Pulse electric field pretreatment method for extracting water-soluble dietary fibers from peanut shells

Abstract

The invention relates to the technical field of spectral analysis, in particular to a pulsed electric field pretreatment method for extracting water-soluble dietary fibers from peanut shells, which comprises the following steps: acquiring spectral data of peanut shell extracts pretreated under different pulsed electric field intensities, and dividing absorption peaks in the spectral data into characteristic peaks and non-characteristic peaks; calculating a characteristic difference parameter between the same characteristic peaks in any two spectral data and a non-characteristic difference parameter between any same non-characteristic peaks in any two spectral data, and further obtaining a difference index between any two spectral data; performing hierarchical clustering by combining difference indexes among the spectral data, and calculating classification indexes of a hierarchical clustering tree; and acquiring the pulse electric field intensity corresponding to the maximum classification index as the optimal pulse electric field intensity. According to the method, the optimal electric field intensity can be accurately found, and the extraction quality of the water-soluble dietary fibers of the peanut shells can be further improved.

Description

Technical Field

[0001] The invention relates to the technical field of spectral analysis, and in particular to a pulse electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells. Background Art

[0002] As an important nutrient, dietary fiber has gradually attracted extensive research and application. Dietary fiber not only plays an important role in promoting intestinal health, regulating blood sugar and blood lipids, but also improves the occurrence and development of chronic diseases such as obesity and diabetes. Peanut shells, as a byproduct of peanut processing, are rich in dietary fiber, especially water-soluble dietary fiber, so they have high development potential. However, due to the complex structure and hard outer shell of peanut shells, it is difficult to extract dietary fiber from peanut shells. In order to solve these problems, in recent years, pulsed electric field (PEF) technology has been widely used in the fields of food and biotechnology as an emerging physical treatment method. Pulsed electric field destroys cell membranes and tissue structures by applying high-intensity electric field pulses, thereby promoting the release of cell contents and the extraction of substances. In the process of dietary fiber extraction, pulsed electric field can induce cell membrane rupture and structural reorganization through the action of electric field, reduce the structural strength of peanut shell cell wall, and thus improve the extraction rate of water-soluble dietary fiber.

[0003] During the extraction process, the magnitude of the pulse electric field intensity will affect the extraction efficiency and fiber structure. Therefore, when using the pulse electric field to extract water-soluble dietary fiber from peanut shells, inappropriate selection of the pulse electric field intensity should be avoided, which will lead to poor dietary fiber extraction effects. Summary of the invention

[0004] The invention provides a pulse electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells to solve the existing problems.

[0005] The pulse electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells of the present invention adopts the following technical scheme: One embodiment of the present invention provides a pulsed electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells, the method comprising the following steps: Acquire the spectral data of peanut shell extract pretreated with different pulse electric field intensities; Obtaining the absorption peak in any spectral data, and dividing the absorption peak in the spectral data into characteristic peaks and non-characteristic peaks according to the wave number corresponding to the absorption peak; According to the peak width and peak distribution of the same characteristic peak in any two spectral data, the characteristic difference parameters between the same characteristic peaks in the any two spectral data are calculated; according to the width and peak difference between the same non-characteristic peaks in any two spectral data, the non-characteristic difference parameters between any same non-characteristic peaks in the any two spectral data are calculated; according to the characteristic difference parameters and non-characteristic difference parameters, and in combination with the numbers corresponding to the characteristic peaks and non-characteristic peaks, the difference index between any two spectral data is obtained; For all spectral data under any pulse electric field intensity, hierarchical clustering is performed in combination with the difference index between the spectral data to obtain a hierarchical clustering tree containing several clusters. According to the distribution level of the difference index between the spectral data in the cluster, the clustering volatility of the cluster is obtained; according to the difference in the aggregation volatility between adjacent layers in the hierarchical cluster and the difference index of the spectral data in different clusters in the same layer, the classification index of the hierarchical clustering tree is determined; The pulse electric field intensity corresponding to the maximum classification index is obtained as the optimal pulse electric field intensity.

[0006] Furthermore, the characteristic difference parameter between the same characteristic peaks in any two spectral data is calculated according to the peak width and peak value distribution of the same characteristic peaks in any two spectral data, including the specific method of: Obtaining the minimum wavenumber and the maximum wavenumber corresponding to any characteristic peak in any spectral data, and recording the interval range formed by the minimum wavenumber and the maximum wavenumber as the characteristic interval of the any characteristic peak; According to the difference in the number of peaks of the same characteristic peaks in any two spectral data and the deviation of the characteristic peak interval, the characteristic difference parameter between the same characteristic peaks in the any two spectral data is calculated.

[0007] Furthermore, the specific calculation method of the characteristic difference parameter is: in, Indicates and The characteristic difference parameters between the characteristic peaks in the spectral data, Indicates The number of peak values ​​contained in the characteristic peaks in the spectral data; Indicates The number of peak values ​​contained in the characteristic peaks in the spectral data; represents the absolute value symbol, Indicates The characteristic interval of the characteristic peak in the spectral data; Indicates The characteristic interval of the characteristic peak in the spectral data; Indicates the first parameter of the preset.

[0008] Furthermore, the specific calculation method of the non-characteristic difference parameter is: in, Indicates and Non-characteristic difference parameters between non-characteristic peaks in spectral data; Indicates the peak width corresponding to the non-characteristic peak with the largest peak width among the two non-characteristic peaks. Indicates the peak width corresponding to the non-characteristic peak with the smallest peak width among the two non-characteristic peaks; Indicates The peak value of non-characteristic peaks in the spectral data; Indicates The peak value of non-characteristic peaks in the spectral data.

[0009] Furthermore, the specific calculation method of the difference index between any two spectral data is: in, Indicates and The difference index between the spectral data; Indicates and The spectral data Non-characteristic difference parameters between non-characteristic peaks; Indicates and The spectral data Characteristic difference parameters between characteristic peaks; Indicates the number of characteristic peaks in the spectral data; Represents the number of non-characteristic peaks in the spectral data.

[0010] Furthermore, the hierarchical clustering is performed by combining the difference indexes between the spectral data to obtain a hierarchical clustering tree containing several clusters, including the following specific methods: According to the difference index between the spectral data, all the spectral data were clustered from top to bottom using the Hierarchical K-means hierarchical clustering algorithm to obtain a hierarchical clustering tree.

[0011] Furthermore, the method of obtaining the aggregate volatility of the clusters according to the distribution level of the difference index between the spectral data in the clusters includes the following specific methods: Obtain any cluster in the hierarchical clustering tree, and determine the aggregation volatility of the arbitrary cluster according to the difference between the difference index between any two spectral data in the arbitrary cluster and the overall difference index between all spectral data in the cluster.

[0012] Furthermore, the classification index of the hierarchical clustering tree is determined according to the difference in aggregation volatility between adjacent layers in the hierarchical clustering clusters and the difference index of spectral data in different clustering clusters in the same layer, including the specific method of: The specific calculation method of the classification index of the hierarchical clustering tree is: in, Represents the classification index of the hierarchical clustering tree; Indicates Aggregate volatility of clusters in layers; Indicates Layer The aggregate volatility of the clusters; Indicates the absolute value symbol; Indicates Layer The average difference index of all pairs of spectral data in the cluster; Indicates Layer The average difference index of all pairs of spectral data in the cluster; Indicates the number of levels in the hierarchical clustering tree; Indicates The number of clusters in the layer.

[0013] Furthermore, the absorption peaks in the spectral data are divided into characteristic peaks and non-characteristic peaks according to the wave numbers corresponding to the absorption peaks, including the specific method of: Get the spectral data with wave number at 4000 ~1800 The absorption peak in the range is taken as the characteristic peak; Get the spectral data with wave number at 1800 ~400 The absorption peaks in the range are recorded as non-characteristic peaks.

[0014] Furthermore, the specific method of obtaining the spectral data of the peanut shell extract pretreated with different pulse electric field intensities includes: The same batch of peanut shells were selected as experimental materials, and different pulse electric field intensities were set to pre-treat the peanut shells. The water-soluble dietary fiber is extracted from the peanut shells treated with a pulse electric field, the extracted water-soluble dietary fiber is dried, and then mixed and ground with potassium bromide to prepare a sample sheet suitable for infrared spectroscopy testing; The infrared spectrometer test range and resolution are adjusted to scan the sample piece, and the spectral data corresponding to the sample piece under different pulse electric field intensity pretreatment are obtained.

[0015] The beneficial effects of the technical solution of the present invention are: by specifically analyzing the characteristic differences of spectral data under each pulse electric field intensity, the effect of pulse electric field treatment on peanut shell extract can be more accurately evaluated to help determine the optimal electric field intensity conditions. Efficient pulse electric field treatment can improve the quality of the extract or increase the extraction rate of its effective ingredients, and the optimization of this process is crucial for the production of industrial applications (such as food, health products, etc.); by dividing the absorption peaks and calculating the differential parameters, the composition changes of peanut shell extracts under different pretreatment conditions can be revealed by quantitatively analyzing the differences between characteristic peaks and non-characteristic peaks of spectral data; by extracting the differences between characteristic peaks and non-characteristic peaks, the molecular characteristics and chemical composition changes of peanut shell extracts can be better understood; by analyzing the difference indicators between different clustering clusters, the spectral characteristic differences of peanut shell extracts under different treatment conditions can be revealed; through hierarchical clustering trees and clustered volatility analysis, the different degrees of influence of pulse electric field intensity on spectral data can be clearly identified, and the optimal electric field intensity can be accurately found, which further helps to improve the extraction quality of water-soluble dietary fiber from peanut shells. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 The present invention is a flow chart of the steps of the pulse electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the pulsed electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells proposed by the present invention, its specific implementation method, structure, characteristics and effects are described in detail as follows 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.

[0019] 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 invention belongs.

[0020] The specific scheme of the pulse electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells provided by the present invention is described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1 , which shows a flow chart of the steps of a pulsed electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells provided by an embodiment of the present invention, the method comprising the following steps: Step S001: Acquire spectral data of peanut shell extract pretreated with different pulse electric field intensities.

[0022] It should be noted that when using a pulsed electric field to extract water-soluble dietary fiber from peanut shells, pulsed electric fields of different intensities will have different degrees of impact on the peanut shells, resulting in a certain degree of difference in the extracted components. In order to improve the extraction effect of water-soluble dietary fiber from peanut shells, the embodiment of the present invention obtains and analyzes the spectral data of peanut shell extracts under different pulsed electric field intensities to obtain the pulsed electric field intensity suitable for the batch of peanut shells.

[0023] Specifically, in order to implement the pulsed electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells proposed in this embodiment, it is first necessary to collect spectral data of the peanut shell extract. The specific process is as follows: Firstly, the same batch of peanut shells were selected as experimental materials, and different pulse electric field intensities were set to pre-treat the peanut shells.

[0024] Then, the water-soluble dietary fiber was extracted from the peanut shells treated with a pulsed electric field. The extracted water-soluble dietary fiber was dried and then mixed and ground with potassium bromide (KBr) to prepare sample sheets suitable for infrared spectroscopy testing.

[0025] Finally, the test range and resolution of the infrared spectrometer are adjusted to scan the sample, and the spectral data corresponding to the sample under different pulse electric field intensities pretreatment are obtained.

[0026] It should be noted that in the embodiment of the present invention, the pulse electric field strength is preset to be between 18 and 30 The specific value can be selected according to the actual situation, and the embodiment of the present invention does not make any specific limitation; in addition, when using an infrared spectrometer to obtain spectral data, the test range of the infrared spectrometer is preset to be 400 to 4000. , resolution 4 Similarly, the specific numerical value can be selected according to the actual situation and is not specifically limited in the embodiment of the present invention.

[0027] So far, the spectral data of peanut shell dietary fiber has been obtained through the above method.

[0028] Step S002: Obtain an absorption peak in any spectral data, and divide the absorption peak in the spectral data into characteristic peaks and non-characteristic peaks according to the wave number corresponding to the absorption peak.

[0029] It should be noted that when classifying peanut shells, since the principles of obtaining dietary fiber from the characteristic peaks and non-characteristic peaks of peanut shells are different, the changes in the characteristic peaks and non-characteristic peaks at different maturity levels are also different. Therefore, the non-characteristic peaks and characteristic peaks can be distinguished, and the differences between different peanut shells in the current graph can be determined. Further hierarchical clustering is performed, and the optimal pulses of different pulse intensities are determined through the clustering differences in maturity between the upper and lower clusters after clustering.

[0030] In addition, the wavelength range of the spectral data for obtaining water-soluble dietary fiber from peanut shells is usually between 25 and 25 microns, and the corresponding wavenumber range is approximately 4000 to 400 , the spectral data can be divided into two intervals according to the size of the wave number, namely 4000 ~1800 and 1800 ~400 , where the wave number interval is 4000 ~1800 It indicates the high wave number group frequency area, that is, the peak of this interval corresponds to the vibration form of the specific chemical bond or group of each molecule in the peanut shell extract. The vibration frequency of the same group always appears in a certain area, so the wave number interval is 4000 ~1800 The peaks in the graph reflect the vibration forms on chemical bonds in peanut shell extract; the wave number interval is 1800 ~400 It represents the fingerprint region in the low wavenumber band. The absorption peak in the fingerprint region is highly characteristic and highly sensitive to changes in molecular structure. It can distinguish slight differences in the structures of different compounds.

[0031] Specifically, first, the peak in the spectrum data under any pulse electric field intensity is obtained.

[0032] Then, obtain the spectral data with a wave number of 4000 ~1800 The absorption peak in the range is taken as the characteristic peak; It should be noted that after obtaining the characteristic peaks in the spectral data, the functional groups corresponding to each characteristic peak can be marked. For example, the CH functional group is usually distributed in the wave number 3000~270 within the range.

[0033] Finally, obtain the spectral data with a wave number of 1800 ~400 The absorption peaks in the range are recorded as non-characteristic peaks.

[0034] So far, the characteristic peaks and non-characteristic peaks in the spectral data are obtained by the above method.

[0035] Step S003: Calculate the characteristic difference parameters between the same characteristic peaks in any two spectral data according to the peak width and peak distribution of the same characteristic peaks in the any two spectral data; calculate the non-characteristic difference parameters between any same non-characteristic peaks in the any two spectral data according to the width and peak difference between the same non-characteristic peaks in the any two spectral data; obtain the difference index between any two spectral data according to the characteristic difference parameters and non-characteristic difference parameters, and in combination with the corresponding numbers of characteristic peaks and non-characteristic peaks respectively.

[0036] It should be noted that, since the principles of obtaining dietary fiber from the characteristic peaks and non-characteristic peaks of peanut shells are different, the changes in the characteristic peaks and non-characteristic peaks at different maturity levels are also different. Therefore, the embodiment of the present invention can comprehensively determine the differences between different peanut shells at different maturity levels by analyzing the peak width and peak distribution of each characteristic peak, the movement of the non-characteristic peak, and the ratio of the number of peaks occupied by the two.

[0037] Specifically, in step S301, characteristic difference parameters between the same characteristic peaks in any two spectral data are calculated according to the peak width and peak value distribution of the same characteristic peaks in the any two spectral data.

[0038] It should be noted that for the characteristic peaks in peanut shells, the characteristic peaks are associated with certain structural units. In different degrees of maturity, these peaks correspond to the vibration form of a chemical bond or group in the molecule, and the vibration frequency of the same group always appears in a certain area. Since different peanut shells have different degrees of maturity, the compound components contained in the peanut shell dietary fiber may vary, so the presence of multiple compounds will cause the current peak to become wider, or multiple peaks will appear in the current peak, resulting in the existence of multiple peaks.

[0039] First, the minimum wavenumber and the maximum wavenumber corresponding to any characteristic peak in any spectral data are obtained, and the interval range formed by the minimum wavenumber and the maximum wavenumber is recorded as the characteristic interval of the arbitrary characteristic peak.

[0040] Then, according to the difference in the number of peaks of the same characteristic peaks in any two spectral data and the deviation of the characteristic peak interval, the characteristic difference parameter between the same characteristic peaks in the any two spectral data is calculated.

[0041] As an embodiment, for any characteristic peak in the spectral data, the specific calculation method of the characteristic difference parameter is: in, Indicates and The characteristic difference parameters between the characteristic peaks in the spectral data, Indicates The number of peak values ​​contained in the characteristic peaks in the spectral data; Indicates The number of peak values ​​contained in the characteristic peaks in the spectral data; represents the absolute value symbol, Indicates The characteristic interval of the characteristic peak in the spectral data; Indicates The characteristic interval of the characteristic peak in the spectral data; Indicates the first parameter of the preset.

[0042] It should be noted that the first parameter is preset based on experience It is 0.1, and can be adjusted according to actual conditions, and is not specifically limited in the embodiment of the present invention.

[0043] It should be noted that Reflected in the and The embodiment of the present invention takes into account that when the number of peaks corresponding to the characteristic peaks in any two spectral data is the same, The situation of zero affects the subsequent analysis of characteristic peak difference parameters, so we choose to use the first parameter by preset parameters. To avoid this situation, , in order to analyze the characteristic peak interval of spectral data. It indicates the deviation degree of the current corresponding characteristic peak in different maturity of peanuts. The larger the value, the greater the maturity difference of peanut shells.

[0044] Step S302, calculating the non-characteristic difference parameter between any same non-characteristic peaks in any two spectral data according to the width and peak value difference between the same non-characteristic peaks in the any two spectral data.

[0045] It should be noted that, due to the differences in the molecular components contained in peanut shells at different maturity levels, non-characteristic peaks are highly sensitive to changes in molecular structure and can distinguish between small differences in molecular structure of different compounds. The intensity and position of non-characteristic peaks may vary due to small molecular changes in peanut shells caused by maturity. In addition, since non-characteristic peaks can reflect the strength of intermolecular interactions, such as the strength of hydrogen bonds, when the chemical composition changes in peanut shells of different maturity levels, the peaks in the spectral data will become wider, and the position of the peaks will move at different wave numbers with the strength of intermolecular interactions (such as the strength of hydrogen bonds). Taking the CO bond in a molecule as an example, it usually appears at 1034 in the spectral data. The contents of cellulose and hemicellulose in peanut shells of different maturity may be different, which will affect the stretching vibration peak of the CO bond, causing the width and height of the CO stretching vibration peak to change, thus reflecting the change in the strength of intermolecular interactions.

[0046] As an embodiment, for any non-characteristic peak in the spectral data, the specific calculation method of the non-characteristic difference parameter is: in, Indicates and Non-characteristic difference parameters between non-characteristic peaks in spectral data; Indicates the peak width corresponding to the non-characteristic peak with the largest peak width among the two non-characteristic peaks. Indicates the peak width corresponding to the non-characteristic peak with the smallest peak width among the two non-characteristic peaks; Indicates The peak value of non-characteristic peaks in the spectral data; Indicates The peak value of non-characteristic peaks in the spectral data.

[0047] It should be noted that Indicates the increase of non-characteristic peaks caused by different maturity levels.

[0048] Step S303, obtaining a difference index between any two spectral data according to the characteristic difference parameter and the non-characteristic difference parameter and in combination with the numbers corresponding to the characteristic peaks and the non-characteristic peaks respectively.

[0049] As an embodiment, the specific calculation method of the difference index between any two spectral data is: in, Indicates and The difference index between the spectral data; Indicates and The spectral data Non-characteristic difference parameters between non-characteristic peaks; Indicates and The spectral data Characteristic difference parameters between characteristic peaks; Indicates the number of characteristic peaks in the spectral data; Represents the number of non-characteristic peaks in the spectral data.

[0050] It should be noted that Indicates and The difference degree of characteristic peaks in the spectral data is Indicates the quantitative weight of characteristic peaks in spectral data; Indicates and The difference degree of non-characteristic peaks of spectral data is Expresses the weight of the current non-characteristic peak in the spectrum peak.

[0051] So far, the difference index between any two spectral data is obtained through the above method.

[0052] Step S004: For all spectral data under any pulse electric field intensity, hierarchical clustering is performed in combination with the difference index between the spectral data to obtain a hierarchical clustering tree containing several clusters, and the clustering volatility of the cluster is obtained according to the distribution level of the difference index between the spectral data in the cluster; the classification index of the hierarchical clustering tree is determined according to the difference in clustering volatility between adjacent layers in the hierarchical cluster and the difference index of spectral data in different clusters in the same layer.

[0053] It should be noted that after determining the difference index of the spectral data of peanut shells, the embodiment of the present invention classifies the current batch of peanuts through hierarchical clustering. Since hierarchical clustering has multiple layers of clustering, the classification index of water-soluble dietary fiber in peanut shells under different pulse intensities can be determined according to the maturity clustering between the upper and lower layers and the clustering of the difference characteristics between the clusters, so as to subsequently determine the optimal pulse electric field intensity.

[0054] Specifically, in step S401, hierarchical clustering is performed in combination with the difference index between the spectral data to obtain a hierarchical clustering tree including a plurality of clusters, and the clustered volatility of the clusters is obtained according to the distribution level of the difference index between the spectral data in the clusters.

[0055] First, hierarchical clustering was performed based on the difference indicators of the spectral data.

[0056] As an embodiment, according to the difference index between the spectral data, and using the Hierarchical K-means hierarchical clustering algorithm, all the spectral data are clustered from top to bottom to obtain a hierarchical clustering tree.

[0057] It should be noted that after clustering the difference indicators between the current peanut shell spectral data, a tree structure formed by multiple layers of clusters is obtained, namely a hierarchical clustering tree. Among them, the upper clusters are generally relatively rough in classification results compared to the lower clusters. Through further classification, the maturity of peanuts in the lower clusters is more concentrated, and the difference characteristics between the lower clusters are also greater.

[0058] Then, any cluster in the hierarchical clustering tree is obtained, and the aggregation volatility of the arbitrary cluster is determined according to the difference between the difference index between any two spectral data in the arbitrary cluster and the overall difference index between all spectral data in the cluster.

[0059] As an embodiment, for any cluster, the specific calculation method of the aggregate volatility is: Take any two spectral data in the cluster as a pair, and get several pairs of spectral data in the cluster; get the clustered volatility of the cluster: in, represents the aggregate volatility of clusters; Indicates the cluster The difference index for spectral data, Represents the average difference index of spectral data of all pairs in the cluster; Indicates the absolute value symbol; Indicates the number of spectral data pairs in the cluster.

[0060] Step S402, determining the classification index of the hierarchical clustering tree according to the difference in aggregation volatility between adjacent layers in the hierarchical clustering clusters and the difference index of spectral data in different clusters in the same layer.

[0061] As an embodiment, the specific calculation method of the classification index of the hierarchical clustering tree is: in, Represents the classification index of the hierarchical clustering tree; Indicates Aggregate volatility of clusters in layers; Indicates Layer The aggregate volatility of the clusters; Indicates the absolute value symbol; Indicates Layer The average difference index of all pairs of spectral data in the cluster; Indicates Layer The average difference index of all pairs of spectral data in the cluster; Indicates the number of levels in the hierarchical clustering tree; Indicates The number of clusters in the layer.

[0062] It should be noted that The larger the value, the greater the volatility of the aggregation of the upper layer relative to the lower layer, indicating that the maturity of the lower layer is more concentrated; The larger the value, the greater the difference in the average difference index between the clusters.

[0063] So far, the classification index of the hierarchical clustering tree is obtained through the above method.

[0064] Step S005: obtaining the pulse electric field strength corresponding to the maximum classification index as the optimal pulse electric field strength.

[0065] Specifically, since it is currently necessary to distinguish the maturity of peanuts to the greatest extent, after hierarchical clustering, the larger the classification index, the better. Therefore, it is necessary to determine the maximum value of the classification index obtained by the peanut shells with different pulse electric field strengths, determine the pulse electric field strength corresponding to the current maximum value, and determine the current electric field strength as the optimal electric field strength for water-soluble dietary fiber analysis of peanut shells. When conducting other batches of peanut shells, the electric field strength can be used to decompose their water-soluble dietary fiber to determine the different maturity conditions of the current peanuts.

[0066] At this point, this embodiment is completed.

[0067] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A pulsed electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells, characterized in that: The method comprises the following steps: Acquire the spectral data of peanut shell extract pretreated with different pulse electric field intensities; Obtaining the absorption peak in any spectral data, and dividing the absorption peak in the spectral data into characteristic peaks and non-characteristic peaks according to the wave number corresponding to the absorption peak; According to the peak width and peak distribution of the same characteristic peak in any two spectral data, the characteristic difference parameters between the same characteristic peaks in the any two spectral data are calculated; according to the width and peak difference between the same non-characteristic peaks in any two spectral data, the non-characteristic difference parameters between any same non-characteristic peaks in the any two spectral data are calculated; according to the characteristic difference parameters and non-characteristic difference parameters, and in combination with the numbers corresponding to the characteristic peaks and non-characteristic peaks, the difference index between any two spectral data is obtained; For all spectral data under any pulse electric field intensity, hierarchical clustering is performed in combination with the difference index between the spectral data to obtain a hierarchical clustering tree containing several clusters. According to the distribution level of the difference index between the spectral data in the cluster, the clustering volatility of the cluster is obtained; according to the difference in the aggregation volatility between adjacent layers in the hierarchical cluster and the difference index of the spectral data in different clusters in the same layer, the classification index of the hierarchical clustering tree is determined; The pulse electric field intensity corresponding to the maximum classification index is obtained as the optimal pulse electric field intensity.

2. The pulse electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells according to claim 1, characterized in that: The specific method of calculating the characteristic difference parameter between the same characteristic peaks in any two spectral data according to the peak width and peak value distribution of the same characteristic peaks in the any two spectral data is as follows: Obtaining the minimum wavenumber and the maximum wavenumber corresponding to any characteristic peak in any spectral data, and recording the interval range formed by the minimum wavenumber and the maximum wavenumber as the characteristic interval of the any characteristic peak; According to the difference in the number of peaks of the same characteristic peaks in any two spectral data and the deviation of the characteristic peak interval, the characteristic difference parameter between the same characteristic peaks in the any two spectral data is calculated.

3. The pulse electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells according to claim 2, characterized in that: The specific calculation method of the characteristic difference parameter is: in, Indicates and The characteristic difference parameters between the characteristic peaks in the spectral data, Indicates The number of peak values ​​contained in the characteristic peaks in the spectral data; Indicates The number of peak values ​​contained in the characteristic peaks in the spectral data; represents the absolute value symbol, Indicates The characteristic interval of the characteristic peak in the spectral data; Indicates The characteristic interval of the characteristic peak in the spectral data; Indicates the first parameter of the preset.

4. The pulse electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells according to claim 1, characterized in that: The specific calculation method of the non-characteristic difference parameter is: in, Indicates and Non-characteristic difference parameters between non-characteristic peaks in spectral data; Indicates the peak width corresponding to the non-characteristic peak with the largest peak width among the two non-characteristic peaks. Indicates the peak width corresponding to the non-characteristic peak with the smallest peak width among the two non-characteristic peaks; Indicates The peak value of non-characteristic peaks in the spectral data; Indicates The peak value of non-characteristic peaks in the spectral data; Represents the absolute value symbol.

5. The pulse electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells according to claim 1, characterized in that: The specific calculation method of the difference index between any two spectral data is: in, Indicates and The difference index between the spectral data; Indicates and The spectral data Non-characteristic difference parameters between non-characteristic peaks; Indicates and The spectral data Characteristic difference parameters between characteristic peaks; Indicates the number of characteristic peaks in the spectral data; Represents the number of non-characteristic peaks in the spectral data.

6. The pulse electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells according to claim 1, characterized in that: The hierarchical clustering is performed by combining the difference indexes between the spectral data to obtain a hierarchical clustering tree containing several clusters, and the specific method includes: According to the difference index between the spectral data, all the spectral data were clustered from top to bottom using the Hierarchical K-means hierarchical clustering algorithm to obtain a hierarchical clustering tree.

7. The pulse electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells according to claim 1, characterized in that: The method of obtaining the aggregate volatility of the clusters according to the distribution level of the difference index between the spectral data in the clusters includes: Obtain any cluster in the hierarchical clustering tree, and determine the aggregation volatility of the arbitrary cluster according to the difference between the difference index between any two spectral data in the arbitrary cluster and the overall difference index between all spectral data in the cluster.

8. The pulse electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells according to claim 1, characterized in that: The method of determining the classification index of the hierarchical clustering tree according to the difference in aggregation volatility between adjacent layers in the hierarchical clustering cluster and the difference index of spectral data in different clusters in the same layer includes the following specific methods: The specific calculation method of the classification index of the hierarchical clustering tree is: in, Represents the classification index of the hierarchical clustering tree; Indicates Aggregate volatility of clusters in layers; Indicates Layer The aggregate volatility of the clusters; Indicates the absolute value symbol; Indicates Layer The average difference index of all pairs of spectral data in the cluster; Indicates Layer The average difference index of all pairs of spectral data in the cluster; Indicates the number of levels in the hierarchical clustering tree; Indicates The number of clusters in the layer.

9. The pulse electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells according to claim 1, characterized in that: The specific method of dividing the absorption peaks in the spectral data into characteristic peaks and non-characteristic peaks according to the wave numbers corresponding to the absorption peaks includes: Get the spectral data with wave number at 4000 ~1800 The absorption peak in the range is taken as the characteristic peak; Get the spectral data with wave number at 1800 ~400 The absorption peaks in the range are recorded as non-characteristic peaks.

10. The pulse electric field pretreatment method for extracting water-soluble dietary fiber from peanut shells according to claim 1, characterized in that: The specific method of obtaining the spectral data of the peanut shell extract pretreated with different pulse electric field intensities is as follows: The same batch of peanut shells were selected as experimental materials, and different pulse electric field intensities were set to pre-treat the peanut shells. The water-soluble dietary fiber is extracted from the peanut shells treated with a pulse electric field, the extracted water-soluble dietary fiber is dried, and then mixed and ground with potassium bromide to prepare a sample sheet suitable for infrared spectroscopy testing; The infrared spectrometer test range and resolution are adjusted to scan the sample piece, and the spectral data corresponding to the sample piece under different pulse electric field intensity pretreatment are obtained.

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