Food pathogenic bacteria detection method based on sandwich structure SERS technology

Abstract

The application provides a food pathogenic bacteria detection method based on sandwich structure SERS technology, and relates to the field of food safety detection. The method uses the specific reaction principle of antibody and antigen, loads commercial signal molecule 4-mercaptobenzoic acid (MBA) on 60nm gold nanospheres or gold-silver core-shell nanospheres, and further couples with specific antibodies to prepare SERS nanoprobes. At the same time, the sputtering film technology is used to prepare gold nanoparticle SERS substrates on tin paper or customized microfluidic chips. During detection, the target pathogenic bacteria are captured by the SERS substrate, combined with the SERS nanoprobes to form a "sandwich" structure, and detected by a confocal Raman spectrometer, a table Raman spectrometer or a handheld Raman instrument. The method can realize the synchronous detection of various foodborne pathogenic bacteria, the detection limit can reach 5-20 CFU / mL, the detection time is less than 30 minutes, and the method has the significant advantages of instant, low cost, high sensitivity and high specificity, and has a wide application prospect in the field of food safety rapid detection.

Description

Technical Field

[0001] This invention relates to the field of food safety testing technology, specifically to a method for detecting food pathogens based on sandwich structure SERS technology, which is used for rapid, highly sensitive, and highly specific detection of pathogens in food. Background Technology

[0002] Foodborne pathogens are one of the main causes of foodborne illnesses, seriously threatening human health and public health safety. Escherichia coli, Staphylococcus aureus, Salmonella spp., Listeria monocytogenes, Vibrio parahaemolyticus, Bacillus cereus, and Clostridium botulinum are among the most common pathogens found in food, causing a range of illnesses from mild gastrointestinal discomfort to severe food poisoning and even death.

[0003] Traditional methods for detecting pathogens mainly include culture methods, biochemical identification methods, and molecular biological methods. Culture methods are currently the "gold standard" for detecting foodborne pathogens, but this method is cumbersome and time-consuming, typically requiring 24-72 hours to obtain results, which cannot meet the needs of rapid detection in food production and distribution. Enzyme-linked immunosorbent assay (ELISA), while relatively simple to perform, has limited sensitivity, with a detection limit typically around 10⁻⁶. 3 -10 4 CFU / mL level. Polymerase chain reaction (PCR) technology has high sensitivity, but requires expensive equipment and skilled technicians, and is easily affected by sample matrix interference.

[0004] Surface-enhanced Raman scattering (SERS) is a spectroscopic detection technique based on the plasmon resonance effect on the surface of metallic nanomaterials, capable of enhancing Raman signals by 10⁻⁶. 6 -10 12 The detection speed can be several times higher, even reaching the level of single-molecule detection. SERS technology has "fingerprint" recognition characteristics, which can provide structural information of molecules. It has the advantages of high sensitivity, high specificity and non-destructive testing, and shows great application potential in the field of food safety testing.

[0005] In recent years, pathogen detection methods based on SERS technology have been extensively studied. Sandwich-structured SERS sensors have attracted much attention due to their unique signal amplification mechanism. This structure typically consists of three parts: a trapping substrate, a target analyte, and a signal probe. By forming plasma hotspots on both sides of the target analyte, the SERS signal is significantly enhanced. However, existing sandwich SERS detection methods still have the following problems: (1) the nanoprobe preparation process is complex and has poor reproducibility; (2) the SERS substrate enhancement effect is unstable; (3) the ability to detect multiple targets simultaneously is limited; and (4) the portability for rapid on-site detection is insufficient.

[0006] Therefore, developing a SERS detection method that is easy to operate, highly sensitive, highly specific, and suitable for rapid on-site detection is of great practical significance. Summary of the Invention

[0007] This invention provides a method for detecting food pathogens based on sandwich structure SERS technology, which solves at least one of the problems of existing sandwich SERS detection methods mentioned in the background art.

[0008] To address the aforementioned technical problems, this invention discloses a method for detecting food pathogens based on sandwich-structured SERS technology, comprising the following steps: Step (1): Preparation of SERS nanoprobes: Gold nanospheres or gold-silver core-shell nanospheres of the target particle size are synthesized. 4-Mercaptobenzoic acid (MBA) signal molecules are loaded onto the surface of the nanoparticles through Au-S bonds. Then, specific antibodies are covalently linked to the surface of the MBA-modified nanoparticles by coupling 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) and N-hydroxysuccinimide (NHS) to form SERS nanoprobes. Step (2): Preparation of SERS substrate: A layer of gold nanoparticles is sputtered onto the surface of the SERS substrate using a sputtering coating instrument to form a SERS substrate with surface-enhanced Raman scattering activity; Step (3): Sandwich structure assembly: The food sample to be tested is brought into contact with the SERS substrate so that the target pathogen in the sample is captured by the SERS substrate. Then, SERS nanoprobes are added. Through the specific binding of antibodies with the surface antigens of pathogens, a "SERS substrate-pathogen-SERS nanoprobe" sandwich structure is formed. Step (4): SERS detection: The sandwich structure is subjected to Raman spectroscopy detection using a Raman spectroscopy detection device. The pathogen is qualitatively or quantitatively analyzed based on the SERS signal intensity of the MBA characteristic peak.

[0009] Preferably, the SERS substrate body includes: tin foil or microfluidic chip; The Raman spectroscopy detection equipment is a confocal Raman spectrometer, a benchtop Raman spectrometer, or a handheld Raman spectrometer.

[0010] Preferably, the MBA characteristic peak includes 1075 cm⁻¹. -1 and 1582 cm -1 .

[0011] Preferably, the target particle size is 60±5nm.

[0012] Preferably, the gold nanospheres are prepared by sodium citrate reduction method. The preparation steps are as follows: 100 mL of 0.01% chloroauric acid (HAuCl4) solution is heated to boiling, 1-5 mL of 1% sodium citrate solution is quickly added, the reaction solution changes from pale yellow to wine red, and boiling and refluxing is continued for 10-30 minutes. After cooling to room temperature, a colloidal solution of gold nanospheres with a particle size of 60±5 nm is obtained.

[0013] The gold-silver core-shell nanospheres are prepared by a seed growth method. Using gold nanospheres as seeds, silver shells are coated onto the surface of AuNPs (gold nanoparticles) by reduction with silver nitrate (AgNO3) in the presence of hydroxylamine reducing agent, forming Au@Ag core-shell structured nanoparticles with a silver shell thickness of 5-20 nm.

[0014] Preferably, the method for loading the MBA signaling molecules is as follows: adding an MBA ethanol solution to an aqueous solution of gold nanospheres or gold-silver core-shell nanospheres, to a final MBA concentration of 10. -6 -10 -4 M, stir and react at room temperature in the dark for 2-4 hours to form stable Au-S or Ag-S bonds with the strong affinity of the thiol group of MBA molecules to the surface of gold / silver. Centrifuge and wash to remove unbound MBA molecules to obtain MBA modified nanoparticles. The method for conjugating the antibody with MBA-modified nanoparticles is as follows: MBA-modified nanoparticles are dispersed in MES buffer (pH 5.5-6.5), and 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) and NHS are added to activate the carboxyl groups. The concentration of 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) is 5-20 mM, and the concentration of N-hydroxysuccinimide (NHS) is 2-10 mM. After reacting at room temperature for 15-30 minutes, a specific antibody solution is added, with a final antibody concentration of 10-100 μg / mL. The reaction is carried out at 4°C in the dark with shaking for 2-4 hours. The unconjugated antibody is removed by centrifugation and washing to obtain the SERS nanoprobe.

[0015] Preferably, the SERS substrate is prepared by placing the SERS substrate in a sputtering coating apparatus and evacuating it to a vacuum level of 8×10⁻⁶. -6Below Torr, argon is used as the sputtering gas, with a flow rate of 20-40 sccm and a sputtering pressure of 3 × 10⁻⁶. -3 -6×10 -3 Torr sputtering at a rate of 0.2–0.5 Å / s for 100–200 minutes forms a uniform gold nanoparticle film on the substrate surface.

[0016] Preferably, the target pathogen includes one or more of the following: Escherichia coli, Staphylococcus aureus, Salmonella spp., Listeria monocytogenes, Vibrio parahaemolyticus, Bacillus cereus, and Clostridium botulinum.

[0017] Preferably, the excitation wavelength for the SERS detection is 532nm, 633nm, or 785nm, the laser power is 0.1-10mW, the integration time is 1-30 seconds, and the scanning range is 400-1800cm. -1 .

[0018] Preferably, the quantitative analysis method is: using MBA at 1582 cm⁻¹ -1 A standard curve was constructed with the SERS peak intensity at a given location as the ordinate and the logarithm of the pathogen concentration as the abscissa, with a linear range of 10. 1 -10 7 CFU / mL, correlation coefficient R 2 >0.99, calculate the concentration of pathogens in the sample to be tested based on the standard curve.

[0019] Preferably, the SERS nanoprobe is formed by using 60±5nm gold nanospheres or gold-silver core-shell nanospheres as the plasma core, MBA as the Raman signal molecule loaded on the surface of the nanoparticles via Au-S bonds, and specific antibodies covalently linked to the surface of the MBA-modified nanoparticles via EDC / NHS coupling.

[0020] Preferably, the SERS substrate is formed by depositing a layer of gold nanoparticles on its surface using tin foil or microfluidic chip as the support material, and the gold nanoparticle layer has a thickness of 20-50 nm and has surface-enhanced Raman scattering activity.

[0021] Preferably, the microfluidic chip includes: a sample inlet, a Y-shaped or serpentine microchannel, a reaction chamber, a detection window, and a waste outlet; the microchannel has a width of 100-200 μm and a depth of 50-100 μm; gold nanoparticles are deposited on the inner surface of the reaction chamber as a SERS substrate using sputtering coating technology; the detection window is made of transparent quartz or glass and is used for Raman spectroscopy detection.

[0022] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0023] Compared with the prior art, the present invention has the following beneficial effects: (1) High sensitivity: The present invention adopts a sandwich structure SERS detection strategy, and a plasma hot spot is formed between the SERS nanoprobe and the SERS substrate, which generates a significant electromagnetic field enhancement effect, thereby enhancing the Raman signal of the MBA signal molecules by 10%. 6 -10 8 The detection limit is 5-20 CFU / mL, which is much lower than that of traditional ELISA and culture methods.

[0024] (2) High specificity: This invention utilizes the specific recognition function of antibodies and antigens to effectively avoid interference from non-target bacteria. The specific recognition rate of the target pathogen is greater than 95%, and the cross-reactivity rate is less than 5%, ensuring the accuracy of the detection results.

[0025] (3) Rapid detection: The entire detection process of this invention can be completed within 30 minutes, including 5 minutes of sample pretreatment, 15 minutes of sandwich structure assembly, and 10 minutes of SERS detection, which is much faster than the traditional culture method (24-72 hours) and meets the needs of rapid on-site detection.

[0026] (4) Multi-target detection capability: This invention can simultaneously detect multiple pathogens such as Escherichia coli, Staphylococcus aureus, and Salmonella by using SERS nanoprobes modified with different antibodies, thereby improving detection efficiency.

[0027] (5) Easy to operate: The SERS nanoprobe and SERS substrate preparation process of this invention are mature and reproducible. The detection process does not require complicated sample pretreatment and expensive instruments and equipment. The detection can be completed with a handheld Raman instrument, which is suitable for field application.

[0028] (6) Low cost: The present invention uses tin foil as the SERS substrate material, which is extremely low cost; the gold nanospheres are prepared by the classic sodium citrate reduction method, which is easy to obtain and simple to process, and is suitable for large-scale promotion and application.

[0029] (7) Good stability: In the SERS nanoprobe of this invention, MBA is bound to nanoparticles through stable Au-S bonds, and the antibody is coupled through covalent bonds. The probe has good stability and can be stored at 4°C for more than 6 months.

[0030] (8) Wide range of applications: The detection method of the present invention is applicable to various food matrices, including meat, dairy products, aquatic products, fruits and vegetables, etc., and has broad application prospects in food safety testing, clinical diagnosis, environmental monitoring and other fields. Attached Figure Description

[0031] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram illustrating the SERS detection principle of a sandwich structure. Figure 2 Flowchart of SERS nanoprobe fabrication process; Figure 3 A complete flowchart of the SERS detection method; Figure 4 Graph showing the sensitivity results of SERS detection for different pathogens; Figure 5 This is a graph illustrating the specificity analysis of the SERS detection method. Figure 6 This is a schematic diagram of a customized microfluidic chip structure. Detailed Implementation

[0032] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0033] Furthermore, in this invention, the use of terms such as "first" and "second" is for descriptive purposes only and does not specifically refer to any order or sequence, nor is it intended to limit the invention. They are merely used to distinguish components or operations described using the same technical terms and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions and features of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If a combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0034] The present invention provides the following embodiments: This invention provides a method for detecting food pathogens based on sandwich-structured SERS technology, such as... Figures 1-6 As shown, it includes: Example 1: Synthesis and characterization of gold nanospheres: (1) Reagents and instruments: Chloroauric acid (HAuCl4·3H2O, analytical grade) was purchased from Sinopharm Chemical Reagent Co., Ltd.; sodium citrate (Na3C6H5O7·2H2O, analytical grade) was purchased from Xilong Scientific Co., Ltd.; ultrapure water (resistivity 18.2 MΩ·cm) was prepared by a Milli-Q ultrapure water system. The main instruments included: magnetic stirrer, reflux condenser, ultraviolet-visible spectrophotometer (UV-Vis), transmission electron microscope (TEM), and dynamic light scattering instrument (DLS).

[0035] (2) Synthesis of gold nanospheres: 60 nm gold nanospheres were prepared using the classic sodium citrate reduction method (Turkevich method). The specific steps are as follows: 100 mL of 0.01% (w / v) chloroauric acid solution was placed in a clean round-bottom flask, a magnetic stir bar was added, and the solution was heated and stirred on a magnetic stirrer until it boiled vigorously. 3.5 mL of 1% (w / v) sodium citrate solution was quickly added, and the solution color rapidly changed from pale yellow to colorless, then to black, purple, and finally to a stable wine red. The mixture was boiled and refluxed for 15 minutes to ensure the reaction was complete. Heating was then stopped, and the mixture was allowed to cool naturally to room temperature. The prepared gold nanosphere colloidal solution was transferred to a brown reagent bottle and stored at 4°C in the dark for later use.

[0036] (3) Characterization results: UV-Vis spectroscopy showed that the gold nanospheres had a distinct surface plasmon resonance absorption peak at 520 nm, with a symmetrical and sharp peak shape, indicating good dispersion of the nanoparticles. TEM observation showed that the gold nanospheres were spherical with uniform particle size, and the average particle size was 60±5 nm (n=100). DLS measured the hydrated particle size to be 65±8 nm, the polydispersity index (PDI) to be 0.08, and the Zeta potential to be -32±3 mV, indicating that the nanoparticles carried a negative charge on their surface and had good colloidal stability.

[0037] Example 2: Preparation of SERS nanoprobes: (1) MBA loading: Take 10 mL of gold nanosphere colloidal solution (approximately 0.05 mg / mL), add 100 μL of 10 -3 MMBA ethanol solution, with a final MBA concentration of 10. -5M. The reaction was carried out at room temperature in the dark with stirring for 3 hours to allow MBA molecules to firmly bind to the surface of gold nanospheres via Au-S bonds. After the reaction was complete, the mixture was centrifuged at 8000 rpm for 10 minutes, the supernatant was discarded, and the precipitate was washed three times with PBS buffer (pH 7.4) to remove unbound MBA molecules. The precipitate was then dispersed in 10 mL of PBS buffer to obtain MBA-modified gold nanospheres (AuNPs-MBA).

[0038] (2) Antibody conjugation: AuNPs-MBA was dispersed in 10 mL of MES buffer (25 mM, pH 6.0), and EDC and NHS were added. The final concentration of EDC was 10 mM and the final concentration of NHS was 5 mM. The mixture was stirred at room temperature for 20 minutes to activate the carboxyl groups of MBA. After centrifugation at 8000 rpm for 10 minutes, the supernatant was discarded, and the precipitate was dispersed in 10 mL of PBS buffer. Specific antibody (such as anti-Escherichia coli O157:H7 antibody) was added, with a final antibody concentration of 50 μg / mL. The mixture was incubated at 4°C in the dark for 3 hours with shaking to covalently conjugate the antibody to the surface of AuNPs-MBA via amide bonds. After centrifugation at 8000 rpm for 10 minutes, the precipitate was washed three times with PBS buffer to remove unconjugated antibody. The precipitate was dispersed in 10 mL of PBS buffer (containing 1% BSA as a blocking agent) and stored at 4°C in the dark to obtain the SERS nanoprobe (AuNPs-MBA-Ab).

[0039] (3) Probe characterization: UV-Vis spectroscopy showed that the SERS nanoprobe had an absorption peak at 524 nm, which was red-shifted by 4 nm relative to AuNPs, indicating that MBA and antibody were successfully modified. Raman spectroscopy showed that the SERS nanoprobe had an absorption peak at 1075 cm⁻¹. -1 and 1582 cm -1 The presence of a distinct MBA characteristic peak confirms successful MBA loading. BCA protein quantification showed an antibody conjugation efficiency of approximately 60%, meaning about 15-20 antibody molecules were attached to the surface of each gold nanosphere. After storage at 4°C for 6 months, the SERS nanoprobe maintained over 95% of its initial signal intensity, indicating good probe stability.

[0040] Example 3: Preparation of SERS substrate: (1) Tin Foil SERS Substrate: Cut commercially available food-grade tin foil into 1cm × 1cm square pieces, and ultrasonically clean them sequentially with acetone, ethanol, and ultrapure water for 10 minutes each to remove surface oil and impurities. After drying with nitrogen, fix the tin foil pieces onto the sample stage of a sputtering coating instrument (such as Emitech K550X). Evacuate to 8 × 10⁻⁶. -6 Below Torr, argon was used as the sputtering gas at a flow rate of 30 sccm and a sputtering pressure of 4 × 10⁻⁶. -3The sputtering rate was 0.3 Å / s, and the sputtering time was 150 minutes. After sputtering, a uniform gold nanoparticle film with a thickness of approximately 30 nm was formed on the surface of the tin foil. The prepared SERS substrate was then stored in a desiccator for later use.

[0041] (2) SERS substrate for microfluidic chip: The PDMS microfluidic chip was fabricated using standard soft lithography. The chip design included: a sample inlet (2 mm in diameter), a serpentine microchannel (100 μm wide, 50 μm deep, and 5 cm long), a reaction chamber (3 mm in diameter), a detection window (5 mm in diameter), and a waste outlet (2 mm in diameter). After plasma bonding the fabricated PDMS chip to a glass substrate, gold nanoparticles were sputtered onto the inner surface of the reaction chamber. The sputtering conditions were the same as those for the tin foil substrate, with a sputtering time of 120 minutes, resulting in a gold nanoparticle layer thickness of approximately 25 nm.

[0042] (3) Substrate characterization: Scanning electron microscopy (SEM) observation showed that the sputtered gold nanoparticles were uniformly distributed on the substrate surface, with a particle size of 20-50 nm and an interparticle distance of 5-20 nm, forming abundant plasma hot spots. Crystal violet (CV) was used as a probe molecule to evaluate the SERS substrate enhancement effect, and the enhancement factor (EF) was approximately 10. 6 The signal reproducibility RSD < 10% (n=20) indicates that the substrate has good SERS activity and reproducibility.

[0043] Example 4: SERS detection of Escherichia coli O157:H7: (1) Preparation of standard bacterial culture: Escherichia coli O157:H7 (ATCC 700728) was inoculated into LB liquid medium and cultured at 37°C and 200 rpm for 18 hours with shaking. The culture was then serially diluted 10-fold to prepare a standard concentration of 10. 1 -10 7 Standard bacterial suspensions at CFU / mL were used. The bacterial concentration was verified using the plate count method.

[0044] (2) Sandwich structure assembly: Take 100 μL of E. coli O157:H7 standard bacterial solutions of different concentrations and add them to the tin foil SERS substrate. Incubate at 37°C for 15 minutes to allow the bacteria to be captured by the substrate. Gently wash the substrate three times with PBS buffer to remove unbound bacteria. Add 100 μL of SERS nanoprobe solution (modified with anti-E. coli O157:H7 antibody) and incubate at 37°C for 15 minutes to allow the nanoprobes to bind to the bacteria through antigen-antibody reaction, forming a sandwich structure. Gently wash three times with PBS buffer to remove unbound nanoprobes.

[0045] (3) SERS detection: The sandwich structure was subjected to SERS detection using a portable Raman spectrometer (such as B&W Tek i-Raman Plus, excitation wavelength 785nm, laser power 5mW, integration time 10 seconds). The scanning range was 400-1800cm². -1 For each sample, spectra were collected from three different locations and averaged. The MBA was recorded at 1582 cm⁻¹. -1 The SERS peak intensity at that location.

[0046] (4) Results Analysis: A standard curve was established with SERS peak intensity as the ordinate and the logarithm of bacterial concentration as the abscissa. The results showed that SERS intensity and E. coli O157:H7 concentration were related within a range of 10. 1 -10 7 The correlation coefficient R0 showed good linearity within the CFU / mL range, with the linear equation being y = 185.2 + 702.5x and a correlation coefficient R0. 2 = 0.993. The limit of detection (LOD, S / N=3) was 7 CFU / mL. Spiked recovery experiments showed recoveries of 89.5%-115.3% in food matrices such as milk, lettuce, and chicken, with RSD <12%.

[0047] Example 5: SERS detection of Staphylococcus aureus: Following the method in Example 4, SERS nanoprobes modified with anti-Staphylococcus aureus antibodies were used to detect Staphylococcus aureus (ATCC 25923). The results showed that the SERS intensity was correlated with the concentration of Staphylococcus aureus within a 10-1 range. 1 -10 7 The correlation coefficient R0 showed good linearity within the CFU / mL range, with the linear equation being y = 142.8 + 688.3x. 2 = 0.991. The limit of detection (LOD, S / N=3) is 5 CFU / mL. In artificially contaminated milk samples, when the concentration of Staphylococcus aureus is 10... 2 At CFU / mL, the positive detection rate was 100%, consistent with the results of the plate count method.

[0048] Example 6: SERS detection of Salmonella: Following the method in Example 4, SERS nanoprobes modified with anti-Salmonella antibodies were used to detect Salmonella Typhimurium (CICC 10871). The results showed that the SERS intensity was correlated with Salmonella concentration at a range of 10⁻⁶. 1 -10 7 The correlation coefficient R0 showed good linearity within the CFU / mL range, with the linear equation being y = 168.5 + 695.8x.2 =0.992. The limit of detection (LOD, S / N=3) was 8 CFU / mL. In artificially contaminated egg samples, the recoveries ranged from 92.1% to 108.7%, with RSD <10%.

[0049] Example 7: SERS detection of Listeria monocytogenes: Following the method in Example 4, SERS nanoprobes modified with anti-Listeria monocytogenes antibodies were used to detect Listeria monocytogenes (ATCC PTA-5562). The results showed that the SERS intensity was correlated with the Listeria monocytogenes concentration at a range of 10... 1 -10 7 The correlation coefficient R0 showed good linearity within the CFU / mL range, with the linear equation being y = 155.3 + 678.2x. 2 = 0.990. The limit of detection (LOD, S / N=3) is 10 CFU / mL. In artificially contaminated ready-to-eat cooked meat products, the recovery rate is 87.6%-112.4%, and the RSD is <13%.

[0050] Example 8: Simultaneous detection of multiple targets: Escherichia coli O157:H7, Staphylococcus aureus, Salmonella, and Listeria monocytogenes were prepared into 10... 5 A mixed bacterial culture was prepared by mixing equal volumes of bacterial suspensions at CFU / mL. Four specific antibody-modified SERS nanoprobes (AuNPs-MBA-Ab-E. coli, AuNPs-MBA-Ab-S. aureus, AuNPs-MBA-Ab-Salmonella, and AuNPs-MBA-Ab-L. mono) were then used to detect the mixed bacterial culture. The results showed that each SERS nanoprobe could only specifically recognize its corresponding target bacterium, with no cross-reactivity to non-target bacteria. When using the mixed solution of the four nanoprobes, the presence of all four pathogens could be detected simultaneously, and the SERS signal intensity of each target bacterium was essentially consistent with that of individual detection, indicating that simultaneous multi-target detection is feasible.

[0051] Example 9: Specificity analysis: Escherichia coli O157:H7, Staphylococcus aureus, Salmonella, and Listeria monocytogenes were selected as target bacteria, while Bacillus subtilis, Pseudomonas aeruginosa, Enterococcus faecalis, and Staphylococcus epidermidis were selected as non-target bacteria. The concentration of all strains was 10. 5 CFU / mL. SERS nanoprobes modified with anti-Escherichia coli O157:H7 antibody were used to detect each strain. Results showed that the target strain, Escherichia coli O157:H7, produced a strong SERS signal (approximately 850 au), while the non-target strains showed a weak signal (<50 au), with a specificity of 94.4%. The same method was used to test three other SERS nanoprobes, with specificities of 96.2% against Staphylococcus aureus, 95.7% against Salmonella, and 94.8% against Listeria monocytogenes. These results demonstrate that the method of this invention has excellent specificity.

[0052] Example 10: Actual food sample testing: Samples of commercially available milk, lettuce, chicken, and shrimp were collected for artificial contamination experiments. *Escherichia coli* O157:H7 and *Staphylococcus aureus* were added to the food samples to prepare concentrations of 10... 2 10 3 10 4 The sample was contaminated with CFU / g. Sample pretreatment: 25g of sample was added to 225mL of sterile PBS buffer, homogenized for 2 minutes, and the supernatant was collected for detection. The SERS detection method of this invention was used for detection, and plate counting was performed using the national standard GB 4789 method for verification. The results showed that the detection limit of Escherichia coli O157:H7 in milk, lettuce, chicken, and shrimp using the method of this invention was 10. 2 The CFU / g results were consistent with those of the plate count method; the recovery rate was 89.5%-115.3%, and the RSD was <12%. The entire detection process took approximately 30 minutes, significantly faster than the national standard method (24-48 hours). Testing was conducted on 50 commercially available food samples, and the results of this invention were completely consistent with the national standard method, achieving a 100% compliance rate.

[0053] Figure 1This is a schematic diagram illustrating the sandwich structure SERS detection principle. As shown, after the SERS substrate (tin foil with sputtered gold nanoparticles or a microfluidic chip) captures the target pathogen, the MBA-antibody-modified SERS nanoprobes bind to the bacteria through antigen-antibody specific binding, forming a "SERS substrate-pathogen-SERS nanoprobe" sandwich structure. During laser excitation, the nano-gap between the SERS nanoprobes and the SERS substrate generates a strong electromagnetic field enhancement (hotspot effect), significantly enhancing the Raman signal of the MBA signal molecules. This is achieved by detecting the MBA characteristic peak (1075 cm⁻¹). -1 and 1582 cm -1 The intensity of the pathogen is used to achieve quantitative analysis of the pathogen.

[0054] Figure 2 The flowchart for the preparation of SERS nanoprobes is shown. The preparation process consists of three steps: (1) Synthesis of gold nanospheres (AuNPs): 60±5nm gold nanospheres are prepared by sodium citrate reduction method; (2) MBA loading: MBA molecules are fixed on the surface of AuNPs by Au-S bond through Au-S bond by utilizing the strong affinity between the thiol group of MBA molecules and the gold surface; (3) Antibody conjugation: the carboxyl group on the surface of MBA-modified AuNPs is covalently linked to the amino group of antibody by using EDC / NHS conjugation chemistry to form a complete SERS nanoprobe (AuNPs-MBA-Ab).

[0055] Figure 3 This is a complete flowchart of the SERS detection method. The detection process includes: food sample collection and pretreatment, SERS substrate preparation, SERS nanoprobe preparation, sandwich structure assembly, Raman detection (confocal / desktop / handheld Raman spectrometer), data analysis, and result output. The entire detection process can be completed within 30 minutes, enabling rapid screening of food pathogens.

[0056] Figure 4 Figure 1 shows the sensitivity results of SERS detection for different pathogens. (A) Limits of detection (LOD) for seven foodborne pathogens: Staphylococcus aureus 5 CFU / mL, Escherichia coli O157:H7 7 CFU / mL, Salmonella 8 CFU / mL, Listeria monocytogenes 10 CFU / mL, Vibrio parahaemolyticus 12 CFU / mL, Bacillus cereus 15 CFU / mL, and Clostridium botulinum 20 CFU / mL. (B) The calibration curve shows a good linear relationship between SERS intensity and the logarithm of bacterial concentration (R0). 2 >0.99), linear range is 10 1 -10 7 CFU / mL.

[0057] Figure 5This is a specificity analysis diagram of the SERS detection method. (A) Target pathogens (Escherichia coli O157:H7, Staphylococcus aureus, Salmonella, Listeria monocytogenes) produce strong SERS signals after reacting with SERS nanoprobes, while non-target bacteria (Bacillus subtilis, Pseudomonas aeruginosa, Enterococcus faecalis, Staphylococcus epidermidis) show weak signals, with a specificity greater than 95%. (B) The cross-reactivity heatmap shows that the cross-reactivity rate between different antibody-nanopeptide strains and bacterial strains is less than 5%. (C) The specificity analysis table lists the specificity values ​​for each target pathogen.

[0058] Figure 6 This is a schematic diagram of a customized microfluidic chip structure. The chip is fabricated using PDMS material and includes a sample inlet, a serpentine microchannel (100 μm wide), a reaction chamber, a detection window, and a waste outlet. Gold nanoparticles are sputtered onto the inner surface of the reaction chamber as a SERS substrate, and the detection window is made of transparent quartz for easy Raman spectroscopy acquisition. The bottom of the chip is a glass substrate, resulting in a compact structure suitable for portable detection.

[0059] This invention discloses a SERS detection method based on a sandwich structure for rapid, highly sensitive, and highly specific detection of foodborne pathogens. The method involves preparing MBA-antibody-modified SERS nanoprobes and a gold nanoparticle SERS substrate, utilizing antigen-antibody specific recognition to form a sandwich structure, thereby significantly enhancing the SERS signal. This method achieves detection limits of 5-20 CFU / mL for various foodborne pathogens, including *Escherichia coli*, *Staphylococcus aureus*, *Salmonella*, *Listeria monocytogenes*, *Vibrio parahaemolyticus*, *Bacillus cereus*, and *Botrytis cinerea*, with a detection time of less than 30 minutes. It offers significant advantages such as immediacy, low cost, high sensitivity, and high specificity. The method is simple to operate, low in cost, and stable, making it suitable for rapid on-site detection and possessing broad application prospects in the field of food safety testing.

[0060] The beneficial effects of this invention are: (1) High sensitivity: This invention adopts a sandwich structure SERS detection strategy, in which a plasma hotspot is formed between the SERS nanoprobe and the SERS substrate, generating a significant electromagnetic field enhancement effect, which enhances the Raman signal of the MBA signal molecules by 10%. 6 -10 8 The detection limit is 5-20 CFU / mL, which is much lower than that of traditional ELISA and culture methods.

[0061] (2) High specificity: This invention utilizes the specific recognition function of antibodies and antigens to effectively avoid interference from non-target bacteria. The specific recognition rate of the target pathogen is greater than 95%, and the cross-reactivity rate is less than 5%, ensuring the accuracy of the detection results.

[0062] (3) Rapid detection: The entire detection process of this invention can be completed within 30 minutes, including 5 minutes of sample pretreatment, 15 minutes of sandwich structure assembly, and 10 minutes of SERS detection, which is much faster than the traditional culture method (24-72 hours) and meets the needs of rapid on-site detection.

[0063] (4) Multi-target detection capability: This invention can simultaneously detect multiple pathogens such as Escherichia coli, Staphylococcus aureus, and Salmonella by using SERS nanoprobes modified with different antibodies, thereby improving detection efficiency.

[0064] (5) Easy to operate: The SERS nanoprobe and SERS substrate preparation process of this invention are mature and reproducible. The detection process does not require complicated sample pretreatment and expensive instruments and equipment. The detection can be completed with a handheld Raman instrument, which is suitable for field application.

[0065] (6) Low cost: The present invention uses tin foil as the SERS substrate material, which is extremely low cost; the gold nanospheres are prepared by the classic sodium citrate reduction method, which is easy to obtain and simple to process, and is suitable for large-scale promotion and application.

[0066] (7) Good stability: In the SERS nanoprobe of this invention, MBA is bound to nanoparticles through stable Au-S bonds, and the antibody is coupled through covalent bonds. The probe has good stability and can be stored at 4°C for more than 6 months.

[0067] (8) Wide range of applications: The detection method of the present invention is applicable to various food matrices, including meat, dairy products, aquatic products, fruits and vegetables, etc., and has broad application prospects in food safety testing, clinical diagnosis, environmental monitoring and other fields.

[0068] In one optimized embodiment, a reference timing process parameter dataset is determined experimentally before each SERS substrate is mass-produced. Current pre-batch testing and preparation methods for SERS substrates include: Step A: Place the SERS substrate in the sputtering coating apparatus and evacuate to 8×10⁻⁶. -6 Below Torr; Step B: Sputter the SERS substrate body using the preset sputtering control parameters corresponding to the current SERS substrate (including preset sputtering gas inlet flow rate, preset sputtering electrical parameters, and preset sputtering chamber exhaust valve opening); Step C: During sputtering, detect the pressure inside the sputtering chamber, the temperature of the SERS substrate, and the cumulative film thickness online using QCM; based on the detection results within the initial set duration of the sputtering process (5-10 min; the duration of each sub-period must be divisible by the initial set duration to ensure complete coverage of the entire initial set duration without omissions or overlaps), record and statistically analyze the online detection results of each process parameter according to the sub-period (15-30 s), construct an actual time-series process parameter dataset, and perform a first analysis on the actual time-series process parameter dataset to determine the equivalent pressure change rate; combine the actual time-series process parameter dataset and the reference time-series process parameter dataset to perform correlation analysis to determine the equivalent pressure deposition characteristic coefficient and deposition temperature rise characteristic coefficient; In the time-series process parameter dataset, the column dimensions include: sub-time period number, sputtering duration interval corresponding to the sub-time period ([sputtering duration of a single substrate corresponding to the start time of the sub-time period, sputtering duration of a single substrate corresponding to the end time of the sub-time period), average value of the pressure detection value in the sputtering cavity corresponding to the sub-time period, cumulative film thickness corresponding to the end of the sub-time period, temperature of the SERS substrate corresponding to the end of the sub-time period, and film thickness change rate corresponding to the sub-time period. Step D: Based on the analysis results of Step C, determine the target sputtering control parameters corresponding to the current batch production of the current SERS substrate; determine the target sputtering electrical parameters (sputtering power) based on the equivalent pressure deposition characteristic coefficient and the deposition temperature rise characteristic coefficient; and determine the opening degree of the target sputtering cavity exhaust valve based on the equivalent pressure change rate.

[0069] Step E: Based on the target sputtering control parameters corresponding to the current batch sputtering mass production of the current SERS substrate, continue to complete the current batch sputtering mass production of the current SERS substrate.

[0070] 1. For each SERS substrate, ideal sputtering process parameters (ideal sputtering gas inlet flow rate range, ideal sputtering rate range, ideal sputtering chamber exhaust valve opening range) are determined before mass production. Before mass production, each SERS substrate must undergo process experiments to determine the ideal sputtering process parameters, including the ideal sputtering gas inlet flow rate range (ranging from 20-40 sccm), the ideal sputtering electrical parameter range (ideal sputtering power of 5-7W for microfluidic chip substrates; ideal sputtering power of 8-10W for tin foil substrates), and the ideal sputtering chamber exhaust valve opening range (capable of achieving a sputtering pressure of 3×10⁻⁶ under the ideal sputtering gas inlet flow rate and ideal sputtering electrical parameter ranges). -3 -6×10 -3Torr; the specific opening range is actually matched according to the specific valve type and sputtering cavity size, and is not specifically limited here); the ideal process parameters are the range of qualified parameters that can be stably prepared to meet the requirements of SERS enhancement performance, film thickness uniformity and substrate integrity, obtained through multiple sets of sputtering tests and performance characterization.

[0071] The preset sputtering gas inlet flow rate and the preset sputtering chamber exhaust valve opening are selected from the center values ​​of the ideal sputtering electrical parameter range and the ideal sputtering chamber exhaust valve opening range, respectively. The preset sputtering electrical parameters are selected from the center value of the corresponding ideal sputtering electrical parameter range minus 0.2W (preset sputtering power of 5.8W for microfluidic chip substrate; preset sputtering power of 8.8W for tin foil substrate). The initial preset values ​​for the sputtering power of both substrates are selected by subtracting a fixed difference from the center value of the interval. Under the premise of ensuring stable plasma ignition and no abnormal fluctuations in chamber pressure, the upper limit of the power range is avoided, the heat accumulation effect of long-term sputtering is reduced, and the substrate deformation and performance degradation are prevented. 3. Before mass production of each SERS substrate in multiple batches, baseline data is first constructed under stable operating conditions of the sputtering equipment: The sputtering power supply operates stably with no abnormal fluctuations in output power, and can maintain the set electrical parameters. The intake and exhaust systems are working normally, the pipelines are unblocked and leak-free, and the gas flow and chamber pressure can be stably controlled. The vacuum chamber is clean and free of impurities, and the magnetron target material undergoes standard pre-sputtering treatment, resulting in stable sputtering yield. Based on this, multiple batches of samples with qualified process conditions and substrate conditions were selected. Based on the above-mentioned preset sputtering gas inlet flow rate, preset sputtering electrical parameters, and preset sputtering cavity exhaust valve opening, the SERS substrate was sputtered. The detection results within the initial set time of the sputtering process of the qualified sputtered products were selected. The time-series process parameter dataset for each batch was constructed according to the above method. The average value of the parameters corresponding to each sub-time period (the average value of the sputtering cavity pressure detection value corresponding to the sub-time period, the cumulative film thickness corresponding to the end of the sub-time period, the temperature of the SERS substrate corresponding to the end of the sub-time period, and the film thickness change rate corresponding to the sub-time period) was taken to form a unified reference time-series process parameter dataset that reflects a stable and qualified process state.

[0072] Furthermore, based on the time-series process parameter datasets for each batch, a reasonable range for the equivalent pressure change rate is determined: Furthermore, based on the time-series process parameter datasets for each batch, a reasonable range for the equivalent pressure change rate is determined: First, the equivalent pressure change rate for each qualified batch is calculated; second, the actual distribution range of the equivalent pressure change rate for all qualified batches is statistically analyzed, and the common range into which all batches fall is taken as the reasonable range; this reasonable range directly reflects the inherent fluctuation range of the chamber pressure of qualified batches over time under stable operating conditions. When the equivalent pressure change rate of subsequent mass-produced batches falls within this range, it is determined that the pressure control state is stable.

[0073] 4. Equivalent pressure change rate = (average value of pressure detection in sputtering chamber corresponding to the last sub-period in the actual time-series process parameter dataset - average value of pressure detection in sputtering chamber corresponding to the first sub-period in the actual time-series process parameter dataset) ÷ (center value of the sputtering duration interval of the last sub-period in the actual time-series process parameter dataset - center value of the sputtering duration interval of the first sub-period in the actual time-series process parameter dataset); The film thickness change rate corresponding to the current sub-period = (cumulative film thickness at the end of the current sub-period - cumulative film thickness at the end of the previous sub-period) ÷ duration of the sub-period; duration of the sub-period = sputtering time of a single substrate corresponding to the end of the sub-period - sputtering time of a single substrate corresponding to the start of the sub-period; 5. Pressure deposition characteristic coefficient of sub-time period i = ; These are the average value of the sputtering cavity pressure detection value corresponding to sub-time period i in the actual time-series process parameter dataset, and the film thickness change rate corresponding to sub-time period i in the actual time-series process parameter dataset, respectively. These are the average value of the sputtering cavity pressure detection value corresponding to sub-time period i in the reference time series process parameter dataset, and the film thickness change rate corresponding to sub-time period i in the reference time series process parameter dataset, respectively. Equivalent pressure deposition characteristic coefficient = the average value of the pressure deposition characteristic coefficients of all sub-time periods corresponding to the actual time-series process parameter dataset; A higher pressure deposition characteristic coefficient indicates that the coupling relationship between pressure and velocity has deviated from the baseline, and the energy utilization efficiency or particle scattering characteristics of sputtering have changed. For example, changes in the target material state or gas ionization characteristics may lead to changes in the "deposition capability under the same pressure". A higher pressure deposition characteristic coefficient indicates that the overall sputtering deposition capability is stronger under the same pressure conditions, which can easily lead to coarse film grains, increased surface roughness, and excessive bombardment of the target material.

[0074] A small pressure deposition characteristic coefficient indicates that the coupling characteristics between pressure and deposition rate have shifted, resulting in an overall decrease in sputtering deposition efficiency and a deviation of the film formation process from the baseline state.

[0075] Deposition temperature rise characteristic coefficient = ; These are the temperatures of the SERS substrate at the end of the sub-period corresponding to the last time period in the actual timing process parameter dataset, and the substrate temperature when the substrate is placed into the sputtering cavity in step A of the actual timing process parameter dataset. These are the temperatures of the SERS substrate body at the end of the sub-period corresponding to the last time period in the reference time process parameter dataset, and the substrate body temperature when the substrate body is placed into the sputtering cavity in step A corresponding to the reference time process parameter dataset. This represents the average film thickness change rate across all sub-time periods in the actual time-series process parameter dataset. The average film thickness change rate is the value of all sub-time periods in the reference time-series process parameter dataset; A deposition temperature rise characteristic coefficient greater than 1 indicates that the overall intensity of film formation efficiency and thermal effect is higher than the baseline, and the sputtering mode deviates from the baseline state, potentially posing a thermal risk to microfluidic and other thermosensitive substrates. For microfluidic and other thermosensitive substrates: at the same deposition rate, a higher temperature rise indicates an abnormally enhanced thermal effect, posing a risk of thermal deformation and channel damage to the substrate. Regarding film quality: excessively high temperature rise can lead to intensified gold atom surface migration, particle agglomeration, uncontrolled morphology, uneven distribution of SERS hotspots, and poor signal repeatability.

[0076] A deposition temperature rise characteristic coefficient less than 1 indicates that the overall strength of film formation efficiency and thermal effect is lower than the baseline, the overall sputtering conditions are weak, and the film formation process deviates from the baseline state.

[0077] The deposition rate represents the film growth state, and the temperature rise represents the thermal environment state associated with sputtering. 6. Based on the analysis results of step C, the specific target sputtering control parameters corresponding to the current batch production of the current SERS substrate are determined as follows: 1) Based on the equivalent pressure deposition characteristic coefficient, the sputtering electrical parameters are corrected: The stable range of the equivalent pressure deposition characteristic coefficient is set to [0.9, 1.1]. If the equivalent pressure deposition characteristic coefficient falls within the stable range, it indicates that the coupling characteristics between pressure and deposition rate are consistent with the benchmark, and the preset sputtering electrical parameters can be directly used for the current batch. Corrected sputtering electrical parameters = Preset sputtering electrical parameters; If the equivalent pressure deposition characteristic coefficient is greater than 1.1, it indicates that the "deposition capability under the same pressure" is enhanced. To avoid film roughness or excessive sputtering of the target material, the preset sputtering electrical parameters are reduced: when the equivalent pressure deposition characteristic coefficient is [1.1, 1.2], the corrected sputtering electrical parameter = preset sputtering electrical parameter - 0.1W; when the equivalent pressure deposition characteristic coefficient is [1.2, 1.3], the corrected sputtering electrical parameter = preset sputtering electrical parameter - 0.2W; when the equivalent pressure deposition characteristic coefficient is greater than 1.3, an alarm is triggered. If the equivalent pressure deposition characteristic coefficient is less than 0.9, it indicates that the overall sputtering deposition efficiency has decreased. The preset sputtering electrical parameters should be increased: when the equivalent pressure deposition characteristic coefficient is [0.8, 0.9], the corrected sputtering electrical parameter = preset sputtering electrical parameter + 0.1W; when the equivalent pressure deposition characteristic coefficient is [0.7, 0.8], the corrected sputtering electrical parameter = preset sputtering electrical parameter + 0.2W; when the equivalent pressure deposition characteristic coefficient is less than 0.7, an alarm should be triggered. 2) Based on the deposition temperature rise characteristic coefficient, the sputtering electrical parameters are finely adjusted in two stages: the stable range of the deposition temperature rise characteristic coefficient is set to [0.9, 1.1]; If the deposition temperature rise characteristic coefficient is greater than 1.1, it indicates that the overall strength of film formation and thermal effect is too high. For heat-sensitive substrates such as microfluidics, further reduce the electrical parameters: when the deposition temperature rise characteristic coefficient is [1.1, 1.2], the target sputtering electrical parameter = corrected sputtering electrical parameter - 0.05W; when the deposition temperature rise characteristic coefficient is (1.2, 1.3], the target sputtering electrical parameter = corrected sputtering electrical parameter - 0.1W; thereby reducing the temperature rise rate and avoiding thermal deformation of the substrate; If the deposition temperature rise characteristic coefficient is less than 0.9, it indicates that the overall sputtering conditions are weak and the film formation efficiency is insufficient. Further improvements to the electrical parameters are needed: when the deposition temperature rise characteristic coefficient is [0.8, 0.9], the target sputtering electrical parameter = corrected sputtering electrical parameter + 0.05W; when the deposition temperature rise characteristic coefficient is [0.7, 0.8), the target sputtering electrical parameter = corrected sputtering electrical parameter + 0.1W. An alarm should be triggered if the deposition temperature rise characteristic coefficient is less than 0.7 or greater than 1.3. 3) Adjust the opening of the sputtering chamber exhaust valve based on the equivalent pressure change rate: the reasonable range of the equivalent pressure change rate is [a,b]; Determine the upper limit of proximity (upper limit proximity = (b - equivalent pressure change rate) ÷ "0.5 × (maximum value - minimum value of reasonable range of equivalent pressure change rate)") and the lower limit of proximity (lower limit proximity = (equivalent pressure change rate - a) ÷ "0.5 × (maximum value - minimum value of reasonable range of equivalent pressure change rate)"). If both min (upper limit proximity and lower limit proximity) are greater than 0.3, the target sputtering cavity exhaust valve opening is equal to the preset sputtering cavity exhaust valve opening. If the upper limit proximity is less than the lower limit proximity, and 0 ≤ upper limit proximity ≤ 0.3, it indicates that the chamber pressure continues to rise over time, and the pumping capacity is insufficient. Increase the opening of the exhaust valve to improve the pumping efficiency and suppress the pressure rise; Target sputtering chamber exhaust valve opening = (1 + 3%) preset sputtering chamber exhaust valve opening; If the lower limit proximity is less than the upper limit proximity, and 0 ≤ lower limit proximity ≤ 0.3, it indicates that the chamber pressure continues to decrease over time, and the pumping capacity is too strong. Reduce the opening of the exhaust valve to reduce the pumping intensity and bring the pressure change back to the stable range. Target sputtering chamber exhaust valve opening = (1-3%) preset sputtering chamber exhaust valve opening.

[0078] An alarm is triggered when min(upper limit proximity, lower limit proximity) is less than 0.

[0079] When any of the above alarms occurs, it indicates that the current batch process status has seriously deviated from the qualified benchmark, and the parameter drift exceeds the adjustable range. At this time, the following abnormal handling procedure should be executed: suspend the sputtering process and stop the automatic production of subsequent batches; prioritize checking the equipment status, including: checking whether there are abnormal fluctuations in the sputtering power supply output; checking whether there are blockages, leaks or flow control failures in the air intake system; checking whether the exhaust valves, vacuum pumps and vacuum pipelines are working properly; checking the cleanliness of the vacuum chamber, the status of the magnetron sputtering target and the pre-sputtering effect; after completing equipment maintenance or troubleshooting, reconstruct the reference timing process parameter dataset for this SERS substrate, and re-perform pre-mass production testing and preparation. Mass production can only be resumed after confirming that the process status has returned to stability.

[0080] The beneficial effects of the solution in this embodiment are as follows: This invention addresses the inherent shortcomings of traditional magnetron sputtering mass production processes that rely on fixed preset parameters. It considers the unavoidable realities of sputtering production, such as slight batch variations in raw materials, equipment performance drift over long-term operation, gradual shifts in vacuum paths and exhaust systems, and target material consumption and degradation. It abandons the crude, fixed-parameter production model and establishes a closed-loop adaptive control system covering the entire process, from initial preset parameter selection and benchmark process establishment to full-time process monitoring, multi-dimensional characteristic parameter characterization, vacuum opening control, progressive correction of sputtering electrical parameters, process anomaly alarms, and priority equipment troubleshooting. This system solves the problems of fixed process parameters failing to adapt to batch fluctuations, gradual equipment shifts leading to decreased film quality, substrate thermal runaway, and poor batch consistency.

[0081] First, preliminary trial production testing and selection using preset basic process parameters has significant pre-emptive benefits. It identifies initial preset process parameters suitable for the current equipment, substrate material, and coating material, providing a reasonable and reliable starting point for subsequent mass production. Second, a benchmark process dataset is established based on complete time-series data from qualified mass production batches. This solution comprehensively collects continuous time-series information such as chamber pressure, film thickness growth status, and substrate temperature rise changes at different production stages throughout the sputtering process. Standard process characteristics are constructed using the average state of multiple qualified batches, accurately reproducing the dynamic changes throughout the stable sputtering process. Third, mass production batches simultaneously acquire data in real-time using the same sequence and dimensions, achieving dynamic monitoring of the entire sputtering process and eliminating reliance on post-production sampling to determine quality. This step can capture early, subtle shifts in chamber pressure trends, minor drifts in film thickness growth rates, and slow changes in substrate temperature rise rates in real time.

[0082] By introducing the equivalent pressure change rate, instead of using a single instantaneous pressure value as the evaluation criterion, it characterizes the overall gradual trend of chamber pressure over time throughout the entire sputtering cycle. This accurately reflects actual operating condition deviations caused by long-term equipment operation, such as micro-leakage in vacuum pipelines, performance degradation of vacuum pumps, changes in exhaust pipeline resistance, and fluctuations in intake and supply gas. By defining a reasonable operating condition range for the equivalent pressure change rate, which aligns with the equipment's normal allowable natural fluctuation range, and then quantifying the distance between the current actual operating condition and the upper and lower boundaries of the reasonable range using upper and lower limit proximity measurements, stable operating conditions and near-warning operating conditions are divided using fixed thresholds. This perfectly matches the actual operating pattern of the equipment, which does not change abruptly but only gradually deviates. Compared to the rigid logic of directly judging whether parameters exceed limits, using proximity prediction can reserve a process safety margin, identify drift trends approaching the boundary in advance, and avoid process runaway caused by direct exceeding of limits. The opening of the sputtering chamber exhaust valve is controlled based on the equivalent pressure change rate and proximity, adapting to the actual mechanical adjustment characteristics of the equipment and the requirements of the sputtering vacuum environment. This control is optimized specifically for the steady state of the vacuum environment, providing a stable foundation for subsequent deposition rate control and substrate thermal effect control, serving as a prerequisite for the entire process control system. The equivalent pressure deposition characteristic coefficient breaks through the limitations of traditional processes that rely solely on single film thickness or pressure parameters. By directly linking the chamber pressure with the film thickness deposition rate, it achieves a precise quantitative characterization of the overall deposition efficiency under actual sputtering conditions. In actual production, long-term equipment operation leads to target material attenuation, power supply performance deviations, changes in vacuum pipeline conditions, and slight differences in raw materials from different batches, all of which cause natural fluctuations in deposition efficiency. Fixed process parameters cannot adapt to these gradual changes. This scheme relies on the coefficient's ability to characterize coupling relationships. Using the pressure-rate matching relationship under the benchmark operating conditions as a reference, it accurately identifies abnormal deviations in deposition efficiency during actual production and adjusts the sputtering electrical parameters accordingly: when the coefficient is too high, the electrical parameters are lowered to suppress excessive sputtering of the target material and avoid coarse film grains and deterioration of surface roughness; when the coefficient is too low, the electrical parameters are raised to compensate for insufficient sputtering energy and stabilize the film thickness growth rate.

[0083] The deposition temperature rise characteristic coefficient, based on the current deposition rate, quantitatively characterizes the substrate temperature rise level corresponding to a unit film thickness during growth. It accurately reflects the matching degree between the thermal effect and film formation efficiency during sputtering, and can effectively identify abnormal substrate temperature rise caused by operational deviations. In actual sputtering production, operational deviations not only affect film thickness but also alter plasma thermal radiation and particle bombardment heat input, easily causing thermal deformation, microstructure damage, and device performance failure in precision thermosensitive substrates such as microfluidics. Based on the equivalent pressure deposition characteristic coefficient for primary adjustment of deposition efficiency, the deposition temperature rise characteristic coefficient is used for minor electrical parameter fine-tuning: when the thermal effect is too high, the electrical parameter output is further suppressed to reduce the substrate temperature rise rate and protect the integrity of the precision substrate structure; when the thermal effect is too low, the electrical parameter energy is appropriately supplemented to ensure film density, adhesion, and structural continuity. This progressive approach of first stabilizing the deposition rate and then finely controlling the substrate thermal load balances film quality and substrate protection, overcoming the shortcomings of traditional fixed-parameter processes that can only consider film thickness and cannot adapt to temperature rise fluctuations.

[0084] This invention establishes a full-process timeline benchmark for a qualified process, then performs synchronous full-process dynamic monitoring of mass production batches. Relying on multiple characteristic parameters, it objectively characterizes the actual process deviation from multiple dimensions, including vacuum pressure change trends, deposition coupling conditions, and substrate thermal effect matching. Then, it adaptively compensates for process drift by first stabilizing the chamber vacuum environment and then progressively correcting the sputtering electrical parameters. At the same time, it sets up abnormal alarms and follows the principle of prioritizing the investigation of equipment hardware. This fundamentally avoids the defects of fixed parameters being unable to adapt to batch fluctuations and equipment deviations, effectively ensuring production quality.

[0085] In one optimized embodiment, a reference timing process parameter dataset is determined experimentally before each SERS substrate is mass-produced. In the current mass production process, the actual cavity pressure data for corresponding sub-periods can be periodically selected, and the actual equivalent pressure change rate for a single sputtering operation (each sputtering operation involves placing multiple substrates in the sputtering cavity) can be calculated using a predetermined formula. This rate is then compared with a reasonable range of equivalent pressure change rates (typically [0.04, 0.06] × 10). -3 The actual equivalent pressure change rate is compared with the rate of change of the equivalent pressure (Torr / min). If the actual equivalent pressure change rate exceeds the reasonable range of the equivalent pressure change rate, it is determined that the current sputtering condition has an abnormal cavity pressure drift. The system alarm is immediately triggered and the current sputtering process is suspended to prevent abnormal coating from causing batch quality deviation.

[0086] Simultaneously, a sputtering operation sequence number-actual equivalent pressure change rate curve is established. For large-volume orders with large total quantities, which cannot be completed in a single sputtering operation, multiple independent sputtering rounds are required. Each sputtering round under the same large-volume order, arranged sequentially, is used as the horizontal axis for the operation sequence number, and the vertical axis corresponds to the equivalent pressure change rate of each sputtering round. When the slope of the established sputtering operation sequence number-actual equivalent pressure change rate curve does not meet the preset reasonable slope range ([-0.0005×10...),... -3 +0.0005×10 -3 The curve is calculated as Torr / (min·rounds) to trigger an alarm. This curve can continuously track the gradual performance degradation and operating condition drift of the vacuum intake and pumping system under multiple sputtering operations, and identify hidden faults such as pipeline micro-leakage, vacuum pump speed reduction, and valve opening deviation in advance. It provides data basis for preventive maintenance of equipment and dynamic correction of process parameters, and ensures the consistency and performance stability of film formation for each round of sputtering and each furnace of multiple substrates under a large-volume order.

[0087] This approach can be implemented based on the pre-mass production testing and preparation method of the previous optimized embodiment, or it can be carried out in the actual mass production process after other existing mass production parameters have been determined.

[0088] The beneficial effects of the above scheme are as follows: By periodically collecting chamber pressure data and calculating the equivalent pressure change rate, and comparing it in real time with a preset reasonable range, abnormal chamber pressure drift can be quickly identified and alarms triggered, pausing the process, effectively avoiding problems such as unstable ignition and deposition efficiency fluctuations caused by chamber pressure exceeding the process window. This solution does not require additional complex monitoring links such as film thickness and temperature; monitoring can be achieved solely based on chamber pressure time-series data. The control logic is simple and reliable, and can flexibly adapt to mass production parameters determined by different pre-process methods, demonstrating good versatility in the mass production of SERS substrates of different equipment models and specifications. By constructing a "sputtering operation sequence number - equivalent pressure change rate" curve and monitoring its slope, the gradual performance degradation of the vacuum intake and pumping systems in multiple sputtering operations can be continuously tracked, revealing hidden faults that are difficult to detect by traditional static monitoring, such as pipeline micro-leakage, decreased vacuum pumping speed, and valve opening deviation. Compared to post-fault troubleshooting, this solution can identify equipment performance drift trends in advance, guiding preventive maintenance and avoiding large-scale product quality scrap due to long-term operating condition deviations.

[0089] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for detecting food pathogens based on sandwich-structured SERS technology, characterized in that: Includes the following steps: Step (1): Preparation of SERS nanoprobes: Gold nanospheres or gold-silver core-shell nanospheres of the target particle size are synthesized. 4-Mercaptobenzoic acid signal molecules are loaded onto the surface of the nanoparticles through Au-S bonds. Then, specific antibodies are covalently linked to the surface of MBA-modified nanoparticles by coupling 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide and N-hydroxysuccinimide to form SERS nanoprobes. Step (2): Preparation of SERS substrate: A layer of gold nanoparticles is sputtered onto the surface of the SERS substrate using a sputtering coating instrument to form a SERS substrate with surface-enhanced Raman scattering activity; Step (3): Sandwich structure assembly: The food sample to be tested is brought into contact with the SERS substrate so that the target pathogen in the sample is captured by the SERS substrate. Then, SERS nanoprobes are added. Through the specific binding of antibodies with the surface antigens of pathogens, a "SERS substrate-pathogen-SERS nanoprobe" sandwich structure is formed. Step (4): SERS detection: The sandwich structure is subjected to Raman spectroscopy detection using a Raman spectroscopy detection device. The pathogen is qualitatively or quantitatively analyzed based on the SERS signal intensity of the MBA characteristic peak.

2. The method for detecting food pathogens based on sandwich structure SERS technology according to claim 1, characterized in that: The SERS substrate body includes: tin foil or microfluidic chip; The Raman spectroscopy detection equipment is a confocal Raman spectrometer, a benchtop Raman spectrometer, or a handheld Raman spectrometer.

3. The method for detecting food pathogens based on sandwich-structure SERS technology according to claim 1, characterized in that: The characteristic peak of MBA includes 1075 cm⁻¹ -1 and 1582cm -1 .

4. The method for detecting food pathogens based on sandwich structure SERS technology according to claim 1, characterized in that: The target particle size is 60±5nm.

5. The method for detecting food pathogens based on sandwich structure SERS technology according to claim 1, characterized in that: The gold nanospheres were prepared by sodium citrate reduction method. The preparation steps were as follows: chloroauric acid solution was heated to boiling, sodium citrate solution was added, the reaction solution changed from light yellow to wine red, boiling and refluxing continued for 10-30 minutes, and after cooling to room temperature, a colloidal solution of gold nanospheres with a particle size of 60±5nm was obtained. The gold-silver core-shell nanospheres were prepared using a seed growth method. Gold nanospheres were used as seeds, and in the presence of hydroxylamine reducing agent, silver nitrate was used to coat the surface of AuNPs with a silver shell layer, forming Au@Ag core-shell structured nanoparticles with a silver shell layer thickness of 5-20 nm.

6. The method for detecting food pathogens based on sandwich structure SERS technology according to claim 1, characterized in that: The loading method for the MBA signaling molecules is as follows: MBA ethanol solution is added to an aqueous solution of gold nanospheres or gold-silver core-shell nanospheres, with a final MBA concentration of 10. -6 -10 -4 M, stir and react at room temperature in the dark for 2-4 hours to form stable Au-S or Ag-S bonds with the strong affinity of the thiol group of MBA molecules to the surface of gold / silver. Centrifuge and wash to remove unbound MBA molecules to obtain MBA modified nanoparticles. The method for conjugating the antibody with MBA-modified nanoparticles is as follows: MBA-modified nanoparticles are dispersed in MES buffer, and 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide and NHS-activated carboxyl groups are added. The concentration of 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide is 5-20 mM, and the concentration of N-hydroxysuccinimide is 2-10 mM. After reacting at room temperature for 15-30 minutes, a specific antibody solution is added, with a final antibody concentration of 10-100 μg / mL. The reaction is carried out at 4°C in the dark with shaking for 2-4 hours. After centrifugation and washing, unconjugated antibody is removed to obtain SERS nanoprobes.

7. The method for detecting food pathogens based on sandwich structure SERS technology according to claim 1, characterized in that: The SERS substrate is prepared by placing the SERS substrate in a sputtering coating apparatus and evacuating it to a vacuum level of 8×10⁻⁶. -6 Below Torr, argon is used as the sputtering gas, with a flow rate of 20-40 sccm and a sputtering pressure of 3 × 10⁻⁶. -3 -6×10 -3 Torr sputtering at a rate of 0.2–0.5 Å / s for 100–200 minutes forms a uniform gold nanoparticle film on the substrate surface.

8. The method for detecting food pathogens based on sandwich structure SERS technology according to claim 1, characterized in that: The target pathogens include one or more of the following: Escherichia coli, Staphylococcus aureus, Salmonella, Listeria monocytogenes, Vibrio parahaemolyticus, Bacillus cereus, and Clostridium botulinum.

9. The method for detecting food pathogens based on sandwich structure SERS technology according to claim 1, characterized in that: The SERS detection uses an excitation wavelength of 532nm, 633nm, or 785nm, a laser power of 0.1-10mW, an integration time of 1-30 seconds, and a scanning range of 400-1800cm. -1 .

10. The method for detecting food pathogens based on sandwich structure SERS technology according to claim 1, characterized in that: The quantitative analysis method is as follows: using MBA at 1582 cm⁻¹ -1 A standard curve was constructed with the SERS peak intensity at a given location as the ordinate and the logarithm of the pathogen concentration as the abscissa, with a linear range of 10. 1 -10 7 CFU / mL, correlation coefficient R 2 >0.99, calculate the concentration of pathogens in the sample to be tested based on the standard curve.

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