CN111665216A - Method for judging pollution degree of escherichia coli and staphylococcus aureus in quick-frozen rice-flour product - Google Patents
Method for judging pollution degree of escherichia coli and staphylococcus aureus in quick-frozen rice-flour product Download PDFInfo
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- 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
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- 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
- G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR
Abstract
The invention provides a method for judging the pollution degree of escherichia coli and staphylococcus aureus in a quick-frozen rice noodle product, which comprises the steps of (1) preparing a quick-frozen rice noodle sample polluted by escherichia coli or staphylococcus aureus; step (2), acquiring a near infrared spectrum of a quick-frozen polished rice sample polluted by escherichia coli or staphylococcus aureus, and preprocessing and correcting the acquired near infrared spectrum to obtain spectrum data of an enhanced signal; and (3) combining digital assignment of different positions of the vector space, and performing discriminant analysis on the spectrum data of the enhanced signal obtained in the step (2) by adopting a multivariate correction method to realize the identification of the food pollution degree. The method successfully realizes the identification of the bacterial pollution degree of the quick-frozen rice-flour products, has high identification rate, stable method and high accuracy, and has great analysis potential in the future food safety detection process.
Description
Technical Field
The invention relates to the technical field of food quality control, in particular to a method for judging the pollution degree of escherichia coli and staphylococcus aureus in quick-frozen rice flour products.
Background
Staphylococcus aureus and escherichia coli are common bacteria in food, and the caused food toxicity is an important food safety problem. Coli, also known as escherichia coli, belongs to gram-negative bacteria, which are mainly distributed in the intestinal tracts of humans and animals, being the most predominant and predominant bacteria in the intestinal tracts. In general, Escherichia coli and human are in a mutualistic symbiosis relationship, but under certain conditions, the bacteria which seem beneficial cause a lot of injuries to human bodies, such as diarrhea, parenteral infection and the like. In addition to E.coli, there is a bacterial infection that is not much weaker than E.coli, Staphylococcus aureus. Staphylococcus aureus is a gram-positive bacterium, also known as staphylococcus aureus. The range of staphylococcus aureus distribution is extremely wide, so the probability of contamination by staphylococcus aureus in food is high. Staphylococcus aureus is a common bacterium causing food poisoning, and can cause severe local suppurative infection, pericarditis, pneumonia and the like under certain conditions. Therefore, the detection of the pollution degree of escherichia coli and staphylococcus aureus in food is particularly important.
Nowadays, methods for detecting the bacterial pollution degree in food are many, such as DNA extraction method, spot enzyme-linked immunosorbent assay, magnetic resonance method, and the like. These methods, while effective, are time consuming and labor intensive. .
Disclosure of Invention
The invention aims to provide a quality control problem method for rapidly, simply, objectively, accurately, and nondestructively judging the bacterial pollution degree of a food, which is characterized in that near-infrared map information of a quick-frozen rice sample polluted by enterobacter coli and staphylococcus aureus is obtained, digital assignment of different positions in a vector space is carried out on the pollution degree of the quick-frozen rice product by escherichia coli or staphylococcus aureus, then a pattern recognition model is constructed on the map information of each quick-frozen rice product sample and the vector spaces with different assignments by using a multivariate correction method, and according to a regression modeling result, the assignment position closest to 1 is judged as the category of the sample by prediction, so that the identification and the judgment of the pollution degree of the quick-frozen rice product are realized.
The purpose of the invention is realized by the following technical scheme:
a method for discriminating and analyzing the pollution degree of quick-frozen rice-flour products comprises the following steps:
step (1), preparation of a quick-frozen rice product sample, which comprises the following specific steps:
freeze-drying the quick-frozen rice product and grinding the frozen rice product into powder;
preparing a concentration gradient of staphylococcus aureus and escherichia coli;
fully mixing the ground powder with staphylococcus aureus or escherichia coli liquid with different concentrations to obtain a quick-frozen polished rice sample polluted by escherichia coli or staphylococcus aureus;
step (2), acquiring a near infrared spectrum of a quick-frozen rice sample polluted by escherichia coli or staphylococcus aureus, and preprocessing and correcting the acquired near infrared spectrum to obtain spectrum data of an enhanced signal;
and (3) combining digital assignment of different positions of the vector space, and performing discriminant analysis on the spectrum data of the enhanced signal obtained in the step (2) by adopting a multivariate correction method to realize the identification of the food pollution degree.
Further, grinding of the instant frozen rice product in the step (1) is carried out under an infrared lamp, and the grinding mode is agate mortar grinding.
Further, the preparation of the concentration gradient of staphylococcus aureus and escherichia coli in the step (1) adopts distilled water to dilute to 15mL as mother liquor, and the dilution is sequentially carried out to a series of concentration gradients.
Further, the gradient concentration of staphylococcus aureus and escherichia coli in the step (1) is 2, 5, 10, 50, 100, 500, 1000, 5000 and 10000 times of mother liquor dilution.
Further, the step (1) is fully mixed in two times, the first mixing is the mixing between staphylococcus aureus or escherichia coli bacterial liquid with different concentrations and the quick-frozen rice flour powder, and the second mixing is grinding and mixing by using an agate mortar after the first staphylococcus aureus or escherichia coli bacterial liquid and the quick-frozen rice flour powder are mixed and dried.
Further, the near infrared spectrum of the quick-frozen polished rice sample polluted by the escherichia coli or the staphylococcus aureus is obtained in the step (2), and the near infrared spectrum full wave band (4000-10000 cm) is carried out by taking gold foil as a reference-1) And (6) scanning.
Further, the step (2) of preprocessing and correcting the acquired spectrum to perform multivariate scattering correction processing on the near infrared spectrum specifically comprises: firstly, obtaining an average value of all spectrum data as an 'ideal spectrum', secondly, performing linear regression on the spectrum of each sample and the average spectrum, and solving a least square problem to obtain a baseline translation amount and an offset of each sample; correcting the spectrum of each sample: subtracting the obtained baseline translation amount and then dividing by the offset amount to obtain a corrected spectrum, namely a multivariate scattering spectrum; multivariate scatter correction is used to overcome the different degrees of influence that may exist for different wavelength points and the differences caused by scattering of the spectrum by physical properties such as size and refractive index of the sample particles.
Further, the preprocessing correction of the acquired spectrum in the step (2) is a second derivative correction, that is, a corrected spectrum obtained by performing a second derivation on the acquired original spectrum, that is, a second derivative spectrum, and the second derivative correction is used for eliminating errors caused by baseline drift and inclination and improving a spectrum resolution peak.
Further, the discriminant analysis in the step (3) is to perform preliminary decision analysis according to 3 spectrograms, wherein the 3 spectrograms include an original spectrum, a multivariate scattering spectrum and a second derivative spectrum, the preliminary decision is to see the coincidence degree of lines of one spectrogram, if the coincidence degree is too serious, the spectrogram cannot be used for distinguishing the pollution degree, and after the preliminary decision, in order to make the decision result more accurate, a PLSDA algorithm is selected for decision.
The method comprises the steps of carrying out digital assignment on quick-frozen polished rice samples of different degrees and types polluted by escherichia coli and staphylococcus aureus at different space vector positions, sequentially setting n types of samples as n digital assignment positions, assigning the positions of each sample type in different orders as 1, assigning the rest positions as 0, combining the positions into a matrix of different vector space combinations, constructing a pattern recognition model based on multivariate correction, judging the type of the sample by predicting the assignment position nearest to 1 according to a regression modeling result, and judging the type of the sample by predicting the assignment position nearest to 1 according to the regression modeling result. The method successfully realizes the identification of the bacterial pollution degree of the quick-frozen rice-flour products, has high identification rate, stable method and high accuracy, and has great analysis potential in the future food safety detection process.
Compared with the prior art, the invention has the beneficial effects that:
1. the experimental materials are easy to obtain, the sample preparation is simple, and the experimental period is short;
2. the near infrared spectrum is combined with digital assignment of different positions of a vector space, and then modeling and analysis are performed by utilizing multivariate correction, so that the method is a green, lossless, objective and accurate discrimination method and has huge application potential.
Drawings
FIG. 1 is a near infrared spectrum of a steamed bun contaminated with Escherichia coli of different concentrations, wherein (a) is an original spectrum; (b) is a multivariate scattering spectrum; (c) is a second derivative spectrum;
FIG. 2 is a near infrared spectrum of dumplings contaminated with E.coli of different concentrations, wherein (a) is the original spectrum; (b) is a multivariate scattering spectrum; (c) is a second derivative spectrum;
FIG. 3 is a near infrared spectrum of a glue pudding contaminated with different concentrations of Escherichia coli, wherein (a) is the original spectrum; (b) is a multivariate scattering spectrum; (c) is a second derivative spectrum;
FIG. 4 is a near infrared spectrum of a steamed bun contaminated with different concentrations of Staphylococcus aureus, wherein (a) is an original spectrum; (b) is a multivariate scattering spectrum; (c) is a second derivative spectrum;
FIG. 5 is a near infrared spectrum of a dumpling contaminated with different concentrations of Staphylococcus aureus, wherein (a) is the original spectrum; (b) is a multivariate scattering spectrum; (c) is a second derivative spectrum;
FIG. 6 is a near infrared spectrum of a rice dumpling contaminated with different concentrations of Staphylococcus aureus, wherein (a) is the original spectrum; (b) is a multivariate scattering spectrum; (c) is a second derivative spectrum; (ii) a
FIG. 7 is a diagram showing the identification and judgment of dumplings contaminated by Escherichia coli with different concentrations in a training mode established by the method of the present invention.
Detailed Description
The method of the present invention will be described in further detail with reference to specific examples so that those skilled in the art can more clearly understand the present invention. The following should not be construed as limiting the scope of the claimed invention.
Example 1 discrimination analysis of map information of steamed bread contaminated by Escherichia coli of different concentrations
The main instrument is an Antaris II Fourier transform near infrared spectrometer.
1. Near infrared spectrum information acquisition of steamed bread polluted by escherichia coli with different concentrations
Freeze-drying steamed bread, grinding into powder with agate mortar, and mixing with bacterial mother liquor and bacterial sample diluted to 2, 5, 10, 50, 100, 500, 1000, 5000, and 10000 times under infrared lamp to obtain sample. And f 01-f 11 represent samples of steamed bread polluted by escherichia coli with different concentrations, and the division of the training set and the prediction set is shown in table 1. Gold foil is used as a reference, and the concentration is 4000-10000cm-1The wave number range of the infrared spectrum is scanned, and 110 pieces of near infrared spectrum information are collected.
TABLE 1 partitioning of training and prediction sets of steamed bun samples contaminated with varying degrees of Escherichia coli
2. Carrying out different spectrum preprocessing on the enhanced near infrared spectrum signals of the steamed bun samples polluted by escherichia coli with different degrees, wherein the preprocessing process is as follows, an original spectrum is an original spectrum scanned by near infrared, a second derivative spectrum is a spectrum obtained by carrying out secondary derivation on the obtained original spectrum, and the processing process of the multivariate scattering spectrum is as follows: firstly, obtaining an average value of all spectrum data as an 'ideal spectrum', secondly, performing linear regression on the spectrum of each sample and the average spectrum, and solving a least square problem to obtain a baseline translation amount and an offset of each sample; correcting the spectrum of each sample: and subtracting the obtained baseline translation amount and dividing by the offset amount to obtain a corrected spectrum.
Then, numerical assignment is carried out on different positions in a vector space of the contamination degree of the escherichia coli in the steamed bread (the assignment is manual assignment, for the example of the experiment, 11 samples of the steamed bread contaminated by the escherichia coli are taken as the f1-f11, respectively, the steamed bread is not contaminated, the steamed bread contaminated by the bacterial mother liquor is diluted by 2, 5, 10, 50, 100, 500, 1000, 5000 and 10000 times, the expression mode of f1 (1,0,0,0,0,0,0,0,0, 0), f2(0,1,0,0,0,0,0,0,0,0 and 0), and the like 11 sample expression modes are repeated, wherein f1 is taken as the example, 1 is a sample not contaminated by the escherichia coli, 0 is taken as the example of other samples, f2 is taken as the example, 1 is a sample contaminated by 2 times of the escherichia coli, and 0 is taken as the other samples), and then, a pattern recognition model is established for the pattern signals of the steamed bun samples and the vector spaces of different assignments by using a multivariate correction method, and the assignment positions closest to 1 obtained by prediction are judged as the classes of the samples according to the regression modeling result, wherein the analysis and judgment results are shown in Table 2.
TABLE 2 PLSDA discrimination results of the contamination degree of steamed bread by Escherichia coli of different contents
FIG. 1 is a near-infrared spectrum of a steamed bun contaminated with Escherichia coli of different concentrations: (a) an original spectrum; (b) multivariate scattering spectra; (c) a second derivative spectrum.
Example 2 discriminatory analysis of atlas information of dumplings contaminated with Escherichia coli to varying degrees
The main instrument is an Antaris II Fourier transform near infrared spectrometer.
1. Near infrared spectrum information acquisition of dumplings polluted by escherichia coli with different concentrations
The dumplings are freeze-dried, then ground into powder by an agate mortar, mixed with bacterial mother liquor and bacterial samples diluted to 2, 5, 10, 50, 100, 500, 1000, 5000 and 10000 times respectively under an infrared lamp to prepare samples, and f 01-f 11 represent dumplings samples polluted by escherichia coli with different concentrations, and the division of a training set and a prediction set is shown in table 3. Gold foil is used as a reference, and the concentration is 4000-10000cm-1The wave number range of the infrared spectrum is scanned, and 110 pieces of near infrared spectrum information are collected.
TABLE 3 division of training and prediction sets of dumpling samples contaminated with varying degrees of E.coli
The method comprises the steps of performing different spectrum preprocessing on the obtained enhanced near infrared spectrum signals of dumpling samples polluted by escherichia coli with different degrees (same as example 1), performing digital assignment on different positions in a vector space on the dumpling polluted by the escherichia coli (same as example 1), analyzing dumpling sample map signals and vector space structure modeling identification models with different assignments by using a multivariate calibration method (shown in figure 7), judging the class of the sample according to a regression modeling result and an assignment position closest to 1 obtained through prediction, wherein the analysis and judgment results are all 100%, and are shown in table 4.
TABLE 4 PLSDA discrimination of contamination degree of dumplings with different contents of E.coli
Example 3 discrimination analysis of map information of different degrees of contamination of glutinous rice balls by Escherichia coli
The main instrument is an Antaris II Fourier transform near infrared spectrometer.
1. Near infrared spectrum information acquisition of glue pudding polluted by escherichia coli with different concentrations
The glue pudding is freeze-dried, then ground into powder by an agate mortar, mixed with bacterial mother liquor and bacterial samples diluted to 2, 5, 10, 50, 100, 500, 1000, 5000 and 10000 times under an infrared lamp to prepare samples respectively, and f 01-f 11 represent glue pudding samples polluted by escherichia coli with different concentrations, and the division of a training set and a prediction set is shown in table 5. Gold foil is used as a reference, and the concentration is 4000-10000cm-1The wave number range of the infrared spectrum is scanned, and 110 pieces of near infrared spectrum information are collected.
TABLE 5 partitioning of training and prediction sets of dumpling samples contaminated with different degrees of E.coli
The enhanced near infrared spectrum signals of the glue pudding samples polluted by escherichia coli with different degrees are subjected to different spectrum preprocessing (same as example 1), then the glue pudding samples are subjected to digital assignment of different positions in a vector space according to the escherichia coli pollution degree (same as example 1), then a multivariate correction method is used for carrying out vector space structure modeling identification models on glue pudding sample map information and different assignments, according to a regression modeling result, the assignment position closest to 1 obtained through prediction is judged as the class of the sample, and the analysis and judgment results are all 100%, as shown in table 6.
TABLE 6 PLSDA discrimination results of contamination degree of rice dumpling by Escherichia coli of different contents
Example 4 analysis of Pattern information of steamed bread contaminated with Staphylococcus aureus of different degrees
The main instrument is an Antaris II Fourier transform near infrared spectrometer.
1. Near infrared spectrum information acquisition of soup steamed bun polluted by staphylococcus aureus with different concentrations
The steamed bun is freeze-dried, then the steamed bun is ground into powder by an agate mortar, the powder is respectively mixed with bacterial mother liquor and bacterial samples diluted to 2, 5, 10, 50, 100, 500, 1000, 5000 and 10000 times under an infrared lamp to prepare samples, f 01-f 11 represent the steamed bun samples polluted by staphylococcus aureus with different concentrations, and the division of a training set and a prediction set is shown in table 7. Gold foil is used as a reference and is arranged at 4000-10000cm-1The wave number range of the infrared spectrum is scanned, and 110 pieces of near infrared spectrum information are collected.
TABLE 7 partitioning of training and prediction sets of steamed bread samples contaminated with varying degrees of Staphylococcus aureus
The method comprises the steps of performing different spectrum preprocessing on enhanced near infrared spectrum signals of steamed bun samples polluted by staphylococcus aureus with different degrees (same as example 1), performing digital assignment on the staphylococcus aureus-polluted degrees in the steamed buns at different positions in a vector space (same as example 1), constructing a mode identification model for the steamed bun sample spectrum signals and the vector spaces with different assignments by using a multivariate correction method, judging the class of the sample according to a regression modeling result and an assignment position closest to 1 obtained through prediction, wherein the judgment result is shown in table 8.
TABLE 8 PLSDA discrimination of the degree of contamination of steamed bread by Staphylococcus aureus in different amounts
Example 5 discriminatory analysis of Profile information of dumplings contaminated with Staphylococcus aureus to various degrees
The main instrument is an Antaris II Fourier transform near infrared spectrometer.
1. Near infrared spectrum information acquisition of dumplings polluted by staphylococcus aureus with different concentrations
The dumplings were freeze-dried, then ground into powder with an agate mortar, and mixed with a mother solution of bacteria and a bacterial sample diluted to 2, 5, 10, 50, 100, 500, 1000, 5000, 10000, 50000 times under an infrared lamp to prepare samples, and the samples of the dumplings contaminated with staphylococcus aureus at different concentrations were represented by f 01-f 11, and the training set and the prediction set were divided as shown in table 9. Gold foil is used as reference and is arranged at 4000-10000cm-1The wave number range of the infrared spectrum is scanned in a near-infrared full spectrum mode, and 110 pieces of near-infrared spectrum information are collected.
TABLE 9 division of training and prediction sets of dumpling samples contaminated with varying degrees of Staphylococcus aureus
Performing different spectrum preprocessing on the enhanced near infrared spectrum signals of the dumpling samples polluted by the staphylococcus aureus with different degrees, performing digital assignment on different positions in a vector space on the pollution degree of the staphylococcus aureus in the dumplings, constructing a pattern recognition model on the dumpling sample spectrum signals and the vector space of different assignments by using a multivariate correction method, analyzing, judging the class of the sample according to a regression modeling result and a most-adjacent 1 assignment position obtained by prediction, wherein the judgment result is shown in a table 10.
TABLE 10 PLSDA discrimination of contamination degree of dumplings with different amounts of Staphylococcus aureus
Example 6 analysis of the discrimination of the pattern information of contamination of the dumplings with Staphylococcus aureus to various degrees
The main instrument is an Antaris II Fourier transform near infrared spectrometer.
1. Near infrared spectrum information acquisition of glue pudding polluted by staphylococcus aureus with different concentrations
The glue pudding is freeze-dried, then ground into powder by an agate mortar, mixed with bacterial mother liquor and bacterial samples diluted to 2, 5, 10, 50, 100, 500, 1000, 5000 and 10000 times under an infrared lamp to prepare samples, f 01-f 11 represent glue pudding samples polluted by staphylococcus aureus with different concentrations, and the division of a training set and a prediction set is shown in table 11. Gold foil is used as reference and is arranged at 4000-10000cm-1The wave number range of the infrared spectrum is scanned, and 110 pieces of near infrared spectrum information are collected.
TABLE 11 division of training and prediction sets of dumpling samples contaminated with varying degrees of Staphylococcus aureus
The enhanced near-infrared spectrum signals of the glue pudding samples polluted by the staphylococcus aureus with different degrees are subjected to different spectrum preprocessing (same as embodiment 1), then the degrees of the glue pudding polluted by the enterobacter coli are subjected to digital assignment at different positions in a vector space (same as embodiment 1), then a multivariate correction method is used for carrying out model identification on glue pudding sample map information and vector space structures of different assignments, the class of the sample is judged according to a regression modeling result through predicting the position of the assigned value nearest to 1, and the judgment result is shown in a table 12.
TABLE 12 PLSDA discrimination of the degree of contamination of the dumplings with different amounts of Staphylococcus aureus
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A method for judging the pollution degree of escherichia coli and staphylococcus aureus in quick-frozen rice flour products is characterized by comprising the following steps:
step (1), preparation of a quick-frozen rice product sample, which comprises the following specific steps:
freeze-drying the quick-frozen rice product and grinding the frozen rice product into powder;
preparing a concentration gradient of staphylococcus aureus and escherichia coli;
fully mixing the ground powder with staphylococcus aureus or escherichia coli liquid with different concentrations to obtain a quick-frozen polished rice sample polluted by escherichia coli or staphylococcus aureus;
step (2), acquiring a near infrared spectrum of a quick-frozen polished rice sample polluted by escherichia coli or staphylococcus aureus, and preprocessing and correcting the acquired near infrared spectrum to obtain spectrum data of an enhanced signal;
and (3) combining digital assignment of different positions of the vector space, and performing discriminant analysis on the spectrum data of the enhanced signal obtained in the step (2) by adopting a multivariate correction method to realize the identification of the food pollution degree.
2. The method for determining the degree of contamination by escherichia coli and staphylococcus aureus in the quick-frozen rice noodle product according to claim 1, wherein: and (2) grinding the frozen rice product in the step (1) under an infrared lamp in an agate mortar grinding mode.
3. The method for determining the degree of contamination by escherichia coli and staphylococcus aureus in the quick-frozen rice noodle product according to claim 1, wherein: and (2) preparing the concentration gradient of the staphylococcus aureus and the escherichia coli in the step (1) by diluting the staphylococcus aureus and the escherichia coli to 15mL by using distilled water as mother liquor, and sequentially diluting the mother liquor to a series of concentration gradients.
4. The method for judging the degree of contamination by escherichia coli and staphylococcus aureus in the quick-frozen rice noodle product according to claim 1 or 3, wherein: the gradient concentration of the staphylococcus aureus and the escherichia coli in the step (1) is 2, 5, 10, 50, 100, 500, 1000, 5000 and 10000 times of the dilution of the mother liquor respectively.
5. The method for determining the degree of contamination by escherichia coli and staphylococcus aureus in the quick-frozen rice noodle product according to claim 1, wherein: the step (1) is fully mixed in two times, the first mixing is the mixing between staphylococcus aureus or escherichia coli liquid with different concentrations and the quick-frozen rice flour powder, and the second mixing is grinding and mixing by using an agate mortar after the first staphylococcus aureus or escherichia coli liquid and the quick-frozen rice flour powder are mixed and dried.
6. The method for determining the degree of contamination by escherichia coli and staphylococcus aureus in the quick-frozen rice noodle product according to claim 1, wherein: the near infrared spectrum of the quick-frozen polished rice sample polluted by the escherichia coli or the staphylococcus aureus is obtained in the step (2), and the full waveband (4000-10000 cm) of the near infrared spectrum is carried out by taking a gold foil as a reference-1) And (6) scanning.
7. The method for determining the degree of contamination by escherichia coli and staphylococcus aureus in the quick-frozen rice noodle product according to claim 1, wherein: the step (2) of preprocessing and correcting the acquired spectrum to perform multivariate scattering correction processing on the near infrared spectrum specifically comprises the following steps: firstly, obtaining an average value of all spectrum data as an 'ideal spectrum', secondly, carrying out unary linear regression on the spectrum of each sample and the average spectrum, and solving a least square problem to obtain a baseline translation amount and an offset of each sample; correcting the spectrum of each sample: and subtracting the obtained baseline translation amount and then dividing by the offset amount to obtain a corrected spectrum, namely a multivariate scattering spectrum.
8. The method for determining the degree of contamination by escherichia coli and staphylococcus aureus in the quick-frozen rice noodle product according to claim 1, wherein: and (2) preprocessing and correcting the acquired spectrum into a corrected spectrum obtained by performing second derivative correction, namely performing second derivative on the acquired original spectrum, namely a second derivative spectrum.
9. The method for determining the degree of contamination by escherichia coli and staphylococcus aureus in the quick-frozen rice noodle product according to claim 1, wherein: the discriminant analysis in the step (3) is to perform preliminary decision analysis according to 3 spectrograms, wherein the 3 spectrograms comprise an original spectrum, a multiple scattering spectrum and a second order guided spectrum, the preliminary decision is to see the coincidence degree of lines of one spectrogram, if the coincidence degree is too serious, the spectrogram cannot discriminate the pollution degree, and after the preliminary decision, in order to make the decision result more accurate, a PLSDA algorithm is selected for decision.
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