CN111122492B - Near-infrared detection-based fast water-injected meat screening method - Google Patents
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- 235000013372 meat Nutrition 0.000 title claims abstract description 72
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 72
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000001514 detection method Methods 0.000 title claims abstract description 25
- 238000012216 screening Methods 0.000 title claims abstract description 12
- 102000004169 proteins and genes Human genes 0.000 claims abstract description 36
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 36
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 9
- 235000015277 pork Nutrition 0.000 claims description 29
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- 238000001228 spectrum Methods 0.000 claims description 17
- 238000002347 injection Methods 0.000 claims description 14
- 239000007924 injection Substances 0.000 claims description 14
- 210000003205 muscle Anatomy 0.000 claims description 8
- 238000006243 chemical reaction Methods 0.000 claims description 4
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- 230000004060 metabolic process Effects 0.000 claims description 2
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- 244000144972 livestock Species 0.000 abstract description 3
- 235000013594 poultry meat Nutrition 0.000 abstract description 2
- 230000000694 effects Effects 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 8
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- 238000007781 pre-processing Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
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- 238000004458 analytical method Methods 0.000 description 5
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- 238000012937 correction Methods 0.000 description 4
- 238000003672 processing method Methods 0.000 description 3
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- 238000010200 validation analysis Methods 0.000 description 3
- 238000005481 NMR spectroscopy Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 2
- 239000003814 drug Substances 0.000 description 2
- 229940079593 drug Drugs 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
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- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- 238000007696 Kjeldahl method Methods 0.000 description 1
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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
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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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
- G01N2201/00—Features of devices classified in G01N21/00
- G01N2201/12—Circuits of general importance; Signal processing
- G01N2201/129—Using chemometrical methods
- G01N2201/1293—Using chemometrical methods resolving multicomponent spectra
Abstract
The invention discloses a near-infrared detection-based water-injected meat rapid screening method. The method comprises the steps of establishing a moisture, protein and fat content prediction model by collecting a near infrared spectrum of a sample; analyzing the distribution rule of water/protein and fat by using a statistical median method; according to the limit value of 77% of the national standard GB 18394-2001 livestock and poultry meat water limit and the median of water/protein and fat as screening indexes, suspected 'water-injected meat' is rapidly screened. The method has the advantages of simple and convenient detection process, rapidness, no damage, suitability for on-site on-line detection, and popularization and promotion to meat detection of markets and business surpasses.
Description
Technical Field
The invention belongs to the field of food detection, and relates to a rapid screening method of water-injected meat based on near-infrared detection.
Background
The behavior of 'water injection' is prohibited since the meat market was released and operated in the last 80 th century, which has become a food safety problem seriously harming the health of consumers. The detection technology of water-injected meat in the project disclosed by Chinese patent mainly comprises the following steps: test paper, electric conduction, nuclear magnetic resonance, near infrared spectrum and other technologies.
The test paper technology is that whether forbidden drugs are added into meat is identified by utilizing the color change principle of chemical reaction, and additives such as water, glue and other substances are determined according to different reaction intervals; the electric conduction technology is to detect the water content in meat to identify whether the meat is water-injected meat by utilizing the electrode principle of physics and the electric conduction characteristic of the meat; from practical effects, the two speed detection methods are seriously lagged, the accuracy needs to be improved, and the deterrence effect is not good.
The nuclear magnetic resonance technology is to analyze the distribution state of water in meat by utilizing the relaxation time of hydrogen molecules in the meat to identify 'water-injected meat'; the technology has high requirements on equipment, needs a professional inspector to operate and detect, is long in detection time, and is not beneficial to on-site online rapid screening.
In the current near-infrared detection method patent, a moisture prediction model is established mainly by collecting near-infrared spectrum of meat, and water-injected meat is identified by utilizing a moisture content value or combining other detection items. The water injection model of the muscle water injection model basically adopts a pure muscle water injection mode, has great difference compared with the water injection meat of living body injection (the main means of the existing illegal water injection), and can not accurately display the real state of water in muscle tissues.
Disclosure of Invention
The invention aims to provide a fast screening method of water-infused meat based on near-infrared detection.
The method for rapidly screening the water-injected meat provided by the invention comprises the following steps:
carrying out near infrared spectrum detection on the pork sample to be screened, and obtaining the water mass percentage content, the protein mass percentage content and the fat content in the pork sample to be screened by using the spectrum information in the obtained near infrared spectrogram, wherein the water mass percentage content, the protein mass percentage content and the fat content are respectively marked as a, b and c;
y = a/(b + c) formula I
In the formula I, a is the mass percentage of water in the pork sample to be screened;
b is the mass percentage content of protein in the pork sample to be screened;
c is the fat content in the pork sample to be screened;
if a of the pork sample to be screened is less than or equal to 77% and y is less than 3.10, the pork sample to be screened is normal meat;
if a of the pork sample to be screened is less than or equal to 77% and y is more than or equal to 3.10 and less than or equal to 3.56, the pork sample to be screened is suspected to be 'water-injected pork';
if a of the pork sample to be screened is more than 77%, or y is more than 3.56, the pork sample to be screened is 'water-injected meat'.
In the method, in the near infrared spectrum detection step, the detection wavelength is 1000-2500nm; the spectral resolution is 10nm; the wavelength repeatability was 0.05nm.
The spectral information is absorbance and wavelength.
The method for obtaining the water content, the protein content and the fat content in the pork sample to be screened by using the spectral information in the obtained near-infrared spectrogram comprises the following steps: and establishing a component content prediction model by using the spectral information. The above-described method of establishing a component content prediction model is a known conventional method.
Specifically, a suppr ir-2750 near infrared analyzer manufactured by the optical concentration technology (hang state) ltd may be used, the spectral range is 1000-2500nm, the spectral resolution is 10nm, the wavelength repeatability is 0.05nm, during spectral scanning, spectral scanning is performed on three different positions on each sample to obtain three spectral curves, and then an average spectral curve is obtained on average.
In order to ensure the accuracy of the model and reduce baseline drift and instability of the spectrum caused by measurement error factors, the spectrum needs to be preprocessed. By utilizing the model building function of chemometrics CM2000 software, preprocessing methods such as smoothing, multivariate scattering correction and derivative processing are adopted for the map, and noise and drift in the map are reduced. The smoothing processing method comprises a moving average method and convolution smoothing, and reduces the signal-to-noise ratio of the map data; the derivative processing method comprises first derivative processing and second derivative processing, and spectral band characteristics of the picture are enhanced. The method adopts a partial least square method to establish a water content, protein content and fat content prediction model, and correlation coefficients R2C are respectively 0.94,0.84 and 0.87; the corrected standard deviation SEC (SEC) is 0.47,0.40 and 0.56, respectively.
Specifically, a moisture, protein and fat content prediction model can be established by using the chemometrics CM2000 software according to the following steps:
in the software of the CM2000 for stoichiometry,
1. a new model analysis project is established, original spectra of samples are imported, the average spectrum of the original spectra of each sample is taken, and the detection value of the edible index of the sample is input;
2. in "spectrum pre-processing", first a random distribution of 96 calibration samples and 24 validation samples is performed on 120 spectra; preprocessing the map by adopting a moving average method, a convolution smoothing method, a first derivative method and a second derivative method to generate modeling map data;
3. in the 'modeling', a quantitative correction method is selected, a least square method (PLS) is adopted, model factors are set to be 8, and a modeling process and a statistical test method meet the standard of ASTM E1655;
4. in the model evaluation, the model effect is output, and the prediction model is stored;
5. in the model verification, the atlas of the verification sample is introduced into an analysis item, and an index prediction model is used for calculation to obtain a usable index prediction value.
The water injection of the living body inevitably causes complex metabolism and compensation reaction of the organism, and in addition, the illegal additives also contain auxiliary drugs, so that the state of water in muscle tissues is definitely different from that of the simple muscle water injection. According to the method, a moisture, protein and fat content prediction model is established by collecting the near infrared spectrum of a sample; analyzing the distribution rule of water/protein and fat by using a statistical median method; according to the limit value% of the national standard GB 18394-2001 livestock and poultry meat water limit and the median of water/protein and fat as screening indexes, suspected 'water-injected meat' is rapidly screened. The method adopts the sample collected after the living body is perfused, has authenticity and representativeness, has simple, convenient, quick and lossless detection process, is suitable for on-site online detection, can be popularized and popularized to meat detection of market and business surpasses, and has important application value.
Drawings
FIG. 1 is a raw spectrum of a sample.
FIG. 2 shows the moisture content distribution of fresh meat and water-infused meat.
FIG. 3 is a graph of the protein content distribution of raw fresh meat and "waterflooded meat".
Fig. 4 is a graph of fat content distribution for raw fresh meat and "water-infused meat".
Figure 5 is the moisture/protein and fat distribution of raw fresh meat and "waterflood meat".
FIG. 6 shows the predicted effect of the moisture content prediction model.
FIG. 7 shows the predicted effect of the protein content prediction model.
FIG. 8 shows the effect of the fat content prediction model.
Fig. 9 is a graph of the water, protein and fat content profiles of the validation samples.
Detailed Description
The present invention will be further illustrated with reference to the following examples, but the present invention is not limited to the following examples. The method is a conventional method unless otherwise specified. The starting materials are commercially available from the open literature unless otherwise specified.
Examples 1,
The experimental procedures used in the following examples are all conventional procedures unless otherwise specified.
1) Sample model
234 healthy three-way hybrid pigs weighing more than 95kg are selected under the same feeding conditions. 153 fresh meat groups are randomly selected, and the rest 81 meat groups are used as water-injected meat groups and used for establishing a water-injected meat model. The illegal additives of water injection collected on site by quality control departments are used for operation according to the common injection dosage and water injection method. Injecting illegal additives into the pig muscle for 2 hours, then filling 6 kilograms of clear water, filling once every 3 hours, and continuously for 2 times; slaughtering after 2h of last drenching. The slaughtering process is carried out according to GB/T17236-2008 pig slaughtering operation regulations.
2) Establishing a water content, protein content and fat content prediction model
Using a suppr ir-2750 near infrared analyzer manufactured by the optical concentration technology (hang state) ltd, the spectral range is 1000-2500nm, the spectral resolution is 10nm, the wavelength repeatability is 0.05nm, during the spectral scanning, the spectral scanning is performed on three different positions on each sample to obtain three spectral curves, and then an average spectral curve is formed on the average, as shown in fig. 1.
In order to ensure the accuracy of the model and reduce baseline drift and instability of the spectrum caused by measurement error factors, the spectrum needs to be preprocessed. By utilizing the model building function of chemometrics CM2000 software, preprocessing methods such as smoothing, multivariate scattering correction and derivative processing are adopted for the map, and noise and drift in the map are reduced. The smoothing method comprises a moving average method and convolution smoothing, and reduces the signal-to-noise ratio of the map data; the derivative processing method comprises first derivative processing and second derivative processing, and the spectral band characteristics of the picture are enhanced. The method adopts a partial least square method to establish a water content, protein content and fat content prediction model, and correlation coefficients R2C are respectively 0.94,0.84 and 0.87; the corrected standard deviation SEC (SEC) is 0.47,0.40 and 0.56, respectively.
Specifically, a moisture, protein and fat content prediction model can be established by using chemometrics CM2000 software according to the following steps:
in the software of the stoichiometric CM2000 system,
1. a new model analysis project is established, original spectrums of samples are imported, an average spectrum of the original spectrum of each sample is taken, and a detection value of an edible index of the sample is input;
2. in "spectrum pre-processing", first a random distribution of 188 calibration samples and 46 validation samples was performed on 234 spectra; preprocessing the map by adopting a moving average method, a convolution smoothing method, a first derivative method and a second derivative method to generate modeling map data;
3. in the process of establishing the model, a quantitative correction method is selected, a least square method (PLS) is adopted, model factors are set to be 8, and a modeling process and a statistical test method meet the standard of ASTM E1655;
4. in the model evaluation, the model effect is output, and the prediction model is stored;
5. in the model verification, the map of the verification sample is introduced into an analysis item, and an index prediction model is used for calculation to obtain a predicted value of a usable index, namely the content of water, protein and fat.
The statistical table of the effect of the obtained component content prediction model is shown in table 1.
The prediction effects of the obtained moisture, protein and fat content prediction models are shown in fig. 6, 7 and 8, respectively.
TABLE 1 content prediction model effect statistics Table
3) Moisture, protein and fat content determination
The moisture content is measured by referring to a method of GB5009.3-2016 (determination of moisture in national food safety standard) for determining the moisture content;
referring to the first method in GB5009.5-2016 (determination of proteins in food safety national standard food): measuring the protein content by a Kjeldahl method;
refer to the first method in GB5009.6-2016 (determination of fat in food safety national standard food): the fat content was determined by soxhlet extraction.
As shown in the results in Table 2, the moisture content of the fresh meat is significantly less than that of the water injected meat (P < 0.05), but the values of the fresh meat and the water injected meat do not exceed the 77% limit value specified in the GB 18394-2001 livestock meat moisture limit;
the protein of the fresh meat is obviously higher than that of water-injected meat (P is less than 0.05);
the fat content of the fresh meat is slightly higher than that of 'water-injected meat'; the moisture/protein and fat of the fresh meat is significantly less than that of "water injected meat" (P < 0.05).
TABLE 2 nutritional content and moisture/protein and fat determination results
4) Screening index analysis
As shown in table 3, the distribution of water, protein and fat showed that the distribution of water, protein and fat in fresh meat ranged from 2.81 to 3.39, and was mainly concentrated around 3.05; the "waterflood meat" has a moisture/protein and fat value of 3.28 to 4.36, with the main distribution area centered around 3.50. The distribution areas of the two are obviously different, and most values can be distinguished.
TABLE 3 moisture/protein and fat percentiles
Further investigation of the percentiles of moisture, protein and fat showed that (table 3) the percentile range for "water-infused meat" moisture, protein and fat was below 0% and the value was 3.10. Thus, it is proposed that a water/protein to fat ratio (i.e., y) of less than 3.10 is judged as normal meat; 3.10 to 3.56 are suspected water-injected meat; if the water content is more than 3.56, the meat is judged to be water-injected meat.
5) Screening process
Firstly, scanning a near-infrared spectrum of a pork sample, and obtaining values of moisture, protein and fat through a near-infrared nutrient content model; the pork sample to be detected with the moisture content of less than or equal to 77 percent and the ratio of the moisture/protein to the sum of fat of less than 3.10 is required to be used as normal pork; a "water-infused meat" if the moisture content is 77% or less, or the moisture/protein to fat ratio is in the range of 3.10 to 3.56; a "water-infused meat" is judged if the moisture content is greater than 77%, or the moisture/protein to fat ratio is greater than 3.56.
Establishing 21 verification samples according to the sample model, and randomly selecting 7 fresh meat groups; the remaining 14 heads were used as the "waterflood meat" group to verify the moisture, protein and fat predictions for the samples, with the results shown in table 4.
TABLE 4 verification of moisture, protein and fat predictions for samples
The content distribution diagram is shown in fig. 9.
Through the judgment of the method, 6 normal fresh meats in the fresh meat group account for 86 percent; 1 suspected water-injected meat accounts for 14 percent. In the water-injected meat group, 1, 7 percent of normal fresh meat is added; 11 suspected water-injected meats, 79%; 2 pieces of water-injected meat, 14 percent.
Claims (1)
1. A method for rapidly screening water-infused meat, comprising the following steps:
carrying out near infrared spectrum detection on the pork sample to be screened, and obtaining the water mass percentage content, the protein mass percentage content and the fat content in the pork sample to be screened by using the spectrum information in the obtained near infrared spectrogram, wherein the water mass percentage content, the protein mass percentage content and the fat content are respectively marked as a, b and c;
y = a/(b + c) formula I
In the formula I, a is the mass percentage of water in the pork sample to be screened;
b is the mass percentage content of protein in the pork sample to be screened;
c is the fat content in the pork sample to be screened;
if a of the pork sample to be screened is less than or equal to 77% and y is less than 3.10, determining that the pork sample to be screened is normal meat;
if a of the pork sample to be screened is less than or equal to 77% and y is more than or equal to 3.10 and less than or equal to 3.56, the pork sample to be screened is suspected to be 'water-injected pork';
if a of the pork sample to be screened is more than 77%, or y is more than 3.56, the pork sample to be screened is 'water-injected meat';
in the near infrared spectrum detection step, the detection wavelength is 1000-2500nm; the spectral resolution is 10nm; the wavelength repeatability is 0.05nm;
the spectral information is absorbance and wavelength;
the method for obtaining the water content, the protein content and the fat content in the pork sample to be screened by using the spectral information in the obtained near-infrared spectrogram comprises the following steps: establishing a component content prediction model by using the spectral information; the samples used for establishing the prediction model comprise a fresh meat group and a water-injected meat group, wherein the water-injected meat group is a sample collected after living body perfusion, and the living body can cause complex metabolism and compensation reaction after water injection, so that the state of water in muscle tissues is different from that of pure muscle water injection.
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