CN106770004A - Method based on the species and content of microorganism in near-infrared spectrum technique detection dairy products - Google Patents
Method based on the species and content of microorganism in near-infrared spectrum technique detection dairy products Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 37
- 235000013365 dairy product Nutrition 0.000 title claims abstract description 28
- 238000001514 detection method Methods 0.000 title claims abstract description 27
- 244000005700 microbiome Species 0.000 title claims abstract description 21
- 241000894007 species Species 0.000 title claims abstract description 18
- 241000894006 Bacteria Species 0.000 claims abstract description 46
- 239000008267 milk Substances 0.000 claims abstract description 33
- 210000004080 milk Anatomy 0.000 claims abstract description 33
- 235000013336 milk Nutrition 0.000 claims abstract description 33
- 230000001580 bacterial effect Effects 0.000 claims abstract description 11
- 238000004611 spectroscopical analysis Methods 0.000 claims abstract description 9
- 208000035143 Bacterial infection Diseases 0.000 claims abstract description 8
- 208000022362 bacterial infectious disease Diseases 0.000 claims abstract description 8
- 238000012937 correction Methods 0.000 claims description 11
- 230000003595 spectral effect Effects 0.000 claims description 10
- 230000004044 response Effects 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 6
- 239000010453 quartz Substances 0.000 claims description 6
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 6
- 238000004445 quantitative analysis Methods 0.000 claims description 5
- 241000295644 Staphylococcaceae Species 0.000 claims description 4
- 238000010521 absorption reaction Methods 0.000 claims description 4
- 235000009754 Vitis X bourquina Nutrition 0.000 claims description 3
- 235000012333 Vitis X labruscana Nutrition 0.000 claims description 3
- 235000014787 Vitis vinifera Nutrition 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 241001478240 Coccus Species 0.000 claims description 2
- 230000005856 abnormality Effects 0.000 claims description 2
- 230000001419 dependent effect Effects 0.000 claims description 2
- 240000006365 Vitis vinifera Species 0.000 claims 1
- 238000010239 partial least squares discriminant analysis Methods 0.000 abstract description 13
- 238000001228 spectrum Methods 0.000 description 9
- 238000002474 experimental method Methods 0.000 description 6
- 238000012360 testing method Methods 0.000 description 5
- 241000191967 Staphylococcus aureus Species 0.000 description 4
- 238000002835 absorbance Methods 0.000 description 4
- 101100190801 Staphylococcus aureus pls gene Proteins 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 239000000843 powder Substances 0.000 description 3
- 241000219095 Vitis Species 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 208000015181 infectious disease Diseases 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000002203 pretreatment Methods 0.000 description 2
- 238000013519 translation Methods 0.000 description 2
- FFRBMBIXVSCUFS-UHFFFAOYSA-N 2,4-dinitro-1-naphthol Chemical compound C1=CC=C2C(O)=C([N+]([O-])=O)C=C([N+]([O-])=O)C2=C1 FFRBMBIXVSCUFS-UHFFFAOYSA-N 0.000 description 1
- 206010012735 Diarrhoea Diseases 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 238000003113 dilution method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 230000001717 pathogenic effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 238000004879 turbidimetry Methods 0.000 description 1
- 210000004916 vomit Anatomy 0.000 description 1
- 230000008673 vomiting Effects 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
- G01N21/359—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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- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
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- Biochemistry (AREA)
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- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The present invention relates to a kind of method of the species and content of microorganism in detection dairy products based on near-infrared spectrum technique, comprise the following steps：Step S1：Obtain spectroscopic data：Dairy products sample is carried out near infrared spectrum scanning, the atlas of near infrared spectra of sample is obtained；Step S2：Set up model：Model is set up after being pre-processed according to the near infrared spectrum obtained in step S1, qualitative detection model is set up using partial least squares discriminant analysis PLS DA, it is used to determine the species of now sample institute bacterial infection, quantitative determination model is set up using PLS PLS simultaneously, it is used to determine the concentration of now sample institute bacterial infection, so that confirmatory sample freshness；Step S3：Prediction unknown sample：Using the model set up in step S2, whether prediction unknown sample is infected by bacterial and the now type and content of bacterium.The method that the present invention detects the species and content of microorganism in milk by near-infrared spectrum technique, quick, accurate, low cost.
Description
Technical field
It is micro- in more particularly to a kind of detection dairy products based on near-infrared spectrum technique the present invention relates to analytical chemistry field
The method of biological species and content.
Background technology
In recent years, the dairy products such as milk, milk powder are due to rich in protein, and contain various micro units needed by human
Element, thus it is increasingly becoming main nutrient product.At the same time, the dairy products such as milk, milk powder be easy to by microorganism infection from
And it is gone bad.The bacterium that infection is easiest in the dairy products such as milk, milk powder is the rugged bacterium of slope, staphylococcus aureus etc., and this
Class bacterium all has stronger pathogenic, and the dairy products that have been infected are very big to human health damage once eaten by mistake.
In general, once invaded by microorganisms such as bacteriums in the dairy products such as milk, it is small, can make one to suffer from diarrhoea, vomit, greatly
Then critical life.The classification of microorganism and the document of content and standard are mainly turbidimetry, directly count in detection milk at present
Method, plate count, dilution method etc., although this kind of detection method is accurate, time-consuming, it is impossible to accomplishes real-time detection, less
On-line checking can be used for, inspection cost to be higher, time-consuming.
The content of the invention
In view of this, it is an object of the invention to provide microorganism in a kind of detection dairy products based on near-infrared spectrum technique
The method of species and content, the species and content of microorganism in quick, accurate, low cost detection milk.
The present invention is realized using following scheme：The species of microorganism in a kind of detection dairy products based on near-infrared spectrum technique
With the method for content, comprise the following steps：
Step S1：Obtain spectroscopic data：Dairy products sample is carried out near infrared spectrum scanning, the near infrared light of sample is obtained
Spectrogram；
Step S2：Set up model：According to model is set up after the near infrared spectrum pretreatment obtained in step S1, using inclined
Least square discriminant analysis PLS-DA sets up qualitative detection model, is used to determine the species of now sample institute bacterial infection, while
Quantitative determination model is set up using PLS PLS, is used to determine the concentration of now sample institute bacterial infection, so as to confirm
Sample freshness；
Step S3：Prediction unknown sample：Using the model set up in step S2, whether prediction unknown sample is infected by bacterial
And the now type and content of bacterium.
Further, in the step S1, sample is scanned using Fourier transform near infrared spectrometer, setting is swept
Scope is retouched for 12400-4000cm-1, resolution ratio is 16cm-1, scan 32 times.
Further, in the step S1, dairy products sample includes the milk sample containing the rugged bacterium of slope, contains golden grape ball
The milk sample of bacterium.The milk sample containing the rugged bacterium of slope is contained in respectively with the milk sample containing golden staphylococci
Be scanned in 10mm quartz colorimetric utensils, obtain the atlas of near infrared spectra of sample, and rejecting abnormalities sample data.
Further, multiplicative scatter correction method MSC is used when being pre-processed near infrared spectrum in the step S2,
It is used to eliminate scattering influence, strengthens the spectral absorption information related to component content.Due to choosing 12400-4000cm-1Characteristic wave
The lower spectral information of section, respectively with utilization multiplicative scatter correction (Multiplicative Scatter Correction,
MSC), standard normal variable correction (Standard Normal Variate, SNV), first derivative spectral information is carried out
Treatment.Experiment proves MSC effects preferably, and scattering influence can be effectively eliminated by the spectroscopic data obtained after scatter correction,
Enhance the spectral absorption information related to component content.The use of the method requires to set up " a reason for testing sample first
Think spectrum ", i.e. the change of spectrum meets direct linear relationship with the content of composition in sample, right as standard requirement with the spectrum
The near infrared spectrum of every other sample is modified, including baseline translation and offset correction etc..
Further, in the step S2, when setting up qualitative detection model with quantitative determination model, after pre-processing first
Spectroscopic data set up Known Species bacterium and treatment using PLS-DA after relation, PLS-DA models between spectral signature matrix X
In dependent variable be Y, its columns be equal to bacterium species, for example bacterial species be 3 kinds, Y=[100], Y=[010], Y=
[001] represent the ownership of bacterium, known bacterial species and spectrum corresponding relation can be just set up after setting up PLS-DA models,
The species of the bacterium that can be used to predict unknown.
Specifically, PLS-DA models first are by sample class dummy variable δijDeal with, i.e.,：
Then, the relational model set up between explanatory variable and response variable with PLS, wherein reacting
Variable is dummy variable, and explanatory variable is X；
Finally, the classification of each sample, even certain dummy variable are determined by the response variable predicted value size of comparison model
The predicted value of component is maximum, then judge that the sample belongs to the classification corresponding to the dummy variable.
Further, quantitative identification includes as follows：Contain different bacterium and the bacterial contents such as the rugged bacterium of slope, golden staphylococci
Different milk samples, surveys its atlas of near infrared spectra at room temperature.
Prepare and a series of contain two kinds of bacteriums, but the dairy products sample, wherein bacterium such as the different milk of bacterial concentration simultaneously
Content (log CFU/mL) be respectively:(8.000,5.301)、(7.699,6.000)、(7.301,6.699)、(7.000,
5.000)、(6.699,5.699)、(6.301,4.699)、(6.000,3.301)、(5.699,4.000)、(5.301,1.699)、
(5.000,1.301), (4.699,2.699), (4.301,7.699), (4.000,8.000), (3.699,7.000), (3.301,
1.000), (3.000,2.301), (2.699,7.301), (2.301,6.301), (2.000,4.301), (1.699,3.699),
(1.301,2.000)、(1.000,3.000).Detection testing sample is contained using 10mm quartz colorimetric utensils, its near infrared spectrum is surveyed
Figure.Set sweep limits as 12400-4000cm-1, resolution ratio is 16cm-1, scan 32 times.Using with air as measurement background,
Air humidity is 60%, is determined at room temperature, and Quantitative Analysis Model is set up respectively to different bacterium with PLS, while to containing mixed
The milk sample for closing bacterium sets up Quantitative Analysis Model, predicts the respective concentration of different bacterium in milk, then by PLS models just
The concentration of bacterium in unknown milk can be measured.
Compared with prior art, the invention has the advantages that：The present invention by near-infrared spectrum technique, to milk
In microorganism set up the qualitative model and PLS quantitative models of PLS-DA respectively, our experiments show that with good precision, and
Have real-time good than traditional method, it is simple to operate, offer reference and contribute for follow-up milk microorganism detection, due to ox
Milk microorganism detection is closely bound up with life, with display meaning and good application prospect well.
Brief description of the drawings
Fig. 1 is the near-infrared original absorbance spectrogram for implementing the rugged bacterium of the slope in the present invention.
Fig. 2 is the near-infrared original absorbance spectrogram for implementing the staphylococcus aureus in the present invention.
Fig. 3 predicts the outcome schematic diagram to implement the staphylococcus aureus PLS quantitative models in the present invention.
Fig. 4 predicts the outcome schematic diagram to implement the rugged bacterium PLS quantitative models of the slope in the present invention.
Fig. 5 is partial least squares discriminant analysis (PLS-DA) basic principle schematic in the present invention.
Fig. 6 is method of the present invention schematic flow sheet.
Specific embodiment
Below in conjunction with the accompanying drawings and embodiment is specifically further described to the present invention.
The present embodiment provides a kind of side of the species and content of microorganism in detection dairy products based on near-infrared spectrum technique
Method, as shown in fig. 6, comprising the following steps：
Step S1：Obtain spectroscopic data：Dairy products sample is carried out near infrared spectrum scanning, the near infrared light of sample is obtained
Spectrogram；
Step S2：Set up model：According to model is set up after the near infrared spectrum pretreatment obtained in step S1, using inclined
Least square discriminant analysis PLS-DA sets up qualitative detection model, is used to determine the species of now sample institute bacterial infection, while
Quantitative determination model is set up using PLS PLS, is used to determine the concentration of now sample institute bacterial infection, so as to confirm
Sample freshness；
Step S3：Prediction unknown sample：Using the model set up in step S2, whether prediction unknown sample is infected by bacterial
And the now type and content of bacterium.
In the present embodiment, the method is specific as follows：
1st, reagent and material：Milk (Australia ox), the rugged bacterium of slope, staphylococcus aureus, this time Qualitive test and quantitative knowledge
Not using both bacteriums as demonstration.
2nd, instrument and equipment：(Thermo fisher are public in the U.S. for Fourier transform near infrared spectrometer nicolet6700
Department), 10mm quartz colorimetric utensils；MATLAB software programming programs.
3rd, experiment condition is set：
Sample is contained with 10mm quartz colorimetric utensils in experiment, and sweep limits is 12400-4000cm-1, resolution ratio is 16cm-1, scan 32 times.Determine at room temperature, with air as measurement background, air humidity is 60%.
4th, experimental technique and spectrum are obtained：
Qualitative spectrochemical analysis model sample is extracted：Take one group and contain the different bacterium milk sample such as the rugged bacterium of slope, golden staphylococci
Product, survey its atlas of near infrared spectra at room temperature.
Quantitative spectrochemical analysis model sample is extracted：Prepare a series of simultaneously containing two kinds of bacteriums, but bacterial concentration is different
The content (log CFU/mL) of the dairy products sample such as milk, wherein bacterium is respectively:(8.000,5.301)、(7.699,
6.000)、(7.301,6.699)、(7.000,5.000)、(6.699,5.699)、(6.301,4.699)、(6.000,3.301)、
(5.699,4.000)、(5.301,1.699)、(5.000,1.301)、(4.699,2.699)、(4.301,7.699)、(4.000,
8.000), (3.699,7.000), (3.301,1.000), (3.000,2.301), (2.699,7.301), (2.301,6.301),
(2.000,4.301), (1.699,3.699), (1.301,2.000), (1.000,3.000).Contained using 10mm quartz colorimetric utensils
Dress detection testing sample, surveys its atlas of near infrared spectra.Sample Scan scope is 12400-4000cm-1, resolution ratio is 16cm-1, sweep
Retouch 32 times.So that with air, used as background is measured, air humidity is 60%.
5th, the treatment of spectrum
For 12400-4000cm-1Spectral information under characteristic wave bands, respectively with multiplicative scatter correction
(Multiplicative Scatter Correction, MSC), standard normal variable correct (Standard Normal
Variate, SNV), first derivative spectral information is processed.Experiment proves MSC effects preferably, by scatter correction
The spectroscopic data for obtaining afterwards can effectively eliminate scattering influence, enhance the spectral absorption information related to component content.Should
The use of method requires to set up one " the preferable spectrum " of testing sample first, i.e., the content of composition in the change of spectrum and sample
Meet direct linear relationship, the near infrared spectrum of every other sample is modified by standard requirement of the spectrum, wherein
Including baseline translation and offset correction etc..
6th, qualitative and quantitative model foundation
After the spectroscopic data after treatment is set up into Known Species bacterium and treatment using PLS-DA between spectral signature matrix X
Relation.PLS-DA models first deal with sample class with dummy variable, using Kronecker symbol (Kronecker δ),
I.e.：
Then, the relational model set up between explanatory variable X and response variable (dummy variable) with PLS.
Finally, the classification of each sample is determined by the response variable predicted value size of comparison model, even certain dummy variable component
Predicted value is maximum, then judge that the sample belongs to the classification corresponding to the dummy variable.See Fig. 5.The model prediction that will be set up is unknown
The milk sample of bacterial species simultaneously detects its accuracy.In addition, setting up fixed respectively to different bacterium with PLS (PLS)
Amount analysis model, while setting up Quantitative Analysis Model to the milk sample containing mixed cell, different bacterium is each in prediction milk
From concentration.
1st, PLS-DA qualitative discrimination models are as shown in table 1 in the effect of different pretreatments.Table 1 implements the rugged bacterium of the slope, gold
Staphylococcus aureus PLS-DA qualitative models predict the unknown sample degree of accuracy under different pretreatments.
Table 1
8th, as shown in table 2, table 2 is real for the actual value of the experimental result of PLS quantitative judges model, predicted value and relative error magnitudes
The rugged bacterium of the slope, staphylococcus aureus PLS quantitative models are applied for recognizing in the milk that Mixed Microbes polluted two kinds of bacteriums
Predict the outcome.
Table 2
In addition, Fig. 1 is the near-infrared original absorbance spectrogram for implementing the rugged bacterium of slope；Fig. 2 is the implementation golden yellow Portugal
The near-infrared original absorbance spectrogram of grape coccus；Fig. 3 predicts the outcome to implement the staphylococcus aureus PLS quantitative models；
Fig. 4 predicts the outcome to implement the rugged bacterium PLS quantitative models of slope.
The foregoing is only presently preferred embodiments of the present invention, all impartial changes done according to scope of the present invention patent with
Modification, should all belong to covering scope of the invention.
Claims (7)
1. it is a kind of based on near-infrared spectrum technique detection dairy products in microorganism species and content method, it is characterised in that：
Comprise the following steps：
Step S1：Obtain spectroscopic data：Dairy products sample is carried out near infrared spectrum scanning, the near infrared spectrum of sample is obtained
Figure；
Step S2：Set up model：According to model is set up after the near infrared spectrum pretreatment obtained in step S1, using partially minimum
Two multiply discriminant analysis PLS-DA sets up qualitative detection model, is used to determine the species of now sample institute bacterial infection, while using
PLS PLS sets up quantitative determination model, is used to determine the concentration of now sample institute bacterial infection, so that confirmatory sample
Freshness；
Step S3：Prediction unknown sample：Using in step S2 set up model, prediction unknown sample whether be infected by bacterial and
The now type and content of bacterium.
2. it is according to claim 1 it is a kind of based on near-infrared spectrum technique detection dairy products in microorganism species and content
Method, it is characterised in that：In the step S1, sample is scanned using Fourier transform near infrared spectrometer, set
Sweep limits is 12400-4000cm-1, resolution ratio is 16cm-1, scan 32 times.
3. it is according to claim 1 it is a kind of based on near-infrared spectrum technique detection dairy products in microorganism species and content
Method, it is characterised in that：In the step S1, dairy products sample includes the milk sample containing the rugged bacterium of slope, contains golden grape
The milk sample of coccus.
4. it is according to claim 3 it is a kind of based on near-infrared spectrum technique detection dairy products in microorganism species and content
Method, it is characterised in that：The milk sample containing the rugged bacterium of slope is contained respectively with the milk sample containing golden staphylococci
Be scanned in 10mm quartz colorimetric utensils, obtain the atlas of near infrared spectra of sample, and rejecting abnormalities sample data.
5. it is according to claim 1 it is a kind of based on near-infrared spectrum technique detection dairy products in microorganism species and content
Method, it is characterised in that：Multiplicative scatter correction method is used when being pre-processed near infrared spectrum in the step S2
MSC, is used to eliminate scattering influence, strengthens the spectral absorption information related to component content.
6. it is according to claim 1 it is a kind of based on near-infrared spectrum technique detection dairy products in microorganism species and content
Method, it is characterised in that：In the step S2, when setting up qualitative detection model with quantitative determination model, first will pretreatment
Spectroscopic data afterwards set up Known Species bacterium and treatment using PLS-DA after relation, PLS-DA moulds between spectral signature matrix X
Dependent variable in type is Y, and its columns is equal to the species of bacterium, and PLS-DA models first are by sample class dummy variable δijAt work
Reason, i.e.,：
Then, the relational model set up between explanatory variable and response variable with PLS, wherein response variable
It is dummy variable, explanatory variable is X；
Finally, the classification of each sample is determined by the response variable predicted value size of comparison model, even certain dummy variable component
Predicted value it is maximum, then judge that the sample belongs to the classification corresponding to the dummy variable.
7. it is according to claim 1 it is a kind of based on near-infrared spectrum technique detection dairy products in microorganism species and content
Method, it is characterised in that：It is a series of simultaneously containing two kinds of bacteriums by preparing in the step S2, but bacterial concentration is different
Dairy products sample carry out near infrared spectrum scanning, the content (log CFU/mL) of wherein bacterium is respectively：(8.000,
5.301)、(7.699,6.000)、(7.301,6.699)、(7.000,5.000)、(6.699,5.699)、(6.301,4.699)、
(6.000,3.301)、(5.699,4.000)、(5.301,1.699)、(5.000,1.301)、(4.699,2.699)、(4.301,
7.699), (4.000,8.000), (3.699,7.000), (3.301,1.000), (3.000,2.301), (2.699,7.301),
(2.301,6.301), (2.000,4.301), (1.699,3.699), (1.301,2.000), (1.000,3.000), with sky
Used as measurement background, air humidity is 60% to gas, is determined at room temperature, and the sample containing different bacterium is set up respectively with PLS
Quantitative Analysis Model, while setting up Quantitative Analysis Model to the sample containing mixed cell.
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