gaussian function of any characteristic peak and the first-order derivative of the plant fiber protein atlas data within a preset range;

[0023] Determine the inter-class confidence of any characteristic peak based on the fluctuation of the characteristic peak fuzziness weight of any characteristic peak in all secondary structures;

[0024] The ratio of the inter-class confidence to the fitting confidence is used as the characteristic peak confidence.

[0025] Preferably, the method for determining the fitting confidence comprises:

[0026] Obtaining the wave number value of any characteristic peak within a preset range to form a range sequence of any characteristic peak;

[0027] The first-order derivative of the Gaussian function of any characteristic peak is obtained, and the elements in the first-order derivative of the Gaussian function obtained by taking the range sequence as the independent variable are normalized to obtain a change rule sequence of any characteristic peak;

[0028] The first-order derivative of the plant fiber protein map data is obtained, and the elements in the first-order derivative of the plant fiber protein map data obtained by taking the range sequence as an independent variable are normalized to obtain a change rule sequence of the plant fiber protein map data;

[0029] The inverse of the difference between the two change rule sequences is taken as the fitting confidence of any characteristic peak.

[0030] Preferably, the method for determining the content difference comprises:

[0031] Calculate the sum of the characteristic peak confidences of the arbitrary two characteristic peaks, and record it as the first characteristic peak coefficient;

[0032] Calculate the overlapping area ratio of any two characteristic peaks, and record it as the second characteristic peak coefficient;

[0033] Calculate the sum of the characteristic peak ambiguity weights of any two characteristic peaks under any secondary structure, and record it as the third characteristic peak coefficient;

[0034] The first, second and third characteristic peak coefficients are merged to obtain the characteristic peak difference of any two characteristic peaks under any secondary structure;

[0035] The cumulative sum of the characteristic peak differences between all two arbitrary characteristic peaks under any secondary structure is recorded as the content difference.

[0036] Preferably, the calculation method of the overlapping area ratio is: taking the overlapping area of ​​any two characteristic peaks as the numerator, taking the sum of the non-overlapping area and the overlapping area of ​​any two characteristic peaks as the denominator, and taking the ratio of the numerator to the denominator as the overlapping area ratio.

[0037] Preferably, the method for calculating the characteristic peak difference comprises: taking the product of the first, second and third characteristic peak coefficients as the characteristic peak difference between any two characteristic peaks under any secondary structure.

[0038] Preferably, the method for training the input data set to obtain the conformational change evaluation decision function is a support vector machine algorithm.

[0039] This application has at least the following beneficial effects:

[0040] The present application aims at evaluating the imitation ability of plant fiber protein on animal protein, and proposes a method for evaluating the conformational changes of plant fiber protein molecules. The method solves the problem that the molecular weight of artificial plant fiber protein molecules is too large, which makes it difficult to extract features by traditional secondary structure infrared spectroscopy processing methods, and thus it is difficult to accurately evaluate the conformation of plant protein fiber molecules. The method includes: obtaining multiple characteristic peaks from the protein spectrum of the amide I band through Gaussian fitting; constructing characteristic peak fuzziness weights according to the position characteristics of the characteristic peaks and the bands corresponding to the four protein secondary structures, and corresponding the characteristic peaks to the four protein secondary structures, thereby avoiding the traditional method based on technical The subjective errors caused by directly classifying and dividing characteristic peaks based on the experience of technical personnel; by comparing the changing rules of characteristic peaks and amide I band protein spectra, combined with the fluctuation of characteristic peak fuzziness weight of characteristic peaks, characteristic peak confidence is obtained, avoiding the adverse effects of pseudo peaks caused by noise on the final comparison data; finally, by comparing the characteristic peak areas of amide I band infrared spectra of the two proteins, combined with characteristic peak fuzziness weight and characteristic peak confidence, the difference in secondary structure content is obtained, and data training is performed on the difference in secondary structure content to construct a conformational change evaluation decision function, which is used to evaluate the imitation ability of plant fibrous protein relative to animal protein. This application completes the comparison of the secondary structure similarity of the two proteins by comparing the amide I band infrared spectra of the two proteins, solving the problem that the conformational evaluation of plant fibrous protein molecules is difficult to accurately conduct due to the excessive molecular weight and complex spectrum of artificial plant fibrous protein molecules. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 A flow chart of a method for evaluating conformational changes of plant fiber protein molecules provided in one embodiment of the present application;

[0043] Figure 2 A schematic diagram of the bands corresponding to the four secondary structures of the amide I band in a protein provided in one embodiment of the present application;

[0044] Figure 3 A flowchart of the steps of training a conformational change evaluation decision function provided in one embodiment of the present application;

[0045] Figure 4 A flowchart of a method for determining the confidence level of a characteristic peak provided in one embodiment of the present application;

[0046] Figure 5 A flow chart of the steps of a method for determining content difference provided in one embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the plant fiber protein molecular conformation change evaluation method proposed in the present application, its specific implementation, structure, characteristics and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0049] The specific scheme of the method for evaluating conformational changes of plant fiber protein molecules provided by the present application is described in detail below with reference to the accompanying drawings.

[0050] An embodiment of the present application provides a method for evaluating conformational changes of plant fiber protein molecules.

[0051] Specifically, the following method for evaluating the conformational change of plant fiber protein molecules is provided. Figure 1 , the method comprises the following steps:

[0052] The first step is to obtain plant fiber protein map data and animal protein map data.

[0053] In practical applications, plant fibrous protein usually comes from plant materials such as soybeans, peas, wheat, soybeans, tofu, etc. The processing involves protein extraction, hydrolysis, polymerization or fiberization to achieve the desired food function and nutritional value.

[0054] In the present application, the texture of meat fiber is presented by processing the plant protein wire drawing structure. The processing method of the plant protein wire drawing structure is as follows: a certain ratio of protein mixture is used, with large protein isolate and gluten as the main raw materials, and the hydrophilicity of soy protein and wheat protein is utilized, and corn starch is added to fully expand. After high-temperature expansion and extrusion, the fiber texture is elastic and not sticky.

[0055] In the present application, the ratio of plant raw materials in the processing of the above-mentioned plant protein drawing structure is: 40-50 parts of soy protein isolate, 20-30 parts of gluten, 15-25 parts of corn starch, and 10-20 parts of low-temperature soybean meal.

[0056] It should be noted that the purpose of using puffing extrusion equipment to perform high-temperature expansion and extrusion on the protein mixture in this application is to utilize the characteristics of soy protein and wheat protein being hydrophilic proteins, so that the proteins can be easily mixed during the puffing and extrusion process, and the maximum degree of protein modification can be achieved during the heating process. At the same time, by using a screw to extrude it, the protein structure of soybeans and wheat is modified from a spherical shape to a fibrous drawing structure, so that it achieves a fine and tough protein fiber structure, thereby changing the taste of the meat.

[0057] In addition, in addition to the elastic meat fiber texture, the overall chewy feel and meaty feel of plant protein lean meat need to be achieved by mixing with an emulsified slurry of soy protein isolate.

[0058] Accordingly, the present application makes full use of the respective adhesiveness and protein emulsification characteristics of soy protein isolate, gluten powder and distarch phosphate, and mixes the processed protein mixture with the emulsified slurry of soy protein isolate, thereby improving the overall chewiness and meatiness of the processed protein mixture.

[0059] Among them, the main proportions of the emulsified slurry of soy protein isolate are: 50-60 parts of water, 15-20 parts of soy protein isolate, 5-10 parts of vegetable oil, 5-10 parts of gluten, 5-10 parts of distarch phosphate, and 3-5 parts of corn starch.

[0060] It is worth noting that the vegetable protein lean meat of the present invention is prepared by mixing and maturing the vegetable protein drawing structure and the emulsified material mainly composed of soy protein isolate. The key lies in the processing of the vegetable protein drawing structure and the ratio of the emulsified material mainly composed of soy protein isolate. Moreover, the vegetable protein lean meat can be used in industrial production channels such as meat products, sauces, fillings, and toppings to replace lean meat, and the application channels are very wide. The main characteristics of the product are:

[0061] a. After high-temperature steaming, it will not mix with other materials in the product, and can maintain a clear sense of meat fiber; after frying or boiling, it can also maintain the granular state containing meat fiber;

[0062] b. It has very good oil and water retention properties, which can lock the oil and water in the mixture of the product, making the product feel moisturizing after chewing;

[0063] c. Soy protein isolate can make the vegetable protein lean meat maintain a certain elastic taste and increase the chewiness of the lean meat;

[0064] d. The protein content of plant protein lean meat is 27.8g, fat is 1.5g, and carbohydrates are 3.5g per 100g. These nutrients are much healthier than animal protein lean meat, especially the protein content is significantly higher than animal protein lean meat;

[0065] e. The plant protein drawing structure in the product is not mushy, greasy, sticky or loose after boiling or frying, and can still maintain the elasticity of the fiber;

[0066] f. When the product is added to various hams, luncheon meats, and meatballs, it will not shrink like lean meat, but will expand after absorbing water and oil, thereby increasing the output rate of the product and improving the cost performance of the product.

[0067] Next, the present application evaluates the ability of plant fiber protein molecules to imitate animal protein molecules by comparing the secondary structure spectrum parts in the infrared spectra of plant fiber protein molecules and animal protein molecules.

[0068] The specific method for obtaining plant fiber protein atlas data and animal protein atlas data is as follows:

[0069] Potassium bromide is added to the processed protein mixture for grinding, mixing and tableting, and the mixture is scanned by an infrared spectrometer to obtain the FT IR spectrum of the plant fiber protein, from which the wavelength band of the amide I band is intercepted to obtain the plant fiber protein spectrum data.

[0070] Preferably, in one embodiment of the present application, 5 mg of the processed protein mixture is weighed, 0.2 g of potassium bromide is added, the mixture is ground and mixed evenly, and then the tablet is pressed using an infrared tablet press, and the temperature is balanced in a constant temperature box for 5 minutes. The tablet is then scanned using an infrared spectrometer with a scanning band of 400 to 4000 cm -1 , scanning band interval is 1cm -1 , thereby obtaining the FT IR spectrum of plant fibrous protein, the vertical axis of the FT IR spectrum of plant fibrous protein is absorbance, and the horizontal axis is wavelength.

[0071] In this embodiment, the weight of the processed protein mixture and potassium bromide, as well as the time for the thermostat to equilibrate the temperature, can be set by the implementer according to actual conditions.

[0072] According to experience, the FT IR spectrum of protein secondary structure is located between 1615 and 1700 cm -1 The wavelength band is called amide I band; therefore, the FT IR spectrum of plant fiber protein is intercepted at 1615-1700 cm -1 The spectrum of the band is used to obtain the plant fiber protein spectrum data. The storage data of the plant fiber spectrum data is a vector with a length of N=85, where the nth element represents a wavelength of (1614+n) cm -1 The absorbance of the sample.

[0073] It should be noted that the position and shape of the amide I band can provide information about the structure of protein molecules, such as the composition and changes of its secondary structure. Through infrared spectroscopy analysis, the structure of proteins can be preliminarily evaluated and compared, which is especially useful when studying the folding state, conformational changes or interactions of proteins with other molecules.

[0074] Potassium bromide is added to freeze-dried animal protein powder with a meaty texture similar to that of the processed protein mixture, and the powder is ground, mixed and pressed into tablets. The powder is scanned with an infrared spectrometer to obtain an FT IR spectrum of the animal protein, from which the band of amide I is intercepted to obtain animal protein spectrum data.

[0075] In one embodiment of the present application, the schematic diagram of the bands corresponding to the four secondary structures of the amide I band in the protein is shown in the attached Figure 2 shown.

[0076] In the attached Figure 2 In the figure, A represents α-helix structure, B represents β-fold structure, C represents random curl structure, and D represents β-turn structure. The horizontal axis represents the wave number in cm. -1 , among which, 1615~1700cm -1 The band is amide I band.

[0077] In the second step, the conformational change evaluation decision function is trained by performing feature analysis on the characteristic peaks in the two atlas data within different secondary structure ranges.

[0078] The traditional infrared spectrum processing method of the characteristic peak of the amide I band uses processing methods such as Gaussian fitting to decompose this part of the infrared spectrum into multiple characteristic peaks to complete the feature extraction of the aliased infrared spectrum features. Although this method decomposes too many characteristic peaks when facing large molecules and complex protein molecules, and it is difficult to correspond to the four protein secondary structures, it can still eliminate the interference caused by the overlapping of characteristic peaks. Therefore, this embodiment performs the same processing on the plant fiber protein spectrum data to obtain the secondary structure characteristic peak data set of the plant fiber protein spectrum data.

[0079] In one embodiment of the present application, the process of acquiring the secondary structure characteristic peak data set of the plant fiber protein atlas data is as follows:

[0080] The plant fibrous protein atlas data was used as input, and the Gaussian fitting algorithm was used. The fitting stop condition was the fitting correlation coefficient R 2≥0.98, the output is K characteristic peaks, each characteristic peak corresponds to a data pair, where the kth data pair is (m, w, h, s), each data pair represents the relevant information of the corresponding characteristic peak, m, w, h, s represent the central wave number m, half-height width w, peak height h, peak area s of the kth characteristic peak respectively; where the number of data pairs K is determined by the calculation results of the algorithm, and the K data pairs constitute the secondary structure characteristic peak data set of plant fibrous protein. Gaussian fitting algorithm is a commonly used well-known processing technology for protein data, and this application will not repeat it.

[0081] The same processing is performed on the animal protein spectrum data to obtain the secondary structure characteristic peak data set of the animal protein spectrum data.

[0082] In one embodiment of the present application, by performing feature analysis on characteristic peaks in two atlas data within different secondary structure ranges, a flow chart of the steps for training a conformational change evaluation decision function is shown in the attached figure. Figure 3 As shown, specifically:

[0083] A1, based on the distribution area and central wavenumber difference of any characteristic peak under any secondary structure, determine the characteristic peak ambiguity weight of any characteristic peak for any secondary structure.

[0084] Since the molecular weight of plant protein fiber is too large, the number of characteristic peaks in its secondary structure characteristic peak data set is usually much greater than 4, resulting in a relatively uniform distribution of these characteristic peaks on the amide I band, and sometimes spanning the band range of two or more secondary structures. At this time, it is necessary to perform subordinate division on these characteristic peaks to determine the secondary structure content of the plant protein fiber.

[0085] Based on this, this embodiment uses fuzzy mathematics to perform subordinate division on the characteristic peaks, that is, the characteristic peaks are divided into different parts according to the secondary structure band range, where the rth part corresponds to the rth secondary structure. The larger the area of ​​the rth part occupies in the whole characteristic peak, the more likely the secondary structure represented by the characteristic peak is to be the rth secondary structure, that is, the characteristic peak fuzziness weight for the rth secondary structure is also greater.

[0086] Specifically, the method for determining the characteristic peak ambiguity weight includes:

[0087] Obtain a corresponding sequence of the wavenumber range corresponding to each secondary structure at a preset step size; accumulate and sum the values ​​corresponding to the wavelength of any characteristic peak in the corresponding sequence of any secondary structure to obtain the area of ​​any characteristic peak in any secondary structure band; determine the central wavenumber conformity of any characteristic peak in any secondary structure band based on the central wavenumber difference between any characteristic peak and any secondary structure; and use the normalized value of the product of the area and the central wavenumber conformity as the characteristic peak fuzziness weight.

[0088] In this embodiment, a specific analysis is performed by taking the kth characteristic peak and the rth secondary structure as an example:

[0089] For the kth characteristic peak in the secondary structure characteristic peak data set of plant fibrous protein, since the Gaussian fitting algorithm is used in this embodiment to obtain the characteristic peak, the kth characteristic peak corresponds to a Gaussian function f(x): k , that is, when the wave number is λ, the function value of the kth characteristic peak is f(λ) k .

[0090] At the same time, the wave number range corresponding to the rth secondary structure can be expanded to form a corresponding sequence with a step size of 1. In other embodiments of the present application, the step size can be set according to actual conditions. Among them, the wave number range of the α-helix structure can be represented by the sequence (1646, 1647...1664), and the corresponding sequence of the rth secondary structure is Num r .

[0091] Furthermore, in this embodiment, the corresponding sequence Num of the rth secondary structure r As the independent variable, the Gaussian function f(x) of the kth characteristic peak is k is a function, and the corresponding sequence of the rth secondary structure is obtained under the Gaussian function f(x) k The element values ​​in the rth secondary structure band are summed up as the area DS of the kth characteristic peak in the rth secondary structure band. k,r .

[0092] In addition, the closer the central wavenumber m in the data pair of the k-th characteristic peak is to the central wavenumber of the r-th secondary structure, the more likely the k-th characteristic peak represents the r-th secondary structure. Therefore, the characteristic peak ambiguity weight of the k-th characteristic peak to the r-th secondary structure should also be greater.

[0093] Therefore, this embodiment calculates the absolute value of the difference between the central wavenumber of the k-th characteristic peak data pair and the central wavenumber of the r-th secondary structure, then finds the inverse of the absolute value of the difference, and finally performs normalization to obtain the central wavenumber conformity Ci of the k-th characteristic peak in the r-th secondary structure band. k,r It should be understood that the larger the conformity value, the more likely the k-th characteristic peak is to represent the r-th secondary structure.

[0094] It should be noted that when calculating the reciprocal of the absolute value of the difference, an extremely small number is added to the absolute value of the difference, and the value is 0.1 in this embodiment to prevent the denominator from being zero.

[0095] Finally, the fuzziness weight of the k-th characteristic peak on the r-th secondary structure is calculated, and the area DS of the k-th characteristic peak in the r-th secondary structure band isk,r The degree of conformity Ci with the central wave number of the kth characteristic peak in the rth secondary structure band k,r The normalized value of the product of is used as the characteristic peak ambiguity weight of the kth characteristic peak for the rth secondary structure.

[0096] It should be understood that the greater the fuzziness weight of the k-th characteristic peak for the r-th secondary structure, the more likely the k-th characteristic peak is the characteristic peak of the r-th secondary structure. Using the fuzzy characteristic weight as a method for judging the expression characteristics of the characteristic peak for the secondary structure avoids the subjective error caused by directly classifying and dividing the characteristic peaks based on the experience of technicians in the traditional method.

[0097] At this point, the characteristic peak fuzziness weight for each secondary structure has been calculated for each characteristic peak in the secondary structure characteristic peak data set of the plant fiber protein atlas data. The same calculation can be performed for each characteristic peak in the secondary structure characteristic peak data set of the animal protein atlas data to obtain the characteristic peak fuzziness weight of each characteristic peak.

[0098] A2, determining the characteristic peak confidence of any characteristic peak based on the difference in the first-order derivative between the Gaussian function of any characteristic peak and the spectrum data, and the fluctuation of the characteristic peak ambiguity weight of any characteristic peak in all secondary structures.

[0099] Since the present embodiment characterizes the difference in protein taste by comparing the difference in secondary structure content of two macromolecular proteins, when comparing the characteristic peaks of the secondary structures of the two proteins, the more the characteristic peak conforms to the changing trend of the original data, the better the fitting effect of the characteristic peak and the higher the confidence; the greater the fluctuation of the characteristic peak fuzziness weight of the characteristic peak, the more the characteristic peak characterizes a single and clear secondary structure, the more clearly it can represent a certain secondary structure, and the higher the confidence; the higher the confidence of the characteristic peak, the clearer its expression of the secondary structure content characteristics and the better the expression effect, and when comparing the characteristic peaks, the greater the calculation weight of the characteristic peak should be.

[0100] Due to the characteristic peak separation method of infrared spectroscopy, such as Gaussian fitting, when decomposing the infrared spectrum of amide I band, noise interference may occur and produce false peaks. This peak is called a pseudo peak. When performing secondary structure infrared spectroscopy analysis on large molecular weight proteins, the pseudo peaks that appear are difficult to distinguish due to too many characteristic peaks. The characteristic peak confidence can eliminate the adverse effects of pseudo peaks on the final comparison data.

[0101] Specifically, the method for determining the characteristic peak confidence includes:

[0102] The fitting confidence of any characteristic peak is determined based on the numerical difference between the first-order derivative of the Gaussian function of any characteristic peak and the first-order derivative of the plant fiber protein atlas data within a preset range; the inter-class confidence of any characteristic peak is determined based on the fluctuation of the characteristic peak fuzziness weight of any characteristic peak in all secondary structures; the ratio of the inter-class confidence to the fitting confidence is used as the characteristic peak confidence.

[0103] In one embodiment of the present application, the method for determining the confidence level of a characteristic peak is shown in the flowchart of the attached Figure 4 shown.

[0104] Wherein, the method for determining the fitting confidence includes:

[0105] Obtain the wave number value of any characteristic peak within a preset range to form a range sequence of any characteristic peak; calculate the first-order derivative of the Gaussian function of any characteristic peak, and normalize the elements in the first-order derivative of the Gaussian function obtained by taking the range sequence as the independent variable to obtain a change law sequence of any characteristic peak; calculate the first-order derivative of the plant fiber protein spectrum data, and normalize the elements in the first-order derivative of the plant fiber protein spectrum data obtained by taking the range sequence as the independent variable to obtain a change law sequence of the plant fiber protein spectrum data; and use the inverse of the difference between the two change law sequences as the fitting confidence of any characteristic peak.

[0106] In one embodiment of the present application, taking the kth characteristic peak as an example, the fitting confidence of the kth characteristic peak is analyzed, specifically:

[0107] For the kth characteristic peak in the secondary structure characteristic peak data set of plant fibrous protein, obtain its Gaussian function f(x) k The central wave number m k According to the 3sigma principle, the main range of the kth characteristic peak is mainly in the interval [m k -3×σ k ,m k +3×σ k ], obtain the wave number value in this interval to form the range sequence Int of the kth characteristic peak k .

[0108] At the same time, for the Gaussian function f(x) k Find the first-order derivative and use the range sequence Int k Substitute the first-order derivative for the independent variable to obtain the first-order derivative sequence of the k-th characteristic peak. Normalize the first-order derivative sequence to obtain the change law sequence of the k-th characteristic peak. This sequence represents the numerical change law of the k-th characteristic peak, where the positive or negative value represents the rise or fall of the k-th characteristic peak, and the size of the value represents the severity of the change.

[0109] The plant fiber protein map data is a vector, which is used as input to calculate using a first-order derivative vector calculation algorithm, and the output is the first-order derivative vector of the plant fiber protein map data; the first-order derivative vector calculation method is a commonly used technology in the field of data processing.

[0110] Further, according to the range sequence Int of the kth characteristic peak k The first-order derivative vector is intercepted within the wavenumber range to obtain the first-order derivative sequence of the plant fiber protein spectrum data. The first-order derivative sequence is normalized to obtain the change law sequence of the plant fiber protein spectrum data. The sequence represents the numerical change law of the plant fiber protein spectrum data within the k-th characteristic peak range, wherein the positive or negative value represents the rise or fall of the plant fiber protein spectrum, and the size of the value represents the severity of the change.

[0111] Finally, the reciprocal of the cumulative sum of the absolute values ​​of the differences between the k-th characteristic peak and the corresponding elements of the change rule sequence of the plant fiber protein spectrum is taken as the fitting confidence of the k-th characteristic peak.

[0112] In another embodiment of the present application, the reciprocal of the DTW distance between the kth characteristic peak and the variation regularity sequence of the plant fiber protein spectrum is used as the fitting confidence of the kth characteristic peak. The DTW distance is a well-known technology and will not be described in detail.

[0113] In other embodiments of the present application, the L1 norm of the sequence composed of the differences between the kth characteristic peak and the variation rule sequence of the plant fiber protein spectrum is calculated, and the inverse of the L1 norm is calculated and recorded as the fitting confidence of the kth characteristic peak.

[0114] It should be understood that the greater the fitting confidence of the k-th characteristic peak, the better the fitting effect of the k-th characteristic peak to the original data is, and the higher the confidence is.

[0115] It should be noted that before calculating the reciprocal, a very small number is added to the L1 norm, and the value in this embodiment is 0.1 to prevent the denominator from being zero.

[0116] The method for determining the inter-class confidence includes:

[0117] The fluctuation of the characteristic peak fuzziness weight of any characteristic peak in all secondary structures is used as the inter-class confidence of any characteristic peak.

[0118] In one embodiment of the present application, taking the kth characteristic peak as an example, the inter-class confidence of the kth characteristic peak is analyzed, and the standard deviation of the characteristic peak fuzziness weights of the kth characteristic peak in all secondary structures is used as the inter-class confidence of the kth characteristic peak. In other embodiments of the present application, variance, information entropy, etc. can also be used as fluctuation conditions for analysis.

[0119] It should be understood that the greater the inter-class confidence of the k-th characteristic peak, the more the k-th characteristic peak represents a single and clear secondary structure, the more clearly it can represent a certain secondary structure, and the higher the confidence.

[0120] Finally, the inter-class confidence is divided by the fitting confidence to obtain the characteristic peak confidence β of the kth characteristic peak k The larger the value, the better the expression effect of the kth characteristic peak on the secondary structure, and the higher the confidence. The confidence can be used to screen the characteristic peaks that express the secondary structure content characteristics well, thereby avoiding the adverse effects of pseudo peaks caused by noise on the final comparison data.

[0121] The above characteristic peak confidence is calculated for each characteristic peak in the secondary structure characteristic peak data set of the plant fiber protein atlas data. The same calculation can be performed for each characteristic peak in the secondary structure characteristic peak data set of the animal protein atlas data to obtain the characteristic peak confidence of each characteristic peak.

[0122] A3, based on the characteristic peak confidence, overlapping area ratio, and characteristic peak fuzziness weight of any two characteristic peaks between the two spectral data, determine the content difference of any secondary structure.

[0123] When comparing the conformations of two proteins, the greater the difference in the characteristic peaks of the amide I band in the infrared spectra of the two proteins, the greater the difference in their secondary structures and the greater the difference in their edible taste. Therefore, this embodiment compares the differences in the characteristic peaks of the amide I band of plant fiber protein and animal protein.

[0124] This embodiment uses area comparison as one of the methods for comparing the difference of characteristic peaks. When comparing, a numerical relationship is established based on the overlapping area and the non-overlapping area. The larger the overlapping area accounts for the total area, the smaller the difference; the larger the non-overlapping area accounts for the total area, the greater the difference. At the same time, the characteristic peak confidence between characteristic peaks and the characteristic peak fuzziness weights between characteristic peaks under different secondary structures are combined to jointly analyze the content difference of any secondary structure.

[0125] Wherein, the method for determining the content difference includes:

[0126] Calculate the sum of the characteristic peak confidences of the arbitrary two characteristic peaks, recorded as the first characteristic peak coefficient; calculate the overlapping area ratio of the arbitrary two characteristic peaks, recorded as the second characteristic peak coefficient; calculate the sum of the characteristic peak fuzziness weights of the arbitrary two characteristic peaks under any secondary structure, recorded as the third characteristic peak coefficient; merge the first, second and third characteristic peak coefficients to obtain the characteristic peak difference of the arbitrary two characteristic peaks under any secondary structure; and record the cumulative sum of the characteristic peak differences of all arbitrary two characteristic peaks under any secondary structure as the content difference.

[0127] It can be understood that fusion can be divided into forward fusion and reverse fusion. This embodiment adopts the forward fusion method. Forward fusion is a fusion method such as addition and multiplication between data. The specific forward fusion method is determined by the implementer according to the actual situation. The application does not impose any special restrictions.

[0128] In one embodiment of the present application, the method for determining the content difference is shown in the flowchart of the attached Figure 5 shown.

[0129] In this embodiment, the k1th characteristic peak of the secondary structure characteristic peak data set of plant fibrous protein and the k2th characteristic peak of the secondary structure characteristic peak data set of animal protein are taken as examples for analysis, specifically:

[0130] First, the characteristic peak confidences of the two characteristic peaks are summed and recorded as the first characteristic peak coefficients of the two characteristic peaks. The larger the value, the better the expression effect of the comparison result between the two on the secondary structure and the higher the confidence.

[0131] Secondly, the overlapping area between the two characteristic peaks is recorded as S1, and the non-overlapping area between the two characteristic peaks is recorded as S2; the overlapping area ratio between the k1th characteristic peak and the k2th characteristic peak is: S1 is taken as the numerator, the sum of S1 and S2 is taken as the denominator, and the ratio of the numerator to the denominator is taken as the overlapping area ratio between the k1th characteristic peak and the k2th characteristic peak, which is recorded as the second characteristic peak coefficient of the two characteristic peaks.

[0132] Then, the rth characteristic peak fuzziness weights of the two characteristic peaks are summed and recorded as the third characteristic peak coefficient of the two characteristic peaks. The larger the value, the more the comparison result of the k1th and k2th characteristic peaks represents the difference of the rth secondary structure.

[0133] Finally, the product of the first, second and third characteristic peak coefficients was calculated as the characteristic peak difference between the two characteristic peaks under the rth secondary structure. The larger the value, the more significant the difference between the two proteins in the rth secondary structure based on the comparison results of the k1 and k2 characteristic peaks.

[0134] The characteristic peak differences between all characteristic peaks under the r-th secondary structure are summed to obtain the content difference ΔB of the r-th secondary structure of the two proteins. r .

[0135] In another embodiment of the present application, the sum of the first, second and third characteristic peak coefficients is calculated as the characteristic peak difference between the two characteristic peaks under the rth secondary structure.

[0136] In other embodiments of the present application, the sum of the first and third characteristic peak coefficients is calculated and then multiplied by the second characteristic peak coefficient to obtain the characteristic peak difference between the two characteristic peaks under the rth secondary structure.

[0137] To facilitate data processing, the R secondary structure content differences were arranged into a sequence, recorded as the content difference characteristic sequence. The larger the r-th value in the content difference characteristic sequence, the more obvious the content difference between the two proteins in the r-th secondary structure was.

[0138] A4, construct an input data set based on the content differences of all secondary structures in any two atlas data under all types of combinations and the manually evaluated evaluation values, and train the input data set to obtain a conformational change evaluation decision function.

[0139] Based on the characteristic sequences of the differences in the secondary structure contents of plant fiber proteins and animal proteins, the differences in the secondary structure contents of the two proteins can be determined, and then the ability of plant fiber proteins to imitate the taste of animal proteins can be evaluated; during the evaluation, the differences in different secondary structures have different effects on the ability to imitate the taste, and this embodiment uses a support vector machine method to process the characteristic sequences of the differences in the secondary structure contents to complete the evaluation of the changes in the molecular conformation of plant fiber proteins; optionally, neural networks and PCA algorithms can also be used as alternative evaluation methods.

[0140] Among them, the specific method of processing the secondary structure content difference feature sequence by support vector machine and training the conformational change evaluation decision function is as follows:

[0141] First, different plant fiber protein map data and different animal protein map data are combined in pairs to obtain multiple combinations; for one of the combinations, a secondary structure content difference characteristic sequence is obtained according to the above-mentioned calculation method of this embodiment; the artificial meat products of the plant fiber protein of this combination and the meat products corresponding to the animal protein are evaluated by tasters, and the evaluation results of the tasters are collected, and the evaluation results are sorted and classified into 4 types of evaluation, 1 represents that the two proteins have similar tastes, 2 represents that the two proteins have relatively similar tastes, 3 represents that the two proteins have different tastes, and 4 represents that the two proteins have very different tastes.

[0142] Finally, each combination can obtain a secondary structure content difference characteristic sequence as the input part of a set of data; each combination can also obtain an evaluation value, the evaluation values ​​are 1, 2, 3, 4, as a data evaluation of a set of data; the input parts composed of all types and the corresponding evaluation values ​​constitute the input data set.

[0143] Taking the input data set as input, the support vector machine algorithm is used to output the decision function, which is recorded as the conformational change evaluation decision function. The support vector machine algorithm is a commonly used technology in the field of data processing.

[0144] The third step is to use a conformational change evaluation decision function to obtain an evaluation value of the conformational change of the plant fiber protein molecule to be tested based on the content difference in all secondary structures between the plant fiber protein molecule to be tested and the animal protein sample it imitates.

[0145] During the detection process, for the artificial meat plant fiber protein molecule to be detected, its protein samples and the target imitation animal meat are obtained, and then a secondary structure content difference characteristic sequence is obtained according to the above method of this embodiment, and the secondary structure content difference characteristic sequence is used as the input of the conformational change evaluation decision function, and an evaluation value is output. According to the evaluation values ​​1, 2, 3, and 4, the taste evaluation of the artificial meat plant fiber protein molecule is obtained accordingly: the two proteins have similar tastes, the two proteins have relatively similar tastes, the two proteins have different tastes, and the two proteins have very different tastes.

[0146] Since this embodiment uses fuzzy mathematics method to extract the characteristics of the difference in the characteristic peaks of the amide I band of the infrared spectra of two proteins, it solves the problem that the conformational evaluation of plant fiber protein molecules is difficult to accurately perform due to the complex spectrum of large molecular weight proteins.

[0147] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0148] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "including one..." does not exclude the existence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items.

[0149] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not invented by the present application.

[0150] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A method for evaluating conformational changes of plant fibrous protein molecules. It is characterized in that The method comprises the following steps: S1, adding potassium bromide to the processed protein mixture for grinding, mixing and tableting, and scanning with an infrared spectrometer to obtain an FTIR spectrum of the plant fiber protein, from which the wavelength band of the amide I band is intercepted to obtain the plant fiber protein spectrum data; accordingly, the animal protein spectrum data is obtained; S2, using Gaussian fitting algorithm to obtain several characteristic peaks of plant fiber protein map data and animal protein map data and their corresponding data pairs; by performing characteristic analysis on the characteristic peaks in the two map data within different secondary structure ranges, training the conformational change evaluation decision function, specifically: A1, based on the distribution area and central wave number difference of any characteristic peak under any secondary structure, determine the characteristic peak ambiguity weight of any characteristic peak for any secondary structure; A2, determining the characteristic peak confidence of any characteristic peak based on the difference between the first-order derivatives of the Gaussian function of any characteristic peak and the spectrum data, and the fluctuation of the characteristic peak ambiguity weight of any characteristic peak in all secondary structures; A3, based on the characteristic peak confidence, overlapping area ratio, and characteristic peak fuzziness weight of any two characteristic peaks between two spectral data, determine the content difference of any secondary structure; A4, constructing an input data set based on the content differences of all secondary structures and manually evaluated values ​​in any two atlas data under all types of combinations, and training the input data set to obtain a conformational change evaluation decision function; S3, based on the content difference of all secondary structures between the plant fiber protein molecule to be detected and the animal protein sample it imitates, a conformational change evaluation decision function is used to obtain the evaluation value of the conformational change of the plant fiber protein molecule to be detected.

2. A method for evaluating conformational changes of plant fiber protein molecules as claimed in claim 1, It is characterized in that The data pair is related information of the corresponding characteristic peak, and the related information of the corresponding characteristic peak contained in the data pair includes: the central wave number, half-height width, peak height and peak area of ​​the characteristic peak.

3. A method for evaluating conformational changes of plant fiber protein molecules as claimed in claim 2, It is characterized in that The method for determining the characteristic peak ambiguity weight comprises: Obtain the corresponding sequence of each secondary structure corresponding to the wave number range expanded under the preset step size; Accumulate and sum the values ​​corresponding to the wavelength of any characteristic peak in the corresponding sequence of any secondary structure to obtain the area of ​​any characteristic peak in any secondary structure band; Based on the central wavenumber difference between any characteristic peak and any secondary structure, determining the central wavenumber conformity of any characteristic peak in any secondary structure band; The normalized value of the product of the area and the central wave number conformity is used as the characteristic peak ambiguity weight.

4. A method for evaluating conformational changes of plant fiber protein molecules as claimed in claim 3, It is characterized in that The method for determining the central wavenumber conformity includes: calculating the difference between the central wavenumber of a data pair of any characteristic peak and the central wavenumber of any secondary structure, and taking the normalized value of the reciprocal of the difference as the central wavenumber conformity of any characteristic peak in any secondary structure band.

5. A method for evaluating conformational changes of plant fiber protein molecules as claimed in claim 1, It is characterized in that The method for determining the characteristic peak confidence level includes: Determine the fitting confidence of any characteristic peak based on the numerical difference between the first-order derivative of the Gaussian function of any characteristic peak and the first-order derivative of the plant fiber protein atlas data within a preset range; Determine the inter-class confidence of any characteristic peak based on the fluctuation of the characteristic peak fuzziness weight of any characteristic peak in all secondary structures; The ratio of the inter-class confidence to the fitting confidence is used as the characteristic peak confidence.

6. A method for evaluating conformational changes of plant fiber protein molecules as claimed in claim 5, It is characterized in that The method for determining the fitting confidence includes: Obtaining the wave number value of any characteristic peak within a preset range to form a range sequence of any characteristic peak; The first-order derivative of the Gaussian function of any characteristic peak is obtained, and the elements in the first-order derivative of the Gaussian function obtained by taking the range sequence as the independent variable are normalized to obtain a change rule sequence of any characteristic peak; The first-order derivative of the plant fiber protein map data is obtained, and the elements in the first-order derivative of the plant fiber protein map data obtained by taking the range sequence as an independent variable are normalized to obtain a change rule sequence of the plant fiber protein map data; The inverse of the difference between the two change rule sequences is taken as the fitting confidence of any characteristic peak.

7. A method for evaluating conformational changes of plant fiber protein molecules as claimed in claim 1, It is characterized in that The method for determining the content difference includes: Calculate the sum of the characteristic peak confidences of the arbitrary two characteristic peaks, and record it as the first characteristic peak coefficient; Calculate the overlapping area ratio of any two characteristic peaks, and record it as the second characteristic peak coefficient; Calculate the sum of the characteristic peak ambiguity weights of any two characteristic peaks under any secondary structure, and record it as the third characteristic peak coefficient; The first, second and third characteristic peak coefficients are merged to obtain the characteristic peak difference of any two characteristic peaks under any secondary structure; The cumulative sum of the characteristic peak differences between all two arbitrary characteristic peaks under any secondary structure is recorded as the content difference.

8. A method for evaluating conformational changes of plant fiber protein molecules as claimed in claim 7, It is characterized in that The calculation method of the overlapping area ratio is: taking the overlapping area of ​​any two characteristic peaks as the numerator, taking the sum of the non-overlapping area and the overlapping area of ​​any two characteristic peaks as the denominator, and taking the ratio of the numerator to the denominator as the overlapping area ratio.

9. A method for evaluating conformational changes of plant fiber protein molecules as claimed in claim 7, It is characterized in that The method for calculating the characteristic peak difference includes: taking the product of the first, second and third characteristic peak coefficients as the characteristic peak difference of any two characteristic peaks under any secondary structure.

10. A method for evaluating conformational changes of plant fiber protein molecules as claimed in claim 1, It is characterized in that The method of training the input data set to obtain the conformational change evaluation decision function is the support vector machine algorithm.

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