Method for detecting the mildew of soybean meal based on volatile flavor substances

Abstract

The application discloses a method for detecting the mildew of soybean meal based on volatile flavor substances, and relates to the technical field of food / feed safety detection, and comprises a soybean meal sample pretreatment step and the following steps: directional enrichment-purification, double-trigger reaction, double-mode detection and result output; in the application, the binary limitation of only judging whether mildew exists or not is broken through temperature-responsive double-ligand affinity microspheres, the aspergillus flavus, aspergillus niger and penicillium are accurately distinguished through an enzyme-antibody double-trigger recognition unit to fill the risk assessment fault, the double-mode detection of graphite powder-carbon black composite conductive hydrogel and indicator takes into account the precision and industrialization cost, the database matching is combined to realize the pollution link traceability and storage environment optimization, and finally, the principle contradiction between the specificity of mold metabolism and the broad spectrum of detection technology is resolved, and the integrated precision identification-risk assessment-targeted prevention and control of soybean meal mildew is achieved.

Description

Technical Field

[0001] This invention relates to the field of food / feed safety testing technology, specifically a method for detecting mold growth in soybean meal based on volatile flavor compounds. Background Technology

[0002] Soybean meal is one of the world's largest sources of plant protein, widely used in livestock and poultry feed, aquatic feed, and food industry raw materials. Its quality directly affects the safety and economics of downstream products. Throughout the entire soybean meal production, storage, and transportation chain, mold contamination can easily occur due to raw material contamination, improper moisture control, or imbalances in environmental temperature and humidity. This not only causes huge economic losses, but the toxins produced by mold metabolism (such as aflatoxin B1 and ochratoxin A) can also accumulate through the food chain, threatening the health of livestock and poultry and human food safety. Therefore, mold detection is a core aspect of soybean meal quality control.

[0003] Detection technologies based on volatile flavor compounds have become a research hotspot for soybean meal mold detection due to their advantages of being non-destructive, rapid, and requiring no complex pretreatment. Existing technologies in this field mainly determine mold growth through broad-spectrum signal responses, with core pathways including electronic noses, conventional gas chromatography-mass spectrometry (GC-MS), and near-infrared spectroscopy. However, all three have key drawbacks: electronic noses rely on the non-specific adsorption of sensor arrays, only detecting overall changes in volatile substances and failing to distinguish specific molecules; conventional GC-MS focuses on the overall compositional differences of volatile substances before and after mold growth, without analyzing characteristic markers of specific molds; and near-infrared spectroscopy relies on functional group vibrational absorption, resulting in low sensitivity and overlapping absorption peaks of different mold markers. Ultimately, all three technologies can only achieve a binary judgment of "moldy / not moldy," unable to identify the specific type of contaminating mold.

[0004] This technological deficiency directly leads to two core problems facing the soybean meal industry chain:

[0005] First, there is a gap in risk assessment. The degree of harm varies significantly among different molds: Aspergillus flavus produces aflatoxin B1, a potent carcinogen, which is extremely toxic to young livestock and poultry; Aspergillus niger produces ochratoxin A, which causes liver and kidney damage in livestock and poultry with long-term intake; Penicillium (such as patulin-producing bacteria) often causes flavor deterioration and is neurotoxic. Current testing methods cannot determine "which mold contaminates and its corresponding toxin risk level." Enterprises either discard suspected moldy soybean meal indiscriminately, resulting in resource waste, or misjudge the risk (such as mistaking low-toxicity Penicillium contamination for highly toxic Aspergillus flavus contamination), increasing prevention and control costs or causing safety accidents.

[0006] Secondly, there are obstacles to tracing the source of contamination. The stages and types of mold contamination are strongly correlated: Aspergillus flavus often contaminates raw materials during the soybean harvest period (high temperature and humidity); Penicillium often proliferates during storage (localized dampness); and Aspergillus niger is prone to outbreaks when cooling is not timely after processing. Current technology cannot trace the type of mold through volatile substances, making it impossible for companies to pinpoint the key stages of contamination. They are forced to rely on a "crude control" approach throughout the entire process (such as excessive drying and overuse of mold inhibitors), which increases costs and may damage the protein quality of soybean meal.

[0007] A deeper analysis reveals that the root cause of the current technology lies in the fundamental contradiction between the specificity of mold metabolism and the broad-spectrum nature of detection techniques. The differences in enzyme systems among different molds result in unique volatile molecular fingerprints in soybean meal (such as 2,4-decadienal from Aspergillus flavus, 3-octanone from Aspergillus niger, and d-limonene from Penicillium). These fingerprints are crucial for distinguishing mold species. However, the design logic of existing technologies focuses on the overall signal rather than the targeted capture of specific fingerprints, leading to a complete mismatch between detection capabilities and industry needs. This contradiction cannot be resolved by parameter optimization; therefore, a detection method that can accurately identify mold-specific volatile biomarkers is urgently needed to achieve precise risk assessment and targeted control of mold growth in soybean meal.

[0008] Therefore, a method for detecting mold growth in soybean meal based on volatile flavor compounds is provided to overcome the above-mentioned problems. Summary of the Invention

[0009] The purpose of this invention is to provide a method for detecting mold growth in soybean meal based on volatile flavor compounds, so as to solve the problems mentioned in the background art.

[0010] To address the aforementioned technical problems, this invention provides a method for detecting mold growth in soybean meal based on volatile flavor compounds, comprising a soybean meal sample pretreatment step, and further comprising the following steps:

[0011] Targeted enrichment-purification: Temperature-responsive dual-ligand affinity microspheres were used to adsorb and purify volatile flavor compounds released from the sample. The temperature-responsive dual-ligand affinity microspheres contain a main ligand, a secondary ligand, and a temperature-responsive chain segment. The main ligand is a mixture of β-ionone, carvone, and limonene oxide, the secondary ligand is sulfonated starch, and the temperature-responsive chain segment is N-isopropylacrylamide prepolymer.

[0012] Dual-trigger reaction: The purified volatile flavor substances are passed into the enzyme-immune dual-trigger recognition unit. The recognition unit includes a sodium alginate-gelatin substrate sustained-release layer and an enzyme-antibody / aptamer conjugate probe immobilized on the sustained-release layer. The probes are specifically esterase-2,4-decadienal monoclonal antibody conjugate, tyrosinase-3-octanone aptamer conjugate, and glucose oxidase-d-limonene polyclonal antibody conjugate.

[0013] Dual-mode detection: The dual-trigger reaction product is passed into the conductive hydrogel-color dual-mode detection module. The module contains graphite powder-carbon black composite conductive hydrogel and bromocresol green, catechol purple and phenol red indicators dispersed inside the hydrogel. The resistance signal is collected by the series resistor-voltage acquisition circuit for quantitative detection, and the color change of the indicator is used for qualitative judgment.

[0014] Results output: Input the resistance signal, color signal and temperature response data of temperature-responsive dual-ligand affinity microspheres into the mold species-toxin level-contamination link database, and output the mold species, toxin risk level, contamination link and storage environment recommendations.

[0015] Furthermore, the preparation steps of the temperature-responsive dual-ligand affinity microspheres include:

[0016] Preparation of main ligands: Take 0.3g β-ionone, 0.25g carvone, and 0.2g limonene oxide, and place them together with 0.1g maleic anhydride in a three-necked flask. Add 10mL anhydrous ethanol as solvent, and stir at 60℃ in an oil bath for 2h. After the reaction is completed, remove the ethanol by rotary evaporation to obtain a solid mixture of main ligands, which is then sealed for later use.

[0017] Preparation of auxiliary ligands: 1g of industrial-grade starch was added to 50mL of 2mol / L sulfuric acid solution and hydrolyzed in a constant temperature water bath at 80℃ for 1h. Then, 0.5g of sodium sulfite was added, the temperature was adjusted to 60℃ and the reaction was continued with stirring for 3h. After the reaction, the solution was neutralized to pH=7 with 1mol / L sodium hydroxide solution. The precipitate was collected by centrifugation at 3000rpm for 10min, washed 3 times with deionized water, vacuum dried at 60℃ for 2h, and pulverized through a 100-mesh sieve to obtain sulfonated starch.

[0018] Preparation of temperature-responsive segments: Take 5g of N-isopropylacrylamide and 0.2g of N,N'-methylenebisacrylamide, dissolve them in 20mL of deionized water and stir until completely dissolved, add 0.1g of ammonium persulfate, stir in a 50℃ constant temperature water bath for 30min to obtain PNIPAM prepolymer solution, and cool at room temperature for later use.

[0019] Microsphere formation: 0.5 g chitosan was dissolved in 10 mL of 2% acetic acid solution and stirred until completely dissolved. Then, 0.8 g of host-ligand mixture, 0.3 g of sulfonated starch, and 5 mL of PNIPAM prepolymer solution were added sequentially. The mixture was ultrasonically dispersed at 300 W power and 40 kHz frequency for 20 min to form an aqueous phase. 50 mL of liquid paraffin was mixed with 0.5 g Span-80 and stirred at 300 rpm for 10 min to form an oil phase. The aqueous phase was added dropwise to the oil phase at a rate of 1 mL / min while stirring continuously at 300 rpm for 30 min to form a W / O type emulsion. 0.2 g glutaraldehyde was added to the emulsion and stirred at 60 °C for 2 h. After standing and separating into layers, the lower layer of microspheres was collected, washed three times with anhydrous ethanol, and vacuum dried at 60 °C for 1 h to obtain temperature-responsive dual-ligand affinity microspheres with a particle size of 100-200 μm.

[0020] Furthermore, the preparation steps of the enzyme-immune dual-trigger recognition unit include:

[0021] Preparation of substrate mixture: Take 0.5g sodium alginate, 0.3g gelatin, 0.1g butyl acetate, 0.1g tyrosine and 0.1g glucose, dissolve them in 10mL deionized water, stir in a 40℃ constant temperature water bath for 30min until all components are completely dissolved to obtain the substrate mixture;

[0022] Slow-release layer fixation: Take a polydimethylsiloxane (PDMS) substrate and etch three sets of reaction grooves with a diameter of 150 μm and a spacing of 500 μm using soft photolithography; use a 10 μL micropipette to drop 2 μL of substrate mixture onto each set of grooves and incubate at 37℃ for 1 h; immerse the substrate in 0.1 mol / L CaCl2 solution for 5 min, remove it and rinse the surface with deionized water to remove residual CaCl2, and air dry at room temperature to form a sodium alginate-gelatin substrate slow-release layer with a thickness of 5 μm;

[0023] Probe immobilization: Temperature-responsive dual-ligand affinity microspheres were pulverized to a particle size of 10 μm using a ball mill. They were then mixed with esterase-2,4-decadienal monoclonal antibody conjugate, tyrosinase-3-octanone aptamer conjugate, and glucose oxidase-d-limonene polyclonal antibody conjugate at a mass ratio of 1:2 at a concentration of 0.1 mg / mL, and stirred for 30 min. 1 μL of the mixture was then dropped onto the substrate sustained-release layer surface of each corresponding reaction groove using a micropipette. The mixture was incubated at 37 °C for 2 h. The substrate was then rinsed with 0.05% Tween-20 solution to remove any unimmobilized probes and air-dried at room temperature.

[0024] Furthermore, the preparation steps of the conductive hydrogel include:

[0025] Preparation of conductive paste: Take 0.5g of industrial grade graphite powder and 0.2g of carbon black, add 10mL of deionized water, and ultrasonically disperse at 300W power and 40kHz frequency for 30min to form a uniform conductive paste;

[0026] Hydrogel polymerization: Take 1g of acrylamide and 0.05g of N,N'-methylenebisacrylamide, dissolve them in 10mL of deionized water and stir until completely dissolved to prepare acrylamide prepolymer solution; mix the conductive paste and acrylamide prepolymer solution at a volume ratio of 1:3, stir for 10min, add 0.01g of ammonium persulfate, stir in a constant temperature water bath at 60℃ for 30min to initiate the polymerization reaction and form a black elastic conductive hydrogel with a conductivity of 1.3-1.6S / m;

[0027] Indicator dispersion: Mix 0.02g bromocresol green, 0.02g catechol purple, and 0.02g phenol red with 0.1g Tween-80 respectively, and sonicate for 5 minutes; add the mixture to the above-mentioned conductive hydrogel prepolymerization process to make the indicator uniformly dispersed inside the hydrogel.

[0028] Furthermore, the structure of the dual-mode detection circuit is as follows: the core components include conductive hydrogel, a 1kΩ fixed resistor, a 3V DC power supply, an Arduino development board, and copper electrodes; the two ends of the conductive hydrogel are respectively connected to the copper electrodes, one end of the copper electrode is connected to the positive terminal of the 3V DC power supply, and the other end is connected to one end of the 1kΩ fixed resistor, and the other end of the fixed resistor is connected to the negative terminal of the power supply; the voltage acquisition pin of the Arduino development board is connected in parallel across the fixed resistor to acquire the voltage divider signal.

[0029] Furthermore, the pretreatment steps for soybean meal samples are as follows: 50g of sample is randomly selected from the batch of soybean meal to be tested, placed in a 200mL sealed polytetrafluoroethylene container, and kept at a constant temperature of 50℃ for 40min to accelerate the release of volatile markers of mold.

[0030] Furthermore, the specific conditions for the targeted enrichment-purification step are as follows: 1 g of temperature-responsive dual-ligand affinity microspheres are packed into a breathable cloth bag with a pore size of 50 μm, and suspended in a sealed container containing the pretreated sample, with the hydrophilic layer of the microspheres facing the sample; the microspheres are first adsorbed at 30 °C for 30 min, then heated to 35 °C for equilibration for 10 min, and finally heated to 40 °C. The microspheres are then eluted with 10 mL of 0.1 mol / L pH 7.4 PBS buffer, and the eluent is collected.

[0031] Furthermore, in the result output step, the specific rules for database matching are as follows:

[0032] If bromocresol green changes from blue to yellow, and the resistance signal corresponds to the characteristic concentration of 2,4-decadienal, then it is determined to be aflatoxin contamination, the toxin risk level is high toxicity, and the contamination stage is the harvest period.

[0033] If the catechol purple color changes from purple to pink and the resistance signal corresponds to the characteristic concentration of 3-octanone, it is determined to be contaminated by Aspergillus niger, with a toxin risk level of medium to high toxicity, and the contamination occurs during the cooling period after processing.

[0034] If phenol red changes from red to yellow, and the resistance signal corresponds to the characteristic concentration of d-limonene, then it is determined to be penicillin contamination, the toxin risk level is low toxicity, and the contamination point is the storage period.

[0035] Based on the temperature response data of temperature-responsive dual-ligand affinity microspheres, if the ambient temperature exceeds 35℃ during the adsorption process, a storage temperature adjustment suggestion is given, which is to control the storage temperature at 25-30℃.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] 1. Temperature-responsive dual-ligand affinity microspheres are used to achieve "targeted enrichment-purification". The main ligand (a mixture of β-ionone, carvone, and limonene oxide) specifically targets the characteristic volatile biomarkers of Aspergillus flavus (2,4-decadienal), Aspergillus niger (3-octanone), and Penicillium (d-limonene). The secondary ligand (sulfonated starch) repels positively charged protein degradation volatiles (such as trimethylamine and ethyl acetate) in soybean meal. The temperature-responsive segment (N-isopropylacrylamide prepolymer) achieves integrated "enrichment-purification-release" through temperature control of adsorption at 30℃, equilibration and impurity removal at 35℃, and elution at 40℃. This design overcomes the shortcomings of existing technologies where passive adsorption is easily interfered with by impurities, providing pure biomarker samples for subsequent accurate identification and breaking the binary detection limitation of "only being able to determine moldiness / non-moldiness".

[0038] 2. The enzyme-immune dual-trigger recognition unit utilizes a sodium alginate-gelatin substrate slow-release layer to achieve the slow release of enzyme-catalyzed substrates such as butyl acetate, tyrosine, and glucose, avoiding signal fluctuations caused by substrate loss. Simultaneously, temperature-responsive dual-ligand affinity microspheres are pulverized and used as carriers to enhance the loading and recognition specificity of three probes: esterase-2,4-decadienal monoclonal antibody conjugate, tyrosinase-3-octone aptamer conjugate, and glucose oxidase-d-limonene polyclonal antibody conjugate. This allows for precise differentiation between Aspergillus flavus, Aspergillus niger, and Penicillium. This design addresses the core deficiency of existing technologies in identifying mold species, filling a "risk assessment gap"—clearly defining the toxin risk levels corresponding to different molds (Aspergillus flavus is highly toxic, Aspergillus niger is moderately to highly toxic, and Penicillium is low toxic). This avoids resource waste caused by companies indiscriminately discarding suspected moldy soybean meal, or increased control costs and safety accidents due to misjudgment of risk (such as mistaking low-toxicity Penicillium contamination for highly toxic Aspergillus flavus contamination).

[0039] 3. The conductive hydrogel-color dual-mode detection module uses industrial-grade graphite powder and carbon black composite as the conductive medium, replacing the high-cost carbon nanotubes. A simple series resistor-voltage circuit (including a 1kΩ fixed resistor, a 3V DC power supply, and an Arduino development board) collects the resistance signal, enabling quantitative detection of marker concentration. Simultaneously, bromocresol green, catechol purple, and phenol red indicators dispersed within the hydrogel assist in qualitative judgment through characteristic color changes (blue to yellow for Aspergillus flavus, purple to pink for Aspergillus niger, and red to yellow for Penicillium). This design solves the problems of existing technologies relying on precision instruments, complex operation, and high cost, balancing detection accuracy and convenience. The materials used (graphite powder, carbon black, etc.) and equipment (Arduino development board, etc.) are all common and readily available types, significantly reducing the threshold and cost for industrialization.

[0040] 4. The results output stage inputs the resistance signal (quantitative), color signal (qualitative), and temperature response data of the temperature-responsive dual-ligand affinity microspheres (e.g., if the adsorption environment temperature exceeds 35℃, it indicates that the temperature range is exceeded) into the "Mold Type - Toxin Level - Contamination Stage" database. This allows for precise matching and output of the contamination stage (Aspergillus flavus corresponds to the harvest period, Aspergillus niger to the post-processing cooling period, and Penicillium to the storage period) and storage environment recommendations (e.g., if the temperature range exceeds the limit, it is recommended to control it at 25-30℃). This design overcomes the obstacle of existing technologies' inability to trace the contamination stage, enabling enterprises to accurately identify key control points without relying on a "broad-based" approach to control (e.g., excessive drying, misuse of antifungal agents). This reduces control costs while avoiding damage to soybean meal protein quality. Simultaneously, by optimizing storage conditions through storage environment recommendations, it reduces the risk of subsequent mold growth, forming a closed loop of "detection-control-optimization."

[0041] 5. Through a closed-loop design of "temperature-responsive dual-ligand affinity microspheres (targeted enrichment) - enzyme-catalyzed - immune dual-trigger recognition unit (precise differentiation) - conductive hydrogel - color dual-mode detection module (precise detection) - database matching (precise output)," the unique volatile molecular fingerprints of different molds are specifically captured. This fundamentally resolves the fundamental contradiction between "mold metabolic specificity and broad-spectrum detection technology" in existing technologies, achieving integrated "precise identification - risk assessment - targeted prevention and control" of soybean meal mold, and meeting the core needs of the soybean meal industry chain for mold detection. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of a method for detecting mold growth in soybean meal based on volatile flavor compounds, according to the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Please see Figure 1 The present invention provides a technical solution:

[0045] See Figure 1 The following is an example of a method for detecting mold growth in soybean meal based on volatile flavor compounds:

[0046] Example 1: Preparation of temperature-responsive dual-ligand affinity microspheres (directional enrichment module):

[0047] 1.1 Design Logic:

[0048] To address the shortcomings of existing enrichment technologies where "passive adsorption leads to interference in the detection of protein degradation volatiles (trimethylamine, ethyl acetate, etc.) in soybean meal," a ternary mechanism of "dual-ligand synergistic recognition + temperature dynamic screening" was designed. This mechanism achieves integrated "enrichment-purification-functional linkage" by using a main ligand to target mold characteristic markers, a secondary ligand to exclude impurities, and a temperature-responsive chain segment to control adsorption / purification / release. Furthermore, the materials (chitosan, starch, etc.) and processes (emulsification, cross-linking) used are conventional technologies that can be implemented in ordinary chemical plants.

[0049] 1.2 Preparation of core raw materials:

[0050] 1.2.1 Preparation of the main ligand (specific ligand for fungal biomarkers):

[0051] Take 0.3g of β-ionone (specific ligand for 2,4-decadienal from Aspergillus flavus), 0.25g of carvone (specific ligand for 3-octanone from Aspergillus niger), and 0.2g of limonene oxide (specific ligand for d-limonene from Penicillium), and place them separately with 0.1g of maleic anhydride in a three-necked flask. Add 10mL of anhydrous ethanol as a solvent, and stir at 60℃ in an oil bath for 2h to generate a carboxyl-containing "ligand-maleic anhydride derivative" (the carboxyl group is used for subsequent crosslinking with the carrier). After the reaction is completed, remove the ethanol by rotary evaporation to obtain a solid mixture of host and ligand, which is then sealed for later use.

[0052] 1.2.2 Preparation of secondary ligands (impurity repellents):

[0053] 1g of industrial-grade starch was added to 50mL of 2mol / L sulfuric acid solution and hydrolyzed in a constant temperature water bath at 80℃ for 1h (to break down starch chains and increase reactivity); then 0.5g of sodium sulfite was added, the temperature was adjusted to 60℃, and the reaction was continued with stirring for 3h to replace the starch hydroxyl groups with sulfonyl groups, thus obtaining sulfonated starch (auxiliary ligand); after the reaction, the solution was neutralized to pH=7 with 1mol / L sodium hydroxide solution, centrifuged (3000rpm, 10min) to collect the precipitate, washed 3 times with deionized water, vacuum dried at 60℃ for 2h, and then pulverized and passed through a 100-mesh sieve for later use (the sulfonyl groups are negatively charged and can repel positively charged protein degradation volatiles in soybean meal).

[0054] 1.2.3. Preparation of temperature-responsive chain segments (temperature-sensitive control unit):

[0055] Take 5g of N-isopropylacrylamide (PNIPAM, phase transition temperature 32℃, matching the common temperature range for soybean meal storage) and 0.2g of N,N'-methylenebisacrylamide (crosslinking agent), dissolve them in 20mL of deionized water, and stir until completely dissolved; add 0.1g of ammonium persulfate (initiator), stir in a 50℃ constant temperature water bath for 30min to initiate free radical polymerization, and obtain PNIPAM prepolymer (viscous, the chain segments can shrink / expand with temperature changes), cool to room temperature and set aside.

[0056] 1.3. Affinity Microsphere Molding Process:

[0057] 1.3.1 Preparation of W / O type emulsion:

[0058] Aqueous phase preparation: Dissolve 0.5 g of chitosan (carrier, to increase the porosity of microspheres) in 10 mL of 2% acetic acid solution and stir until completely dissolved; add the main ligand mixture (0.8 g) prepared in 1.2.1, the sulfonated starch (0.3 g) prepared in 1.2.2, and the PNIPAM prepolymer solution (5 mL) prepared in 1.2.3 in sequence, and ultrasonically disperse for 20 min (power 300 W, frequency 40 kHz) to form a homogeneous aqueous emulsion.

[0059] Oil phase preparation: Mix 50 mL of liquid paraffin with 0.5 g of Span-80 (emulsifier) ​​and stir at 300 rpm for 10 min to ensure uniform dispersion of the emulsifier.

[0060] Emulsion compounding: Under continuous stirring at 300 rpm, the aqueous phase emulsion was slowly added dropwise to the oil phase (dropping rate 1 mL / min). After the addition was completed, stirring was continued for 30 min to form a stable W / O type emulsion (the droplet diameter was controlled at 100-200 μm, and the stirring rate was adjusted by observation with an optical microscope).

[0061] 1.3.2 Crosslinking, Curing, and Purification:

[0062] 0.2 g of glutaraldehyde (a crosslinking agent to form a three-dimensional network structure of chitosan, ligands, and PNIPAM) was added to the above emulsion, and the mixture was stirred at 60 °C for 2 h to complete the crosslinking and curing. After the reaction was completed, the mixture was allowed to stand for 30 min, and the emulsion naturally separated into layers. The lower layer was a microsphere precipitate, and the upper layer was an oil phase. The lower layer of microspheres was collected and washed three times with anhydrous ethanol (20 mL each time) to remove the residual oil phase. Finally, the mixture was vacuum dried at 60 °C for 1 h to obtain white granular "temperature-responsive dual-ligand affinity microspheres" (referred to as "temperature-sensitive affinity microspheres") with a particle size of 100-200 μm.

[0063] Example 2: Preparation of an enzyme-immune dual-trigger recognition unit (mold-specific recognition module):

[0064] 2.1 Design Logic:

[0065] To address the issues of "substrate loss and signal fluctuation" in existing recognition units, a "sodium alginate-gelatin substrate slow-release layer" is added before the recognition probe is fixed, achieving synergy of "slow substrate release, accurate probe recognition, and stable output of enzyme-catalyzed signal". At the same time, temperature-sensitive affinity microspheres are pulverized and used as probe carriers, and their porous structure is used to increase the probe loading capacity and further enhance the recognition sensitivity.

[0066] 2.2 Preparation of substrate sustained-release layer:

[0067] 2.2.1 Preparation of substrate mixture:

[0068] Take 0.5g sodium alginate (slow-release substrate, which can form a gel through calcium ion cross-linking), 0.3g gelatin (enhancing the adhesion of the slow-release layer), 0.1g butyl acetate (aspergillus flavus detection enzyme substrate, which esterase can catalyze its hydrolysis to acetic acid), 0.1g tyrosine (aspergillus niger detection enzyme substrate, which tyrosinase can catalyze its production of dopaquinone), and 0.1g glucose (Penicillium detection enzyme substrate, which glucose oxidase can catalyze its production of hydrogen peroxide), dissolve them in 10mL deionized water, and stir in a 40℃ constant temperature water bath for 30min until all components are completely dissolved to obtain a homogeneous and transparent substrate mixture.

[0069] 2.2.2. Fixation of the sustained-release layer:

[0070] A polydimethylsiloxane (PDMS) substrate (2cm × 3cm) was etched using soft photolithography to create three sets of 150μm diameter reaction grooves (corresponding to the detection of Aspergillus flavus, Aspergillus niger, and Penicillium, respectively), with a groove spacing of 500μm between each set. The substrate mixture was then drop-coated into the reaction grooves using a micropipette (10μL capacity), with 2μL drop-coated into each groove. The mixture was incubated at 37℃ for 1 hour to allow it to form a uniform thin film within the grooves. Subsequently, the substrate was immersed in a 0.1mol / L CaCl2 solution for 5 minutes to allow sodium alginate to crosslink and solidify, forming a 5μm thick "sodium alginate-gelatin substrate slow-release layer". The substrate was then removed, rinsed with deionized water to remove residual CaCl2, and air-dried at room temperature for later use.

[0071] 2.3 Dual-trigger identification probe fixation:

[0072] 2.3.1 Preparation of probe mixture:

[0073] Aspergillus flavus probe: Esterase (EC3.1.1.3) and 2,4-decadienal monoclonal antibody were mixed at a molar ratio of 1:2 and 0.1 mol / L PBS buffer (pH 7.4) were added to prepare an esterase-antibody conjugate solution with a concentration of 0.1 mg / mL.

[0074] Aspergillus niger probe: Tyrosinase (EC1.14.18.1) and 3-octanone aptamer (sequence: 5'-GGTGGTGGTGGTTGTGGTGGTGGTGG-3') were mixed at a molar ratio of 1:3 and 0.1 mol / L PBS buffer (pH 7.4) were added to prepare a tyrosinase-aptamer conjugate solution with a concentration of 0.1 mg / mL.

[0075] Penicillium probe: Glucose oxidase (EC1.1.3.4) and d-limonene polyclonal antibody were mixed at a molar ratio of 1:2 and added to 0.1 mol / L PBS buffer (pH 7.4) to prepare a glucose oxidase-antibody conjugate solution with a concentration of 0.1 mg / mL.

[0076] 2.3.2 Probe Fixing Process:

[0077] The thermosensitive affinity microspheres prepared in Example 1 were pulverized to a particle size of 10 μm using a ball mill (to increase the specific surface area). They were then mixed with the three probe solutions mentioned above at a mass ratio of 1:2 and stirred for 30 min (to allow the probes to be adsorbed into the porous structure of the microspheres). Subsequently, the mixture was drop-coated onto the substrate slow-release layer surface of the corresponding reaction groove using a micropipette (1 μL per groove). The mixture was incubated at 37°C for 2 h to achieve fixation through the "amino-probe carboxyl condensation reaction on the microsphere surface". After incubation, the substrate was rinsed with 0.05% Tween-20 solution to remove unfixed probes and air-dried at room temperature to obtain the "enzyme-immune dual-trigger recognition unit".

[0078] Example 3: Fabrication of a conductive hydrogel-color dual-mode detection module (signal detection module):

[0079] 3.1 Design Logic:

[0080] Overcoming the shortcomings of existing detection modules that rely on carbon nanotubes (high cost) and require precision instruments (complex operation), this paper adopts a graphite powder-carbon black composite conductive medium to reduce costs and designs a series resistor-voltage acquisition circuit to simplify the circuit, realizing dual-mode detection of "resistance signal (quantitative) + color signal (qualitative)," thus balancing detection accuracy and industrialization convenience.

[0081] 3.2 Preparation of conductive hydrogels:

[0082] 3.2.1 Preparation of conductive paste:

[0083] Take 0.5g of industrial-grade graphite powder (50μm particle size, low cost) and 0.2g of carbon black (50nm particle size, enhances conductivity), add 10mL of deionized water, and ultrasonically disperse for 30min (300W power, 40kHz frequency) to form a uniform conductive slurry (avoid particle agglomeration and ensure uniform conductivity).

[0084] 3.2.2 Hydrogel polymerization:

[0085] Take 1g of acrylamide (monomer) and 0.05g of N,N'-methylenebisacrylamide (crosslinking agent), dissolve them in 10mL of deionized water, and stir until completely dissolved to prepare acrylamide prepolymer solution; mix the conductive slurry with the acrylamide prepolymer solution at a volume ratio of 1:3 and stir for 10min; add 0.01g of ammonium persulfate (initiator), and stir in a constant temperature water bath at 60℃ for 30min to initiate the polymerization reaction, forming a black elastic conductive hydrogel (conductivity 1.3-1.6S / m, comparable to carbon nanotube hydrogel).

[0086] 3.2.3 Optimized Dispersion of Indicators:

[0087] 0.02g of bromocresol green (aspergillus detection indicator, turns yellow upon contact with acetic acid), 0.02g of catechol purple (aspergillus niger detection indicator, turns pink upon contact with dopaquinone), and 0.02g of phenol red (penicillium detection indicator, turns yellow upon contact with hydrogen peroxide) were each mixed with 0.1g of Tween-80 (dispersant) and ultrasonically dispersed for 5 minutes (to form tiny droplets of the indicator). The mixture was then added to the prepolymerization process of the conductive hydrogel to ensure that the indicator was uniformly dispersed within the hydrogel (improving dispersion uniformity and avoiding interpretation errors caused by color spots).

[0088] 3.3 Dual-mode detection circuit design:

[0089] 3.3.1 Circuit Structure:

[0090] Core components: conductive hydrogel (detection unit), 1kΩ fixed resistor (voltage divider), 3V DC power supply (power supply), Arduino development board (voltage acquisition, cost <50 yuan), copper electrodes (signal conduction).

[0091] Connection method: Connect the two ends of the conductive hydrogel to copper electrodes respectively. Connect one end of the copper electrode to the positive terminal of the 3V power supply and the other end to one end of the 1kΩ fixed resistor. Connect the other end of the fixed resistor to the negative terminal of the power supply. Connect the voltage acquisition pin of the Arduino development board in parallel across the fixed resistor to acquire the voltage divider signal.

[0092] Example 4: Complete testing process:

[0093] 4.1 Testing Process (from sample to result output):

[0094] 4.1.1 Sample Pretreatment:

[0095] Take 50g of soybean meal sample (randomly selected from the batch to be tested, which is highly representative), place it in a 200mL sealed polytetrafluoroethylene container, and equilibrate at 50℃ for 40min (to accelerate the release of volatile markers of mold and shorten the enrichment time).

[0096] 4.1.2 Targeted enrichment-purification:

[0097] The thermosensitive affinity microspheres (1g) prepared in Example 1 were placed in a breathable cloth bag (50μm pore size to prevent microsphere loss) and suspended in a sealed container (hydrophilic layer facing the sample); first, the microspheres were adsorbed at 30℃ for 30min (the main ligand adsorbs the marker, and the secondary ligand repels impurities); then the temperature was raised to 35℃ for equilibration for 10min (PNIPAM shrinks and squeezes out residual impurities); finally, the temperature was raised to 40℃, and the microspheres were eluted with 10mL of 0.1mol / L PBS buffer (pH 7.4), and the eluent was collected.

[0098] 4.1.3, Dual-trigger reaction:

[0099] The eluent was injected into the dual-trigger recognition unit prepared in Example 2 via a micro-peristaltic pump (flow rate 0.5 mL / min); the fungal markers in the eluent bound to the corresponding probes, activating the enzymatic reaction (Aspergillus flavus: esterase catalyzes butyl acetate → acetic acid; Aspergillus niger: tyrosinase catalyzes tyrosine → dopaquinone; Penicillium: glucose oxidase catalyzes glucose → hydrogen peroxide), and the reaction was carried out for 15 min.

[0100] 4.1.4 Dual-mode testing:

[0101] The reacted mixture was injected into the reaction chamber of the dual-mode detection module prepared in Example 3 (in contact with the conductive hydrogel):

[0102] Resistance signal: The enzymatic reaction products change the ionic environment of the conductive hydrogel, resulting in a change in resistance; the voltage divider signal of the fixed resistor is collected by the Arduino development board and converted into the concentration of the marker.

[0103] Color signal: The enzyme reaction product reacts with the indicator to produce a characteristic color (Aspergillus flavus → blue to yellow; Aspergillus niger → purple to pink; Penicillium → red to yellow). Photos taken with a smartphone can be used to assist in qualitative judgment.

[0104] 4.1.5 Result Output:

[0105] Input the resistance signal (quantitative), color signal (qualitative), and temperature response data of the thermosensitive affinity microspheres (e.g., a high impurity removal rate at 35℃ indicates that the storage temperature range is exceeded) into the supporting software, match it with the "mold type-toxin level-contamination link" database, and output a complete test report:

[0106] Types of mold: such as "Aspergillus flavus".

[0107] Toxin risk level: such as "high toxicity (aflatoxin B1 risk)".

[0108] Contamination points: such as "harvest period (Aspergillus flavus often contaminates during the high temperature and humidity harvest period, and the storage temperature exceeds 35℃, which is a supplementary verification)".

[0109] Storage environment recommendations: If the temperature exceeds 35℃, it is recommended to adjust the storage temperature to 25-30℃.

[0110] Summarize:

[0111] A closed-loop detection solution was constructed using a combination of temperature-responsive dual-ligand affinity microspheres, an enzyme-catalyzed and immunologically triggered dual-trigger recognition unit, a conductive hydrogel, and a color-based dual-mode detection module. This solution specifically addresses the following issues: the temperature-sensitive affinity microspheres target the volatile molecular fingerprints of mold characteristics using dual ligands, and the temperature-responsive mechanism filters out impurities, overcoming the limitation of existing technologies that can only perform binary judgments of mold growth. The dual-trigger recognition unit, combined with the substrate slow-release layer and the microsphere carrier, enhances specificity, accurately distinguishing between Aspergillus flavus, Aspergillus niger, and Penicillium, filling the "risk assessment gap" (clearly identifying mold species and their corresponding toxin risk levels, avoiding "one-size-fits-all" detection). (This eliminates the risk of "knife-cutting" discarding or misjudging); the complete detection process combines the environmental response data of temperature-sensitive microspheres with the "mold type-contamination link" correlation logic to accurately locate key contamination links (such as harvest period, storage period, and processing cooling period), replacing the extensive prevention and control of the entire process and solving the "contamination source traceability obstacle"; at the same time, by selecting conventional materials and simplifying circuit design, it reduces impurity interference and detection costs, realizes industrialization, fundamentally resolves the principle contradiction between "mold metabolic specificity and broad-spectrum detection technology", and ultimately achieves the integration of "precise identification-risk assessment-targeted prevention and control" of soybean meal mold.

Claims

1. A method for detecting mold growth in soybean meal based on volatile flavor compounds, comprising a soybean meal sample pretreatment step, characterized in that, It also includes the following steps: Targeted enrichment-purification: Temperature-responsive dual-ligand affinity microspheres were used to adsorb and purify volatile flavor compounds released from the sample. The temperature-responsive dual-ligand affinity microspheres contain a main ligand, a secondary ligand, and a temperature-responsive chain segment. The main ligand is a mixture of β-ionone, carvone, and limonene oxide, the secondary ligand is sulfonated starch, and the temperature-responsive chain segment is N-isopropylacrylamide prepolymer. Dual-trigger reaction: The purified volatile flavor substances are passed into the enzyme-immune dual-trigger recognition unit. The recognition unit includes a sodium alginate-gelatin substrate sustained-release layer and an enzyme-antibody / aptamer conjugate probe immobilized on the sustained-release layer. The probes are specifically esterase-2,4-decadienal monoclonal antibody conjugate, tyrosinase-3-octanone aptamer conjugate, and glucose oxidase-d-limonene polyclonal antibody conjugate. Dual-mode detection: The dual-trigger reaction product is passed into the conductive hydrogel-color dual-mode detection module. The module contains graphite powder-carbon black composite conductive hydrogel and bromocresol green, catechol purple and phenol red indicators dispersed inside the hydrogel. The resistance signal is collected by the series resistor-voltage acquisition circuit for quantitative detection, and the color change of the indicator is used for qualitative judgment. Results output: Input the resistance signal, color signal and temperature response data of temperature-responsive dual-ligand affinity microspheres into the mold species-toxin level-contamination link database, and output the mold species, toxin risk level, contamination link and storage environment recommendations.

2. The method for detecting mold growth in soybean meal based on volatile flavor compounds as described in claim 1, characterized in that: The preparation steps of temperature-responsive dual-ligand affinity microspheres include: Preparation of main ligands: Take 0.3g β-ionone, 0.25g carvone, and 0.2g limonene oxide, and place them together with 0.1g maleic anhydride in a three-necked flask. Add 10mL anhydrous ethanol as solvent, and stir at 60℃ in an oil bath for 2h. After the reaction is completed, remove the ethanol by rotary evaporation to obtain a solid mixture of main ligands, which is then sealed for later use. Preparation of auxiliary ligands: 1g of industrial-grade starch was added to 50mL of 2mol / L sulfuric acid solution and hydrolyzed in a constant temperature water bath at 80℃ for 1h. Then, 0.5g of sodium sulfite was added, the temperature was adjusted to 60℃ and the reaction was continued with stirring for 3h. After the reaction, the solution was neutralized to pH=7 with 1mol / L sodium hydroxide solution. The precipitate was collected by centrifugation at 3000rpm for 10min, washed 3 times with deionized water, vacuum dried at 60℃ for 2h, and pulverized through a 100-mesh sieve to obtain sulfonated starch. Preparation of temperature-responsive segments: Take 5g of N-isopropylacrylamide and 0.2g of N,N'-methylenebisacrylamide, dissolve them in 20mL of deionized water and stir until completely dissolved, add 0.1g of ammonium persulfate, stir in a 50℃ constant temperature water bath for 30min to obtain PNIPAM prepolymer solution, and cool at room temperature for later use. Microsphere formation: 0.5 g chitosan was dissolved in 10 mL of 2% acetic acid solution and stirred until completely dissolved. Then, 0.8 g of host-ligand mixture, 0.3 g of sulfonated starch, and 5 mL of PNIPAM prepolymer solution were added sequentially. The mixture was ultrasonically dispersed at 300 W power and 40 kHz frequency for 20 min to form an aqueous phase. 50 mL of liquid paraffin was mixed with 0.5 g Span-80 and stirred at 300 rpm for 10 min to form an oil phase. The aqueous phase was added dropwise to the oil phase at a rate of 1 mL / min while stirring continuously at 300 rpm for 30 min to form a W / O type emulsion. 0.2 g glutaraldehyde was added to the emulsion and stirred at 60 °C for 2 h. After standing and separating into layers, the lower layer of microspheres was collected, washed three times with anhydrous ethanol, and vacuum dried at 60 °C for 1 h to obtain temperature-responsive dual-ligand affinity microspheres with a particle size of 100-200 μm.

3. The method for detecting mold growth in soybean meal based on volatile flavor compounds as described in claim 2, characterized in that: The preparation steps of the enzyme-immune dual-trigger recognition unit include: Preparation of substrate mixture: Take 0.5g sodium alginate, 0.3g gelatin, 0.1g butyl acetate, 0.1g tyrosine and 0.1g glucose, dissolve them in 10mL deionized water, stir in a 40℃ constant temperature water bath for 30min until all components are completely dissolved to obtain the substrate mixture; Slow-release layer fixation: Take a polydimethylsiloxane (PDMS) substrate and etch three sets of reaction grooves with a diameter of 150 μm and a spacing of 500 μm using soft photolithography; use a 10 μL micropipette to drop 2 μL of substrate mixture onto each set of grooves and incubate at 37℃ for 1 h; immerse the substrate in 0.1 mol / L CaCl2 solution for 5 min, remove it and rinse the surface with deionized water to remove residual CaCl2, and air dry at room temperature to form a sodium alginate-gelatin substrate slow-release layer with a thickness of 5 μm; Probe immobilization: Temperature-responsive dual-ligand affinity microspheres were pulverized to a particle size of 10 μm using a ball mill. They were then mixed with esterase-2,4-decadienal monoclonal antibody conjugate, tyrosinase-3-octanone aptamer conjugate, and glucose oxidase-d-limonene polyclonal antibody conjugate at a mass ratio of 1:2 at a concentration of 0.1 mg / mL, and stirred for 30 min. 1 μL of the mixture was then dropped onto the substrate sustained-release layer surface of each corresponding reaction groove using a micropipette. The mixture was incubated at 37 °C for 2 h. The substrate was then rinsed with 0.05% Tween-20 solution to remove any unimmobilized probes and air-dried at room temperature.

4. The method for detecting mold growth in soybean meal based on volatile flavor compounds as described in claim 3, characterized in that: The preparation steps of conductive hydrogels include: Preparation of conductive paste: Take 0.5g of industrial grade graphite powder and 0.2g of carbon black, add 10mL of deionized water, and ultrasonically disperse at 300W power and 40kHz frequency for 30min to form a uniform conductive paste; Hydrogel polymerization: Take 1g of acrylamide and 0.05g of N,N'-methylenebisacrylamide, dissolve them in 10mL of deionized water and stir until completely dissolved to prepare acrylamide prepolymer solution; mix the conductive paste and acrylamide prepolymer solution at a volume ratio of 1:3, stir for 10min, add 0.01g of ammonium persulfate, stir in a constant temperature water bath at 60℃ for 30min to initiate the polymerization reaction and form a black elastic conductive hydrogel with a conductivity of 1.3-1.6S / m; Indicator dispersion: Mix 0.02g bromocresol green, 0.02g catechol purple, and 0.02g phenol red with 0.1g Tween-80 respectively, and sonicate for 5 minutes; add the mixture to the above-mentioned conductive hydrogel prepolymerization process to make the indicator uniformly dispersed inside the hydrogel.

5. The method for detecting mold growth in soybean meal based on volatile flavor compounds as described in claim 4, characterized in that: The structure of the dual-mode detection circuit is as follows: the core components include conductive hydrogel, 1kΩ fixed resistor, 3V DC power supply, Arduino development board, and copper electrodes; the two ends of the conductive hydrogel are connected to the copper electrodes respectively, one end of the copper electrode is connected to the positive terminal of the 3V DC power supply, and the other end is connected to one end of the 1kΩ fixed resistor, and the other end of the fixed resistor is connected to the negative terminal of the power supply; the voltage acquisition pin of the Arduino development board is connected in parallel across the fixed resistor to acquire the voltage divider signal.

6. The method for detecting mold growth in soybean meal based on volatile flavor compounds as described in claim 5, characterized in that: The pretreatment steps for soybean meal samples are as follows: 50g of sample is randomly selected from the batch of soybean meal to be tested, placed in a 200mL sealed polytetrafluoroethylene container, and kept at a constant temperature of 50℃ for 40min to accelerate the release of volatile markers of mold.

7. The method for detecting mold growth in soybean meal based on volatile flavor compounds as described in claim 6, characterized in that: The specific conditions for the targeted enrichment-purification step are as follows: 1 g of temperature-responsive dual-ligand affinity microspheres are placed in a breathable cloth bag with a pore size of 50 μm and suspended in a sealed container containing the pretreated sample, with the hydrophilic layer of the microspheres facing the sample; the microspheres are first adsorbed at 30 °C for 30 min, then heated to 35 °C for equilibration for 10 min, and finally heated to 40 °C. The microspheres are then eluted with 10 mL of 0.1 mol / L pH 7.4 PBS buffer, and the eluent is collected.

8. The method for detecting mold growth in soybean meal based on volatile flavor compounds as described in claim 7, characterized in that: In the result output step, the specific rules for database matching are as follows: If bromocresol green changes from blue to yellow, and the resistance signal corresponds to the characteristic concentration of 2,4-decadienal, then it is determined to be aflatoxin contamination, the toxin risk level is high toxicity, and the contamination stage is the harvest period. If the catechol purple color changes from purple to pink and the resistance signal corresponds to the characteristic concentration of 3-octanone, it is determined to be contaminated by Aspergillus niger, with a toxin risk level of medium to high toxicity, and the contamination occurs during the cooling period after processing. If phenol red changes from red to yellow, and the resistance signal corresponds to the characteristic concentration of d-limonene, then it is determined to be penicillin contamination, the toxin risk level is low toxicity, and the contamination point is the storage period. Based on the temperature response data of temperature-responsive dual-ligand affinity microspheres, if the ambient temperature exceeds 35℃ during the adsorption process, a storage temperature adjustment suggestion is given, which is to control the storage temperature at 25-30℃.

Images