CN111678884A - Method for detecting estrogen in milk - Google Patents
Method for detecting estrogen in milk Download PDFInfo
- Publication number
- CN111678884A CN111678884A CN202010734426.5A CN202010734426A CN111678884A CN 111678884 A CN111678884 A CN 111678884A CN 202010734426 A CN202010734426 A CN 202010734426A CN 111678884 A CN111678884 A CN 111678884A
- Authority
- CN
- China
- Prior art keywords
- estrogen
- sample
- near infrared
- infrared spectrum
- concentration
- 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.)
- Pending
Links
- 229940011871 estrogen Drugs 0.000 title claims abstract description 63
- 239000000262 estrogen Substances 0.000 title claims abstract description 63
- 238000000034 method Methods 0.000 title claims abstract description 42
- 235000013336 milk Nutrition 0.000 title claims abstract description 32
- 210000004080 milk Anatomy 0.000 title claims abstract description 32
- 239000008267 milk Substances 0.000 title claims abstract description 31
- 238000012937 correction Methods 0.000 claims abstract description 42
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 27
- 238000001514 detection method Methods 0.000 claims abstract description 23
- 238000012795 verification Methods 0.000 claims abstract description 21
- 238000001228 spectrum Methods 0.000 claims description 9
- 230000003595 spectral effect Effects 0.000 claims description 8
- 238000009795 derivation Methods 0.000 claims description 4
- 230000004069 differentiation Effects 0.000 claims description 4
- 238000009499 grossing Methods 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 3
- ZKFNOUUKULVDOB-UHFFFAOYSA-N 1-amino-1-phenylmethyl phosphonic acid Chemical compound OP(=O)(O)C(N)C1=CC=CC=C1 ZKFNOUUKULVDOB-UHFFFAOYSA-N 0.000 description 27
- 229960003846 melengestrol acetate Drugs 0.000 description 27
- 238000002965 ELISA Methods 0.000 description 7
- 239000012086 standard solution Substances 0.000 description 7
- 239000008363 phosphate buffer Substances 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 239000000243 solution Substances 0.000 description 6
- 239000003153 chemical reaction reagent Substances 0.000 description 5
- 229940088597 hormone Drugs 0.000 description 4
- 239000005556 hormone Substances 0.000 description 4
- 238000010200 validation analysis Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- QAOWNCQODCNURD-UHFFFAOYSA-N Sulfuric acid Chemical compound OS(O)(=O)=O QAOWNCQODCNURD-UHFFFAOYSA-N 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000000903 blocking effect Effects 0.000 description 2
- 239000011248 coating agent Substances 0.000 description 2
- 238000000576 coating method Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 238000005303 weighing Methods 0.000 description 2
- 201000009273 Endometriosis Diseases 0.000 description 1
- 102000004190 Enzymes Human genes 0.000 description 1
- 108090000790 Enzymes Proteins 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000000262 chemical ionisation mass spectrometry Methods 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 235000013601 eggs Nutrition 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 201000007741 female breast cancer Diseases 0.000 description 1
- 201000002276 female breast carcinoma Diseases 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 238000001294 liquid chromatography-tandem mass spectrometry Methods 0.000 description 1
- 210000004995 male reproductive system Anatomy 0.000 description 1
- 210000005075 mammary gland Anatomy 0.000 description 1
- 235000013372 meat Nutrition 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 231100000915 pathological change Toxicity 0.000 description 1
- 230000036285 pathological change Effects 0.000 description 1
- 230000000149 penetrating effect Effects 0.000 description 1
- 239000000843 powder Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- RJKFOVLPORLFTN-UHFFFAOYSA-N progesterone acetate Natural products C1CC2=CC(=O)CCC2(C)C2C1C1CCC(C(=O)C)C1(C)CC2 RJKFOVLPORLFTN-UHFFFAOYSA-N 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 238000003127 radioimmunoassay Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000010561 standard procedure Methods 0.000 description 1
- 238000000528 statistical test Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
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
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
-
- 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
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (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)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention relates to a method for detecting estrogen in milk, which comprises the following steps: preparing samples with different estrogen concentrations as a calibration set; measuring the near infrared spectrum of the correction set sample, and making an estrogen concentration correction model by using chemometrics software; preparing samples with different estrogen concentrations as a verification set; wherein, the variation range of the estrogen concentration in the correction set sample is larger than that in the verification set sample; measuring the near infrared spectrum of the sample of the verification set, calculating the estrogen concentration of the sample of the verification set by using the established estrogen concentration correction model, and verifying the accuracy and the effectiveness of the estrogen concentration correction model; and (3) measuring the near infrared spectrum of the sample to be detected, and calculating the estrogen concentration of the sample to be detected by using the established estrogen concentration correction model. The method has the advantages of convenient and quick operation, cost saving, time reduction, good reproducibility of detection results and high accuracy.
Description
Technical Field
The invention relates to the technical field of water food detection, in particular to a method for detecting estrogen in milk.
Background
In the process of feeding and processing edible animals, hormone abuse phenomenon exists, which causes hormone residue pollution in aquatic products, meat, eggs and milk. The estrogen can make female children develop in advance, make male children mammary gland develop into female, make male reproductive system abnormal development and pathological changes, and increase incidence of female breast cancer and endometriosis.
There are many methods for measuring hormone in milk at home and abroad, wherein the biological technologies such as a radioimmunoassay, an enzyme-linked immunosorbent assay and the like can only measure a few hormones and the measuring method is single; the existing detection methods for estrogen residue in milk specified in national standards comprise gas chromatography-negative chemical ionization mass spectrometry and liquid chromatography-tandem mass spectrometry, and the required instruments are expensive, complex to operate and long in time consumption.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, the invention aims to provide a method for detecting estrogen in milk. The method solves the problem that only some estrogens can be detected but a plurality of estrogens cannot be detected when the residual quantity of estrogens in milk is detected in the prior art; in the prior art, the sample pretreatment is long in time consumption, complicated in steps and complex in operation, the cost is high, and errors are easily generated due to improper operation in the detection process; the data report obtained by the detection at the present stage is not timely, so that the timeliness and effectiveness of the decision in the production process are easily influenced; the method for detecting the estrogen in the milk can detect various estrogens, can obtain results immediately, does not need to carry out sample pretreatment, is convenient and quick to operate, saves the cost, reduces the time, and has good reproducibility and high accuracy of the detection results.
To this end, in one aspect of the invention, the invention proposes a method for detecting estrogen in milk, comprising the following steps:
s1, preparing samples with different estrogen concentrations as a calibration set;
s2, measuring the near infrared spectrum of the correction set sample, and making an estrogen concentration correction model by using chemometrics software;
s3, preparing samples with different estrogen concentrations to serve as a verification set; wherein, the variation range of the estrogen concentration in the correction set sample is larger than that in the verification set sample;
s4, determining the near infrared spectrum of the verification set sample, calculating the estrogen concentration of the verification set sample by using the established estrogen concentration correction model, and verifying the accuracy and the effectiveness of the estrogen concentration correction model;
s5, measuring the near infrared spectrum of the sample to be detected, and calculating the estrogen concentration of the sample to be detected by using the established estrogen concentration correction model.
According to the method for detecting estrogen in milk, provided by the embodiment of the invention, the near infrared spectrum technology is adopted, so that the sample can be simply and conveniently pretreated or does not need to be treated; the analysis speed is high, and the efficiency is high; the chemical quality of a detected sample is not damaged by pure physical optical detection, and a reagent is not needed, so that the analysis cost is extremely low; the sample is not damaged, the sample can be repeatedly detected, and the test reproducibility is good; the component information of the sample can be rapidly obtained, and qualitative analysis or quantitative analysis is carried out on the sample; the sample is detected based on a correction model manufactured by a chemometrics method, and the obtained result has small relative error; the same sample is detected for many times, and the reproducibility is good; under the same mode, a plurality of components can be measured, and the operation process is simplified.
In addition, the method for detecting estrogen in milk according to the above embodiment of the present invention may further have the following additional technical features:
according to the embodiment of the invention, in S1, the calibration set sample is prepared by analytically and purely mixing pure milk without estrogen and estrogen, and the mixing temperature is 60-80 ℃.
According to the embodiment of the invention, the detection wavelength of the near infrared spectrum is 1750-2150 nm, and the spectral resolution is 10 nm.
According to the embodiment of the invention, during near infrared spectrum detection, the temperature of a sample is 30-38 ℃, and the environment temperature required by detection is 5-40 ℃.
According to an embodiment of the present invention, in S2, the chemometric software creating an estrogen concentration correction model includes:
s21, importing near infrared spectrum information of all data in a correction set converted into a TXT text format in MATLAB software, and inputting an estrogen concentration value corresponding to each near infrared spectrum information;
s22, preprocessing the near infrared spectrum information by using a Savitzky-Golay convolution derivation method for smoothing, variable standardization and data differentiation to obtain spectrum data required by modeling;
s23, using partial least squares PLS, a model for correcting estrogen concentration is constructed using the above spectral data.
According to the embodiment of the invention, in S4, when the accuracy and effectiveness of the estrogen concentration correction model are not enough, the number of the correction set samples extracted in S1 is increased until the accuracy of the estrogen concentration correction model reaches the relative error of less than 5%, and the effectiveness reaches the relative standard deviation of less than 1%.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a near infrared spectrum of a calibration set sample according to an embodiment of the present invention;
FIG. 2 is a near infrared spectrum of a validation set sample in accordance with an embodiment of the present invention;
fig. 3 is a melengestrol acetate concentration correction model established in an example according to the present invention.
Detailed Description
The technical solution of the present invention is illustrated by specific examples below. It is to be understood that one or more method steps mentioned in the present invention do not exclude the presence of other method steps before or after the combination step or that other method steps may be inserted between the explicitly mentioned steps; it should also be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Moreover, unless otherwise indicated, the numbering of the various method steps is merely a convenient tool for identifying the various method steps, and is not intended to limit the order in which the method steps are arranged or the scope of the invention in which the invention may be practiced, and changes or modifications in the relative relationship may be made without substantially changing the technical content.
In order to better understand the above technical solutions, exemplary embodiments of the present invention are described in more detail below. While exemplary embodiments of the invention have been shown, it should be understood that the invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The test materials adopted by the invention are all common commercial products and can be purchased in the market.
The invention will now be described with reference to specific examples, which are intended to be illustrative only and not to be limiting in any way.
Example Merrill progesterone acetate is taken as an example to establish a near-infrared correction model
1. Samples with different melengestrol acetate concentrations were prepared as calibration sets:
selecting fresh sterilized pure milk meeting the production requirements. Before the experiment, the purchased milk is subjected to melengestrol acetate detection according to the detection scheme of melengestrol acetate in the milk and milk powder of the national standard method (GB/T22973-2008), and the detection result proves that the sterilized pure milk does not contain melengestrol acetate. Utilizing the purchased melengestrol acetate analytical reagent (the purity is 99.7%), weighing 50 groups of melengestrol acetate analytical reagents with different masses, fully mixing the weighed melengestrol acetate analytical reagents with the purchased pure milk, preparing 50 melengestrol acetate milks with the concentration uniformly distributed in the variation range of 0mg/L-10mg/L as correction set samples, and oscillating. Wherein, there are 5 concentration variation ranges of 0mg/L-1mg/L, 5 concentration variation ranges of 1mg/L-2mg/L, 5 concentration variation ranges of 2mg/L-3mg/L, 5 concentration variation ranges of 3mg/L-4mg/L, 5 concentration variation ranges of 4mg/L-5mg/L, 5 concentration variation ranges of 5mg/L-6mg/L, 5 concentration variation ranges of 6mg/L-7mg/L, 5 concentration variation ranges of 7mg/L-8mg/L, 5 concentration variation ranges of 8mg/L-9mg/L, and 5 concentration variation ranges of 9mg/L-10mg/L, the concentrations of the respective samples are shown in Table 1.
TABLE 1 true values for the concentrations of the individual samples
2. And (3) measuring the near infrared spectrum of the correction set sample, and making a melengestrol acetate concentration correction model by using chemometrics software:
the calibration set samples were measured at a wavelength range of 1750nm to 2150nm at 35 ℃, and each sample was subjected to signal measurement three times and averaged to obtain near infrared spectrum information, the results of which are shown in fig. 1.
In order to improve the measurement accuracy of the model and reduce the measurement error caused by baseline drift and the instability of the spectrum, the spectrum is preprocessed firstly. Using a model building function of chemometrics software MATLAB, smoothing the spectrum by a Savitzky-Golay convolution derivation method, and removing high-frequency signals; then, carrying out variable standardization on the map to eliminate deviation caused by different dimensions, data sources and dimension units; and finally, carrying out data differentiation on the map to eliminate spectrum baseline drift. Then, a partial least square method PLS is used for establishing a milk estrogen content prediction model and a correlation coefficient R2Is 0.97; the relative error RE is less than 5 percent; the relative standard deviation RSD is less than 1 percent.
Specifically, the steps of establishing a melengestrol acetate concentration correction model in milk by using MATLAB software are as follows:
1) and (3) importing the spectral information of all data in the correction set converted into the TXT text format into MATLAB software, and inputting the actual value of the melengestrol acetate concentration corresponding to each spectral information.
2) And smoothing by using a Savitzky-Golay convolution derivation method, standardizing variables, and preprocessing a spectrum by data differentiation to obtain spectrum data required by modeling.
3) The selected cross validation method is Partial Least Squares (PLS), and the preprocessed spectral data is used for establishing a correction model. The modeling procedure and statistical test method meet the ASTM E1655-2017 standard.
4) And outputting the correction model, and selecting an MAT format to store the file. The final correction model is shown in fig. 2.
3. Samples of 15 different melengestrol acetate concentrations were prepared as validation set (the specific concentrations are shown as the true values in table 2) according to the method described in step 1. Wherein, the sample concentration variation range selected by the verification set is smaller than the sample concentration range of the correction set
4. And (3) measuring the near infrared spectrum of the verification set sample, and measuring the concentration of the sample by using the calibration model established in the step (2), wherein the accuracy and the effectiveness of the verification model are as follows: and (3) introducing all the spectral information of the tested verification set into the correction model, and calculating by using the melengestrol acetate concentration correction model to obtain a predicted value of the melengestrol acetate concentration of the verification set, wherein the obtained verification result is shown in table 2.
Table 2: validation data of validation set samples
| Sample numbering | Mean value (mg/L) | True value (mg/L) | Difference value | RE(％) | RSD(％) |
| 1 | 1.612 | 1.593 | 0.019 | 1.19 | 0.099 |
| 2 | 1.143 | 1.167 | -0.024 | 2.05 | 0.106 |
| 3 | 3.039 | 3.131 | -0.092 | 2.94 | 0.539 |
| 4 | 8.194 | 8.172 | 0.022 | 0.27 | 0.023 |
| 5 | 5.348 | 5.368 | -0.020 | 0.37 | 0.277 |
| 6 | 8.647 | 8.475 | 0.172 | 2.09 | 0.480 |
| 7 | 7.277 | 7.357 | -0.080 | 1.08 | 0.048 |
| 8 | 6.327 | 6.433 | -0.106 | 1.65 | 0.024 |
| 9 | 9.248 | 9.316 | -0.068 | 0.72 | 0.287 |
| 10 | 5.989 | 5.864 | 0.125 | 2.13 | 0.203 |
| 11 | 6.297 | 6.435 | -0.138 | 2.14 | 0.583 |
| 12 | 1.567 | 1.588 | -0.021 | 1.32 | 0.521 |
| 13 | 7.151 | 7.324 | -0.173 | 2.36 | 0.109 |
| 14 | 4.691 | 4.567 | 0.124 | 2.71 | 0.045 |
| 15 | 5.234 | 5.123 | 0.111 | 2.16 | 0.044 |
In the above table:
average value: the average value of 5 times of determination results of the verification set samples;
difference value: the difference between the mean value and the true value;
RE (relative error): difference/true value × 100%;
RSD (relative standard deviation):
therefore, the melengestrol acetate concentration correction model established in the embodiment has good reproducibility when being used for detecting the concentration of the melengestrol acetate in milk, the relative error RE is less than 3%, and the relative standard deviation RSD is less than 1%, so that the model can be used for detecting the residual condition of the melengestrol acetate in the milk.
Weighing melengestrol acetate for analyzing purity, and fully mixing with water to prepare melengestrol acetate standard solution, wherein the concentration of melengestrol acetate standard solution I is 2mg/L, the concentration of melengestrol acetate standard solution II is 4mg/L, the concentration of melengestrol acetate standard solution III is 6mg/L, the concentration of melengestrol acetate standard solution IV is 8mg/L, and the concentration of melengestrol acetate standard solution V is 10mg/L, detecting and making a melengestrol acetate standard curve by an enzyme-linked immunosorbent assay, wherein the enzyme-linked immunosorbent assay specifically comprises the following steps:
1) and (3) sequentially adding the melengestrol acetate standard solution in the step one into a 96-well plate, wherein each well is 10 mu L.
0) mu.L of 0.01mol/L phosphate buffer was added to each well as a coating solution, and after coating overnight at 4 ℃ the wells were washed 5 times with 0.5% BSA in phosphate buffer for 3 minutes each, and patted dry.
3) mu.L of blocking solution (5% BSA in phosphate buffer) was added to each well and blocked at 37 ℃ for 2 hours.
4) The cells were washed 5 times with 0.5% BSA in phosphate buffer for three minutes and blotted dry, 100. mu.L of blocking solution diluted antiserum was added to each well and incubated at 37 ℃ for 1 hour.
5) The cells were washed 5 times with 0.5% BSA in phosphate buffer for three minutes each, blotted dry, and incubated at 37 ℃ for 1 hour with blocking-diluted enzyme-labeled secondary antibody.
6) Wash 5 times with phosphate buffer containing 0.5% BSA for three minutes each time and beat dry, add 100 μ L of color reagent TMEDM per well and incubate for 30 minutes at 37 ℃.
7) The wells were stopped by adding 50. mu.L of 2M sulfuric acid solution, and the OD was measured at 450nm using a microplate reader and a standard curve was automatically generated.
7. The enzyme-linked immunosorbent assay is used for detecting the verification set samples, and the obtained results are shown in table 3; the ratio of the parameters in the detection process using the enzyme-linked immunosorbent assay to the parameters in the detection process of the present invention is shown in table 4:
table 3: detection of verification set by enzyme linked immunosorbent assay
| Sample numbering | Mean value (mg/L) | True value (mg/L) | Difference value | RE(％) |
| 1 | 1.609 | 1.593 | 0.016 | 1.00 |
| 2 | 1.145 | 1.167 | -0.022 | 1.89 |
| 3 | 3.046 | 3.131 | -0.085 | 2.71 |
| 4 | 8.181 | 8.172 | 0.008 | 0.10 |
| 5 | 5.358 | 5.368 | -0.010 | 0.19 |
| 6 | 8.637 | 8.475 | 0.162 | 1.91 |
| 7 | 7.293 | 7.357 | -0.064 | 0.87 |
| 8 | 6.341 | 6.433 | -0.093 | 1.45 |
| 9 | 9.267 | 9.316 | -0.049 | 0.53 |
| 10 | 5.978 | 5.864 | 0.114 | 1.94 |
| 11 | 6.309 | 6.435 | -0.126 | 1.96 |
| 12 | 1.569 | 1.588 | -0.019 | 1.20 |
| 13 | 7.170 | 7.324 | -0.154 | 2.10 |
| 14 | 4.682 | 4.567 | 0.115 | 2.52 |
| 15 | 5.226 | 5.123 | 0.103 | 2.01 |
Table 4: comparing enzyme-linked immunoassay and near infrared spectrum detection processes:
therefore, compared with the common method for detecting the estrogen residue in the milk, the near infrared spectrum detection has the following advantages: the sensitivity is high, and the detection limit is 0.01 mg/L; the near infrared light has strong penetrating power, and no pretreatment is needed when a sample is detected; the near infrared technology can detect the sample timely and quickly, the sample is only required to be scanned for about 5s once, and various composition or property data of the sample can be calculated within 2min through one spectrum of the sample; under the same mode, a plurality of components can be simultaneously measured, so that the detection time is saved, and the operation flow is simplified.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above should not be understood to necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples described in this specification can be combined and combined by those skilled in the art.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (6)
1. A method for detecting estrogen in milk is characterized by comprising the following steps:
s1, preparing samples with different estrogen concentrations as a calibration set;
s2, measuring the near infrared spectrum of the correction set sample, and making an estrogen concentration correction model by using chemometrics software;
s3, preparing samples with different estrogen concentrations to serve as a verification set; wherein, the variation range of the estrogen concentration in the correction set sample is larger than that in the verification set sample;
s4, determining the near infrared spectrum of the verification set sample, calculating the estrogen concentration of the verification set sample by using the established estrogen concentration correction model, and verifying the accuracy and the effectiveness of the estrogen concentration correction model;
s5, measuring the near infrared spectrum of the sample to be detected, and calculating the estrogen concentration of the sample to be detected by using the established estrogen concentration correction model.
2. The method for detecting estrogen in milk according to claim 1, wherein in S1, the calibration set sample is prepared by analytically and purely mixing pure milk without estrogen and estrogen, and the mixing temperature is 60-80 ℃.
3. The method for detecting estrogen in milk according to claim 1, wherein the detection wavelength of the near infrared spectrum is 1750 to 2150nm, and the spectral resolution is 10 nm.
4. The method for detecting estrogen in milk according to claim 1, wherein the temperature of the sample is 30-38 ℃ and the environment temperature required for detection is 5-40 ℃ during near infrared spectrum detection.
5. The method of claim 1, wherein the step of generating a corrected estrogen concentration model using chemometric software at S2 comprises:
s21, importing near infrared spectrum information of all data in a correction set converted into a TXT text format in MATLAB software, and inputting an estrogen concentration value corresponding to each near infrared spectrum information;
s22, preprocessing the near infrared spectrum information by using a Savitzky-Golay convolution derivation method for smoothing, variable standardization and data differentiation to obtain spectrum data required by modeling;
s23, using partial least squares PLS, a model for correcting estrogen concentration is constructed using the above spectral data.
6. The method for detecting estrogens in milk as in claim 1, wherein in S4, when the accuracy and effectiveness of the estrogen concentration correction model is not sufficient, the number of the correction set samples extracted in S1 is increased until the accuracy of the estrogen concentration correction model reaches a relative error of < 5% and the effectiveness reaches a relative standard deviation of < 1%.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010734426.5A CN111678884A (en) | 2020-07-27 | 2020-07-27 | Method for detecting estrogen in milk |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010734426.5A CN111678884A (en) | 2020-07-27 | 2020-07-27 | Method for detecting estrogen in milk |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111678884A true CN111678884A (en) | 2020-09-18 |
Family
ID=72438349
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010734426.5A Pending CN111678884A (en) | 2020-07-27 | 2020-07-27 | Method for detecting estrogen in milk |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111678884A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101769866A (en) * | 2010-01-15 | 2010-07-07 | 中国农业机械化科学研究院 | Device for detecting milk components and method thereof |
| CN101929951A (en) * | 2009-06-19 | 2010-12-29 | 西北农林科技大学 | Method for distinguishing milk doped with ewe's milk by near infrared spectrum |
| CN103335960A (en) * | 2013-06-04 | 2013-10-02 | 安徽华润金蟾药业股份有限公司 | Rapid detection method of key indicators in cinobufagin extraction and concentration processes |
| CN105973814A (en) * | 2015-12-16 | 2016-09-28 | 新希望双喜乳业（苏州）有限公司 | Laser near-infrared rapid detecting method for milk freshness |
-
2020
- 2020-07-27 CN CN202010734426.5A patent/CN111678884A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101929951A (en) * | 2009-06-19 | 2010-12-29 | 西北农林科技大学 | Method for distinguishing milk doped with ewe's milk by near infrared spectrum |
| CN101769866A (en) * | 2010-01-15 | 2010-07-07 | 中国农业机械化科学研究院 | Device for detecting milk components and method thereof |
| CN103335960A (en) * | 2013-06-04 | 2013-10-02 | 安徽华润金蟾药业股份有限公司 | Rapid detection method of key indicators in cinobufagin extraction and concentration processes |
| CN105973814A (en) * | 2015-12-16 | 2016-09-28 | 新希望双喜乳业（苏州）有限公司 | Laser near-infrared rapid detecting method for milk freshness |
Non-Patent Citations (7)
| Title |
|---|
| 冯海,徐光明,刘迪霞,徐铸德: "近红外光谱法同时测定多种雌、孕激素", no. 02, pages 1 * |
| 尚廷义 等: "《基于光谱技术的寒地水稻稻瘟病害分析及机理研究》", 哈尔滨工程大学出版社 * |
| 徐琛善;吴楠;海霞;王军;田佳;刘香檬;鲍振博;彭锦星;: "中红外光谱快速检测牛奶中土霉素的可行性研究", no. 03 * |
| 杨仁杰;刘蓉;徐可欣;: "基于近红外光谱对牛奶中掺杂尿素的判别分析", no. 16 * |
| 王丽杰;蔡丽娟;周真;秦勇;苏子美;徐可欣;郭建英;: "直接正交校正用于牛奶成分近红外光谱分析", no. 09 * |
| 董一威;屠振华;朱大洲;刘亚伟;王亚男;黄金丽;孙宝利;范中南;: "利用近红外光谱快速检测牛奶中三聚氰胺的可行性研究", no. 11 * |
| 马小红;赵武奇;谷月;李晓丹;张哲;田蜜;: "牛奶中青霉素近红外光谱定量分析", no. 12, pages 1 - 1 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3033620B1 (en) | Method for the detection of the prozone effect of photometric assays | |
| CN104390927B (en) | The quick determination method of ash content in coal sample | |
| CN103543123A (en) | Infrared spectrum recognition method for adulterated milk | |
| WO2022001090A1 (en) | Hook effect determination method for homogeneous time-resolved fluorescence immunoassay | |
| CN101216419A (en) | Method for quickly detecting yellow wine quality index | |
| CN109142263B (en) | Component dispersion uniformity online test method in solid propellant preparation process | |
| CN115436353A (en) | High-low concentration real-time switching analyzer colorimetric system and using method thereof | |
| Krahn et al. | Thyroglobulin and anti-thyroglobulin assays in thyroid cancer monitoring | |
| EP2778651B1 (en) | Exhaust gas analyzing apparatus | |
| CN106061294A (en) | Systems and methods for computer models of animal feed | |
| CN110376154A (en) | Fruit online test method and system based on spectrum correction | |
| CN109799224A (en) | Quickly detect the method and application of protein concentration in Chinese medicine extract | |
| CN111678884A (en) | Method for detecting estrogen in milk | |
| CN116735811A (en) | Method and system for measuring total acid and total ester content of wine | |
| CN104568811B (en) | The new quick determination method of caloric value in coal sample | |
| CN110567900A (en) | method and device for judging antigen excess in sample reaction and optical detection system | |
| CN102507501A (en) | Special protein testing method and special protein testing device | |
| CN110174362B (en) | Method for detecting content of neutral sugar and acidic sugar | |
| Florin et al. | Evaluation of an automated algorithm for interpretation of lupus anticoagulant testing | |
| CN105866065B (en) | Methenamine content analysis method in a kind of methenamine-acetum | |
| Kurfürst et al. | Uncertainty in analytical results from solid materials with electrothermal atomic absorption spectrometry: a comparison of methods | |
| Yan et al. | A candidate reference method for serum potassium measurement by inductively coupled plasma mass spectrometry | |
| CN107991375A (en) | The kit of iodine in a kind of measure human urine | |
| CN115046828A (en) | Preparation method of cubilose standard substance for detection | |
| CN104181125A (en) | Method for rapidly determining Kol-bach value of beer malt |
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 |