A method for detecting heavy metal cadmium in food

Abstract

The invention discloses a method for detecting heavy metal cadmium in food, and belongs to the technical field of rapid food safety evaluation. The invention uses sodium alginate (SA) and silver nitrate as reactants to prepare green enhanced substrate silver nanoparticles SA-AgNPs, optimizes the concentration of specific molecule trithiocyanate (TMT) and the volume of detection liquid droplets, and utilizes edge enrichment effect to amplify the detection signal, thereby realizing rapid and highly sensitive detection of heavy metal cadmium in food based on SERS technology. A quantitative detection model is established by first-order derivative pretreatment combined with competitive adaptive reweighted partial least squares (CARS-PLS), and the heavy metal cadmium content in food is accurately predicted. The sensing detection method can effectively quantify and predict the heavy metal cadmium content in food, and realize rapid evaluation of heavy metals in food.

Description

Technical Field

[0001] The invention relates to a method for detecting heavy metal cadmium in food, in particular to a SERS specific rapid detection method for heavy metal cadmium in food, and belongs to the technical field of rapid food safety evaluation. Background Art

[0002] Cadmium (Cd) is one of the heavy metal elements commonly found in many foods. It mainly comes from human production activities, such as mining, smelting, and fertilization. In 1933, the International Agency for Research on Cancer listed Cd as a Class I human carcinogen. 2+ The presence of Cd will hinder plant photosynthesis and inhibit root elongation, thereby reducing the plant's absorption of nutrients. 2+ It is usually absorbed by plants through the roots through soil or water. If it accumulates in edible parts, it will cause food safety risks. 2+ The presence of Cd in the human body has no nutritional value, but will damage the human body's functional system, leading to emphysema, end-stage renal failure, osteoporosis and cancer. 2+ Poisoning incidents have occurred in many countries around the world, so it is important to ensure that food is free from Cd 2+ It is very important that contamination or its concentration is below the Maximum Residue Level (MRL).

[0003] Raman spectroscopy is very sensitive to the molecular structure and chemical composition of the analyte. Since the vibration mode of the molecule depends on the structure, when the composition and structure of the compound molecule changes, the corresponding Raman scattering will also change. Therefore, Raman spectroscopy can provide unique "fingerprint" characteristics for the compound. However, its main disadvantage is that the weak signal intensity leads to a decrease in Raman detection ability, so special methods are needed to enhance the signal intensity. Surface enhanced Raman spectroscopy (SERS) relies on the interaction between the excitation light, the relevant analyte and the SERS active substrate to selectively enhance the Raman scattering signal.

[0004] In the SERS application process, the enhancement substrate plays a key role in obtaining a stable Raman signal. The composition, size, spacing, surface composition and charge of the noble metal nanoparticles determine their unique physical properties, which in turn affect the enhancement effect. In order to obtain a suitable Raman enhancement signal, it is necessary to reasonably prepare the enhancement substrate according to the test sample. At present, the commonly used SERS substrate materials in food testing are mainly silver and gold, and the commonly used reducing agents are ethylene glycol, trisodium citrate, sodium borohydride, etc. Most of them are not green and friendly to the environment or organisms. Summary of the invention

[0005] In view of some deficiencies in the prior art, the present invention provides a method for detecting heavy metal cadmium in food. The method of the present invention overcomes the problems of slow speed, susceptibility to interference by impurities, and inaccurate quantification of conventional physical and chemical detection, and significantly improves the detection speed and reliability of the heavy metal cadmium content in food.

[0006] The present invention uses sodium alginate (SA) as a green reducing agent to synthesize silver nanoparticles, mixes with trithiocyanate (TMT) which can specifically identify cadmium ions, and uses the silver nanoparticles for SERS sensing detection of heavy metal cadmium in food. Combined with edge enrichment, a chemometric algorithm is used for data processing and analysis to achieve a rapid quantitative detection function of the cadmium ion content.

[0007] To achieve the above object, the present invention proposes a method for detecting heavy metal cadmium in food, which is based on SERS technology and comprises the following steps:

[0008] (1) Synthesis of silver nanoparticles: SA was dissolved in pure water, to which NaOH solution was added, heated in a water bath and stirred, and after the temperature stabilized, an AgNO3 aqueous solution was added dropwise. The resulting mixture, i.e., SA-AgNPs nanosolution, was stirred for a certain period of time, then cooled at room temperature and stored at an appropriate temperature for later use.

[0009] (2) Preparation of detection reaction solution: The obtained SA-AgNPs nanosolution and TMT solution were mixed in a certain ratio to obtain a detection reaction solution, which was recorded as SA-AgNPs / TMT solution.

[0010] (3) Detection of heavy metal cadmium content in food: Different concentrations of Cd were added to the pretreated food samples. 2+ The standard solution was added with SA-AgNPs / TMT solution to obtain a mixed solution including Cd 2+ Take an appropriate amount of the standard solution and SA-AgNPs / TMT solution and carefully drop it on the surface of the tin foil so that the droplet shape is as round as possible. Wait for it to dry naturally to form a coffee ring, and measure the SERS spectrum intensity on the coffee ring.

[0011] (4) Data processing and analysis, using chemometrics to optimize data results and establish quantitative models;

[0012] (5) Unknown food sample detection: The food sample was pretreated, and SA-AgNPs / TMT solution was added and dropped on the surface of tin foil. After the edge of the coffee ring was enriched and dried, the Raman spectrum of the mixed solution was measured; the established quantitative model was called to calculate the Cd in the unknown food sample. 2+ content.

[0013] In the above scheme,

[0014] The volume ratio of the NaOH solution and the AgNO3 aqueous solution in step (1) is 1:2, and the concentrations are 1 mol·L -1 , 9g·L -1 The concentration ratio of SA, NaOH and AgNO3 in the obtained SA-AgNPs nano-solution was 0.2 mg·mL -1 :0.01mol·L -1 :0.18mg·mL -1 ;

[0015] In the synthesis step of the silver nanoparticles, the water bath heating temperature is 100°C, the stirring time is 30 minutes, preferably magnetic stirring; and the storage temperature of the obtained mixture is 4°C.

[0016] Wherein, the volume ratio of the SA-AgNPs nano solution to the TMT solution in step (2) is 1-3:1-9, preferably 2:3; and the concentration of the TMT solution is 0.5 mM.

[0017] Wherein, the SA-AgNPs / TMT solution and Cd 2+ The volume ratio of the standard solution is 1:1, and the droplet volume of the mixed solution is 0.5 μL-2 μL, preferably 0.5 μL.

[0018] The pretreatment of the food samples in step (3) is performed according to the National Food Safety Standard for the Determination of Cadmium in Food (GB 5009.15-2014) to perform corresponding pretreatment on different foods.

[0019] In the above scheme, the spectrum acquisition uses a confocal micro-Raman imaging spectrometer with the following parameters: the excitation wavelength is 785 nm; 50× objective lens (spot size = 1.28 μm); the acquisition time is 0.5 s; and the power attenuation is 100%.

[0020] Among them, the quantitative model described in step (4) uses first-order derivative preprocessing combined with competitive adaptive reweighted-partial least squares (CARS-PLS) model to optimize and process the data, effectively quantifying and predicting the heavy metal cadmium content in food.

[0021] In the present invention, the addition ratio of TMT and SA-AgNPs and the volume of the detection reaction solution required in the detection process are optimized:

[0022] Optimization of the droplet volume required to form a coffee ring with the total test liquid: The total droplet volume dropped on the tin foil is optimized here. The SA-AgNPs nanosolution obtained in step (1) is mixed with the TMT solution. After standing for a certain period of time, different volumes of the mixed solution are dropped on the smooth tin foil surface. When the coffee ring is completely dried by automatic edge enrichment at room temperature, the droplet volume with the narrowest coffee ring width and the most uniform interior under a high-power microscope is selected.

[0023] The volume ratio of the SA-AgNPs nano solution to the TMT solution was 1:1, the concentration of the TMT solution was 0.5 mM, the standing time was 10 min, the volumes of the mixed solutions were 0.5, 1, 1.2, 1.5, 1.8 and 2 μL, respectively, and the microscope magnification was 50×.

[0024] Optimization of the addition ratio of TMT and SA-AgNPs: Add different volumes of TMT solution and SA-AgNPs nano-solution into the test tube to make the total volume equal, let it stand for a certain period of time, and add Cd 2+ The standard solution was dropped onto the smooth tin foil surface. After the edge of the coffee ring was enriched and dried, the SERS spectrum was collected, and the Raman intensities of the main characteristic peaks were compared. The addition ratio corresponding to the coffee ring with the highest Raman intensity was selected.

[0025] The concentration of the TMT solution was 0.5 mM, and the volume ratio of the TMT solution to the SA-AgNPs mixed solution was 1:9, 1:4, 3:7, 2:3, and 1:1, respectively. The standing time was 10 min. The SA-AgNPs / TMT solution and Cd 2+ The volume ratio of the standard solution is 1:1, Cd 2+ The concentration of the standard solution is 100 μg / L, and the volume of the droplet on the tin foil surface is 0.5 μL.

[0026] Beneficial effects of the present invention:

[0027] (1) The system of green enhanced substrate SA-AgNPs combined with TMT specific recognition molecules involved in the present invention is a stable and good SERS specific cadmium ion detection system. The silver nanoparticles synthesized with biopolysaccharide sodium alginate (SA) as a reducing agent have no obvious toxic effect on living organisms. SA molecules have abundant carboxyl and hydroxyl groups. The active hydroxyl groups have a certain reducing ability, and the carboxyl groups have a strong pairing effect with the silver nanoparticles. In the detection process, the silver nanoparticles (SA-AgNPs) synthesized with SA as a green reducing agent have good dispersibility, long-term stability and high SERS enhancement effect. The specific binding of TMT and cadmium ions and the edge enrichment effect increase the concentration of the target analyte in the coffee ring area, which plays a role in signal amplification.

[0028] (2) The present invention establishes a label-free Cd 2+The SERS detection system uses tin foil as a carrier to reduce background interference and enhance the SERS detection effect. At the same time, the coffee rings on the surface of the tin foil dry naturally, and no hydrophobic treatment is required. The operation steps are simple. The confocal micro-Raman imaging spectrometer is used for spectrum collection, and its spot size is 1.28μm. The SERS spectrum of a small area is collected at multiple points, which improves the stability and accuracy of SERS detection.

[0029] (3) The SERS-specific rapid detection method for heavy metal cadmium in food disclosed by the present invention can increase the Raman scattering signal intensity of the sample by 2.68×10 5 times, the minimum detection limit reached 0.1μg·L -1 . A correlation is established between the heavy metal cadmium content in food and the peak intensity change of surface enhanced Raman spectroscopy. Data preprocessing and subsequent rapid data processing using a chemometric model enable accurate detection and prediction of extremely low levels of heavy metal cadmium in food. The SERS-specific rapid detection method for heavy metal cadmium in food disclosed by the present invention has high sensitivity, accuracy and repeatability, and can replace the traditional ICP-MS method to detect the content of heavy metal cadmium in food. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Schematic diagram of the SERS specific rapid detection method for heavy metal cadmium in food.

[0031] Figure 2 Figure 1 is the transmission electron microscopy image of SA-AgNPs (a); the UV spectra of SA-AgNPs after 0, 7 and 30 days of storage (b); the FTIR spectra of SA and SA-AgNPs (c); the enhancement effect of SA-AgNPs on the Raman signal molecule 4-MBA (d), the concentration of 4-MBA is 10 -3 mol / L.

[0032] Figure 3 Microscopic images of coffee rings formed by droplets of TMT / SA-AgNPs mixed solutions with different volumes.

[0033] Figure 4 Optimization results of coffee ring droplet volume (a); optimization results of TMT and SA-AgNPs addition ratio (b); SERS response results of the detection system to various ions (c); Raman spectra of TMT powder (bottom) and Cd 2+ (100 μg / L) Raman spectrum of specific detection (top) (d).

[0034] Figure 5 Using tin foil and silicon wafer as carriers to detect the enhancement effect of SA-AgNPs on the Raman signal molecule 4-MBA

[0035] Figure 6 is the gradient concentration Cd 2+ Original SERS spectrum of the spiked tea test solution.

[0036] Figure 7 is CARS-PLS(a), where Ⅰ is RMSECV when the optimal variable is selected, Ⅱ is the regression coefficient when the optimal variable is selected, and Ⅲ is the number of optimal variables selected; based on 1 st Derivative preprocessing Cd 2+ Quantitative detection of CARS-PLS modeling results (b). DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Embodiment 1:

[0039] Figure 1 It is a schematic diagram of the SERS specific rapid detection method of heavy metal cadmium in food of the present invention.

[0040] The method for detecting heavy metal cadmium in food according to the present invention is implemented based on SERS technology and comprises the following steps:

[0041] Synthesis of silver nanoparticles: SA was dissolved in pure water, NaOH solution was added thereto, heated in a water bath and stirred, and after the temperature stabilized, AgNO3 aqueous solution was added dropwise, the mixture was stirred for a certain period of time, then cooled at room temperature, and stored at an appropriate temperature for later use.

[0042] In this step, the volume ratio of the NaOH solution to the AgNO3 aqueous solution is 1:2, and the concentrations are 1 mol·L -1 , 9g·L -1 ; The concentration ratio of SA, NaOH and AgNO3 in the final system is 0.2 mg·mL -1 :0.01mol·L -1 :0.18mg·mL -1 The water bath heating temperature is 100°C, the stirring time is 30 min, preferably magnetic stirring; the storage temperature of the obtained mixture is 4°C.

[0043] Preparation of detection reaction solution: The obtained SA-AgNPs mixed solution and TMT solution were mixed in a certain proportion to obtain a detection reaction solution, which was recorded as SA-AgNPs / TMT solution. The concentration of the TMT solution was 0.5 mmol·L -1 The volume ratio of SA-AgNPs mixed solution and TMT solution is 1:1.

[0044] Detection of heavy metal cadmium content in food: Add different concentrations of Cd to the pretreated food samples. 2+ Add the prepared SA-AgNPs / TMT solution to the standard solution, take an appropriate amount of the mixture and carefully drop it on the flat surface of tin foil to make the drop shape as round as possible, wait for it to dry naturally to form a coffee ring, and measure the SERS spectrum intensity on the coffee ring.

[0045] In this step, the SA-AgNPs / TMT solution is mixed with Cd 2+ The volume ratio of the sample solution is 1:1, and the volume of the droplet to be tested dropped on the surface of the tin foil is 0.5 μL. The spectrum acquisition uses a confocal micro-Raman imaging spectrometer with the following parameters: the excitation wavelength is 785 nm; 50× objective lens (spot size = 1.28 μm); the acquisition time is 0.5 s; and the power attenuation is 100%.

[0046] The pretreatment of the food samples is carried out according to the "National Food Safety Standard Determination of Cadmium in Food" (GB 5009.15-2014) for different foods.

[0047] Data processing and analysis: Chemometrics was used to optimize data results and establish quantitative models.

[0048] Preferably, in this step, based on 4 pretreatments and 5 variable selection methods, Cd 2+ PLS model for quantitative detection:

[0049] The standard normal variate transformation (SNV), multiple scatter correction (MSC), first order (1 st ) derivative and the second order (2 nd ) Four derivative preprocessing methods were used to eliminate the interference of baseline drift and noise on the original spectrum.

[0050] Based on five variable selection algorithms, namely cooperative interval (Si), uninformative variable elimination (UVE), genetic algorithm (GA), ant colony optimization (ACO) and competitive adaptive weighted sampling (CARS), a Cd 2+ PLS model for testing.

[0051] The quantitative model is a first-order derivative preprocessing combined with a competitive adaptive reweighting algorithm-partial least squares (CARS-PLS) model, which optimizes and processes data and effectively quantifies and predicts the heavy metal cadmium content in food.

[0052] Unknown food sample detection: The food sample is pretreated, SA-AgNPs / TMT solution is added, and then dropped on the smooth tin foil surface. After the edge of the coffee ring is enriched and dried, the Raman spectrum of the mixed solution is measured; the established quantitative model is called to calculate the Cd in the unknown food sample. 2+ content.

[0053] According to this embodiment, preferably, a silver nano-reduction system combining green enhancement substrate and edge enrichment effect is constructed.

[0054] The synthesis steps of silver nanoparticles described in the above scheme can be summarized as follows:

[0055] Dissolve 20 mg SA in 97 mL pure water and pour into a 150 mL round-bottom flask. Add 1 mL of NaOH solution (1 mol / L) to maintain the alkaline environment of the solution to promote the reduction reaction. Heat the water bath to 100 ° C and stir vigorously. After the temperature stabilizes, add 2 mL of 18 mg AgNO3 aqueous solution dropwise, and the colorless solution immediately turns dark yellow-green. Keep the mixture under magnetic stirring for 30 minutes. Then let the yellow solution cool at room temperature and store at 4 ° C for future use.

[0056] The surface of the sodium alginate molecule used in the present invention has carboxyl and hydroxyl groups, wherein the carboxyl groups have a strong pairing effect with the silver nanoparticles, and the active hydroxyl groups have a certain reducing ability. In this embodiment, the hydroxyl groups on the surface of sodium alginate are used to reduce silver ions to generate silver particles, and then the surface carboxyl groups quickly pair with the silver nanoparticles to make the sodium alginate wrap the silver nanoparticles to control the particle size of the silver nanoparticles and increase the stability of the silver nanoparticles.

[0057] Figure 2 a shows that the particle size of the synthesized SA-AgNPs is between 30 and 40 nm, and the nanoparticles have good dispersibility in aqueous solution. The freshly prepared SA-AgNPs have a strong UV absorption peak at 401 nm and still have good stability and dispersibility after being stored at 4°C for 30 days ( Figure 2 b) The Zeta potential of SA-AgNPs was -47.2, which confirmed that SA reduced silver ions and then coated on the surface of AgNPs as a surfactant.

[0058] Figure 2 c shows the FT-IR spectra of SA and AgNPs. In the FT-IR spectrum of SA, the -1The peaks near 1620 cm-1 can be attributed to the stretching vibration of -OH. -1 The absorption peak at 1320 cm is caused by the stretching vibration of the COO group. -1 The absorption peak near 1060cm is caused by the deformation vibration of -OH. -1 The absorption band at corresponds to the COC stretching mode in the unit. The FT-IR spectra of SA and AgNPs were compared and it was found that in AgNPs, -OH, COO-groups and COC vibration peaks all existed and shifted to a certain extent. This proved the presence of SA on AgNPs. The enhancement effect of SA-AgNPs was verified by using 4-MBA molecular probe. When 4-MBA ethanol solution (10 -3 mol / L), the Raman intensity of 4-MBA was significantly enhanced ( Figure 2 d). The EF value of SA-AgNPs was calculated to quantify its enhancement ability. The enhancement factor of the SA-AgNPs synthesized in this example was calculated to be 2.68×10 5 .

[0059] Furthermore, the detection system was optimized in this embodiment:

[0060] Optimization of the droplet volume for forming a coffee ring with the total test liquid required: The total droplet volume dropped on the tin foil is optimized here. SA-AgNPs solution (1 mL) and TMT solution (0.5 mM) were mixed in equal volumes in a test tube. After standing for 10 min, different volumes (0.5, 1, 1.2, 1.5, 1.8 and 2 μL) of TMT / SA-AgNPs mixed solution were dropped on the smooth tin foil surface. After the coffee ring was completely dried at room temperature by automatic edge enrichment, the width of the coffee ring was observed under a 50× microscope. The droplet volume with the narrowest coffee ring width and the most uniform interior was selected for subsequent research.

[0061] Optimization of the ratio of TMT to SA-AgNPs: Different volumes of TMT solution (100, 200, 300, 400 and 500 μL, 0.5 mM) were added to the test tube, and then SA-AgNPs solution was added to make the total volume 1 mL. After standing for 10 min, the obtained TMT / SA-AgNPs solution (20 μL) was mixed with the same volume of Cd 2+ The standard solution (100 μg / L) was evenly mixed to obtain a mixed solution, and each mixed solution (0.5 μL) was dropped on the smooth tin foil surface. After the coffee rings were edge-enriched and dried at room temperature, SERS spectra were collected and the Raman intensities of the main characteristic peaks were compared. The volume of TMT solution with the highest Raman intensity on the coffee rings was selected for subsequent research.

[0062] The coffee rings were characterized by the confocal Raman imaging spectrometer using the peak-clipping method. 2+ The characteristic Raman spectral peak position of the coffee ring was constructed based on the signal peak intensity. 2+ The pseudo-color image of the distribution. SERS spectra of 15 points on the coffee ring were randomly collected, and Cd 2+ The characteristic peaks were selected and the stability of the detection was determined by calculating the RSD values ​​of the 15 acquisition points.

[0063] According to the experimental method, the total liquid droplet volume to be detected and the addition ratio of TMT and SA-AgNPs were optimized. The TMT / SA-AgNPs mixed solution droplets tended to be automatically enriched, and finally formed a relatively uniform coffee ring. However, with the increase of the TMT / SA-AgNPs mixed solution droplet volume, the width of the coffee ring increased significantly, and the distribution uniformity of the nanoparticles on the coffee ring decreased. In the coffee ring with a TMT / SA-AgNPs mixed solution droplet volume of 1.2 and 1.5 μL, a dendritic distribution appeared ( Figure 3 ); When the droplet volume of TMT / SA-AgNPs mixed solution is 0.5 μL, the width of the coffee ring is the narrowest at 30 μm ( Figure 4 a). The color of the SA-AgNPs solution synthesized in the present invention is orange-yellow, and it quickly turns reddish brown after adding TMT. When the addition ratio of TMT to SA-AgNPs is 2:3, 979.60 cm -1 The Raman signal intensity reaches the highest at Figure 4 b). Therefore, the optimal TMT / SA-AgNPs mixed solution droplet volume was selected as 0.5 μL, and the optimal addition ratio of TMT to SA-AgNPs was 2:3.

[0064] In this example, the method of the present invention is also used to detect Cd 2+ The sensitivity or specificity of

[0065] Different ions (Hg 2+ , Pb 2+ , Mn 2+ , Cu 2+ 、As 3+ 、Ni 2+ Cr 6+ , Fe 3+ , Ba 2+ 、Cd 2+ ) were added to the blank tea sample and mixed evenly, and then mixed with TMT / SA-AgNPs solution according to the experimental method to collect Raman spectra. Figure 4 c shows different ions at 979.60 cm -1Obviously, in the presence of PEX, other metal ions will not interfere with the Cd 2+ The results show that only heavy metal ions with appropriate binding constants can destroy the SH bonds of TMT and form stable N-Cd 2+ -S bond. Therefore, this SERS detection method is effective for Cd in food. 2+ Has good selection specificity.

[0066] In this example, the complexity and intensity of the SERS signals on the carrier tin foil and the silicon wafer involved in the method of the present invention are compared:

[0067] The SA-AgNPs solution and the 4-MBA ethanol solution were mixed in equal volumes, and 0.5 μL was dropped onto the surfaces of tin foil and silicon wafer respectively. After natural drying, the SERS spectra of the edges of the coffee rings were collected respectively. Figure 5 The results show that the SERS signal detected on tin foil has a higher intensity, a more stable baseline, and no signal interference from the carrier itself, which can be directly and more accurately used for quantitative detection of cadmium ions in food. Therefore, the SERS detection method using tin foil as a carrier can reduce background interference, enhance SERS detection signals, and reduce redundant information of spectral signals.

[0068] This example uses tea leaves as a representative food, and the specific method for rapid quantitative detection of heavy metal cadmium in tea leaves is as follows:

[0069] (1) Tea sample pretreatment

[0070] 500g tea sample (Oriental black tea) was crushed with an ultra-fine grinder for about 1 min, then sieved several times until the powder was uniform and put into a sealed bag for later use. The tea powder was microwave digested according to the "National Food Safety Standard Determination of Cadmium in Food" (GB 5009.15-2014): 0.2g of tea sample (accurate to 0.001g) was weighed, 8mL of high-grade pure concentrated nitric acid and Cd 2+ The standard solution was covered and placed overnight, the lid was tightened, and digestion was performed according to the standard operating procedures of the microwave digester. The digestion conditions were as follows: the temperature was controlled at 190°C, the heating time was 20 minutes, the constant temperature time was 10 minutes, and the cooling time was 15 minutes. After taking it out, it was cooled to room temperature, and the lid of the can was slowly opened in the fume hood to exhaust the air. The inner cover was rinsed with a small amount of deionized water, and the digestion tank was placed in an ultrasonic water bath. It was heated at 100°C for 30 minutes to degas. In order to avoid the interference of other metal ions in food and affect the specific detection, PEX solution (100μmol / L, 2.5mL) was added to the digestion solution and the volume was adjusted to 50mL with deionized water. After filtering with a 0.22μm filter membrane, a total of 11 Cd in spiked samples were obtained. 2+The contents of 100, 80, 60, 40, 20, 10, 5, 1, 0.5, 0.1 and 0 μg / L were respectively. These tea samples were stored at 4°C for subsequent SERS and ICP-MS detection.

[0071] (2) Raman spectrum acquisition and analysis

[0072] Add 20 μL of SA-AgNPs / TMT solution and 20 μL of Cd-containing solutions with different concentrations into the test tube. 2+ of tea extract and mix them evenly. Pipette 0.5μL of the mixture and carefully drop it on the flat surface of tin foil to make the droplet shape as round as possible. After it dries naturally to form a coffee ring, collect the SERS spectrum on the coffee ring. Using a confocal micro-Raman imaging spectrometer, the excitation wavelength was 785nm, and all SERS spectral data were collected under a 50× objective lens (spot size = 1.28μm). The acquisition time and power attenuation were set to 0.5s and 100% respectively. 15 spectra were collected for each sample. The LabSpec6 software of the spectrometer was used for smoothing and denoising. The smoothing effect was controlled by adjusting the function type and parameters. The function type was Denoise, Size was 256, Degree was 2, and Factor was 1.

[0073] First, TMT powder and Cd 2+ The Raman spectrum peaks of the mixed test solution (100 μg / L) of the spiked tea sample and SA-AgNPs / TMT probe are as follows: Figure 4 As shown in d, the Raman peaks of TMT mainly appear at 446.15, 1129.80, and 1272.86 cm -1 The Cd collected on the coffee ring formed by the cadmium detection solution belongs to the CS group, CN group and benzene ring vibration. 2+ The characteristic peaks are mainly located at 976.60 and 1226.74 cm -1 419.86, 479.11 and 832 cm -1 The Raman peak at 1390.07 cm may come from CS stretching vibration. -1 The Raman peak at can be attributed to the stretching vibration of COO-. The existence of the Raman peak at this position once again verifies that SA with carboxyl groups has been modified on the surface of silver nanoparticles. Figure 6 10 gradient concentrations of Cd are shown 2+ The complete Raman spectrum of the spiked tea test solution. 2+ With the increase of Cd 2+ It is positively correlated with the Raman intensity.

[0074] (3) Data processing and analysis

[0075] Due to the interference of fluorescence from the external environment and food samples, the collected Raman curve has a certain baseline drift. Therefore, it is necessary to remove the baseline of the curve. The parameters for removing the baseline are as follows: background fitting is poly-nomial, degree = 7, maximum points = 256. Standard normal variable transformation (SNV), multiple scattering correction (MSC), first order (1 st ) derivative and the second order (2 nd ) derivatives were used to eliminate the interference of baseline drift and noise on the original spectrum. A Cd 2+ PLS model for testing.

[0076] The original spectral data is in the range of 300-1800cm -1 The Raman shift range contains 930 variables. 2+ The PLS results are shown in Table 1. The PLS results of SNV and MSC are not satisfactory, and their noise reduction capabilities are not well reflected in this embodiment, but this also proves that the experimental conditions of this embodiment are relatively stable and the performance of the device itself is excellent. The optimization effect of the derivative processing on the original spectrum is well reflected, and the PLS modeling effect is best after the first-order derivative algorithm is implemented (R c =0.9982, RMSEC=2.1159).

[0077] After the first-order derivative preprocessing, the variable selection algorithms of SiPLS, UVE-PLS, GA-PLS, ACO-PLS and CARS-PLS were implemented using MATLAB 2014b to compress the spectral data and improve the calculation speed and accuracy of the PLS model. The PLS modeling results based on variable selection are shown in Table 2. p The values ​​of Cd in tea samples were compared by various models. 2+ The results show that the R p The values ​​are all greater than 0.99. By comparing all variable screening methods, CARS-PLS obtained the best modeling results. Figure 7 a is the CARS-PLS variable optimization diagram, where Ⅰ is the RMSECV when the optimal variable is selected, Ⅱ is the regression coefficient when the optimal variable is selected, and Ⅲ is the number of optimal variables selected. Figure 7 b is based on 1 st Derivative preprocessing Cd 2 +The quantitative detection of CARS-PLS modeling results, the correlation coefficients of the calibration set and the prediction set were R c =0.9982, R p =0.9989.

[0078] In order to verify the accuracy and precision of heavy metal cadmium detection in food, 8 different concentration gradients of Cd were prepared. 2+ (100, 80, 60, 40, 20, 10, 5, 1 μg / L) tea samples. Each sample was tested three times using the standardized ICP-MS method, and the average value was taken to calculate the recovery rate and RSD value. Table 3 compares the detection results of the SERS method and ICP-MS method proposed in the present invention in tea samples. The recovery rate of tea samples detected by this method is 91.37% to 104.69%, and the relative standard deviation is ≤2.24%; the recovery rate of tea samples determined by the ICP-MS method is 91.45% to 110.63%, and the relative standard deviation is ≤2.87%. The results show that the SERS method can predict Cd in tea. 2+ The content has high accuracy and good reproducibility. In addition, this method is effective for Cd 2+ The minimum detection concentration is 0.1μg / L, which can meet the needs of trace detection.

[0079] Table 1. Results of Raman spectroscopy pretreatment combined with PLS model for cadmium ion detection in tea samples

[0080]

[0081] Table 2. Results of multivariate algorithm model for the prediction of cadmium ions in tea samples

[0082]

[0083] Table 3. Comparison of SERS and ICP-MS methods for the determination of cadmium ions in tea

[0084]

[0085] In summary, the present invention uses SA as a green reducing agent to synthesize SA-AgNPs combined with TMT molecules for the removal of Cd in tea. 2+ By optimizing the volume of the detection solution and the ratio of TMT solution to SA-AgNPs, the optimal detection system was established. The edge enrichment effect increased the concentration of the target analyte in the coffee ring area and played a role in signal amplification. In addition, with the participation of PEX, TMT molecules 2+ The SERS method has high selectivity. The standardized ICP-MS method also verifies the accuracy and repeatability of the SERS method. Therefore, the SERS technology combining green enhanced substrate and edge enrichment effect can achieve Cd in food. 2+Accurate, fast, label-free detection.

[0086] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described in the present invention; therefore, although the present invention has been described in detail in this specification with reference to the above embodiments, it should be understood by those skilled in the art that the present invention may still be modified or replaced by equivalents; and all technical solutions and improvements thereof that do not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for detecting heavy metal cadmium in food, characterized in that: The steps include: (1) Synthesis of silver nanoparticles: SA was dissolved in pure water, NaOH solution was added thereto, heated in a water bath and stirred, and after the temperature stabilized, AgNO3 aqueous solution was added dropwise, and the resulting mixture, i.e., SA-AgNPs nanosolution, was stirred and then cooled at room temperature; (2) Preparation of detection reaction solution: SA-AgNPs nanosolution and TMT solution were mixed in a certain ratio to obtain a detection reaction solution, which was recorded as SA-AgNPs / TMT solution; (3) Detection of heavy metal cadmium content in food: Add different concentrations of Cd to the pretreated food samples. 2+ Standard solution, add SA-AgNPs / TMT solution, and remove Cd 2+ The standard solution and SA-AgNPs / TMT mixed solution were dripped onto the surface of tin foil and allowed to dry naturally to form a coffee ring, and the SERS spectrum intensity was measured; (4) Data processing and analysis, using chemometrics to optimize data results and establish quantitative models; (5) Unknown food sample detection: The food sample is pretreated, SA-AgNPs / TMT solution is added, and the solution is dropped on the surface of tin foil. After the edge of the coffee ring is enriched and dried, the Raman spectrum is measured; the established quantitative model is called to calculate the Cd in the unknown food sample. 2+ content.

2. The detection method according to claim 1, characterized in that: The volume ratio of the NaOH solution and the AgNO3 aqueous solution in step (1) is 1:2, and the concentration ratio is 1 mol∙L -1 :9 g∙L -1 ; The concentration ratio of SA, NaOH and AgNO3 contained in the SA-AgNPs nano-solution is 0.2 mg∙mL -1 :0.01 mol·L -1 :0.18 mg∙mL -1 .

3. The detection method according to claim 1, characterized in that: In the synthesis step of the silver nanoparticles in step (1), the water bath heating temperature is 100°C, and the water bath heating and stirring time is 30 min; the storage temperature of the obtained mixture is 4°C.

4. The detection method according to claim 3, characterized in that: The stirring is magnetic stirring.

5. The detection method according to claim 1, characterized in that: The volume ratio of the SA-AgNPs nano solution to the TMT solution in step (2) is 1-3:1-9; the concentration of the TMT solution is 0.5 mM.

6. The detection method according to claim 5, characterized in that: The volume ratio of the SA-AgNPs nano solution to the TMT solution is 2:

3.

7. The detection method according to claim 1, characterized in that: The SA-AgNPs / TMT solution and Cd 2+ The volume ratio of the standard solution is 1:1, and the drop volume of the mixed solution added onto the surface of the tin foil is 0.5 μL-2 μL.

8. The detection method according to claim 7, characterized in that: The droplet volume of the mixed solution added onto the surface of the tin foil is 0.5 μL.

9. The detection method according to claim 1, characterized in that: The pretreatment of the food samples in step (3) is carried out according to the National Food Safety Standard Determination of Cadmium in Food (GB 5009.15-2014) for different foods.

10. The detection method according to claim 1, characterized in that: The spectrum was collected using a confocal micro-Raman imaging spectrometer, with an excitation wavelength of 785 nm, a 50× objective lens, and a spot size of 1.28 μm.

11. The detection method according to claim 1, characterized in that: The optimization is based on 4 preprocessing and 5 variable selection methods; The four preprocessing methods include standard normal variable transformation (SNV), multiple scatter correction (MSC), first order (1 st ) derivative and the second order (2 nd ) derivative; The five variable selection methods include collaborative interval (Si), uninformative variable elimination (UVE), genetic algorithm (GA), ant colony optimization (ACO) and competitive adaptive weighted sampling (CARS).

12. The detection method according to claim 1, characterized in that: The quantitative model adopts the first-order derivative preprocessing combined with the competitive adaptive reweighting algorithm-partial least squares (CARS-PLS) model.

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