Method for identifying and quality controlling natural plant feed raw material based on quality markers

Abstract

The present application relates to the technical field of feed quality and safety, in particular to a natural plant feed raw material identification and quality control method based on quality markers, comprising the following steps: S1, characteristic marker screening step: aiming at the plant-derived components to be measured in the natural plant feed raw material; S2, test sample pretreatment step: according to the physicochemical properties of the quality markers and the matrix type of the natural plant feed raw material; S3, precision detection step: detecting the test sample solution by high-resolution mass spectrometry or tandem mass spectrometry; S4, true and false identification and quality evaluation step: comparing the detection data with the preset standard reference data. The present application can effectively avoid the marker failure problem caused by matrix interference after the mixing, processing and compounding of feed raw materials by screening quality markers with component specificity and matrix stability from the whole process of feed processing and replacing the traditional mode of directly following the index components of traditional Chinese medicinal materials.

Description

Technical Field

[0001] This invention relates to the field of feed quality and safety technology, specifically to a method for identifying and controlling the quality of natural plant feed ingredients based on quality markers. Background Technology

[0002] Natural plant-based feed ingredients are green and safe, and have both nutritional and health benefits. They are high-quality raw materials for replacing antibiotics in livestock and poultry farming, and the scale of industry application continues to expand. Currently, the industry generally applies the evaluation standards of traditional Chinese veterinary medicine and Chinese medicinal materials directly to the quality control of such raw materials, without establishing a dedicated quality control system that is combined with the feed application scenario.

[0003] Feed is a multi-component compound system containing various basic raw materials and auxiliary materials. The production process involves high temperature and mixing, resulting in a complex matrix and many interfering factors. Traditional detection techniques such as thin-layer chromatography and conventional liquid chromatography have low sensitivity and poor anti-interference ability, making it difficult to accurately identify the authenticity of raw materials and prone to problems such as missed detection of components and quantitative deviations. At the same time, the existing indicators of Chinese medicinal materials do not have the specificity of feed matrix and are easily affected by the base components, failing to objectively reflect the true quality of natural plant feed raw materials.

[0004] Currently, there are no specific standards or unified quality control standards for identifying genuine and counterfeit natural plant feed ingredients. Industry regulation is relatively weak, and traditional indicators for Chinese medicinal materials are still used. There is a lack of specific quality markers that are suitable for complex feed matrices, resulting in insufficient accuracy in identifying genuine and counterfeit ingredients. Traditional testing technologies have weak anti-interference capabilities and low sensitivity, making it impossible to achieve accurate qualitative and quantitative detection of target components in complex feed systems. The industry lacks specific legal quality control standards, raw material quality control is not standardized, and market supervision lacks a basis. Summary of the Invention

[0005] The purpose of this invention is to provide a method for identifying and controlling the quality of natural plant feed ingredients based on quality markers in order to solve the above-mentioned problems, thereby resolving the issues mentioned in the background art.

[0006] To address the above problems, this invention provides a technical solution: a method for identifying and controlling the quality of natural plant feed ingredients based on quality markers, comprising the following steps:

[0007] S1. Characteristic marker screening steps: For the plant-derived components to be tested in natural plant feed raw materials, with the goal of eliminating matrix interference factors and the influence of inter-component interactions in the entire feed processing process and retaining component specificity, at least one quality marker with component specificity and matrix stability is screened.

[0008] S2. Sample pretreatment steps: Based on the physicochemical properties of the quality markers and the matrix type of the natural plant feed raw materials, an appropriate extraction and purification scheme is used to pretreat the natural plant feed raw materials to prepare a sample solution.

[0009] S3. Precision detection steps: High-resolution mass spectrometry or tandem mass spectrometry is used to detect the test solution to obtain qualitative and quantitative detection data of the quality marker;

[0010] S4. Authenticity identification and quality evaluation steps: Compare the test data with the preset standard reference data, and determine the authenticity and quality qualification of the tested natural plant feed raw materials based on the comparison results.

[0011] Preferably, in the S1 characteristic marker screening step, the matrix interference factors and interactions between components in the entire feed processing process specifically include: chemical interactions between different plant-derived components during the mixing and formulation of feed raw materials, interference effects of excipients, and the effects of processing temperature, processing pressure, and processing time on the stability of the markers.

[0012] The criteria for obtaining the quality markers are: the relative content change before and after processing does not exceed a preset threshold, and it is not significantly interfered with by other coexisting components in the natural plant feed raw materials.

[0013] Preferably, in the S1 feature marker screening step, the quality marker satisfies at least one of the following specificity evaluation indicators:

[0014] S1.1 The quality marker has a content of not less than the first preset value in the plant-derived component to be tested, and its content in other common plant-derived feed ingredients and excipients is less than the second preset value or not detected.

[0015] S1.2 The mass spectrometry fragmentation characteristic ion pairs or characteristic fragments of the mass marker have uniqueness that distinguishes them from closely related species and other adulterants;

[0016] S1.3 The content ratio of the quality marker is pre-correlated with the doping ratio of the corresponding plant-derived component, and is used to quantitatively identify the degree of adulteration of raw materials.

[0017] Preferably, in the S2 sample pretreatment step, the adaptability extraction and purification scheme is selected from at least one of the following methods based on the matrix type of the natural plant feed raw material:

[0018] S2.1 For high starch or high protein matrices, solid phase extraction purification combined with matrix dispersion solid phase extraction is used for pretreatment;

[0019] S2.2 For high-oil matrices, low-temperature freeze defatting or hexane liquid-liquid extraction defatting followed by extraction is employed;

[0020] S2.3 For multi-raw material composite matrices, gradient solvent extraction combined with pH adjustment is used for selective enrichment;

[0021] S2.4 For feed matrices processed at high temperatures, enzymatic hydrolysis-assisted extraction or pressurized fluid extraction is used to improve the recovery rate of target biomarkers;

[0022] S2.5 For high-fiber matrices, cellulase-assisted extraction or microwave-assisted extraction is used.

[0023] Preferably, in the S3 precision detection step, ultra-high performance liquid chromatography-tandem mass spectrometry or ultra-high performance liquid chromatography-high resolution mass spectrometry is used for detection. The detection conditions are corrected by a matrix matching standard curve to offset the inhibitory or enhancing effect of the natural plant feed raw material matrix on ionization efficiency. The matrix matching standard curve is prepared using a blank matrix that is consistent with the matrix type of the test sample.

[0024] The linear correlation coefficient of the matrix matching standard curve is not lower than a preset threshold, and the quantitative results are corrected by the matrix effect factor.

[0025] Preferably, in the S4 authenticity identification and quality evaluation step, the preset standard reference data includes: a quality marker fingerprint spectrum library, characteristic ion pair abundance ratio range, and content limit range established by measuring no less than three batches of known genuine natural plant feed raw materials according to the same methods in S2 and S3.

[0026] Preferably, in the S4 authenticity verification and quality evaluation step, a multi-index comprehensive discrimination model is used for authenticity verification, and the comprehensive discrimination model includes at least the following discrimination dimensions:

[0027] S4.1 Whether or not quality markers are detected qualitatively;

[0028] S4.2 Deviation in consistency between the abundance ratio of characteristic ion pairs of quality markers and standard reference data;

[0029] S4.3 Whether the quantitative content of the quality marker is within the preset content limit range;

[0030] S4.4 Whether the content ratios among multiple quality markers conform to the preset ratio range;

[0031] If any discrimination dimension fails to meet the preset requirements, the test sample is determined to be unqualified or triggers a suspected adulteration marker, and subsequent judgment is terminated or a retest is initiated.

[0032] Preferably, the S1 feature marker screening step further includes using chemometric methods to screen for differential components in feed samples before and after processing and before and after formulation. Candidates for the quality markers are determined based on the standard that the variable importance projection value or correlation coefficient is not lower than a preset threshold and the relative standard deviation of the content after processing and formulation does not exceed a preset value. The structure of the candidate is confirmed by comparison with reference standards or mass spectrometry library search.

[0033] Preferably, the S2 sample pretreatment step further includes adding at least one internal standard throughout the pretreatment process. The internal standard is an isotope marker or structural analog that is not present in the tested natural plant feed raw material. In the S3 precision detection step, the internal standard is used to quantitatively correct the relative response factor between the internal standard and the target marker, thereby eliminating the influence of pretreatment loss and matrix effect on quantitative accuracy and eliminating systematic errors in the pretreatment and detection processes.

[0034] Preferably, the step between the S1 feature marker screening step and the S3 precision detection step further includes a methodology verification step: verifying the detection limit, quantitation limit, linear range, spiked recovery rate, intra-day and inter-day precision of the quality marker in the tested natural plant feed raw material matrix to ensure that the preset acceptable standard of methodology verification is met before it can be used for subsequent authenticity identification and quality evaluation.

[0035] The preset acceptable standards include: the detection limit is not higher than a predetermined proportion of the preset lower limit of content, the spiked recovery rate is within a predetermined acceptable range, and the relative standard deviation of intra-day and inter-day precision is within a predetermined acceptable range.

[0036] The beneficial effects of this invention are reflected in:

[0037] 1. This invention screens quality markers with component specificity and matrix stability from the entire feed processing process, and replaces the traditional method of directly using the indicator components of Chinese medicinal materials. This can avoid the problem of marker failure caused by matrix interference after feed raw materials are mixed, processed and formulated, improve the identification of authenticity and anti-interference ability, and provide a reliable target for the source quality control of natural plant feed raw materials.

[0038] 2. This invention employs high-resolution mass spectrometry or tandem mass spectrometry detection technology, combined with matrix-matched standard curve correction and internal standard correction for quantitative determination. Combined with methodological validation steps, the detection limit, quantitation limit, linear range, spiked recovery rate, and precision are systematically validated. This can improve the detection sensitivity and quantitative accuracy of trace markers in complex feed matrices, and avoid the problems of stray peak interference and missed detection and misjudgment that exist in traditional thin-layer chromatography and ordinary liquid chromatography in compound and high-temperature processed feed samples.

[0039] 3. This invention constructs a comprehensive discrimination model that includes multiple indicators such as qualitative detection, consistency of characteristic ion pair abundance ratio, content limit range, and content ratio of multiple markers. It also compares and judges the data with standard reference data established from multiple batches of known genuine products. This invention can provide a unified basis for the identification of authenticity and quality evaluation of natural plant feed raw materials, and solve the current problems of lack of standards for supervision and market adulteration caused by inconsistent standards and chaotic references in the industry. Attached Figure Description

[0040] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0041] Figure 1 This is a schematic diagram of the quality marker screening process of the present invention;

[0042] Figure 2 This is a schematic diagram of the sample pretreatment and precision detection process of the present invention;

[0043] Figure 3 This is a schematic diagram of the decision-making process for authenticity identification and quality evaluation of the present invention. Detailed Implementation

[0044] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0045] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0046] Example 1

[0047] like Figures 1-3 As shown, the method for identification and quality control of natural plant feed ingredients based on quality markers includes the following steps:

[0048] S1. Characteristic marker screening steps: For the plant-derived components to be tested in natural plant feed raw materials, with the goal of eliminating matrix interference factors and the influence of inter-component interactions in the entire feed processing process and retaining component specificity, at least one quality marker with component specificity and matrix stability is screened.

[0049] S2. Sample pretreatment steps: Based on the physicochemical properties of the quality markers and the matrix type of the natural plant feed raw materials, an appropriate extraction and purification scheme is used to pretreat the natural plant feed raw materials to prepare the test solution.

[0050] S3. Precision detection steps: High-resolution mass spectrometry or tandem mass spectrometry is used to detect the test solution to obtain qualitative and quantitative detection data of quality markers;

[0051] S4. Authenticity and Quality Evaluation Steps: Compare the test data with the preset standard reference data, and determine the authenticity and quality compliance of the tested natural plant feed raw materials based on the comparison results.

[0052] The specific process for screening quality markers:

[0053] A1. Feed processing simulation experiment design: Take the plant-derived raw material to be tested, and set up a processed group and an unprocessed control group according to the actual feed production process conditions. The processing conditions include, but are not limited to:

[0054] Mixing ratios, for example: Component A: Component B = 1:3, 1:1, 3:1;

[0055] Processing temperature, for example, 80℃, 120℃, 180℃;

[0056] Processing time, for example, 5 min, 30 min, 60 min;

[0057] Processing pressures: atmospheric pressure, 0.2 MPa, and 0.5 MPa; at least three samples should be prepared in parallel for each group.

[0058] A2. Sample extraction and profile data acquisition: Take the above-mentioned samples, extract them according to the unified pretreatment scheme, and use ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) for full scan data acquisition to obtain the base peak chromatogram and total ion chromatogram of each sample. Use peak matching and alignment software to preprocess the raw data to obtain a multidimensional data matrix composed of retention time, mass-to-charge ratio and peak area.

[0059] A3. Screening of Chemometric Differential Biomarkers: Partial Least Squares Discriminant Analysis (PLS-DA) or Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was used to classify and distinguish samples before and after processing and before and after compatibility. The variable importance projection value (VIP) was used as the variable contribution evaluation index, and its calculation formula is as follows:

[0060]

[0061] in: For j variables, the projected importance values ​​are:

[0062] H represents the total number of principal components extracted from the PLS model;

[0063] This is the sum of squares of the contribution of the h-th principal component to the model;

[0064] The weight coefficient of the j-th variable in the h-th principal component;

[0065] J represents the total number of variables;

[0066] by ≥1.0 was used as the initial screening threshold to screen for differential variables that made a significant contribution to group discrimination;

[0067] A4. Processing and compatibility stability evaluation: The candidate biomarkers obtained from the initial VIP screening were further evaluated based on their relative content change rate. Its processing and compatibility stability were evaluated using relative standard deviation (RSD).

[0068] Formula for calculating the relative content change rate before and after processing:

[0069]

[0070] Formula for calculating the relative content change rate before and after compatibility:

[0071]

[0072] Formula for calculating the relative standard deviation of content under multiple batches / conditions:

[0073]

[0074] Where SD is the standard deviation. This represents the average content of the marker across multiple sample groups.

[0075] The screening criteria are set as follows: ≤30% and RSD≥15% indicate that the candidate biomarker has good matrix stability throughout the entire feed processing process;

[0076] A5. Specificity Validation: Take the corresponding reference standards for the candidate biomarkers and establish a multiple reaction monitoring (MRM) method using UHPLC-MS / MS to detect the following two types of samples:

[0077] Category A: Feed samples containing the plant-derived components to be tested (no fewer than 10 batches);

[0078] Category B: Plant feed ingredients from common closely related species and other sources of possible adulteration (no fewer than 20 kinds);

[0079] The specificity discrimination parameter—the discrimination index (DI)—is calculated using the following formula:

[0080]

[0081] in, The lowest concentration of the candidate biomarker in category A samples. The highest concentration of the candidate biomarker in category B samples;

[0082] Standard setting: When DI ≥ 5, the marker is considered to have good specificity, meaning its lowest concentration in the analyte is not less than 5 times the highest concentration in the interfering agent. Simultaneously, the detection rate of this marker in Category B samples is required to be no higher than 10%.

[0083] A6. Confirmation of candidate biomarker structure: The chemical structure of the candidate biomarkers that have passed the above screening will be confirmed by at least one of the following methods: comparison with the retention time and mass spectrometry fragment spectrum of known reference standards; deduction of molecular formula by accurate mass number of high-resolution mass spectrometry, combined with fragment analysis by secondary mass spectrometry; comparison with public mass spectrometry library, with a matching degree not lower than the preset threshold.

[0084] Furthermore, in the S1 characteristic marker screening step, the matrix interference factors and interactions between components in the entire feed processing process specifically include: chemical interactions between different plant-derived components during the mixing and formulation of feed raw materials, interference effects of excipients, and the effects of processing temperature, processing pressure, and processing time on the stability of the markers.

[0085] The criteria for screening quality markers are: the relative content change before and after processing does not exceed a preset threshold, and it is not significantly interfered with by other coexisting components in natural plant feed raw materials.

[0086] Furthermore, in the S1 feature marker screening step, the quality markers must meet at least one of the following specificity evaluation indicators:

[0087] S1.1 The content of the quality marker in the plant-derived component to be tested is not lower than the first preset value, and the content in other common plant-derived feed ingredients and excipients is lower than the second preset value or not detected.

[0088] S1.2 The mass spectrometry fragmentation characteristic ion pairs or characteristic fragments of the quality markers have unique characteristics that distinguish them from closely related species and other adulterants;

[0089] S1.3 The content ratio of quality markers is pre-correlated with the doping ratio of corresponding plant-derived components, which is used to quantitatively identify the degree of adulteration of raw materials.

[0090] Furthermore, in the S2 sample pretreatment step, the adaptability extraction and purification scheme is selected from at least one of the following methods based on the substrate type of the natural plant feed raw material:

[0091] S2.1 For high starch or high protein matrices, solid phase extraction purification combined with matrix dispersion solid phase extraction is used for pretreatment;

[0092] S2.2 For high-oil matrices, low-temperature freeze defatting or hexane liquid-liquid extraction defatting followed by extraction is employed;

[0093] S2.3 For multi-raw material composite matrices, gradient solvent extraction combined with pH adjustment is used for selective enrichment;

[0094] S2.4 For feed matrices processed at high temperatures, enzymatic hydrolysis-assisted extraction or pressurized fluid extraction is used to improve the recovery rate of target biomarkers;

[0095] S2.5 For high-fiber matrices, cellulase-assisted extraction or microwave-assisted extraction is used.

[0096] Furthermore, in the S3 precision detection step, ultra-high performance liquid chromatography-tandem mass spectrometry or ultra-high performance liquid chromatography-high resolution mass spectrometry is used for detection. The detection conditions are corrected by matrix matching standard curve to offset the inhibitory or enhancing effect of natural plant feed raw material matrix on ionization efficiency. The matrix matching standard curve is prepared using a blank matrix consistent with the matrix type of the test sample.

[0097] The linear correlation coefficient of the matrix-matched standard curve is not lower than the preset threshold, and the quantitative results are corrected for matrix effect factors.

[0098] Precision testing conditions and matrix effect correction:

[0099] B1. Typical chromatographic-mass spectrometry detection conditions, taking UHPLC-MS / MS as an example:

[0100] Chromatographic column: ACQUITY-UPLCHSS-T3 (2.1mm*100mm, 1.8μm) or equivalent column;

[0101] Column temperature: 40℃;

[0102] Mobile phase A: 0.1% formic acid aqueous solution; Mobile phase B: acetonitrile (containing 0.1% formic acid);

[0103] Gradient elution program: 0-2 min, 5% B; 2-15 min, 5%-95% B; 15-18 min, 95% B; 18-18.1 min, 95%-5% B; 18.1-20 min, 5% B;

[0104] Flow rate: 0.3 mL / min; injection volume: 2 μL.

[0105] Ion source: Electrospray ionization (ESI) source, full scan mode for both positive and negative ions;

[0106] Ion source temperature: 150℃; Desolvation gas temperature: 500℃; Desolvation gas flow rate: 1000L / h; Conical orifice gas flow rate: 150L / h;

[0107] Capillary voltage: 3.0kV for positive ion mode, 2.5kV for negative ion mode;

[0108] B2. Evaluation and correction of matrix effects, preparation of three types of solutions:

[0109] Solution A (pure solvent standard solution): Prepare a series of standard solutions of different concentrations using the initial mobile phase;

[0110] Solution B (Matrix-Matched Standard Solution): Prepare a series of standard solutions of the same concentration using the pretreated blank matrix extract;

[0111] Solution C (sample spiking solution): A known amount of standard is added to the test sample before pretreatment, and the sample is then measured after pretreatment;

[0112] Formula for calculating matrix effect factor (ME%):

[0113]

[0114] when In this case, the matrix effect is negligible, and quantification can be directly performed using the solvent standard curve.

[0115] when At this time, there is a moderate matrix effect, and matrix matching standard curve correction must be used;

[0116] when At this time, a strong matrix effect exists, and it is necessary to combine the internal standard correction method or the standard addition method for correction;

[0117] In this method, when At that time, matrix matching standard curve calibration is initiated;

[0118] B3. Internal standard method for quantitative correction: Before sample pretreatment, a fixed amount of internal standard solution is added to each test sample. Quantitative calculations use the internal standard correction formula:

[0119]

[0120] in: The content of the target marker in the test sample (mg / kg);

[0121] The peak area (or ion pair response value) of the target marker in the test sample solution;

[0122] The peak area (or ion pair response value) of the internal standard in the test solution;

[0123] The concentration of the added internal standard (μg / mL);

[0124] V is the final volume (mL) of the test solution;

[0125] m is the weight (g) of the test sample;

[0126] f is the dilution factor during the pretreatment process;

[0127] RRF stands for Relative Response Factor. The standard solution was pre-determined.

[0128] Furthermore, in the S4 authenticity identification and quality evaluation step, the preset standard reference data includes: a quality marker fingerprint spectrum library, characteristic ion pair abundance ratio range, and content limit range established by measuring no less than three batches of known genuine natural plant feed raw materials according to the same methods as S2 and S3.

[0129] Furthermore, in the S4 authenticity verification and quality evaluation steps, a multi-index comprehensive discrimination model is used for authenticity verification. The comprehensive discrimination model includes at least the following discrimination dimensions:

[0130] S4.1 Whether or not quality markers are detected qualitatively;

[0131] S4.2 Deviation in consistency between the abundance ratio of characteristic ion pairs of quality markers and standard reference data;

[0132] S4.3 Whether the quantitative content of the quality marker is within the preset content limit range;

[0133] S4.4 Whether the content ratios among multiple quality markers conform to the preset ratio range;

[0134] If any discrimination dimension fails to meet the preset requirements, the test sample is determined to be unqualified or triggers a suspected adulteration marker, and subsequent judgment is terminated or a retest is initiated.

[0135] Judgment rules for multi-indicator comprehensive discrimination model:

[0136] Comprehensive discrimination score The calculation uses a weighted summation model:

[0137]

[0138] in, To comprehensively determine the total score;

[0139] n is the total number of discriminant dimensions; in this method, n=4.

[0140] Let be the weight coefficient for the i-th dimension. The weights for each dimension can be preset based on the characteristics of the marker. ,and ;

[0141] This represents the score for the i-th dimension, either 0 or 1.

[0142] Dimension (a) Qualitative detection:

[0143]

[0144] Dimension (b) Characteristic ion pair abundance ratio bias:

[0145]

[0146] in, This represents the measured abundance ratio of the two characteristic ion pairs of the marker in the test sample. The abundance ratio standard value is the preset standard reference data. To preset the allowable deviation, such as =20%;

[0147] Is the content of dimension (c) within the limit?

[0148]

[0149] in, This represents the measured content of the marker in the test sample. and These are the preset lower and upper limits of content, respectively;

[0150] Dimension (d) Multi-marker content ratio:

[0151]

[0152] in, and The contents of two different quality markers are shown. and These are the preset lower and upper limits of the ratio, respectively.

[0153] Comprehensive Judgment Rules:

[0154] when When the test sample is deemed genuine and of acceptable quality, it is determined that the sample is genuine.

[0155] when If the test sample is determined to be unqualified or triggers a suspected adulteration marker, a retest will be initiated.

[0156] when If the test sample is found to be counterfeit or seriously substandard, it will be directly determined that the sample is counterfeit or seriously substandard.

[0157] Furthermore, the S1 characteristic marker screening step also includes using chemometric methods to screen for differential components in feed samples before and after processing and before and after formulation. Candidates for quality markers are determined based on the criteria that the variable importance projection value or correlation coefficient is not lower than a preset threshold and the relative standard deviation of the content after processing and formulation does not exceed a preset value. The structure of the candidate is then confirmed by comparison with reference standards or by mass spectrometry library search.

[0158] Furthermore, in the S2 sample pretreatment step, at least one internal standard is added throughout the pretreatment process. The internal standard is an isotopic marker or structural analog that is not present in the tested natural plant feed raw material. In the S3 precision detection step, the internal standard is used to quantitatively correct the relative response factor between the internal standard and the target marker, thereby eliminating the influence of pretreatment loss and matrix effect on quantitative accuracy and eliminating systematic errors in the pretreatment and detection processes.

[0159] Furthermore, between the S1 characteristic marker screening step and the S3 precision detection step, there is also a method validation step: the detection limit, quantitation limit, linear range, spiked recovery rate, intra-day and inter-day precision of the quality marker in the tested natural plant feed raw material matrix are validated to ensure that the preset acceptable standards of method validation are met before it can be used for subsequent authenticity identification and quality evaluation.

[0160] The preset acceptable standards include: the detection limit is not higher than a predetermined proportion of the preset lower limit of content, the spiked recovery rate is within the predetermined acceptable range, and the relative standard deviation of intra-day and inter-day precision is within the predetermined acceptable range.

[0161] Example 2

[0162] This example uses licorice extract feed additive:

[0163] 1) Sample information: The sample to be tested is a licorice extract feed additive, labeled with a glycyrrhizic acid content of ≥2.0%. A total of 12 batches of genuine samples and 15 samples of common adulterants and closely related species were collected.

[0164] 2) Quality Marker Screening Results: Through chemometric analysis, glycyrrhizic acid and glycyrrhetinic acid were selected as quality markers. The VIP of glycyrrhizic acid was 2.35, and the VIP of glycyrrhetinic acid was 1.82. The Δ% before and after processing were 12.3% and 15.6%, respectively, the RSD was 8.7% and 10.2%, respectively, and the specificity verification DI values ​​were 8.5 and 6.2, respectively, both ≥5;

[0165] 3) Pretreatment conditions: Weigh 1.0 g of the test sample accurately, add 100 μL of internal standard ammonium glycyrrhizate-d5 solution (2 μg / mL), add 20 mL of 70% ethanol solution, extract by sonication for 30 min, centrifuge (10000 rpm, 5 min), take the supernatant and filter it through a 0.22 μm filter membrane, and use the filtrate as the test solution;

[0166] 4) Detection conditions: Samples were injected and detected according to UHPLC-MS / MS conditions. The monitoring ion pairs for glycyrrhizic acid were: m / z 821.4→351.2 (quantitative) and 821.4→453.3 (qualitative); for glycyrrhetinic acid, the monitoring ion pairs were: m / z 469.3→425.3 (quantitative) and 469.3→409.3 (qualitative); the monitoring ion pair for the internal standard ammonium glycyrrhizate-d5 was: m / z 826.4→356.2.

[0167] 5) Method validation results: Glycyrrhizic acid detection limit 0.05 mg / kg, quantitation limit 0.15 mg / kg, linear range 0.15-500 mg / kg. The spiked recoveries were 92.3%-105.6% (RSD = 3.2%-5.8%), intra-day precision RSD = 2.1%-4.3%, inter-day precision RSD = 3.5%-5.9%, glycyrrhetinic acid limit of detection 0.02 mg / kg, limit of quantitation 0.06 mg / kg, and linear range 0.06-200 mg / kg. The spiked recoveries ranged from 88.7% to 98.9% (RSD = 3.8% to 6.5%).

[0168] 6) Matrix effect evaluation: Glycyrrhizic acid ME%=-28.5%, glycyrrhetinic acid ME%=-22.3%, both |ME%|>20%, therefore, matrix-matched standard curve correction was used, combined with internal standard correction method for quantification.

[0169] 7) Content determination and discrimination results: The glycyrrhizic acid content in the sample was 2.15%, the glycyrrhetinic acid content was 0.32%, the glycyrrhizic acid / glycyrrhetinic acid ratio was 6.72 (within the standard range of 6.5±1.5), the characteristic ion abundance ratio deviation was 5.2% (<20%), and the comprehensive discrimination was... =1, indicating it is a qualified genuine product.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for identifying and controlling the quality of natural plant feed ingredients based on quality markers, characterized in that, Includes the following steps: S1. Characteristic marker screening steps: For the plant-derived components to be tested in natural plant feed raw materials, with the goal of eliminating matrix interference factors and the influence of inter-component interactions in the entire feed processing process and retaining component specificity, at least one quality marker with component specificity and matrix stability is screened. S2. Sample pretreatment steps: Based on the physicochemical properties of the quality markers and the matrix type of the natural plant feed raw materials, an appropriate extraction and purification scheme is used to pretreat the natural plant feed raw materials to prepare a sample solution. S3. Precision detection steps: High-resolution mass spectrometry or tandem mass spectrometry is used to detect the test solution to obtain qualitative and quantitative detection data of the quality marker; S4. Authenticity identification and quality evaluation steps: Compare the test data with the preset standard reference data, and determine the authenticity and quality qualification of the tested natural plant feed raw materials based on the comparison results.

2. The method for identification and quality control of natural plant feed ingredients based on quality markers according to claim 1, characterized in that: In the S1 characteristic marker screening step, the matrix interference factors and interactions between components in the entire feed processing process specifically include: chemical interactions between different plant-derived components during the mixing and formulation of feed raw materials, interference effects of excipients, and the effects of processing temperature, processing pressure, and processing time on the stability of the markers. The criteria for obtaining the quality markers are: the relative content change before and after processing does not exceed a preset threshold, and it is not significantly interfered with by other coexisting components in the natural plant feed raw materials.

3. The method for identification and quality control of natural plant feed ingredients based on quality markers according to claim 1, characterized in that: In the S1 feature marker screening step, the quality marker meets at least one of the following specificity evaluation indicators: S1.1 The quality marker has a content of not less than the first preset value in the plant-derived component to be tested, and its content in other common plant-derived feed ingredients and excipients is less than the second preset value or not detected. S1.2 The mass spectrometry fragmentation characteristic ion pairs or characteristic fragments of the mass marker have uniqueness that distinguishes them from closely related species and other adulterants; S1.3 The content ratio of the quality marker is pre-correlated with the doping ratio of the corresponding plant-derived component, and is used to quantitatively identify the degree of adulteration of raw materials.

4. The method for identification and quality control of natural plant feed ingredients based on quality markers according to claim 1, characterized in that: In the S2 sample pretreatment step, the compatibility extraction and purification scheme is selected from at least one of the following methods based on the matrix type of the natural plant feed raw material: S2.1 For high starch or high protein matrices, solid phase extraction purification combined with matrix dispersion solid phase extraction is used for pretreatment; S2.2 For high-oil matrices, low-temperature freeze defatting or hexane liquid-liquid extraction defatting followed by extraction is employed; S2.3 For multi-raw material composite matrices, gradient solvent extraction combined with pH adjustment is used for selective enrichment; S2.4 For feed matrices processed at high temperatures, enzymatic hydrolysis-assisted extraction or pressurized fluid extraction is used to improve the recovery rate of target biomarkers; S2.5 For high-fiber matrices, cellulase-assisted extraction or microwave-assisted extraction is used.

5. The method for identification and quality control of natural plant feed ingredients based on quality markers according to claim 1, characterized in that: In the S3 precision detection step, ultra-high performance liquid chromatography-tandem mass spectrometry or ultra-high performance liquid chromatography-high resolution mass spectrometry is used for detection. The detection conditions are corrected by matrix matching standard curve to offset the inhibitory or enhancing effect of the natural plant feed raw material matrix on ionization efficiency. The matrix matching standard curve is prepared using a blank matrix that is consistent with the matrix type of the test sample. The linear correlation coefficient of the matrix matching standard curve is not lower than a preset threshold, and the quantitative results are corrected by the matrix effect factor.

6. The method for identification and quality control of natural plant feed ingredients based on quality markers according to claim 1, characterized in that: In the S4 step of authenticity identification and quality evaluation, the preset standard reference data includes: a quality marker fingerprint spectrum library, characteristic ion pair abundance ratio range, and content limit range established by measuring no less than three batches of known genuine natural plant feed raw materials according to the same methods in S2 and S3.

7. The method for identification and quality control of natural plant feed ingredients based on quality markers according to claim 1, characterized in that: In the S4 authenticity verification and quality evaluation step, a multi-index comprehensive discrimination model is used for authenticity verification. The comprehensive discrimination model includes at least the following discrimination dimensions: S4.1 Whether or not quality markers are detected qualitatively; S4.2 Deviation in consistency between the abundance ratio of characteristic ion pairs of quality markers and standard reference data; S4.3 Whether the quantitative content of the quality marker is within the preset content limit range; S4.4 Whether the content ratios among multiple quality markers conform to the preset ratio range; If any discrimination dimension fails to meet the preset requirements, the test sample is determined to be unqualified or triggers a suspected adulteration marker, and subsequent judgment is terminated or a retest is initiated.

8. The method for identification and quality control of natural plant feed ingredients based on quality markers according to claim 1, characterized in that: The S1 feature marker screening step also includes using chemometric methods to screen for differential components in feed samples before and after processing and before and after formulation. Candidates for the quality markers are determined based on the criteria that the variable importance projection value or correlation coefficient is not lower than a preset threshold and the relative standard deviation of the content after processing and formulation does not exceed a preset value. The structure of the candidate is confirmed by comparison with reference standards or mass spectrometry library search.

9. The method for identification and quality control of natural plant feed ingredients based on quality markers according to claim 1, characterized in that: The S2 sample pretreatment step further includes adding at least one internal standard throughout the pretreatment process. The internal standard is an isotope marker or structural analog that is not present in the tested natural plant feed raw material. In the S3 precision detection step, the internal standard is used to quantitatively correct the relative response factor between the internal standard and the target marker, thereby eliminating the influence of pretreatment loss and matrix effect on quantitative accuracy and eliminating systematic errors in the pretreatment and detection processes.

10. The method for identification and quality control of natural plant feed ingredients based on quality markers according to any one of claims 1-9, characterized in that: The step between the S1 feature marker screening step and the S3 precision detection step also includes a methodology verification step: verifying the detection limit, quantitation limit, linear range, spiked recovery rate, intra-day and inter-day precision of the quality marker in the tested natural plant feed raw material matrix to ensure that it meets the preset acceptable standards of methodology verification before it can be used for subsequent authenticity identification and quality evaluation. The preset acceptable standards include: the detection limit is not higher than a predetermined proportion of the preset lower limit of content, the spiked recovery rate is within a predetermined acceptable range, and the relative standard deviation of intra-day and inter-day precision is within a predetermined acceptable range.

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