CN114609119B - A method for detecting heavy metal cadmium in food - Google Patents
A method for detecting heavy metal cadmium in food Download PDFInfo
- Publication number
- CN114609119B CN114609119B CN202210281764.7A CN202210281764A CN114609119B CN 114609119 B CN114609119 B CN 114609119B CN 202210281764 A CN202210281764 A CN 202210281764A CN 114609119 B CN114609119 B CN 114609119B
- Authority
- CN
- China
- Prior art keywords
- solution
- food
- agnps
- tmt
- detection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 235000013305 food Nutrition 0.000 title claims abstract description 67
- 238000000034 method Methods 0.000 title claims abstract description 36
- 229910052793 cadmium Inorganic materials 0.000 title claims abstract description 35
- BDOSMKKIYDKNTQ-UHFFFAOYSA-N cadmium atom Chemical compound [Cd] BDOSMKKIYDKNTQ-UHFFFAOYSA-N 0.000 title claims abstract description 34
- 229910001385 heavy metal Inorganic materials 0.000 title claims abstract description 31
- 238000001514 detection method Methods 0.000 claims abstract description 73
- FOIXSVOLVBLSDH-UHFFFAOYSA-N Silver ion Chemical compound [Ag+] FOIXSVOLVBLSDH-UHFFFAOYSA-N 0.000 claims abstract description 18
- 239000007788 liquid Substances 0.000 claims abstract description 17
- 230000003044 adaptive effect Effects 0.000 claims abstract description 5
- 230000002860 competitive effect Effects 0.000 claims abstract description 5
- 239000000243 solution Substances 0.000 claims description 76
- HEMHJVSKTPXQMS-UHFFFAOYSA-M Sodium hydroxide Chemical compound [OH-].[Na+] HEMHJVSKTPXQMS-UHFFFAOYSA-M 0.000 claims description 26
- ATJFFYVFTNAWJD-UHFFFAOYSA-N Tin Chemical compound [Sn] ATJFFYVFTNAWJD-UHFFFAOYSA-N 0.000 claims description 23
- 239000011259 mixed solution Substances 0.000 claims description 22
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 16
- 239000000203 mixture Substances 0.000 claims description 13
- 238000000479 surface-enhanced Raman spectrum Methods 0.000 claims description 11
- 101710134784 Agnoprotein Proteins 0.000 claims description 10
- 238000001237 Raman spectrum Methods 0.000 claims description 10
- 239000012086 standard solution Substances 0.000 claims description 10
- 238000001228 spectrum Methods 0.000 claims description 9
- 239000007864 aqueous solution Substances 0.000 claims description 8
- 238000010438 heat treatment Methods 0.000 claims description 8
- 238000007781 pre-processing Methods 0.000 claims description 8
- 238000003756 stirring Methods 0.000 claims description 8
- 238000004422 calculation algorithm Methods 0.000 claims description 7
- 230000005284 excitation Effects 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 6
- 230000015572 biosynthetic process Effects 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 5
- 238000012937 correction Methods 0.000 claims description 4
- 238000003786 synthesis reaction Methods 0.000 claims description 4
- 230000008030 elimination Effects 0.000 claims description 3
- 238000003379 elimination reaction Methods 0.000 claims description 3
- 230000002068 genetic effect Effects 0.000 claims description 3
- 238000003760 magnetic stirring Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 238000010187 selection method Methods 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims 2
- 238000001344 confocal Raman microscopy Methods 0.000 claims 1
- 238000003384 imaging method Methods 0.000 claims 1
- 238000002360 preparation method Methods 0.000 claims 1
- 230000009466 transformation Effects 0.000 claims 1
- 238000004416 surface enhanced Raman spectroscopy Methods 0.000 abstract description 32
- 235000010413 sodium alginate Nutrition 0.000 abstract description 24
- 239000000661 sodium alginate Substances 0.000 abstract description 24
- 229940005550 sodium alginate Drugs 0.000 abstract description 24
- IXPNQXFRVYWDDI-UHFFFAOYSA-N 1-methyl-2,4-dioxo-1,3-diazinane-5-carboximidamide Chemical compound CN1CC(C(N)=N)C(=O)NC1=O IXPNQXFRVYWDDI-UHFFFAOYSA-N 0.000 abstract description 23
- 230000000694 effects Effects 0.000 abstract description 14
- 239000000758 substrate Substances 0.000 abstract description 8
- 238000005516 engineering process Methods 0.000 abstract description 4
- 238000011156 evaluation Methods 0.000 abstract description 3
- SQGYOTSLMSWVJD-UHFFFAOYSA-N silver(1+) nitrate Chemical compound [Ag+].[O-]N(=O)=O SQGYOTSLMSWVJD-UHFFFAOYSA-N 0.000 abstract 2
- 239000000376 reactant Substances 0.000 abstract 1
- 238000011896 sensitive detection Methods 0.000 abstract 1
- 229910001961 silver nitrate Inorganic materials 0.000 abstract 1
- 238000001069 Raman spectroscopy Methods 0.000 description 25
- 244000269722 Thea sinensis Species 0.000 description 23
- 235000013616 tea Nutrition 0.000 description 22
- 239000000523 sample Substances 0.000 description 21
- WLZRMCYVCSSEQC-UHFFFAOYSA-N cadmium(2+) Chemical compound [Cd+2] WLZRMCYVCSSEQC-UHFFFAOYSA-N 0.000 description 10
- XHSSRBLTUVPKQU-UHFFFAOYSA-N 4-sulfanylbutanimidamide Chemical compound NC(=N)CCCS XHSSRBLTUVPKQU-UHFFFAOYSA-N 0.000 description 7
- 239000012295 chemical reaction liquid Substances 0.000 description 7
- 238000001095 inductively coupled plasma mass spectrometry Methods 0.000 description 7
- 125000003178 carboxy group Chemical group [H]OC(*)=O 0.000 description 6
- 238000002156 mixing Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 229910052709 silver Inorganic materials 0.000 description 6
- 239000004332 silver Substances 0.000 description 6
- 238000003332 Raman imaging Methods 0.000 description 5
- 239000003638 chemical reducing agent Substances 0.000 description 5
- 125000002887 hydroxy group Chemical group [H]O* 0.000 description 5
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 4
- 238000001157 Fourier transform infrared spectrum Methods 0.000 description 4
- BQCADISMDOOEFD-UHFFFAOYSA-N Silver Chemical compound [Ag] BQCADISMDOOEFD-UHFFFAOYSA-N 0.000 description 4
- 238000010521 absorption reaction Methods 0.000 description 4
- 238000001816 cooling Methods 0.000 description 4
- 230000029087 digestion Effects 0.000 description 4
- 238000001035 drying Methods 0.000 description 4
- 150000002500 ions Chemical class 0.000 description 4
- 239000002245 particle Substances 0.000 description 4
- 239000000843 powder Substances 0.000 description 4
- 239000000126 substance Substances 0.000 description 4
- 241000196324 Embryophyta Species 0.000 description 3
- LYCAIKOWRPUZTN-UHFFFAOYSA-N Ethylene glycol Chemical compound OCCO LYCAIKOWRPUZTN-UHFFFAOYSA-N 0.000 description 3
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 description 3
- -1 polysaccharide Sodium Alginate Chemical class 0.000 description 3
- 238000011084 recovery Methods 0.000 description 3
- 229910052710 silicon Inorganic materials 0.000 description 3
- 239000010703 silicon Substances 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000003321 amplification Effects 0.000 description 2
- 239000012491 analyte Substances 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 2
- 239000008367 deionised water Substances 0.000 description 2
- 229910021641 deionized water Inorganic materials 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 229910021645 metal ion Inorganic materials 0.000 description 2
- 239000002082 metal nanoparticle Substances 0.000 description 2
- 238000000120 microwave digestion Methods 0.000 description 2
- 239000002105 nanoparticle Substances 0.000 description 2
- 229910000510 noble metal Inorganic materials 0.000 description 2
- 238000003199 nucleic acid amplification method Methods 0.000 description 2
- 125000001997 phenyl group Chemical group [H]C1=C([H])C([H])=C(*)C([H])=C1[H] 0.000 description 2
- 238000006722 reduction reaction Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 238000011895 specific detection Methods 0.000 description 2
- 230000003595 spectral effect Effects 0.000 description 2
- 230000002195 synergetic effect Effects 0.000 description 2
- 230000002194 synthesizing effect Effects 0.000 description 2
- 238000002211 ultraviolet spectrum Methods 0.000 description 2
- 206010014561 Emphysema Diseases 0.000 description 1
- 241000282412 Homo Species 0.000 description 1
- 206010028980 Neoplasm Diseases 0.000 description 1
- GRYLNZFGIOXLOG-UHFFFAOYSA-N Nitric acid Chemical compound O[N+]([O-])=O GRYLNZFGIOXLOG-UHFFFAOYSA-N 0.000 description 1
- 208000001132 Osteoporosis Diseases 0.000 description 1
- 238000003723 Smelting Methods 0.000 description 1
- 235000006468 Thea sinensis Nutrition 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 235000020279 black tea Nutrition 0.000 description 1
- 201000011510 cancer Diseases 0.000 description 1
- 230000000711 cancerogenic effect Effects 0.000 description 1
- 231100000357 carcinogen Toxicity 0.000 description 1
- 239000003183 carcinogenic agent Substances 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000012630 chemometric algorithm Methods 0.000 description 1
- 208000020832 chronic kidney disease Diseases 0.000 description 1
- 238000004624 confocal microscopy Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000007872 degassing Methods 0.000 description 1
- 208000028208 end stage renal disease Diseases 0.000 description 1
- 201000000523 end stage renal failure Diseases 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 238000011010 flushing procedure Methods 0.000 description 1
- 239000003517 fume Substances 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 230000002209 hydrophobic effect Effects 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- ZFSLODLOARCGLH-UHFFFAOYSA-N isocyanuric acid Chemical compound OC1=NC(O)=NC(O)=N1 ZFSLODLOARCGLH-UHFFFAOYSA-N 0.000 description 1
- 238000011898 label-free detection Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000001000 micrograph Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000003068 molecular probe Substances 0.000 description 1
- 238000012627 multivariate algorithm Methods 0.000 description 1
- 229910017604 nitric acid Inorganic materials 0.000 description 1
- 235000015816 nutrient absorption Nutrition 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 230000029553 photosynthesis Effects 0.000 description 1
- 238000010672 photosynthesis Methods 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 231100000572 poisoning Toxicity 0.000 description 1
- 230000000607 poisoning effect Effects 0.000 description 1
- 229920001282 polysaccharide Polymers 0.000 description 1
- 239000005017 polysaccharide Substances 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 238000010298 pulverizing process Methods 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000012488 sample solution Substances 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000007873 sieving Methods 0.000 description 1
- 239000012279 sodium borohydride Substances 0.000 description 1
- 229910000033 sodium borohydride Inorganic materials 0.000 description 1
- 239000001509 sodium citrate Substances 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 239000004094 surface-active agent Substances 0.000 description 1
- 239000012085 test solution Substances 0.000 description 1
- 231100000331 toxic Toxicity 0.000 description 1
- 230000002588 toxic effect Effects 0.000 description 1
- 238000011282 treatment Methods 0.000 description 1
- HRXKRNGNAMMEHJ-UHFFFAOYSA-K trisodium citrate Chemical compound [Na+].[Na+].[Na+].[O-]C(=O)CC(O)(CC([O-])=O)C([O-])=O HRXKRNGNAMMEHJ-UHFFFAOYSA-K 0.000 description 1
- 229940038773 trisodium citrate Drugs 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
- 238000000733 zeta-potential measurement Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/65—Raman scattering
- G01N21/658—Raman scattering enhancement Raman, e.g. surface plasmons
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
Landscapes
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
本发明公开一种食品中重金属镉的检测方法，属于食品安全快速评价技术领域。本发明以海藻酸钠(SA)、硝酸银为反应物，制备得到绿色增强底物银纳米粒子SA‑AgNPs，通过优化特异性分子三聚硫氰酸(TMT)的浓度和检测液液滴体积，利用边缘富集效应放大检测信号，实现基于SERS技术的食品中重金属镉的快速、高灵敏检测。通过一阶导数预处理结合竞争性自适应重加权‑偏最小二乘法(CARS‑PLS)建立定量检测模型，对食品中的重金属镉含量进行准确预测。该传感检测方法可以有效定量和预测食品中的重金属镉含量，实现食品重金属的快速评价。
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
The invention relates to a detection method of heavy metal cadmium in food, in particular to a SERS (surface enhanced Raman scattering) specificity rapid detection method of heavy metal cadmium in food, and belongs to the technical field of rapid food safety evaluation.
Background
Cadmium (Cd) is one of the heavy metal elements commonly found in many foods, mainly derived from human production activities such as mining, smelting, fertilizing, and the like. In 1933, cd has long been listed by the international cancer research institute as a human class I carcinogen. The presence of cadmium ions (Cd 2+) can hinder photosynthesis of plants, inhibit root elongation, and thereby reduce nutrient absorption by plants. Cd 2+ is generally absorbed by plants via soil or water from the roots, which can pose a food safety risk if accumulated in the edible parts. The presence of Cd 2+ in humans does not have any nutritional value, but rather can damage the human functional system, leading to emphysema, end-stage renal failure, osteoporosis, and cancer. Since the industrial revolution, the occurrence of Cd 2+ poisoning has occurred in many countries around the world, and it is therefore very important to ensure that foods are not contaminated with Cd 2+ or that their concentration is below the maximum residual amount (MRL).
Raman spectroscopy is sensitive to the molecular structure and chemical composition of the analyte, and since the vibrational mode of the molecule depends on the structure, the corresponding raman scattering changes as the composition and structure of the compound molecule changes. Raman spectroscopy can thus provide compounds with unique "fingerprint" characteristics. But has a major disadvantage in that the weak signal intensity leads to a decrease in raman detection ability, so that a special method is required to enhance the signal intensity. Surface Enhanced Raman Spectroscopy (SERS) selectively enhances raman scattering signals depending on interactions between excitation light, the analyte of interest, and the SERS-active substrate.
In the SERS application process, the enhanced substrate plays a key role in acquiring stable Raman signals, and the composition, size, spacing, surface components, charge and the like of the noble metal nano particles determine the unique physical properties of the noble metal nano particles, so that the enhancement effect is influenced. In order to obtain a suitable raman enhancement signal, it is necessary to rationally prepare the enhancement substrate from the test sample. The SERS substrate materials commonly used in food detection at present mainly comprise silver and gold, and the commonly used reducing agents comprise ethylene glycol, trisodium citrate, sodium borohydride and the like, which are not environment-friendly or biological enough.
Disclosure of Invention
Aiming at some defects of the prior art, the invention provides a method for detecting heavy metal cadmium in food, which solves the problems of low conventional physicochemical detection speed, easy impurity interference and inaccurate quantification, and remarkably improves the detection speed and the detection reliability of the heavy metal cadmium content in food.
According to the invention, sodium Alginate (SA) is used as a green reducing agent to synthesize silver nano particles, cyanuric acid (TMT) capable of specifically identifying cadmium ions is mixed, the silver nano particles are used for SERS sensing detection of heavy metal cadmium in food, and a chemometric algorithm is adopted for data processing and analysis in combination with edge enrichment, so that a rapid quantitative detection function of cadmium ion content is realized.
In order to achieve the above purpose, the invention provides a method for detecting heavy metal cadmium in food, which is realized based on SERS technology and comprises the following steps:
(1) And (3) synthesizing silver nano particles, namely dissolving SA in pure water, adding NaOH solution into the solution, heating in a water bath, stirring, dropwise adding AgNO 3 aqueous solution after the temperature is stable, stirring the obtained mixture, namely the SA-AgNPs nano solution for a certain time, cooling at room temperature, and storing at a proper temperature for standby.
(2) Preparing detection reaction liquid, namely mixing the obtained SA-AgNPs nano solution with TMT solution according to a certain proportion to obtain the detection reaction liquid, and recording the detection reaction liquid as SA-AgNPs/TMT solution.
(3) And (3) detecting the heavy metal cadmium content of the food, namely adding Cd 2+ standard substance solutions with different concentrations into a food sample after pretreatment, adding SA-AgNPs/TMT solution to obtain mixed solution comprising Cd 2+ standard substance solution and SA-AgNPs/TMT solution, taking a proper amount of mixed solution, carefully dripping the mixed solution on the surface of tinfoil to form a circular liquid drop shape as much as possible, naturally drying the liquid drop to form a coffee ring, and measuring the SERS spectrum intensity on the coffee ring.
(4) Data processing and analysis, namely, utilizing chemometrics to optimize data results and establishing a quantitative model;
(5) The unknown food sample detection comprises the steps of preprocessing a food sample, adding SA-AgNPs/TMT solution, dripping on the surface of tinfoil, measuring the Raman spectrum of the mixed solution after the edge of the coffee ring is enriched and dried, and calling an established quantitative model to calculate the Cd 2+ content in the unknown food sample.
In the above-described arrangement, the first and second embodiments,
Wherein, the volume ratio of the NaOH solution to the AgNO 3 aqueous solution in the step (1) is 1:2, the concentration is 1 mol.L -1、9g·L-1, and the concentration ratio of SA, naOH and AgNO 3 contained in the obtained SA-AgNPs nanometer solution is 0.2 mg.mL -1:0.01mol·L-1:0.18mg·mL-1;
In the synthesis step of the silver nano particles, the water bath heating temperature is 100 ℃, the stirring time is 30min, magnetic stirring is preferred, and the reserve temperature of the obtained mixture is 4 ℃.
Wherein the volume ratio of the SA-AgNPs nanometer solution to the TMT solution in the step (2) is 1-3:1-9, preferably 2:3, and the TMT solution concentration is 0.5mM.
Wherein the volume ratio of the SA-AgNPs/TMT solution to the Cd 2+ standard solution in the step (3) is 1:1, and the drop volume of the mixed solution is 0.5-2. Mu.L, preferably 0.5. Mu.L.
Wherein, the pretreatment of the food sample in the step (3) refers to the determination of cadmium in food safety national standard food (GB 5009.15-2014) to correspondingly pretreat different foods.
In the scheme, the spectrum acquisition uses a confocal microscopic Raman imaging spectrometer, and the parameters are that the excitation wavelength is 785nm, the excitation wavelength is 50 times of an objective lens (the light spot size=1.28 mu m), the acquisition time is 0.5s, and the power attenuation is 100%.
The quantitative model in the step (4) adopts a first derivative pretreatment combined with a competitive self-adaptive weighting-partial least squares (CARS-PLS) model to optimize and process data, so that the heavy metal cadmium content in food is effectively quantized and predicted.
In the invention, the adding proportion of TMT and SA-AgNPs and the volume of the detection reaction liquid required in the detection process are optimized:
The drop volume required to detect total liquid formation into a coffee ring is optimised here by the total volume of drops on tinfoil. Mixing the SA-AgNPs nanometer solution obtained in the step (1) with TMT solution, standing for a certain time, dripping different volumes of mixed liquid on the surface of smooth tinfoil, and selecting the liquid drop volume with the narrowest width and the most uniform inside of the coffee ring under a high-power microscope after the coffee ring is fully dried through automatic edge enrichment at room temperature.
Wherein the volume ratio of the SA-AgNPs nanometer solution to the TMT solution is 1:1, and the concentration of the TMT solution is 0.5mM. The standing time was 10min, and the volumes of the obtained mixed solutions were 0.5, 1, 1.2, 1.5, 1.8 and 2. Mu.L, respectively, and the microscope magnification was 50X.
And (3) optimizing the adding proportion of TMT and SA-AgNPs, namely adding TMT solution and SA-AgNPs nanometer solution with different volumes into a test tube, making the total volume equal, standing for a certain time, adding Cd 2+ standard solution, respectively dripping the Cd 2+ standard solution onto the smooth tinfoil surface, collecting SERS spectra after the edges of the coffee rings are enriched and dried, comparing the Raman intensities of main characteristic peaks, and selecting the adding proportion corresponding to the coffee ring with the highest Raman intensity.
The concentration of the TMT solution is 0.5mM, the volume ratio of the mixed solution with SA-AgNPs is 1:9, 1:4, 3:7, 2:3 and 1:1 respectively, the standing time is 10min, the volume ratio of the SA-AgNPs/TMT solution to the Cd 2+ standard solution is 1:1, the concentration of the Cd 2+ standard solution is 100 mug/L, and the volume of liquid drops on the surface of the tin-plated paper is 0.5 mug.
The invention has the beneficial effects that:
(1) The system of combining the green enhanced substrate SA-AgNPs with the TMT specific recognition molecule is a stable and good SERS specific cadmium ion detection system. The silver nano-particles synthesized by using the biological polysaccharide Sodium Alginate (SA) as a reducing agent have no obvious toxic effect on living bodies, SA molecules have rich carboxyl and hydroxyl, active hydroxyl has certain reducing capability, and the carboxyl has strong pairing effect with silver nano-particles. In the detection process, the silver nano particles (SA-AgNPs) synthesized by the SA as the green reducing agent have good dispersibility, long-term stability and higher SERS enhancement effect, and the specific combination of TMT and cadmium ions and the edge enrichment effect improve the concentration of target analytes in the coffee ring area and play a role in signal amplification.
(2) The invention establishes a SERS detection system of the label-free Cd 2+, takes the tinfoil as a carrier to reduce background interference and enhance the SERS detection effect, and meanwhile, the coffee ring on the surface of the tinfoil is naturally dried without hydrophobic treatment, so that the operation steps are simple and convenient. The confocal microscopic Raman imaging spectrometer is used for spectrum acquisition, the spot size is 1.28 mu m, and the SERS spectrum of the micro-area is acquired in multiple points, so that the stability and the accuracy of SERS detection are improved.
(3) The SERS specificity rapid detection method for the heavy metal cadmium in the food can improve the Raman scattering signal intensity of a sample by 2.68 multiplied by 10 5 times, and the minimum detection limit reaches 0.1 mug.L -1. The content of heavy metal cadmium in the food and the peak intensity change of the surface enhanced Raman spectrum establish a correlation, and the data preprocessing and the chemometric model perform subsequent rapid processing on the data so as to realize the accurate detection and prediction of the extremely low content of the heavy metal cadmium in the food. The SERS specificity rapid detection method for the heavy metal cadmium in the food has high sensitivity, accuracy and repeatability, and can replace the traditional ICP-MS method for detecting the content of the heavy metal cadmium in the food.
Drawings
Fig. 1 is a schematic diagram of a rapid SERS-specific detection method for heavy metal cadmium in food.
FIG. 2 shows a transmission electron microscope image (a) of SA-AgNPs, an ultraviolet spectrum image (b) of SA-AgNPs after storage for 0, 7 and 30 days, an FTIR spectrum image (c) of SA and SA-AgNPs, and an enhancement effect image (d) of SA-AgNPs on 4-MBA of Raman signal molecule, wherein the 4-MBA concentration is 10 -3 mol/L.
FIG. 3 is a microscopic image of a coffee ring formed by droplets of a mixed solution of different volumes of TMT/SA-AgNPs.
FIG. 4 shows the result of optimizing the volume of the liquid drop of the coffee ring (a), the result of optimizing the adding proportion of TMT to SA-AgNPs (b), the result of SERS response of the detection system to various ions (c), the Raman spectrum of TMT powder (lower) and the Raman spectrum of Cd 2+ (100 mug/L) which are specifically detected (upper) (d).
FIG. 5 is a graph showing the effect of detection of SA-AgNPs on enhancement of Raman signal molecule 4-MBA by using tin foil and silicon wafer as carriers
FIG. 6 shows the original SERS spectrum of the tea detection liquid with the gradient concentration Cd 2+ added with the standard.
FIG. 7 shows CARS-PLS (a), wherein I is the regression coefficient of RMSECV when the optimal variable is selected, II is the regression coefficient when the optimal variable is selected, III is the number of the optimal variables selected, and Cd 2+ based on 1 st derivative pretreatment quantitatively detects CARS-PLS modeling result (b).
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
fig. 1 is a schematic diagram of a rapid SERS specificity detection method for heavy metal cadmium in food according to the present invention.
The detection method of heavy metal cadmium in food, disclosed by the invention, is realized based on SERS technology, and comprises the following steps:
And (3) synthesizing silver nano particles, namely dissolving SA in pure water, adding NaOH solution into the solution, heating in a water bath, stirring, dropwise adding AgNO 3 aqueous solution after the temperature is stable, stirring the mixture for a certain time, cooling at room temperature, and storing at a proper temperature for standby.
In the step, the volume ratio of the NaOH solution to the AgNO 3 aqueous solution is 1:2, the concentration is 1 mol.L -1、9g·L-1, the concentration ratio of SA, naOH and AgNO 3 contained in a final system is 0.2 mg.mL -1:0.01mol·L-1:0.18mg·mL-1, the water bath heating temperature is 100 ℃, the stirring time is 30min, magnetic stirring is preferred, and the reserve temperature of the obtained mixture is 4 ℃.
Preparing detection reaction liquid, namely mixing the obtained SA-AgNPs mixed liquid with TMT solution according to a certain proportion to obtain detection reaction liquid, and recording the detection reaction liquid as SA-AgNPs/TMT solution. Wherein the concentration of the TMT solution is 0.5 mmol.L -1, and the dosage volume ratio of the SA-AgNPs mixed solution to the TMT solution is 1:1.
And (3) detecting the heavy metal cadmium content of the food, namely adding Cd 2+ standard substance solutions with different concentrations into a food sample after pretreatment, adding the prepared SA-AgNPs/TMT solution, taking a proper amount of mixed solution, carefully dripping the mixed solution on the surface of the flat tinfoil to enable the droplet shape to be as circular as possible, naturally drying the droplet to form a coffee ring, and measuring the SERS spectrum intensity on the coffee ring.
In the step, the volume ratio of the SA-AgNPs/TMT solution to the sample solution containing Cd 2+ is 1:1, and the volume of the liquid drop to be detected, which is dropped on the surface of the tinfoil, is 0.5 mu L. The spectrum acquisition uses a confocal microscopic Raman imaging spectrometer, and the parameters are that the excitation wavelength is 785nm, the excitation wavelength is 50 times of an objective lens (the light spot size=1.28 mu m), the acquisition time is 0.5s, and the power attenuation is 100%.
Pretreatment of food samples different foods are subjected to corresponding pretreatment according to the determination of cadmium in food safety national standard food (GB 5009.15-2014).
And (3) data processing and analysis, namely, utilizing chemometrics to optimize data results and establishing a quantitative model.
Preferably, in this step, a PLS model of quantitative detection of Cd 2+ was established based on 4 pre-treatments and 5 variable selection methods:
The baseline drift and noise interference to the original spectrum were eliminated using four pre-processing methods, standard normal variable transforms (SNV), multiple Scatter Correction (MSC), first (1 st) derivative and second (2 nd) derivative.
The PLS model for Cd 2+ detection was established based on 5 variable selection algorithms, namely synergistic interval (Si), non-informative variable elimination (UVE), genetic Algorithm (GA), ant Colony Optimization (ACO) and contention adaptive weighted sampling (CARS).
The quantitative model is a first derivative pretreatment combined with a competitive self-adaptive re-weighting algorithm-partial least squares (CARS-PLS) model, optimizes and processes the data, and effectively quantifies and predicts the content of heavy metal cadmium in food.
The unknown food sample detection comprises the steps of preprocessing a food sample, adding SA-AgNPs/TMT solution, dripping on a smooth tinfoil surface, measuring the Raman spectrum of the mixed solution after the edge of a coffee ring is enriched and dried, and calling an established quantitative model to calculate the Cd 2+ content in the unknown food sample.
According to this example, preferably, a silver nano-reduction system is constructed in which the green enhancing substrate is combined with the edge enrichment effect.
The synthesis procedure of the silver nanoparticles described in the above schemes can be summarized as follows:
20mg of SA was dissolved in 97mL of pure water, poured into a 150mL round bottom flask, and 1mL of NaOH solution (1 mol/L) was added thereto to maintain the alkaline environment of the solution to promote the progress of the reduction reaction, heated to 100℃in a water bath and stirred strongly by magnetic force. After the temperature had stabilized, 2mL of an aqueous solution containing 18mg of AgNO 3 were added dropwise, and the colorless solution immediately turned dark yellow-green. The mixture was kept magnetically stirred for 30min. The yellow solution was then allowed to cool at room temperature and stored at 4 ℃ for future use.
The sodium alginate molecule surface selected by the invention has carboxyl and hydroxyl, wherein the carboxyl has strong pairing action with silver nano particles, and the active hydroxyl has certain reducing capability. In the embodiment, silver ions are reduced by utilizing hydroxyl groups on the surface of sodium alginate to generate silver particles, and then carboxyl groups on the surface of the silver particles are quickly matched with the silver nanoparticles to enable the sodium alginate to wrap the silver nanoparticles so as to control the particle size of the silver nanoparticles and increase the stability of the silver nanoparticles.
Fig. 2a shows that the synthesized SA-AgNPs has a particle size of 30-40 nm, and the nanoparticles have good dispersibility in aqueous solutions. Freshly prepared SA-AgNPs had a strong UV spectrum absorption peak at 401nm and still had good stability and dispersibility after 30 days of storage at 4 ℃ (FIG. 2 b). The Zeta potential measurement value of SA-AgNPs is-47.2, and the SA is proved to be used as a surfactant to be coated on the surface of AgNPs after reducing silver ions.
FIG. 2c shows FT-IR spectra of SA and AgNPs. In the FT-IR spectrum of SA, the peak located near 3440cm -1 can be attributed to the stretching vibration of-OH. The absorption peak at 1620cm -1 is caused by stretching vibration of COO groups, the absorption peak near 1320cm -1 is caused by-OH deformation vibration, and the absorption band at 1060cm -1 corresponds to the C-O-C stretching mode in the cell. FT-IR spectra comparing SA and AgNPs revealed that in AgNPs, the-OH, COO-groups and C-O-C vibrational peaks were all present and shifted to some extent. The presence of SA on AgNPs was demonstrated. The enhancement of SA-AgNPs was verified with a 4-MBA molecular probe. After adding 4-MBA ethanol solution (10 -3 mol/L) to SA-AgNPs solution, the Raman intensity of 4-MBA is significantly enhanced (FIG. 2 d). The EF value of SA-AgNPs was calculated to quantify the enhancement thereof, and the enhancement factor of SA-AgNPs synthesized in this example was calculated to be 2.68X10. 10 5.
Further, in this embodiment, the detection system is optimized:
The drop volume required to detect total liquid formation into a coffee ring is optimised here by the total volume of drops on tinfoil. The SA-AgNPs solution (1 mL) was mixed with TMT solution (0.5 mM) in medium volume in a test tube. After 10min of rest, different volumes (0.5, 1, 1.2, 1.5, 1.8 and 2. Mu.L) of TMT/SA-AgNPs mixed solution were dropped onto a smooth tinfoil surface. After the coffee ring was completely dried at room temperature by edge automatic enrichment, the width of the coffee ring was observed under a 50 x microscope. The narrowest width of the coffee ring and the most uniform internal drop volume were chosen for subsequent investigation.
Optimization of the ratio of TMT to SA-AgNPs different volumes of TMT solution (100, 200, 300, 400 and 500. Mu.L, 0.5 mM) were added to the tube, followed by the addition of SA-AgNPs solution to make the total volume 1mL. After standing for 10min, the resulting TMT/SA-AgNPs solution (20. Mu.L) was mixed homogeneously with the same volume of Cd 2+ standard solution (100. Mu.g/L) to obtain a mixture, and each mixture (0.5. Mu.L) was dropped onto a smooth tinfoil surface. After edge enrichment drying of the coffee ring at room temperature, SERS spectra were collected and the raman intensities of the main characteristic peaks were compared. The volume of TMT solution on the coffee ring that showed the highest raman intensity was selected for subsequent study.
The coffee ring was characterized by a peak-clipping method using a confocal microscopy raman imaging spectrometer. The invention selects the characteristic Raman spectrum peak position of Cd 2+, and constructs a pseudo-color image distributed by Cd 2+ on the coffee ring according to the signal peak intensity. SERS spectra of 15 points on the coffee ring are randomly collected, characteristic peaks of Cd 2+ are selected, RSD values of the 15 collection points are calculated, and detection stability is determined.
According to the experimental method, the total liquid drop volume to be detected and the addition proportion of TMT and SA-AgNPs are optimized. The TMT/SA-AgNPs mixed solution droplets have an automatic enrichment tendency, and finally a relatively uniform coffee ring is formed, but as the volume of the TMT/SA-AgNPs mixed solution droplets increases, the width of the coffee ring is obviously increased, the uniformity of the distribution of nanoparticles on the coffee ring is reduced, dendritic distribution occurs in the interior of the coffee ring with the volume of the TMT/SA-AgNPs mixed solution droplets of 1.2 and 1.5 mu L (figure 3), and when the volume of the TMT/SA-AgNPs mixed solution droplets is 0.5 mu L, the width of the coffee ring is narrowest to 30 mu m (figure 4 a). The SA-AgNPs solution synthesized by the invention has orange color and turns into reddish brown rapidly after TMT is added. The raman signal intensity at 979.60cm -1 reached the highest when the addition ratio of TMT and SA-AgNPs was 2:3 (fig. 4 b). Thus, the drop volume of the optimal TMT/SA-AgNPs mixed solution was selected to be 0.5. Mu.L, and the optimal addition ratio of TMT to SA-AgNPs was 2:3.
In the embodiment, the sensitivity or specificity of the method for detecting Cd 2+ is also verified:
And adding different ions (Hg2+、Pb2+、Mn2+、Cu2+、As3+、Ni2+、Cr6+、Fe3+、Ba2+、Cd2+) into a blank tea sample, uniformly mixing, and respectively mixing with a TMT/SA-AgNPs solution to acquire a Raman spectrum according to an experimental method. Fig. 4c shows the raman intensity of different ions at 979.60cm -1. Clearly, other metal ions in the presence of PEX do not interfere with the binding of Cd 2+ to TMT in food samples. The result shows that only heavy metal ions with proper binding constants can break the S-H bond of TMT to form a stable N-Cd 2+ -S bond. Therefore, the SERS detection method has good selectivity specificity for Cd 2+ in food.
In this embodiment, the redundancy and strength of the SERS signal on the carrier tinfoil and the silicon wafer related to the method of the present invention are also compared:
Uniformly mixing SA-AgNPs solution and 4-MBA ethanol solution in equal volume, respectively dripping 0.5 mu L of the mixture on the surfaces of the tinfoil and the silicon wafer, and respectively collecting SERS spectra of the edge of the coffee ring after natural drying. FIG. 5 shows that the SERS signal intensity detected on the tinfoil is higher, the baseline is more stable, no signal interference of the carrier itself exists, and the method can be directly and more accurately used for quantitative detection of cadmium ions in food. Therefore, the SERS detection method adopts the tinfoil as the carrier, so that the background interference can be reduced, the SERS detection signal is enhanced, and the redundant information of the spectrum signal is reduced.
The embodiment takes tea as food representative, and the rapid quantitative detection of heavy metal cadmium in the tea comprises the following specific steps:
(1) Pretreatment of tea samples
Pulverizing 500g tea sample (Oriental black tea) with an ultrafine pulverizer for about 1min, sieving for multiple times until powder quality is uniform, and packaging into sealed bag. Referring to the determination of cadmium in food safety national standard food (GB 5009.15-2014), carrying out microwave digestion on tea powder, namely weighing 0.2g (accurate to 0.001 g) of tea leaf sample, adding 8mL of high-grade pure concentrated nitric acid and Cd 2+ standard solution, capping and standing overnight, screwing a tank cap, and carrying out digestion according to the standard operation steps of a microwave digestion instrument. The digestion conditions are that the temperature is controlled to be 190 ℃, the heating time is 20min, the constant temperature time is 10min, and the cooling time is 15min. Cooling to room temperature after taking out, slowly opening a tank cover in a fume hood for exhausting, flushing an inner cover with a small amount of deionized water, placing a digestion tank in an ultrasonic water bath box, heating at 100 ℃ for 30min for degassing, adding PEX solution (100 mu mol/L,2.5 mL) into the digestion solution and fixing the volume to 50mL by using deionized water in order to avoid interference of other metal ions in food and influence specific detection. After filtration through a 0.22 μm filter, a total of 11 labeled samples were obtained with Cd 2+ contents of 100, 80, 60, 40, 20, 10, 5,1, 0.5, 0.1 and 0. Mu.g/L, respectively. These tea samples were stored at 4 ℃ for subsequent SERS and ICP-MS detection.
(2) Raman spectrum acquisition and analysis
Mu.L of SA-AgNPs/TMT solution and 20. Mu.L of Cd 2+ -containing tea extract of different concentrations were added to the tube and mixed well. And (3) taking 0.5 mu L of the mixed solution, carefully dripping the mixed solution on the surface of the flat tinfoil to enable the shape of the liquid drop to be round as much as possible, and collecting SERS spectra on the coffee ring after the liquid drop is naturally dried to form the coffee ring. Using a confocal microscopic raman imaging spectrometer, the excitation wavelength was 785nm and all SERS spectral data were collected at 50 x objective (spot size=1.28 μm). The acquisition time and power decay were set to 0.5s and 100%, respectively. 15 spectra were collected for each sample. Smoothing and denoising were performed using LabSpec software from the spectrometer, and smoothing was controlled by adjusting the function type and parameters, denoise, size 256, degree 2, factor 1.
First, the raman spectrum peaks of a mixed test solution (100 mug/L) of TMT powder, cd 2+ -labeled tea sample and SA-AgNPs/TMT probe were compared, and as shown in FIG. 4d, the raman peaks of TMT mainly appear at 446.15, 1129.80 and 1272.86cm -1, belonging to C-S group, C-N group and benzene ring vibration, respectively. Characteristic peaks of Cd 2+ collected on a coffee ring formed by the cadmium detection solution are mainly positioned at 976.60 and 1226.74cm -1 and can be attributed to benzene ring vibration. The raman peaks at 419.86, 479.11 and 832cm -1 may be from C-S stretching vibrations, and the raman peak at 1390.07cm -1 may be ascribed to COO-stretching vibrations, the presence of which again verifies that the SA with carboxyl groups has been modified at the surface of the silver nanoparticles. Figure 6 shows the overall raman spectra of 10 gradient concentrations Cd 2+ for the labeled tea detection fluid. Research shows that with the increase of Cd 2+ content, the Raman intensity is obviously increased, and the Cd 2+ and the Raman intensity are in positive correlation.
(3) Data processing and analysis
Due to the interference of the external environment and the fluorescence of the food sample, the acquired raman curve has a certain baseline drift. Therefore, it is necessary to perform a de-baselined process on the curve. Parameters for de-baselines were set as follows, background fit to poly-nomial, setting degree=7, maximum points=256. The baseline drift and noise interference to the original spectrum were eliminated using four pre-processing methods, standard normal variable transforms (SNV), multiple Scatter Correction (MSC), first (1 st) derivative and second (2 nd) derivative. The PLS model for Cd 2+ detection is established on the basis of five variable selection algorithms, namely a synergistic interval (Si), an informationless variable elimination (UVE), a Genetic Algorithm (GA), ant Colony Optimization (ACO) and contention adaptive weighted sampling (CARS).
The raw spectral data contains 930 variables in total in the raman shift range of 300-1800cm -1. The result of detecting PLS by Cd 2+ constructed after pretreatment is shown in Table 1. The PLS results of SNV and MSC are poor, and the noise cancellation capability is not well reflected in this example, but this also proves that the experimental conditions of this example are relatively stable, and the device itself is excellent in performance. The optimizing effect of the derivative processing on the original spectrum is better reflected, and the PLS modeling effect is best after the first derivative algorithm is realized (R c = 0.9982, RMSEC = 2.1159).
After the first derivative pretreatment, variable selection algorithms of SiPLS, UVE-PLS, GA-PLS, ACO-PLS and CARS-PLS are realized by utilizing MATLAB 2014b, so that the optical data are compressed, and the operation speed and the operation precision of a PLS model are improved. The PLS modeling results based on variable selection are shown in table 2. The performance of each model in predicting the Cd 2+ content in tea samples is compared with the R p value, and the result shows that the R p value of each model is greater than 0.99. By comparing all variable screening methods, the best modeling result is obtained by CARS-PLS, FIG. 7a is a CARS-PLS variable preference graph, wherein I is the regression coefficient when selecting the optimal variable, II is the regression coefficient when selecting the optimal variable, III is the number of the optimal variables, FIG. 7b is the CARS-PLS modeling result of Cd 2 + quantitative detection based on 1 st derivative pretreatment, and the correlation coefficients of the correction set and the prediction set are R c＝0.9982,Rp =0.9989 respectively.
In order to verify the accuracy and precision of detecting heavy metal cadmium in food, 8 tea samples with different concentration gradients Cd 2+ (100, 80, 60, 40, 20, 10, 5 and 1 mug/L) are prepared. The measurements were repeated 3 times for each sample using the standardized ICP-MS method, and the recovery and RSD values were calculated as averages. Table 3 compares the detection results of SERS method and ICP-MS method in tea samples. The method detects that the recovery rate of the tea sample is 91.37% -104.69%, the relative standard deviation is less than or equal to 2.24%, and the recovery rate of the tea sample is 91.45% -110.63% and the relative standard deviation is less than or equal to 2.87% when the ICP-MS method is used for measuring. The result shows that the SERS method predicts the Cd 2+ content in the tea, and has higher accuracy and good reproducibility. In addition, the minimum detection concentration of Cd 2+ in the method is 0.1 mug/L, so that the trace detection requirement can be met.
TABLE 1 Raman Spectroscopy pretreatment for cadmium ion detection in tea samples combined with PLS model results
TABLE 2 multivariate algorithm model results for cadmium ion prediction in tea samples
TABLE 3 comparison of SERS method with ICP-MS method for determining cadmium ions in tea leaves
In conclusion, the invention synthesizes the SA-AgNPs combined with TMT molecule by taking SA as a green reducing agent for quantitative detection of Cd 2+ in tea. By optimizing the detection liquid volume and the proportion of TMT solution to SA-AgNPs, an optimal detection system is established, and the edge enrichment effect improves the concentration of target analytes in the coffee ring area and plays a role in signal amplification. In addition, TMT molecules have higher selectivity specificity for Cd 2+ in the presence of PEX. The standardized ICP-MS method also verifies the accuracy and repeatability of the SERS method. Therefore, the SERS technology combining the green enhanced substrate and the edge enrichment effect can realize accurate, rapid and label-free detection of Cd 2+ in food.
The above embodiments are only for illustrating the present invention and not for limiting the technical solutions described in the present invention, and therefore, although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the present invention may be modified or substituted by equivalent ones, and all technical solutions and modifications thereof without departing from the spirit and scope of the present invention should be covered in the scope of the claims of the present invention.
Claims (12)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210281764.7A CN114609119B (en) | 2022-03-21 | 2022-03-21 | A method for detecting heavy metal cadmium in food |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210281764.7A CN114609119B (en) | 2022-03-21 | 2022-03-21 | A method for detecting heavy metal cadmium in food |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114609119A CN114609119A (en) | 2022-06-10 |
| CN114609119B true CN114609119B (en) | 2025-02-14 |
Family
ID=81864646
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210281764.7A Active CN114609119B (en) | 2022-03-21 | 2022-03-21 | A method for detecting heavy metal cadmium in food |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114609119B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117483746A (en) * | 2023-11-28 | 2024-02-02 | 浙江理工大学绍兴柯桥研究院有限公司 | A kind of silver nanoparticle solution and preparation method thereof |
| CN117871576B (en) * | 2024-01-12 | 2024-09-27 | 浙江大学 | A visualization method and system for detecting the evolution of rapid reaction products |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110286225A (en) * | 2019-06-06 | 2019-09-27 | 江苏大学 | A detection method for cadmium content in tea based on upconversion and dithizone specific system |
| CN113189083A (en) * | 2021-05-25 | 2021-07-30 | 江苏大学 | SERS (surface enhanced Raman scattering) specificity rapid detection method for heavy metal lead in food |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160377628A1 (en) * | 2015-06-23 | 2016-12-29 | Chandrahas Bansal | Method for quantitative determination of micro molar concentration level of d-glucose using surface enhanced raman spectroscopy (sers) with 2-thienylboronic acid as linker molecule on silver nano-cluster substrates |
| WO2022034225A2 (en) * | 2020-08-14 | 2022-02-17 | Tiotech A/S | An electrode material and a battery as well as their manufacture |
-
2022
- 2022-03-21 CN CN202210281764.7A patent/CN114609119B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110286225A (en) * | 2019-06-06 | 2019-09-27 | 江苏大学 | A detection method for cadmium content in tea based on upconversion and dithizone specific system |
| CN113189083A (en) * | 2021-05-25 | 2021-07-30 | 江苏大学 | SERS (surface enhanced Raman scattering) specificity rapid detection method for heavy metal lead in food |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114609119A (en) | 2022-06-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Green reduction of silver nanoparticles for cadmium detection in food using surface-enhanced Raman spectroscopy coupled multivariate calibration | |
| US20220119610A1 (en) | Preparation Method of Polyurethane-based Nano-silver SERS Substrate | |
| CN114609119B (en) | A method for detecting heavy metal cadmium in food | |
| CN111208101B (en) | Method for conveniently detecting aluminum ions based on carbon quantum dots synthesized by flavonoid glycoside and derivatives thereof | |
| CN112798573A (en) | A surface-enhanced Raman spectroscopic method for detecting total arsenic in food | |
| Barimah et al. | SERS nanosensor of 3-aminobenzeneboronic acid labeled Ag for detecting total arsenic in black tea combined with chemometric algorithms | |
| CN110987881A (en) | A mercury ion detection method based on enzymatic reaction double emission fluorescent probe | |
| Dos Santos et al. | Simultaneous determination of As, Hg, Sb, Se and Sn in sediments by slurry sampling axial view inductively coupled plasma optical emission spectrometry using on-line chemical vapor generation with internal standardization | |
| Xue et al. | A core–shell structured AuNPs@ ZnCo-MOF SERS substrate for sensitive and selective detection of thiram | |
| Firdaus et al. | Quantitative analysis of Cr (III) and Cr (VI) using gold nanoparticles with UV-vis spectrometry and smartphone colorimetric-sensing | |
| CN109187454A (en) | A kind of fluorescence detection method of tealeaves risk substance fluorine | |
| Jin et al. | Innovative paper-based SERS substrate for rapid detection and degradation of pesticides: Mechanistic insights and environmental applications | |
| Liu et al. | Exploring variables optimization methods to screen surface-enhanced Raman spectroscopy characteristic peaks for rapid detection of difenoconazole pesticides in tea | |
| CN108181304A (en) | The detection method of ion concentration of mercury in a kind of water | |
| Li et al. | Analysis and experimental assessment of an optimized SERS substrate used to detect thiabendazole in apples with high sensitivity | |
| Chen et al. | Selective surface passivation of gold nanoparticles: A strategy for enhanced sensitivity in lateral flow immunoassay | |
| Ma et al. | Surface-enhanced Raman scattering sensor based on cysteine-mediated nucleophilic addition reaction for detection of patulin | |
| Gong et al. | Au@ Ag hollow nanoshells-based SERS integrated microfluidic chip as a sample-to-answer platform for the ultra-sensitive detection of geosmin | |
| Lei et al. | A portable optical fiber tip facet LSPR aptasensor for detection of fumonisin B1 | |
| CN113189083B (en) | SERS (surface enhanced Raman scattering) specificity rapid detection method for heavy metal lead in food | |
| CN115078321B (en) | Method for preparing graphene quantum dots based on cane molasses for rapidly detecting food additive pigment | |
| CN108303388B (en) | Method for in-situ quantitative characterization of complex organic matter and metal ion complexing process | |
| CN117664953B (en) | Method for rapidly detecting fumonisin B1 and mercury by using Au-Ag Janus@Au NPs with SERS and nano enzyme activities | |
| Hussain et al. | Facile synthesis of 2-MCE capped silver shell optimized core-shell nanosensor for simultaneous detection of insecticide residues in fruit samples | |
| Lv et al. | A stable and sensitive Au metal organic frameworks resonance Rayleigh scattering nanoprobe for detection of SO32–in food based on fuchsin addition reaction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |