Multi-dimensional high-precision detection method and system for biotoxins in agricultural products

Abstract

The invention discloses a multi-dimensional high-precision detection method and system for biotoxins in agricultural products, and the method comprises the steps: carrying out sterile collection and grading pretreatment on an agricultural product sample to obtain a homogenized sample; extracting the homogenized sample by adopting a mixed solvent system, adding an internal standard substance, carrying out salting-out layering, and collecting supernate; sequentially carrying out QuEChERS purification, multi-mechanism adsorption column purification and molecularly imprinted polymer specific enrichment on the supernate; carrying out dual-mode detection on the target toxin enrichment liquid, respectively carrying out quantitative detection by adopting a liquid chromatography-tandem mass spectrometry method, and carrying out screening detection by adopting an intelligent electrochemical sensing method; performing optimization processing on the dual-mode detection signal, and comprehensively judging a sample detection result; through integrated application of a triple purification mechanism, a dual-mode detection system, machine learning signal analysis and a full-chain quality control technology, efficient enrichment and deep interference removal of target toxins in a complex matrix can be realized, and both precision and applicability are considered.

Description

Technical Field

[0001] This invention relates to the field of detection technology, specifically to the field of agricultural product detection, and particularly to a method and system for multidimensional and high-precision detection of biotoxins in agricultural products. Background Technology

[0002] Throughout the entire chain of planting, harvesting, storage and processing, agricultural products are susceptible to infection by microorganisms such as fungi and bacteria, which produce biotoxins. Among them, fungal toxins (such as aflatoxin, zearalenone, ochratoxin A, etc.) pose a serious threat to food safety and public health due to their wide distribution, high toxicity and easy residue.

[0003] However, traditional detection methods are mostly designed for single toxins and have drawbacks such as cumbersome pretreatment, long detection cycle, strong matrix interference, and insufficient ability to detect multiple toxins simultaneously, making it difficult to meet the high-throughput, high-precision, and fast-response detection needs of modern agriculture. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-dimensional, high-precision detection method and system for biotoxins in agricultural products, thereby solving all or one of the aforementioned problems in the prior art.

[0005] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows: On the one hand, this invention provides a method for multi-dimensional and high-precision detection of biotoxins in agricultural products, comprising the following steps: Aseptic collection and grading pretreatment of agricultural product samples were performed to obtain homogenized samples. The homogenized sample was extracted using a mixed solvent system, and after the addition of an internal standard, it was subjected to salting out and the supernatant was collected. The supernatant was subjected to QuEChERS purification, multi-mechanism adsorption column purification, and molecularly imprinted polymer-specific enrichment in sequence to obtain the target toxin enrichment solution. The target toxin enrichment solution was subjected to dual-mode detection: quantitative detection was performed by liquid chromatography-tandem mass spectrometry, and screening detection was performed by intelligent electrochemical sensing. Machine learning algorithms are used to process the dual-mode detection signals, and combined with preset standard limits, the detection results of multiple types of biotoxins in the sample are comprehensively judged.

[0006] Furthermore, the hierarchical preprocessing further includes: After removing impurities, solid agricultural product samples are ground and sieved. Defatting treatment was performed on high-fat agricultural product samples; Homogenize high-moisture agricultural product samples; Liquid agricultural product samples were filtered to remove impurities. The collected samples were stored and transported in a light-proof, sterile container at low temperature.

[0007] Furthermore, the mixed solvent system includes: a mixed system containing nitrile, water, and organic acid; The extraction process, which uses a mixed solvent system to extract homogenized samples, combines vortex oscillation and ultrasonic extraction. During the salting-out process, an anhydrous magnesium sulfate and sodium chloride combination system is used, and the supernatant is collected after centrifugation.

[0008] Furthermore, the QuEChERS purification system employs an adsorbent combination system; The adsorbent combination system includes: amine adsorbents and carbon-based adsorbents; After purification by QuEChERS, the supernatant is collected by centrifugation and then enters the purification step of the multi-mechanism adsorption column.

[0009] Furthermore, the multi-mechanism adsorption column is a purification column based on the synergistic effect of hydrophobic interaction, ion exchange and hydrogen bonding. Before purification by the multi-mechanism adsorption column, the multi-mechanism adsorption column is activated, the liquid flow rate is controlled to complete the purification, and the effluent is collected. The molecularly imprinted polymer is formed by electropolymerization on the surface of a magnetic nanocomposite substrate, using the target biotoxin as a template molecule. The molecularly imprinted polymer specific enrichment process includes oscillatory adsorption, magnetic field separation, washing, and elution steps. The eluent from the elution step is dried with nitrogen gas, and a methanol-water mixture is used as the volume-fixing solution. After dissolution, the solution is filtered through a filter membrane to obtain the test solution.

[0010] Furthermore, the chromatographic conditions for the liquid chromatography-tandem mass spectrometry method include: The chromatographic column is C10. 18 Reversed-phase chromatography column; Column temperature 40℃; Injection volume: 5 μL; Mobile phase A is a 0.1% formic acid aqueous solution, and mobile phase B is methanol; Gradient elution program: 0-2 min, 30% B; 2-10 min, 30%-80% B; 10-15 min, 80%-95% B; 15-18 min, 95% B; 18-20 min, 30% B equilibration; Flow rate: 0.3 mL / min.

[0011] Furthermore, the mass spectrometry conditions for the liquid chromatography-tandem mass spectrometry method include: The ion source is an ESI source, ionizing in positive ion mode; Spray voltage: 3.5kV; Ion source temperature 350℃; Air curtain pressure 20 psi; Atomizing gas pressure 50 psi; Drying gas pressure 60 psi; A multi-response monitoring mode is adopted.

[0012] Furthermore, the machine learning algorithm includes: a baseline correction algorithm and a feature region selection algorithm; The process of processing the dual-mode detection signal using machine learning algorithms further includes: correcting the baseline drift of the electrochemical detection signal using the baseline correction algorithm, and optimizing the quantitative relationship between the detection signal and the toxin concentration using the feature region screening algorithm. The comprehensive determination of the detection results of multiple types of biotoxins in the sample further includes: determining the results based on the consistency comparison of the dual-mode detection results.

[0013] Furthermore, the aforementioned types of biotoxins include: aflatoxins, fusarium toxins, ochratoxins, and other common biotoxins that contaminate agricultural products; The agricultural products include: grains, oil crops, fruits and vegetables, and silage.

[0014] On the other hand, the present invention also provides a multi-dimensional, high-precision detection system for biotoxins in agricultural products, comprising: The collection module is used for aseptic collection and grading pretreatment of agricultural product samples to obtain homogenized samples. The pretreatment module is used to: extract the homogenized sample using a mixed solvent system, add an internal standard, and then perform salting-out separation to collect the supernatant; The purification and enrichment module is used to sequentially perform QuEChERS purification, multi-mechanism adsorption column purification, and molecularly imprinted polymer-specific enrichment on the supernatant to obtain a target toxin enrichment solution. The dual-mode detection module is used to perform dual-mode detection on the target toxin enrichment solution, using liquid chromatography-tandem mass spectrometry for quantitative detection and intelligent electrochemical sensing for screening detection. The result review module is used to process the dual-mode detection signals using machine learning algorithms, and combine them with preset standard limits to comprehensively determine the detection results of multiple types of biotoxins in the sample.

[0015] On the other hand, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for multidimensional and high-precision detection of biotoxins in agricultural products.

[0016] The beneficial effects of the technical solution of this invention are: 1. The multi-dimensional high-precision detection method for biotoxins in agricultural products described in this invention can achieve efficient enrichment of target toxins and deep removal of interference in complex matrices through the integrated application of triple purification mechanism, dual-mode detection system, machine learning signal analysis and whole-chain quality control technology. It balances detection accuracy and field applicability, eliminates cross-interference between toxins, ensures traceability and reproducibility of detection results, adapts to the simultaneous detection of multiple biotoxins in multiple types of agricultural products, improves the systematization and intelligence of detection, makes up for the deficiencies of existing technologies, and has high application value.

[0017] 2. The multi-dimensional high-precision detection system for biotoxins in agricultural products described in this invention can achieve the multi-dimensional high-precision detection method for biotoxins in agricultural products described in this invention through the cooperation of system modules.

[0018] 3. The computer-readable storage medium of the present invention can guide the system modules of the multi-dimensional high-precision detection system for biotoxins in agricultural products of the present invention to cooperate, thereby realizing the multi-dimensional high-precision detection method for biotoxins in agricultural products of the present invention. The computer-readable storage medium of the present invention effectively improves the operability of the multi-dimensional high-precision detection method for biotoxins in agricultural products. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the multi-dimensional, high-precision detection method for biotoxins in agricultural products as described in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the architecture of the multi-dimensional high-precision detection system for biotoxins in agricultural products described in Embodiment 2 of the present invention. Detailed Implementation

[0021] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby providing a clearer and more explicit definition of the scope of protection of the present invention.

[0022] In the description of this invention, it should be noted that the embodiments described in this invention are only some embodiments of this invention, not all embodiments; based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0023] The terms "first," "second," etc., used in this specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0024] In the description of this invention, it should be noted that the types of agricultural products to which this invention is applicable include at least: cereals (wheat, corn, rice, oats), oil crops (peanuts, soybeans, sesame), fruits and vegetables (apples, grapes, peppers), and silage, etc., and can be adapted to pretreatment optimization schemes with different substrate characteristics.

[0025] In the description of this invention, it should be noted that the target toxins for detection by this invention include at least: aflatoxins (AFB1, AFB2, AFG1, AG2); fusarium toxins (zearalenone ZEN, deoxynivalenol DON, T-2 toxin, HT-2 toxin, fumonisin B1, B2); ochratoxins (OTA); and other toxins (mycophenolic acid MPA, patulin, beauveria bassiana, enfurspone B1).

[0026] I. In the embodiments of the present invention, the instruments, reagents and conditions listed below are all as one implementation method, and the implementation method can be adapted and adjusted according to the implementation environment, objectives or conditions of the method in this embodiment.

[0027] II. The instruments, equipment, and reagents used in the embodiments of the present invention are as follows: 1. Instruments and equipment, including: 1) Liquid chromatography-tandem mass spectrometry (LC-MS / MS, equipped with an ESI ion source, such as an Agilent 6495 triple quadrupole mass spectrometer). 2) Intelligent electrochemical workstation (such as CHI660E, equipped with glassy carbon electrode, platinum wire counter electrode, and saturated calomel reference electrode); 3) High-speed refrigerated centrifuge (speed ≥ 12000 r / min, such as Eppendorf 5810R); 4) Nitrogen blowing device (temperature control accuracy ±1℃, such as Thermo Scientific TRACE 1310); 5) Ultrapure water system (resistivity ≥ 18.2 MΩ·cm); 6) Sterile grinder (equipped with an agate grinding cup, such as the IKA A11 basic); 7) Analytical balance (accuracy 0.1 mg and 0.01 mg); 8) Solid-phase extraction apparatus and multi-mechanism impurity adsorption column; 9) Molecularly imprinted polymer synthesis apparatus (including electropolymerization module).

[0028] 2. Reagents and standards, including: 1) Standard products: For each target toxin standard (purity ≥98%, purchased from Sigma-Aldrich), internal standards (AFB1-D7, ZEN-D6, OTA-D5, etc., purity ≥99%), prepare a 100 μg / mL standard stock solution (dissolved in methanol, stored at -20℃ protected from light, shelf life 3 months). When using, dilute with methanol-water (50:50, v / v) to prepare a series of concentration standard working solutions.

[0029] 2) Reagents: Methanol, acetonitrile, and formic acid (Fisher Chemical), chromatographically pure; Acetic acid, sodium chloride, anhydrous magnesium sulfate, primary and secondary amines (PSA), and ENVI-Carb adsorbent (Sigma-Aldrich) were used, all of analytical grade. Fe3O4-MGO nanomaterials (particle size 50-100nm). Pyrrole-3-carboxylic acid, ammonium persulfate (APS), N,N'-methylenebisacrylamide (MBA) (purity ≥98%); Ultrapure water (meets GB / T 6682-2008 Class I water standard); Sterile physiological saline (0.9% NaCl).

[0030] 3) Buffer solution: Phosphate buffer (PBS, 0.1 mol / L, pH 7.4): Weigh 8.0 g NaCl, 0.2 g KCl, 1.44 g Na2HPO4·12H2O and 0.24 g KH2PO4, dissolve in 1000 mL ultrapure water, and use after sonication to degas; Acetate buffer (0.05 mol / L, pH 4.5) is used for molecularly imprinted polymer synthesis.

[0031] Example 1: This example provides a multi-dimensional, high-precision detection method for biotoxins in agricultural products, such as... Figure 1 As shown, it includes: S100, Sample collection and pretreatment steps, including: In this step, the pretreatment employs a graded purification-specific enrichment strategy to remove matrix interference step by step, ensuring the recovery rate and purity of the target toxin, and adapting to the characteristics of different agricultural product matrices, as detailed below: S101, Sample collection operation, including: As a preferred implementation method, the sampling tool includes: a sterile, non-absorbent stainless steel sampler, which is cleaned and dried with 70% ethanol before sampling to avoid cross-contamination; As a preferred implementation method, the sampling method includes: for bulk samples, a five-point sampling method (covering the four corners and the central area) is used; for packaged samples, 3%-5% of the packaging units are randomly selected, and multiple points are sampled within each unit. As a preferred embodiment, the sample quantity includes: no less than 500g for solid samples and no less than 500mL for liquid samples (such as juice or silage leachate); As a preferred implementation method, the sample storage method includes: immediately after collection, placing the sample into a sterile brown container, transporting it under refrigeration at 4°C, processing it within 24 hours, and if long-term storage is required, freezing it at -20°C in the dark, with a storage period not exceeding 7 days, to avoid toxin degradation.

[0032] S102. Sample pretreatment operations, including: As a preferred implementation method, for solid samples, remove impurities (mud, sand, insect-damaged parts), grind them into uniform powder using a sterile grinder, pass them through a 40-mesh sieve, mix them thoroughly, and then put them into a sterile sealed bag and label the sample information. As a preferred implementation method, for high-fat samples (peanuts, soybeans), add 5 mL of n-hexane / acetone (1:1, v / v), vortex for 2 min, centrifuge (8000 r / min, 5 min), and discard the supernatant to remove the oil; As a preferred implementation method, for high-moisture samples (fruits and vegetables), they are chopped and homogenized using a high-speed homogenizer, and 20g of the homogenate is used for subsequent extraction. As a preferred implementation method, for liquid samples, they are filtered through a 0.45 μm organic phase filter membrane to remove insoluble impurities.

[0033] S103, Extraction operation, including: As a preferred implementation method, a targeted extraction system is selected based on the physicochemical properties of the toxins to ensure simultaneous and efficient extraction of multiple toxins, as detailed below: (i) Weigh 10.0g of the pretreated sample and place it in a 50mL centrifuge tube. Add 20mL of extraction solvent (acetonitrile / water / formic acid = 80:19:1, v / v / v), add internal standard working solution (final concentration 10μg / L), vortex for 3min, and sonicate for 20min (power 300W, temperature 30℃). (ii) Add 4g of anhydrous magnesium sulfate and 1g of sodium chloride, shake vigorously for 1min, let stand in an ice bath for 5min, and centrifuge (10000r / min, 4℃, 10min). (iii) Take 10 mL of the supernatant and transfer it to a new centrifuge tube for later use.

[0034] S104. Graded purification and enrichment operations, including: (i) First-stage purification (QuEChERS purification) procedure: Add 150 mg PSA, 50 mg ENVI-Carb adsorbent and 1 g anhydrous magnesium sulfate to the supernatant above, vortex for 2 min, centrifuge (8000 r / min, 5 min), take 8 mL of supernatant to remove pigments, fatty acids and other matrix interferences. (ii) Secondary purification (multi-mechanism adsorption column purification) operation: Pass the supernatant of the primary purification through a multi-mechanism impurity adsorption column (pre-activated with 5 mL methanol and 5 mL water), control the flow rate at 1-2 mL / min, discard the initial effluent of 3 mL, and collect the subsequent effluent of 5 mL; this column can remove residual interfering substances through multiple mechanisms such as hydrophobic interaction, ion exchange, and hydrogen bonding, and is suitable for the simultaneous purification of 37 kinds of mycotoxins, with a purification time of ≤2 min; Among them, the multi-mechanism adsorption column is an impurity adsorption purification column designed based on a triple synergistic mechanism of hydrophobic interaction, ion exchange, and hydrogen bonding. It is suitable for the simultaneous purification of 37 kinds of mycotoxins in 5 categories. Its technical principle and structure are industrial purification columns developed by the Institute of Quality Standards and Testing Technology of the Chinese Academy of Agricultural Sciences. This type of adsorption column can shorten the sample purification time to less than 2 minutes. Its packing material achieves synergistic effect of multiple adsorption mechanisms through composite modification. It can specifically remove residual pigments, fatty acids, polar impurities and other interfering substances in agricultural product matrices without adsorbing target toxins, thus ensuring purification efficiency and recovery rate.

[0035] (iii) Tertiary enrichment (molecular imprinting enrichment) procedure: Take 5 mL of secondary purification solution, add 50 mg of Fe3O4-MGO / MIP composite material, place on a shaker and shake for 30 min (25℃, 150 r / min), separate the composite material by external magnetic field, and discard the supernatant; then wash the composite material twice with 5 mL of PBS buffer to remove non-specific adsorbents; then add 2 mL of methanol / formic acid (95:5, v / v) as elution buffer, vortex for 5 min, centrifuge (8000 r / min, 5 min), collect the elution buffer, and complete the specific enrichment of the target toxin; It should be noted that in this step, the target toxin is used as a template molecule, and pyrrole-3-carboxylic acid (Py3C) is selected as a bifunctional monomer. A molecularly imprinted polymer film is formed by electropolymerization on the surface of Fe3O4-modified magnetic graphene oxide (Fe3O4-MGO) substrate. Its customized binding cavity can specifically recognize the target toxin. At the same time, the high conductivity and large specific surface area of ​​Fe3O4-MGO improve the enrichment efficiency and signal transduction ability.

[0036] S105. Concentration and volume adjustment operations, including: The eluent was placed in a nitrogen evaporator and dried by nitrogen in a 35°C water bath. 1 mL of methanol-water (50:50, v / v) solution was added to make up the volume. The solution was vortexed for 2 min, sonicated for 10 min, filtered through a 0.22 μm organic phase filter membrane, and transferred to a sample vial for LC-MS / MS and electrochemical sensing detection.

[0037] S200, multi-dimensional detection system operation steps, including: In this step, a dual-mode detection approach is adopted, combining precise LC-MS / MS quantification with rapid screening using intelligent electrochemical sensing. The former ensures detection accuracy and simultaneous detection of multiple toxins, while the latter enables rapid on-site verification. The results of both methods corroborate each other, eliminating biases from a single technique. Details are as follows: S201, LC-MS / MS detection, including: (i) Set the targeted chromatographic conditions as follows: The chromatographic column is C10. 18 Reversed-phase column (2.1 mm × 150 mm, 1.8 μm); Column temperature 40℃; Injection volume: 5 μL; Mobile phase A is a 0.1% formic acid aqueous solution, and mobile phase B is methanol; Gradient elution program: 0-2 min, 30% B; 2-10 min, 30%-80% B; 10-15 min, 80%-95% B; 15-18 min, 95% B; 18-20 min, 30% B equilibration; Flow rate: 0.3 mL / min.

[0038] (ii) Set the appropriate mass spectrometry conditions as follows: The ion source is an ESI source, ionizing in positive ion mode; Spray voltage: 3.5kV; Ion source temperature 350℃; Air curtain pressure 20 psi; Atomizing gas pressure 50 psi; Drying gas pressure 60 psi; The multiple reaction monitoring (MRM) mode is used to optimize the characteristic precursor ion, daughter ion, and collision energy for each toxin to ensure no ion interference between toxins.

[0039] It should be noted that this step uses a high-performance liquid chromatography column to separate different toxins. The mobile phase gradient is optimized based on the difference in the polarity of toxin molecules. The separated components enter a mass spectrometer and are ionized by an electrospray ionization source (ESI). Characteristic precursor ions and daughter ion pairs are selected for multiple reaction monitoring (MRM) to achieve accurate qualitative and quantitative analysis.

[0040] S202, Standard Curve Plotting, including: Prepare a series of standard working solutions at concentrations of 0.1, 0.5, 1.0, 5.0, 10.0, 50.0, and 100.0 μg / L, and add an equal volume of internal standard working solution. Inject the samples according to the instrument parameters described above. Plot a standard curve with the standard concentration on the x-axis and the peak area ratio of the target toxin to the internal standard on the y-axis. Calculate the regression equation and correlation coefficient (R²). 2 ≥0.999).

[0041] S203. Sample testing and qualitative / quantitative analysis, including: (i) Inject the final volume of the sample solution according to the standard curve determination conditions. Set up three parallel determinations for each sample. At the same time, set up a blank control (with methanol-water as the sample matrix) and a quality control sample (a blank sample with a known concentration of standard added). (ii) Qualitative determination: The type of toxin is determined based on the retention time of the target analyte in the sample (deviation from the standard ≤ ±2%) and the abundance ratio of characteristic ion pairs (deviation from the standard ≤ ±15%). (iii) Quantitative calculation: The toxin concentration in the sample was calculated by the regression equation of the standard curve, and the result was the average of three parallel determinations.

[0042] S204, Intelligent electrochemical sensing and detection operation, including: (i) Sensor fabrication: Glassy carbon electrode (GCE) pretreatment: Polished to mirror finish with 0.3μm and 0.05μm alumina powder in sequence, ultrasonically cleaned with ultrapure water and ethanol alternately for 5 min each, and dried with nitrogen gas; Modification of Fe3O4-MGO layer: Take 5 μL of Fe3O4-MGO dispersion (1 mg / mL) and drop it onto the electrode surface, then let it air dry at room temperature; Electropolymerized molecularly imprinted layer: The modified electrode was placed in an acetate buffer solution containing a mixture of 5 mmol / L Py3C and 0.1 mmol / L template toxin, and electropolymerization was performed using cyclic voltammetry (potential range -0.2~1.0V, scan rate 50mV / s, 15 scans). Elution of template molecules: The electrode was placed in methanol / formic acid (95:5, v / v) and ultrasonically eluted for 10 min to remove template toxins and form a cavity-type molecular imprinted sensor, which was then stored at 4℃ for later use.

[0043] (ii) Detection parameter settings: Electrochemical impedance spectroscopy (EIS) was used for detection, with a test frequency range of 0.01 Hz to 100 kHz, an AC signal amplitude of 5 mV, and a test medium of 0.1 mol / L PBS buffer (pH 7.4). Impedance signals are acquired using the software provided with the electrochemical workstation, and the characteristic impedance value (Rct) is recorded.

[0044] (iii) Sample detection and signal analysis: Take 20 μL of sample solution and drop it onto the sensor surface. Incubate at room temperature for 20 min. Rinse the electrode surface with PBS buffer to remove unbound material. Place the electrode in the test medium, collect the impedance spectrum, record the Rct value, and measure each sample in parallel 3 times. (iv) Signal correction: The SWMA algorithm was used to perform baseline correction on the original impedance signal, separating background noise from characteristic signal peaks. The iPLS algorithm was combined to divide potential sub-intervals, select the optimal characteristic region, and establish a quantitative model of Rct change value and toxin concentration. (v) Output results: The toxin concentration in the sample was calculated based on a quantitative model and verified by comparison with LC-MS / MS results. It should be noted that this step constructs an Fe3O4-MGO / MIP / GCE sensor. After the target toxin specifically binds to the imprinted site, it causes a change in the electrode surface impedance. The baseline drift is corrected by the small window moving average algorithm (SWMA), and the quantitative relationship between the impedance signal and the toxin concentration is established by combining the interval partial least squares (iPLS) algorithm, thereby improving the detection specificity and accuracy.

[0045] S300, Data Analysis and Result Determination Steps, including: S301, Basic data processing, including: MassHunter software (LC-MS / MS data) and Origin2023 software (electrochemical data) were used for data processing to calculate the mean (x), standard deviation (SD), and relative standard deviation (RSD) of parallel samples. RSD ≤ 5% was considered acceptable to ensure detection precision.

[0046] S302. Interference cancellation verification, including: (i) Matrix interference verification: Set up blank matrix group, matrix spiked group and pure standard group, and calculate matrix effect (ME) = (peak area of ​​matrix spiked group / peak area of ​​pure standard group) × 100%. There is no obvious matrix interference in the ME range of 80%-120%. (ii) Verification of cross-interference between toxins: Prepare a mixed standard solution of multiple toxins, and measure the peak area / impedance value of single toxins and mixed toxins respectively. If the deviation is ≤±5%, it is confirmed that there is no cross-interference.

[0047] S303, Methodological Validation Metrics, including: (i) Linear range: The linear range for each toxin is 0.1-100 μg / L, R 2 ≥0.999; (ii) Limit of detection (LOD) and limit of quantitation (LOQ): LOD is calculated with S / N=3 and LOQ is calculated with S / N=10. LOD ≤ 0.05 μg / L and LOQ ≤ 0.15 μg / L; (iii) Recovery rate: The recovery rate was between 85% and 105% when three concentration levels of standard (0.5, 10 and 50 μg / L) were added; (iv) Stability: The sensor's cyclic regeneration stability RSD is <2.07%, and the batch-to-batch repeatability RSD is 1.79%.

[0048] S304. Result judgment criteria, including: A comprehensive judgment based on the results of both modes of detection: If the results of LC-MS / MS and electrochemical sensing are both lower than the corresponding toxin limit standard (e.g., the limit of AFB1 in cereals is ≤2.0μg / kg, GB5009.22-2016), and the deviation between the results of the two methods is ≤±10%, it is deemed qualified; If the result of a single method exceeds the limit or the deviation between the results of two methods is >±10%, resampling and testing are required. If the results of both methods still exceed the limit after retesting, the product is deemed unqualified, and the source of contamination must be traced and measures such as isolation and destruction must be taken.

[0049] Additionally, it should be noted that this plan requires the implementation of the following quality control steps, including at least: (i) Quality control of samples: Two blank controls and three concentration level quality control samples (low, medium and high) are set for each batch of samples. No target toxin is detected in the blank control. The recovery rate and RSD of the quality control samples meet the method validation requirements. Otherwise, the test results of the batch are invalid. (ii) Perform quality control on the instrument: Before each daily test, calibrate the LC-MS / MS and electrochemical workstation with standards to ensure instrument accuracy; maintain the chromatographic column by rinsing it with methanol weekly, and clean the sensor with eluent after each use to remove residual conjugates and extend its service life.

[0050] Example 2: This example provides a multi-dimensional, high-precision detection system for biotoxins in agricultural products, such as... Figure 2 As shown, it includes: The collection module is used for aseptic collection and grading pretreatment of agricultural product samples to obtain homogenized samples. The pretreatment module is used to: extract the homogenized sample using a mixed solvent system, add an internal standard, and then perform salting-out separation to collect the supernatant; The purification and enrichment module is used to sequentially perform QuEChERS purification, multi-mechanism adsorption column purification, and molecularly imprinted polymer-specific enrichment on the supernatant to obtain a target toxin enrichment solution. The dual-mode detection module is used to perform dual-mode detection on the target toxin enrichment solution, using liquid chromatography-tandem mass spectrometry for quantitative detection and intelligent electrochemical sensing for screening detection. The result review module is used to process the dual-mode detection signals using machine learning algorithms, and combine them with preset standard limits to comprehensively determine the detection results of multiple types of biotoxins in the sample.

[0051] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0052] Example 3: This example provides a computer-readable storage medium, including: The storage medium is used to store computer software instructions for implementing the multidimensional high-precision detection method for biotoxins in agricultural products as described in the above embodiments. It includes programs for executing the above-described multidimensional high-precision detection method for biotoxins in agricultural products. Specifically, the executable program can be built into the multidimensional high-precision detection system for biotoxins in agricultural products described in Embodiment 2. In this way, the multidimensional high-precision detection method for biotoxins in agricultural products described in Embodiment 1 can be implemented by executing the built-in executable program.

[0053] Furthermore, the computer-readable storage medium in this embodiment can be any combination of one or more readable storage media, wherein the readable storage medium includes an electrical, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof.

[0054] Unlike existing technologies, the multidimensional high-precision detection method and system for biotoxins in agricultural products proposed in this application can achieve efficient enrichment of target toxins and deep removal of interference in complex matrices through the integrated application of triple purification mechanism, dual-mode detection system, machine learning signal analysis and whole-chain quality control technology. It takes into account both detection accuracy and field applicability, eliminates cross-interference between toxins, ensures traceability and reproducibility of detection results, and is suitable for simultaneous detection of multiple biotoxins in multiple types of agricultural products, thereby improving the systematization and intelligence of detection.

[0055] It should be understood that in the various embodiments of this document, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this document.

[0056] It should also be understood that, in the embodiments herein, the term "and / or" is merely a description of the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following associated objects have an "or" relationship.

[0057] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this document.

[0058] In the embodiments provided herein, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, or they may be electrical, mechanical, or other forms of connection.

[0059] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments described herein, depending on actual needs.

[0060] Furthermore, the functional units in the various embodiments of this document can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0061] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this paper, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this paper. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for multidimensional and high-precision detection of biotoxins in agricultural products, characterized in that, Includes the following steps: Aseptic collection and grading pretreatment of agricultural product samples were performed to obtain homogenized samples. The homogenized sample was extracted using a mixed solvent system, and after the addition of an internal standard, it was subjected to salting out and the supernatant was collected. The supernatant was subjected to QuEChERS purification, multi-mechanism adsorption column purification, and molecularly imprinted polymer-specific enrichment in sequence to obtain the target toxin enrichment solution. The target toxin enrichment solution was subjected to dual-mode detection: quantitative detection was performed by liquid chromatography-tandem mass spectrometry, and screening detection was performed by intelligent electrochemical sensing. Machine learning algorithms are used to process the dual-mode detection signals, and combined with preset standard limits, the detection results of multiple types of biotoxins in the sample are comprehensively judged.

2. The method for multi-dimensional and high-precision detection of biotoxins in agricultural products according to claim 1, characterized in that: The hierarchical preprocessing further includes: After removing impurities, solid agricultural product samples are ground and sieved. Defatting treatment was performed on high-fat agricultural product samples; Homogenize high-moisture agricultural product samples; Liquid agricultural product samples were filtered to remove impurities. The collected samples were stored and transported in a light-proof, sterile container at low temperature.

3. The method for multidimensional and high-precision detection of biotoxins in agricultural products according to claim 1, characterized in that: The mixed solvent system includes: a mixed system containing nitrile, water and organic acid; The extraction process, which uses a mixed solvent system to extract homogenized samples, combines vortex oscillation and ultrasonic extraction. During the salting-out process, an anhydrous magnesium sulfate and sodium chloride combination system is used, and the supernatant is collected after centrifugation.

4. The method for multidimensional and high-precision detection of biotoxins in agricultural products according to claim 1, characterized in that: The QuEChERS purification system employs an adsorbent combination system. The adsorbent combination system includes: amine adsorbents and carbon-based adsorbents; After purification by QuEChERS, the supernatant is collected by centrifugation and then enters the purification step of the multi-mechanism adsorption column.

5. The method for multidimensional and high-precision detection of biotoxins in agricultural products according to claim 1, characterized in that: The multi-mechanism adsorption column is a purification column based on the synergistic effect of hydrophobic interaction, ion exchange and hydrogen bonding. Before purification by the multi-mechanism adsorption column, the multi-mechanism adsorption column is activated, the liquid flow rate is controlled to complete the purification, and the effluent is collected. The molecularly imprinted polymer is formed by electropolymerization on the surface of a magnetic nanocomposite substrate, using the target biotoxin as a template molecule. The molecularly imprinted polymer specific enrichment process includes oscillatory adsorption, magnetic field separation, washing, and elution steps. The eluent from the elution step is dried with nitrogen gas, and a methanol-water mixture is used as the volume-fixing solution. After dissolution, the solution is filtered through a filter membrane to obtain the test solution.

6. The method for multidimensional and high-precision detection of biotoxins in agricultural products according to claim 1, characterized in that: The chromatographic conditions for the liquid chromatography-tandem mass spectrometry method include: The chromatographic column is C10. 18 Reversed-phase chromatography column; Column temperature 40℃; Injection volume: 5 μL; Mobile phase A is a 0.1% formic acid aqueous solution, and mobile phase B is methanol; Gradient elution program: 0-2 min, 30% B; 2-10 min, 30%-80% B; 10-15 min, 80%-95% B; 15-18 min, 95% B; 18-20 min, 30% B equilibration; Flow rate: 0.3 mL / min.

7. The method for multidimensional and high-precision detection of biotoxins in agricultural products according to claim 1, characterized in that: The mass spectrometry conditions for the liquid chromatography-tandem mass spectrometry method include: The ion source is an ESI source, ionizing in positive ion mode; Spray voltage: 3.5kV; Ion source temperature 350℃; Air curtain pressure 20 psi; Atomizing gas pressure 50 psi; Drying gas pressure 60 psi; A multi-response monitoring mode is adopted.

8. The method for multidimensional and high-precision detection of biotoxins in agricultural products according to claim 1, characterized in that: The machine learning algorithm includes: a baseline correction algorithm and a feature region selection algorithm; The process of processing the dual-mode detection signal using machine learning algorithms further includes: correcting the baseline drift of the electrochemical detection signal using the baseline correction algorithm, and optimizing the quantitative relationship between the detection signal and the toxin concentration using the feature region screening algorithm. The comprehensive determination of the detection results of multiple types of biotoxins in the sample further includes: determining the results based on the consistency comparison of the dual-mode detection results.

9. The method for multidimensional and high-precision detection of biotoxins in agricultural products according to claim 1, characterized in that: The aforementioned types of biotoxins include: aflatoxins, fusarium toxins, ochratoxins, and other common biotoxins that contaminate agricultural products; The agricultural products include: grains, oil crops, fruits and vegetables, and silage.

10. A high-precision detection system for biotoxins in agricultural products based on any one of claims 1 to 9, characterized in that, The system includes: The collection module is used to aseptically collect and grade agricultural product samples to obtain homogenized samples. The pretreatment module is used to: extract the homogenized sample using a mixed solvent system, add an internal standard, and then perform salting-out separation to collect the supernatant; The purification and enrichment module is used to sequentially perform QuEChERS purification, multi-mechanism adsorption column purification, and molecularly imprinted polymer-specific enrichment on the supernatant to obtain a target toxin enrichment solution. The dual-mode detection module is used to perform dual-mode detection on the target toxin enrichment solution, using liquid chromatography-tandem mass spectrometry for quantitative detection and intelligent electrochemical sensing for screening detection. The result review module is used to process the dual-mode detection signals using machine learning algorithms, and combine them with preset standard limits to comprehensively determine the detection results of multiple types of biotoxins in the sample.

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