CN113189083A - SERS (surface enhanced Raman scattering) specificity rapid detection method for heavy metal lead in food - Google Patents

Abstract

The invention discloses a rapid detection method for SERS specificity of heavy metal lead in food, which takes chloroauric acid, hydrogen peroxide and hydrochloric acid as reactants, a single-layer graphene oxide GO as a reaction activator, and lead ions and specific aptamers M4-16 thereof are added to regulate and control the activity of GO, wherein the generation amount of gold nanoparticles in a system is in direct proportion to the Raman signal intensity of Raman signal molecules 4-mercaptobenzoic acid, so that rapid and high-sensitivity detection of heavy metal lead in food based on the SERS technology is realized. A quantitative detection model is established by combining first-order derivative pretreatment with a cooperative interval-partial least square method, and the content of heavy metal lead in food is accurately predicted. The sensing detection method can effectively quantify and predict the content of heavy metal lead in food, and realize the rapid evaluation of heavy metal in food.

Description

SERS (surface enhanced Raman scattering) specificity rapid detection method for heavy metal lead in food

Technical Field

The invention belongs to the technical field of food, and particularly relates to a rapid detection method for SERS specificity of heavy metal lead in food.

Background

Heavy metals have non-degradable and bioaccumulating properties, and their presence in food products may have detrimental health effects. Lead ion (Pb)²⁺) Is one of the common toxic heavy metals in food, and the Pb is accumulated in different organs of the human body²⁺Can seriously damage human health, leading to cancer, mutation and teratogenicity. Pb in food²⁺Is unavoidable, thus ensuring that the food is free from Pb²⁺Contamination or contents thereof below the Maximum Residual Limit (MRL) are of great significance. Currently, the determination of Pb in food products²⁺The common methods of (A) are Atomic Absorption Spectrometry (AAS), inductively coupled plasma mass spectrometry (ICP-MS) and X-ray absorption spectrometry. These methods are sensitive and accurate enough, but most instruments are expensive, inconvenient for field testing and require the operation of experienced professionals.

Traditional raman spectroscopy signals are weak, and Surface Enhanced Raman Scattering (SERS) can greatly enhance the raman signal of an analysis sample. Amplification of the signal in SERS is mainly caused by electromagnetic interaction between light and noble metals, known as plasmon resonance. SERS is a rapid sensing technology, and compared with other sensors based on nano materials, the SERS has the advantages of high selectivity, high sensitivity and specific recognition of molecules. The establishment of the rapid high-sensitivity SERS detection method of heavy metal lead in food plays an important role in guaranteeing food safety.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides a rapid detection method for SERS specificity of heavy metal lead in food, which overcomes the problems of low conventional physicochemical detection speed, easy impurity interference and inaccurate quantification, and obviously improves the detection speed and the detection reliability of the content of the heavy metal lead in the food. The gold nano reduction system activated by the Graphene Oxide (GO) regulated and controlled by the aptamer is constructed, and is used for SERS sensing detection of heavy metal lead in food, and the rapid quantitative detection of the lead ion content is realized by adopting a chemometrics algorithm for data processing and analysis.

The technical scheme of the invention is as follows: a method for quickly detecting SERS specificity of heavy metal lead in food comprises the following steps:

(1) aptamer M4-16 and lead ion Pb²⁺Specific binding: adding Pb-containing solution into M4-16²⁺The food sample solution and the graphene oxide GO solution are mixed to obtain a mixed solution, and the mixed solution is kept stand for a certain time, wherein M4-16 is preferentially mixed with Pb²⁺Specific binding;

(2) controllable reduction of gold nanoparticles: sequentially adding HAuCl into the mixed solution in the step (1)₄HCl and H₂O₂Heating in water bath to produce reduced AuNPs in the reaction system without adding Pb²⁺The food sample of (a) is a blank control;

(3) establishing a standard curve for detecting the content of heavy metal lead in food: pretreating food sample, and adding Pb with different concentration gradients²⁺Respectively constructing the reaction systems in the step (2) by using standard substance solutions to obtain mixed liquor containing reduced AuNPs, adding Raman signal molecules to determine the Raman spectrum of the mixed liquor, collecting multiple Raman spectra of each sample, and averaging to establish Pb²⁺The linear relation between the content C and the Raman intensity I of the mixed solution is obtained, namely Pb²⁺A content detection standard curve equation;

(4) data processing and model building: processing and analyzing the obtained Raman spectrum data to obtain a quantitative model;

(5) food sample detection: pretreating a food sample to be detected, constructing the reaction system in the step (2) to obtain a mixed solution containing reduced AuNPs, measuring the Raman spectrum of the mixed solution, and calculating Pb in the food sample to be detected through the quantitative model in the step (4)²⁺And (4) content.

In the scheme, M4-16 is in Pb²⁺Preferential specificity with Pb in the Presence of GO²⁺And M4-16 controls the reduction amount of AuNPs in the reaction system by regulating the activity of GO.

In the scheme, in the step (1), the volume ratio of the M4-16 solution to GO is 1:1, and the concentration ratio is 0.4 mu mol. L^-1:230μmol·L^-1；HAuCl₄HCl and H₂O₂The volume ratio of (1) to (5) to (116) is 6:5:116, the concentration ratio is 0.1% to 0.01 mol.L^-1:0.2mol·L^-1。

In the scheme, in the step (1), the standing time is 15-20 min.

In the scheme, in the step (2), the heating temperature is 60 ℃ and the heating time is 25-30 min.

In the above scheme, in the step (3), the Raman signal molecule is 4-mercaptobenzoic acid (4-MBA), and the concentration of 4-MBA is 10-³mol/L, and the volume ratio of the mixed solution to 4-MBA is 1: 1.

In the above scheme, in the step (3), the raman spectrum intensity of the mixed solution is measured, specifically, the raman spectrometer is used for detection, and the acquisition parameters are as follows: the excitation wavelength is 785 nm; the laser power is 100% of the maximum power; the integration time was 0.5 s.

In the scheme, in the step (3), the quantitative detection adopts a labeling method, and Pb is detected²⁺The concentration gradient is 0.1 mug.L^-1-100μg·L^-1The 10 uniformly distributed concentrations are measured by using a Raman spectrometer for Pb with different concentrations²⁺And carrying out spectrum collection by a mixed detection system of the labeled food sample.

In the above aspect, in the step (3), the Raman spectrum of the mixed solution is measured specifically at 1595.80cm under 785nm laser excitation^-1(ii) the intensity of the raman spectrum; the standard curve for detecting the content of heavy metal lead in the food is y-364.44 x +701.82, and the coefficient R is determined²Is 0.9969.

In the above scheme, in the step (4), the quantitative model is a first derivative preprocessing combined with a collaborative interval-partial least squares SI-PLS model, and the data is optimized and processed to effectively quantify and predict the content of heavy metal lead in the food.

In the above scheme, the food pretreatment in step (3): carrying out corresponding pretreatment on different foods according to national food safety standard GB5009.11-2014 determination of heavy metal lead and inorganic arsenic in foods;

in the scheme, the detection of the content of heavy metal lead in the food in the step (3) comprises the following steps: will contain different Pb²⁺Dripping the detection system of the food extract on flat tinfoil, adding 4-MBA of Raman signal molecules, naturally drying, and measuring the Raman spectrum of the mixed solution.

In the scheme, each sample in the step (3) collects 15 Raman spectra, and the average value is taken to establish the linear relation between the lead ion content C and the Raman intensity I of the mixed solution, so that the lead ion content detection standard curve equation is obtained.

In the scheme, the data in the step (4) are processed and analyzed, and a quantitative model is established by utilizing chemometrics to optimize data results.

Compared with the prior art, the invention has the beneficial effects that: the gold nano reduction system activated by the aptamer regulating and controlling graphene oxide is a stable and good SERS detection system, and in the detection process, the aptamer M4-16 has specific affinity to lead ions, can avoid interference of other complex components in the food detection process, releases graphene oxide through specific combination with the lead ions, and activates the gold nano reduction reaction with SERS enhancement effect. The invention relates to a SERS (surface enhanced Raman scattering) specificity rapid detection method for heavy metal lead in food, which is characterized in that the corresponding relation is established between the Raman spectrum intensity of a Raman signal molecule 4-MBA and the content of the heavy metal lead in the food, and the subsequent rapid processing of data is realized by data preprocessing and a chemometrics model, so that the heavy metal lead in the food is reduced to 0.1 mu g.L^-1Accurate detection of very low levels of (a).

Drawings

FIG. 1 is a schematic diagram of a SERS specificity rapid detection method of heavy metal lead in food.

Fig. 2 is a FESEM image of AuNPs and a uv-vis absorption spectrum of AuNPs, wherein fig. 2(a) is a FESEM image of AuNPs, and fig. 2(b) is a uv-vis absorption spectrum of AuNPs generated in the M4-16 aptamer-modulated detection system.

FIG. 3 is a graph comparing the results of the detection system under different conditions, wherein FIG. 3(a) is 1595.80cm after different heating times of the detection system^-1Comparison of Raman intensity at (A), FIG. 3(b) is a graph of different concentrations of HAuCl₄(b) Optimized detection system 1595.80cm^-1Raman ofComparison of spectral intensities, FIG. 3(c) is 1595.80cm detection System optimized for aptamers at different concentrations^-1Graph of intensity comparison of Raman spectra, FIG. 3(d) is 1595.80cm with the addition of other ion optimized detection system^-1Is aligned with Pb²⁺A specific comparison of (A) and (B).

FIG. 4 shows different concentrations of Pb²⁺The surface enhanced Raman spectrogram and the Raman spectrum intensity and concentration linear curve chart of the mixed labeled tea sample and the M4-16-SERS detection system are shown in FIG. 4(a) which is a Pb graph with different concentrations²⁺(0.1-100 mu g/L) surface enhanced Raman spectrum of the mixed tea sample with the M4-16-SERS detection system, and the graph in FIG. 4(b) shows Pb with different concentrations²⁺(0.1-100 mu g/L) Raman spectrum of mixed labeled tea sample and M4-16-SERS detection system is 1595.80cm^-1Left and right raman spectral intensity versus concentration linear plots.

FIG. 5 is a graph of the optimal results of SI-PLS detection in a calibration and prediction set, wherein FIG. 5(a) performs a first derivative pre-processing on the data of M4-16-SERS system to select the optimal results from Pb²⁺Specific wavelength and composition variables, fig. 5(b) is a relation between the PLS modeled SERS prediction data and actual concentration data after first derivative preprocessing, fig. 5(c) is SI variable screening of data of the M4-16-SERS system after first derivative preprocessing, and fig. 5(d) is a relation between the SI prediction data and actual concentration data modeled by SI-PLS after first derivative preprocessing.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.

In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "axial," "radial," "vertical," "horizontal," "inner," "outer," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the present invention and for simplicity in description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.

In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.

Fig. 1 shows a preferred embodiment of the method for quickly detecting the SERS specificity of heavy metal lead in food, and the method for quickly detecting the SERS specificity of heavy metal lead in food comprises the following steps:

(1) aptamer M4-16 and lead ion Pb²⁺Specific binding: adding Pb-containing solution into M4-16²⁺The food sample solution and the graphene oxide GO solution are mixed to obtain a mixed solution, and the mixed solution is kept stand for a certain time, wherein M4-16 is preferentially mixed with Pb²⁺Specific binding;

(2) controllable reduction of gold nanoparticles: sequentially adding HAuCl into the mixed solution in the step (1)₄HCl and H₂O₂Heating in water bath to produce reduced AuNPs in the reaction system without adding Pb²⁺The food sample of (a) is a blank control;

(3) foodEstablishing a standard curve for detecting the content of heavy metal lead in the product: pretreating food sample, and adding Pb with different concentration gradients²⁺Respectively constructing the reaction systems in the step (2) by using standard substance solutions to obtain mixed liquor containing reduced AuNPs, adding Raman signal molecules to determine the Raman spectrum of the mixed liquor, collecting multiple Raman spectra of each sample, and averaging to establish Pb²⁺The linear relation between the content C and the Raman intensity I of the mixed solution is obtained, namely Pb²⁺A content detection standard curve equation;

(4) data processing and model building: processing and analyzing the obtained Raman spectrum data to obtain a quantitative model;

(5) food sample detection: pretreating a food sample to be detected, constructing the reaction system in the step (2) to obtain a mixed solution containing reduced AuNPs, measuring the Raman spectrum of the mixed solution, and calculating Pb in the food sample to be detected through the quantitative model in the step (4)²⁺And (4) content.

According to the method, chloroauric acid, hydrochloric acid and hydrogen peroxide are used as reactants, single-layer Graphene Oxide (GO) is used as a reaction activator, lead ions and specific aptamers (M4-16) thereof are added to regulate and control the activity of GO, the yield of gold nanoparticles in a system is in direct proportion to the Raman signal intensity of Raman signal molecules 4-mercaptobenzoic acid (4-MBA), and the rapid and high-sensitivity detection of heavy metal lead in food based on the SERS technology is realized. A quantitative detection model is established by combining first derivative pretreatment with a combined interval-partial least square method (SI-PLS), so that the content of heavy metal lead in food can be accurately predicted. The sensing detection method can effectively quantify and predict the content of heavy metal lead in food, and realize the rapid evaluation of heavy metal in food.

According to this embodiment, preferably, the aptamer regulates establishment of a reaction system for graphene oxide activated gold nano reduction:

construction of (I) M4-16-SERS reaction system

Aptamer M4-16 is DNA single strand with nucleic acid sequence of 5-AGGGTGGGTGGGAGGG-3, and is added into 5mL test tube in an amount of 100. mu.L and 0.4. mu. mol. L^-1Aptamer solution, 100 μ L of different concentrations containing Pb²⁺The digestion solution of the tea sample is 600 mu L and 230 mu Lg·L^-1Graphene oxide GO solution, when the three substances exist simultaneously, the aptamer M4-16 is preferentially mixed with Pb²⁺Combine to form a stable G-quadruplex structure. After 15min, 60 μ L of 0.1% HAuCl was added₄、30μL、0.01mol·L^-1HCl (g) 50. mu.L, 0.2 mol. L^-1H of (A) to (B)₂O₂Finally, 1160. mu.L of ultrapure water was added to dilute the solution to 1.5 mL. The mixture was shaken gently. In the absence of a reaction activator, H₂O₂Reduction of HAuCl₄The method is a slow process, and free GO in the solution enables a reaction system to generate AuNPs with high Raman activity in a short time. The tube was heated in a water bath at 60 ℃ for 25 minutes and then cooled to room temperature for SERS spectroscopy. Without addition of Pb in the same volume²⁺The tea sample of (a) is blank.

(II) optimization of detection conditions

The reaction system constructed according to (A) and the Raman system is used for the reaction of Pb²⁺The detection process comprises the following steps: adding Pb to the mixture in the presence of the aptamer and GO²⁺Enhancing Raman signal of 4-MBA, thereby realizing the effect on Pb²⁺Belongs to a reaction system for enhancing signals. In the whole Pb²⁺In the process of Raman detection, the aptamer plays a key role in regulation. In order to obtain the best detection sensitivity, the concentration of the aptamer was first optimized, taking M4-16 as an example. In addition, due to heating time and HAuCl₄The concentration of (a) indirectly determines the signal intensity of Raman detection by influencing the reduction of the gold nanoparticles, and therefore, the concentration of (a) is optimized. Aptamer concentrations were 0.1. mu. mol. L, respectively^-1，0.2μmol·L^-1，0.3μmol·L^-1，0.4μmol·L^-1，0.5μmol·L^-1，0.6μmol·L^-1，0.7μmol·L^-1，0.8μmol·L^-1Heating time of 20, 25, 30, 35, 40 minutes, respectively, HAuCl₄The concentrations were 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 0.6%, respectively. 0.7% and 0.8%. Under the condition of keeping other detection conditions unchanged, each influencing factor is tested, the optimal detection condition is selected, and Pb used in the test is optimized²⁺The concentration is 100 mug. L ^-13 Raman spectra were collected for each droplet of the sample and averaged as the final result.

Comparison of scanning electron microscope, ultraviolet and detection effects of (III) M4-16-SERS detection system

First, a tea digesting solution having a high concentration of lead ions and no added lead ions was added to an aptamer system, and a raman spectrum was collected as a blank and comparatively analyzed. The uv absorption of these samples was collected using a mini uv spectrophotometer (agilent technologies, usa) to verify the production of gold nanoparticles in the system.

The secondary structure of the double-stranded DNA is double helix, base complementary pairing conforms to the principles of A-T and G-C, two hydrogen bonds exist between AT, and three hydrogen bonds exist between GC. However, this is not the only pairing that can occur between bases. Different base pairing results in different structures, including triple-and quadruple-stranded structures, where the interaction of the G-quadruplex structure with the metal ion is particularly prominent. In the G-quadruplex structure, four guanines are arranged in a square, and N1-O6 and N2-N7 at the edges of the square are bonded by Hoogsteen hydrogen bonds to form a planar structure called a G-quadruplex. Pb²⁺After coordination to the aptamer, a symmetric binding structure is formed between the two guanine quadruplets. The G tetrad has a large pi plane. Due to pi-pi stacking, the G-quartets also tend to stack on each other, forming a G-quadruplex structure. Pb²⁺Is a toxic heavy metal ion and can cause cognitive and motor disorders. Pb²⁺The genotoxicity of (A) is its extremely strong ability to induce stable folding of DNA structure.

The image in fig. 2a shows the size and shape of the AuNPs. At Pb²⁺The AuNPs formed in the aptamer system at higher concentrations (100. mu.g/L) were relatively uniform, with a diameter of about 80 nm. Fig. 2b shows the uv-vis spectrum of the aptamer SERS system, where a distinct AuNPs characteristic peak appears. In order to prove the enhancement effect of the AuNPs reduced by the SERS system, 4-MBA is taken as a signal molecule, and Raman spectra before and after the AuNPs are reduced are collected, as shown in the upper right part of figure 1. 905.12cm^-1The Raman peak of (a) is the vibrational peak of the S-H bond, 1095.77cm^-1The peak of (A) is a benzene ringSensitive stretching vibration peak of generation base, 1175.99cm^-1And 1595.80cm^-1The peaks of (a) and (b) respectively belong to a C-H deformation vibration peak and a C-C expansion vibration peak. The original Raman spectrum peak of 4-MBA is very weak. When 4-MBA is mixed with AuNPs, the Raman shift of 4-MBA is 1095cm^-1The characteristic peak of the AuNPs is obviously enhanced, which shows that the AuNPs have good capability of enhancing Raman signals. In order to quantify the enhancement effect of AuNPs, enhancement factors of AuNPs were calculated. The calculation formula is as follows: enhancement factor ═ I_SRES/C_SERS)/(I_MBA/C_MBA). The enhancement factor of the gold nano-particles prepared by the invention is 2.36 multiplied by 10⁶。

According to the experimental method, the reaction conditions of the reaction system are optimized. In the optimization of the heating reaction time, the longer the heating time is, the more complete the reaction is, the color of the solution gradually changes from transparent to wine red, but when the reaction time exceeds 40min, a small amount of visible precipitate is generated in the system, and the acquisition of SERS spectrum is not facilitated. Therefore, the heating reaction time was chosen to be 25 minutes, as shown in fig. 3 a. When HAuCl is present₄At a concentration of 0.6%, the system was 1595.80cm^-1Has the largest peak value of Raman spectrum, so HAuCl is selected₄The concentration was 0.6%, as shown in FIG. 3 b. When the concentration of M4-16 is 0.4 mu mol.L^-1At most, 1595.80cm^-1The peak of the Raman spectrum is maximized, so that the aptamer concentration is selected to be 0.4. mu. mol. L^-1As shown in fig. 3 c.

In addition, the addition of other ions, such as Hg, to the system was compared²⁺、Cr⁶⁺、Cu²⁺、Ni²⁺、Ba²⁺、Cd²⁺、Ag²⁺、Fe³⁺、As³⁺At time of 1595.80cm^-1The SERS signal intensity at (a) is shown in fig. 3 d. The results showed that only 100. mu. mol/L Pb was present in the detection system²⁺The Raman spectrum intensity of 4-MBA is obviously enhanced, and other ions do not interfere Pb²⁺Specific binding to M4-16. It can be seen that the detection system constructed has good immunity to other ions.

SERS specificity rapid quantitative detection of heavy metal lead in food, taking tea as an example:

tea sample pretreatment

The black tea which is common in the market is selected as an experimental sample and ground into powder so as to facilitate the subsequent operation. Carrying out microwave digestion on tea powder according to the national standard GB 5009.12-2017: a0.2 g sample of the powder (to an accuracy of 0.001g) was weighed on an electronic balance and added to the microwave digestion tank. 9mL of concentrated HNO was added₃And Pb in various concentrations²⁺And (3) after the standard solution is subjected to pre-digestion for 1h at 100 ℃, cooling to room temperature, and then performing digestion according to the operating procedure instructions of a microwave digestion instrument. The digestion conditions are set to control the temperature to be 190 ℃, the temperature rise time to be 20min, the constant temperature time to be 10min and the cooling time to be 15 min. Taking out, cooling to room temperature, slowly opening the tank cover in a fume hood, exhausting, washing the inner cover with a small amount of deionized water, placing the digestion tank in an ultrasonic water bath box, heating at 100 deg.C for 30min, degassing, and obtaining clear and transparent digestion solution without any solid residue. The digested solution was diluted into a 50mL volumetric flask, mixed and shaken well, filtered through a 0.22 μm filter and stored in a refrigerator at 4 ℃. Pb in the final 11 tea extract samples²⁺The concentrations of (A) were 100. mu.g/L, 80. mu.g/L, 60. mu.g/L, 40. mu.g/L, 20. mu.g/L, 10. mu.g/L, 5. mu.g/L, 1. mu.g/L, 0.5. mu.g/L, 0.1. mu.g/L and 0. mu.g/L, respectively. The tea leaf digestion sample was used for subsequent SERS and ICP-MS analysis.

(II) Raman spectrum acquisition

Taking 5 μ L of the mixed solution from the test tube, dropping it on a flat tin foil, and adding 5 μ L of 4-MBA (10-³μ mol/L), and collecting a raman signal after natural drying. All SERS spectra were measured at 785nm excitation wavelength using confocal micro-raman imaging spectrometer (XploRA plus, HORIBA, usa) in combination with LabSpec6 software and the spectral data processed. The detection time is 0.5s, the accumulation is 1 time, the laser power is set to be 100 percent of the maximum power, the detection time is obviously shortened, and the lead ion specific fingerprint spectrum can be quickly obtained. And (3) acquiring 15 spectra at different random points of each sample, and finally obtaining 150 sets of spectral data for establishing a linear equation and a chemometrics quantitative model for quantifying heavy metal lead in food. A blank test of the tea sample without the added standard is set. Performing inductively coupled plasma mass spectrometry (ICP-MS) on food samplesComparative validation analysis is shown in table 3.

FIG. 4a shows different Pb²⁺And (3) carrying out original SERS spectrum on the tea extract sample with the concentration (0.1-100 mu g/L) in an M4-16-SERS system. Wherein the SERS intensity is along with Pb in the tea sample²⁺The concentration increases. M4-16 aptamer at lower Pb²⁺The sensitivity is still higher at the concentration (0.1. mu.g/L). 1595.80cm of 15 spectra were taken from each sample^-1The average spectral intensity is linearly fitted, as shown in fig. 4b, to obtain a linear equation: y 364.44x +701.82, determining the coefficient R²Is 0.9969.

(III) data processing and analysis

Due to the external environment, complex components of the food and fluorescence interference (for example, lignin and other substances in plant cell walls have fluorescence effect), the Raman spectrum collected in practical application is greatly interfered. Therefore, the algorithm processing and analysis of the raw data can effectively quantify and predict the content of heavy metal lead in the food.

According to the invention, firstly, LabSpec6 software of a spectrometer is adopted for smooth denoising and baseline removing treatment, and the optimal smooth denoising treatment parameters are set as follows: type is Denoise, Size is 256, Degree is 2, Factor is 1. The baseline removal parameter settings were as follows: the background fit is a polynomial fit with the parameters set to Degree-7 and Max points-256. The baseline shift phenomenon of the raman spectrum is initially improved by the de-baseline process. Further, the invention selects various preprocessing methods (SG, MC, SNV, first derivative and second derivative) for the original spectral data (including 200-^-1470 variables in the raman shift range). Pb established by different pretreatment methods²⁺The statistical results of the partial least squares model of (A) are shown in Table 1, and the results of dividing SNV-PLS are not very good (R)_C0.9352), other pretreatment methods can achieve better results (R)_C>0.994). For R_CThe preprocessing of the first derivative works best for spectral data with a mean square error of 0.9960, 0.9949.

Table 1. Raman spectrum pretreatment for lead ion detection in tea sample and PLS model result

The original SERS spectral data is preprocessed by using a data preprocessing method, so that noise in the data is eliminated, and the speed and the performance of a multivariate algorithm applied to the spectral data are improved. The preprocessed SERS spectral data consists of 470 variables, and the spectral range is 200cm^-1-2000cm^-1. First derivative preprocessing combined with PLS model on Pb²⁺The spectral data of the spiked tea samples had the best predictive performance, and furthermore, the model results for the different modeling methods with spiked tea samples are shown in Table 2, where SI-PLS detects the best results in the calibration and prediction set, as shown in FIG. 5, where FIG. 5(a) performs a first derivative pre-processing on the data of the M4-16-SERS system to select the Pb-free spectrum for selection²⁺Specific wavelength and composition variables, fig. 5(b) is a relation between the PLS modeled SERS prediction data and actual concentration data after first derivative preprocessing, fig. 5(c) is SI variable screening of data of the M4-16-SERS system after first derivative preprocessing, and fig. 5(d) is a relation between the SI prediction data and actual concentration data modeled by SI-PLS after first derivative preprocessing. The correlation coefficients of the correction set and the prediction set are respectively R_C＝0.995，R_P＝0.997。

TABLE 2 multivariate algorithm model results for lead ion prediction in tea samples

In order to verify the accuracy and precision of detecting heavy metal lead in food, 10 Pb with different concentrations are used²⁺The recovery experiment was performed with a labeled tea sample. The average recovery of three measurements per concentration was between 98.4-103.60%,the relative standard deviation was between 0.50% and 8.075%, as in table 3. Likewise, for 10 different concentrations of Pb²⁺The standard ICP-MS method analysis is carried out on the standard tea sample, and the T test result shows that the results of the two detection methods have no significant difference (P is 0.415)>0.05)。

TABLE 3 comparison of SERS method and ICP-MS method for determination of lead ions in tea

In conclusion, the invention establishes a rapid and high-sensitivity SERS detection system which can rapidly detect Pb in food²⁺And (4) content. The invention successfully constructs a novel oxidation-reduction reaction system with an aptamer sequence (M4-16) for regulating and controlling the reaction activation capability of graphene oxide. Pb²⁺The concentration is in direct proportion to the reduction amount of AuNPs, so that the Raman signal of 4-MBA accurately reflects Pb in food²⁺The content of (a). In addition, the M4-16-SERS detection system has good binding specificity, signal intensity and accuracy, and can be used for determining Pb in food with ICP-MS²⁺Compared with the prior art, the result has no significant difference (P is 0.415)>0.05). Therefore, the SERS detection method combined with chemometrics can be used for Pb in food samples²⁺Detection and quantification of (3).

It should be understood that although the present description has been described in terms of various embodiments, not every embodiment includes only a single embodiment, and such description is for clarity purposes only, and those skilled in the art will recognize that the embodiments described herein may be combined as suitable to form other embodiments, as will be appreciated by those skilled in the art.

The above-listed detailed description is only a specific description of a possible embodiment of the present invention, and they are not intended to limit the scope of the present invention, and equivalent embodiments or modifications made without departing from the technical spirit of the present invention should be included in the scope of the present invention.

Claims (10)

1. The SERS specificity rapid detection method for heavy metal lead in food is characterized by comprising the following steps:

(1) aptamer M4-16 and lead ion Pb²⁺Specific binding: adding Pb-containing solution into M4-16²⁺The food sample solution and the graphene oxide GO solution are mixed to obtain a mixed solution, the mixed solution is kept stand, and M4-16 is preferentially mixed with Pb²⁺Specific binding;

(2) controllable reduction of gold nanoparticles: sequentially adding HAuCl into the mixed solution in the step (1)₄HCl and H₂O₂Heating in water bath to produce reduced AuNPs in the reaction system without adding Pb²⁺The food sample of (a) is a blank control;

(3) establishing a standard curve for detecting the content of heavy metal lead in food: pretreating food sample, and adding Pb with different concentration gradients²⁺Respectively constructing the reaction systems in the step (2) by using standard substance solutions to obtain mixed liquor containing reduced AuNPs, adding Raman signal molecules to determine the Raman spectrum of the mixed liquor, collecting multiple Raman spectra of each sample, and averaging to establish Pb²⁺The linear relation between the content C and the Raman intensity I of the mixed solution is obtained, namely Pb²⁺A content detection standard curve equation;

(4) data processing and model building: processing and analyzing the obtained Raman spectrum data to obtain a quantitative model;

(5) food sample detection: pretreating a food sample to be detected, constructing the reaction system in the step (2) to obtain a mixed solution containing reduced AuNPs, measuring the Raman spectrum of the mixed solution, and calculating Pb in the food sample to be detected through the quantitative model in the step (4)²⁺And (4) content.

2. The method for detecting the content of heavy metal lead in food by using surface-enhanced Raman spectroscopy according to claim 1, wherein M4-16 is in Pb²⁺Preferential specificity with Pb in the Presence of GO²⁺And M4-16 controls the reduction amount of AuNPs in the reaction system by regulating the activity of GO.

3. The method for detecting the content of heavy metal lead in food by using surface-enhanced Raman spectroscopy as claimed in claim 1, wherein in the step (1), the volume ratio of the M4-16 solution to GO is 1:1, and the concentration ratio is 0.4 [ mu ] mol-L^-1:230μmol·L^-1；HAuCl₄HCl and H₂O₂The volume ratio of (1) to (5) to (116) is 6:5:116, the concentration ratio is 0.1% to 0.01 mol.L^-1:0.2mol·L^-1。

4. The method for detecting the content of heavy metal lead in food by surface-enhanced Raman spectroscopy according to claim 1, wherein in the step (1), the standing time is 15-20 min.

5. The method for detecting the content of heavy metal lead in food by using surface-enhanced Raman spectroscopy according to claim 1, wherein in the step (2), the heating temperature is 60 ℃ and the heating time is 25-30 min.

6. The method for detecting the content of heavy metal lead in food by surface-enhanced Raman spectroscopy according to claim 1, wherein in the step (3), the Raman signal molecule is 4-mercaptobenzoic acid, and the concentration of the 4-mercaptobenzoic acid is 10^-3mol/L, and the volume ratio of the mixed solution to the 4-mercaptobenzoic acid is 1: 1.

7. The method for detecting the content of heavy metal lead in food by using surface-enhanced Raman spectroscopy as claimed in claim 1, wherein in the step (3), the Raman spectroscopy intensity of the mixed solution is measured, specifically, a Raman spectrometer is used for detection, and the collection parameters are as follows: the excitation wavelength is 785 nm; the laser power is 100% of the maximum power; the integration time was 0.5 s.

8. The method for detecting the content of heavy metal lead in food by surface-enhanced Raman spectroscopy (SERS) as claimed in claim 1, wherein in the step (3), the quantitative detection adopts a labeling method, and Pb is detected by a labeling method²⁺The concentration gradient is 0.1 mug.L^-1-100μg·L^-1The 10 uniformly distributed concentrations are measured by using a Raman spectrometer for Pb with different concentrations²⁺And carrying out spectrum collection by a mixed detection system of the labeled food sample.

9. The method for detecting the content of heavy metal lead in food according to claim 1, wherein in the step (3), when the Raman spectrum of the mixed solution is measured, the Raman spectrum is specifically measured at 1595.80cm under the excitation of 785nm laser^-1(ii) the intensity of the raman spectrum; the standard curve for detecting the content of heavy metal lead in the food is y-364.44 x +701.82, and the coefficient R is determined²Is 0.9969.

10. The method for detecting the content of heavy metal lead in food by using surface-enhanced Raman spectroscopy as claimed in claim 1, wherein in the step (4), the quantitative model is a first derivative pretreatment combined with a collaborative interval-partial least squares (SI-PLS) model.

Images