CN105424645A - Method for fast identifying clinical pathogens based on principal component analysis and Fisher discriminance - Google Patents
Method for fast identifying clinical pathogens based on principal component analysis and Fisher discriminance Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000000513 principal component analysis Methods 0.000 title claims description 44
- 244000052769 pathogen Species 0.000 title abstract description 6
- 241000191967 Staphylococcus aureus Species 0.000 claims abstract description 12
- 230000009467 reduction Effects 0.000 claims abstract description 5
- 238000012545 processing Methods 0.000 claims abstract description 4
- 241000894006 Bacteria Species 0.000 claims description 25
- 238000002329 infrared spectrum Methods 0.000 claims description 15
- 235000001674 Agaricus brunnescens Nutrition 0.000 claims description 10
- 241000607142 Salmonella Species 0.000 claims description 10
- 238000004458 analytical method Methods 0.000 claims description 10
- 238000012937 correction Methods 0.000 claims description 7
- 238000002405 diagnostic procedure Methods 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 7
- 241000193830 Bacillus <bacterium> Species 0.000 claims description 5
- 210000001072 colon Anatomy 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 5
- 230000003595 spectral effect Effects 0.000 claims description 5
- 238000004611 spectroscopical analysis Methods 0.000 claims description 4
- 230000005856 abnormality Effects 0.000 claims description 3
- 241000894007 species Species 0.000 claims description 3
- 241000588724 Escherichia coli Species 0.000 abstract description 7
- 238000012847 principal component analysis method Methods 0.000 abstract 1
- 244000052616 bacterial pathogen Species 0.000 description 9
- 238000001228 spectrum Methods 0.000 description 7
- 241001465754 Metazoa Species 0.000 description 4
- 238000000862 absorption spectrum Methods 0.000 description 4
- 238000002835 absorbance Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000001717 pathogenic effect Effects 0.000 description 3
- 241000282412 Homo Species 0.000 description 2
- 241000191940 Staphylococcus Species 0.000 description 2
- 230000001580 bacterial effect Effects 0.000 description 2
- 230000004069 differentiation Effects 0.000 description 2
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 238000004043 dyeing Methods 0.000 description 2
- 235000013305 food Nutrition 0.000 description 2
- 239000001963 growth medium Substances 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 235000015097 nutrients Nutrition 0.000 description 2
- 238000012797 qualification Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- FRXSZNDVFUDTIR-UHFFFAOYSA-N 6-methoxy-1,2,3,4-tetrahydroquinoline Chemical compound N1CCCC2=CC(OC)=CC=C21 FRXSZNDVFUDTIR-UHFFFAOYSA-N 0.000 description 1
- 241000271566 Aves Species 0.000 description 1
- 201000004569 Blindness Diseases 0.000 description 1
- 241000192125 Firmicutes Species 0.000 description 1
- 206010039438 Salmonella Infections Diseases 0.000 description 1
- 206010040047 Sepsis Diseases 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000005842 biochemical reaction Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 230000005670 electromagnetic radiation Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000002550 fecal effect Effects 0.000 description 1
- 239000008187 granular material Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 208000015181 infectious disease Diseases 0.000 description 1
- 235000013372 meat Nutrition 0.000 description 1
- 238000000386 microscopy Methods 0.000 description 1
- 238000001320 near-infrared absorption spectroscopy Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000007918 pathogenicity Effects 0.000 description 1
- 231100000572 poisoning Toxicity 0.000 description 1
- 230000000607 poisoning effect Effects 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 206010039447 salmonellosis Diseases 0.000 description 1
- 208000013223 septicemia Diseases 0.000 description 1
- 208000026775 severe diarrhea Diseases 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 238000013519 translation 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
- G01N21/359—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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- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
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Abstract
The invention discloses a method for fast identifying clinical pathogens (such as staphylococcus aureus, salmonellae and escherichia coli). The method includes the following steps that near infrared spectrograms of staphylococcus aureus, salmonellae and escherichia coli samples are collected; PCA dimension reduction processing is carried out on the near infrared spectrograms of the staphylococcus aureus, salmonellae and escherichia coli samples; a Fisher discrimination function is set up for classification. The adopted principal component analysis method in combination with Fisher discriminance is introduced to identify the common pathogens, the types of the pathogens can be fast identified, and the method has the advantages of being high in accuracy and easy to operate, making a great contribution to follow-up clinical strain identification and having good application prospects.
Description
Technical field
The present invention relates to a kind of sorting technique relating to clinical common three kinds of pathogenic bacteria, build method and the application of differentiating clinical three kinds of common pathogenic bacteria fast in conjunction with principal component analysis (PCA) and Fisher diagnostic method.
Background technology
Staphylococcus aureus (Staphylococcusaureus) is a kind of important pathogen of the mankind, be under the jurisdiction of staphylococcus (this English of Staphylococcus also can not add), there is the another name of " addicted to meat bacterium ", be the representative of gram-positive bacteria, many severe infections can be caused.It is special in human disease that Salmonella has, and what have only causes a disease to animal, also has humans and animals all pathogenic.Salmonellosis refers to the general name multi-form to the mankind, domestic animal and wild birds and beasts caused by all kinds salmonella.Infect the people of salmonella or the fecal pollution food of carrier, human hair uncooked food can be made poisoning.Colon bacillus (E.coli wants full name) is commonly called Escherichia coli, has pathogenicity to humans and animals, especially to baby and cub (fowl), often causes severe diarrhea and septicemia, and it is a kind of common prokaryotes.
Near infrared spectrometer (NearInfraredSpectrumInstrument, NIRS) be between visible ray (Vis) and in electromagnetic radiation as waves between infrared (MIR), near infrared spectrum area definition is the region of 780-2526nm, is first non-visible light district that people find in absorption spectrum.Near infrared spectrum district is consistent with the uptake zone of the sum of fundamental frequencies that hydric group in organic molecule (O-H, N-H, C-H) vibrates and frequency multiplication at different levels, by the near infrared spectrum of scanning samples, the characteristic information of organic molecule hydric group in sample can be obtained, and utilize near-infrared spectrum technique analyze sample have easily and fast, efficiently, accurately lower with cost, do not destroy sample, do not consume chemical reagent, free from environmental pollution, the advantages such as instrument is portable, therefore this technology is subject to the favor of more and more people.
In real work, only need classification and the quality grade of knowing sample, do not need the problem knowing number of components and its content contained in sample, at this time need pattern-recongnition method.The qualitative foundation mainly spectrum of near infrared spectrum itself, because spectrum reflects the Nomenclature Composition and Structure of Complexes information of authentic sample, identical or approximate sample has identical or close spectrum.Actual qualitative analysis is carried out by applying ambiguous mathematics Spectral matching mode.The recognition methods of near infrared spectrum common pattern mainly contains cluster analysis, principal component analysis (PCA) in conjunction with the differentiation of mahalanobis distance, principal component analysis (PCA) in conjunction with the mode etc. of least square method supporting vector machine.Because original spectrum dimension is many, and clustering methodology carries out according to range index (as Euclidean distance), so deal with comparatively complicated, and when sample size is larger, obtain cluster conclusion has certain difficulty.So a lot of research is all by carrying out cluster again after principal component analysis (PCA) on original spectrum, and principal component analysis (PCA) can exaggerate small variable effect in conjunction with the method for mahalanobis distance, by the impact of covariance matrix instability, and principal component analysis (PCA) has certain " blindness " in conjunction with the method for least square method supporting vector machine due to choosing of parameter, adopt the optimization of parameter space exhaustive search method, time-consuming and precision is not high.
Discriminating clinically for pathogen is by carrying out Zengjing Granule at nutrient culture media, observes the shape facility on nutrient culture media through the regular hour, carries out dyeing, microscopy.According to the shape of thalline and the kind of dyeing characteristic preliminary judgement bacterium.The bacterial strain obtained is carried out biochemical identification further, and due to different bacterial strain biochemical reactions, out information is different, finally obtains the affiliated kind of pathogenic bacteria.Whole process qualification is tediously long consuming time, and testing cost is high.
Summary of the invention
In view of this, the present invention proposes a kind of method differentiating clinical common pathogenic bacteria based on principal component analysis (PCA) and Fish method of discrimination quickly and accurately.
The present invention realizes by the following technical solutions: a kind of method differentiating clinical bacteria based on principal component analysis (PCA) and Fish diagnostic method fast, is characterized in that: comprise the following steps: step S1: cultivate pathogenic bacteria sample; Step S2: sample is carried out near infrared spectrum scanning, gathers the near infrared spectrum of these three kinds of bacteriums, and the sample data of rejecting abnormalities; Step S3: choose 4003.497 ~ 12396.18cm
-1spectral information under characteristic wave bands, uses multiplicative scatter correction MSC to carry out processing spectral information; Step S4: utilize principal component analysis (PCA) (PCA method) to carry out the dimension-reduction treatment of data the spectroscopic data after process, chooses major component number under satisfied accumulative confidence level is >98% condition; Step S5: choose corresponding major component after principal component analytical method after, set up Fisher discrimination function, sets up the discriminating model of three kinds of corresponding bacteriums, obtains discriminant function; Step S6: after Fisher discriminatory analysis, selects the number of the discriminant function of needs by eigenwert and the contribution rate of its variance; Step S7: the bacterium of various species, in the data after principal component analysis (PCA), in the discriminant function choosing out, obtains the central value of often kind of kind after each discriminant function dimensionality reduction; Step S8: by differentiating that the central value of unknown mushroom distance often kind of mushroom differentiates its classification.
In an embodiment of the present invention, step S5 sets up Fisher discrimination function and comprises following concrete steps: step S51: solve Fisher discriminatory analysis: totally have C class y=W
tx, sample point covariance matrix in class:
every class distance central overall covariance matrix:
wherein μ
ibe all kinds of centers, μ is central overall, N
iall kinds of sample numbers, N=∑ N
i; Step S52: according to tolerance
the W obtaining projecting depends on
the eigenwert characteristic of correspondence vector solved.
In an embodiment of the present invention, described clinical bacteria is staphylococcus aureus, salmonella, colon bacillus.
Compared with prior art, the present invention has the following advantages: the principal component analysis (PCA) that the present invention takes differentiates to be incorporated in conjunction with Fisher differentiates common pathogenic bacteria, the kind of pathogenic bacteria can not only be identified fast, and there is high precision, simple operation and other advantages, in follow-up clinical bacterium identification work, there is major contribution, have a good application prospect.
Accompanying drawing explanation
Fig. 1 is schematic flow sheet of the present invention.
Fig. 2 implements the near infrared original absorbance spectrogram of described staphylococcus aureus.
Fig. 3 implements the near infrared original absorbance spectrogram of described salmonella.
Fig. 4 implements the near infrared original absorbance spectrogram of described colon bacillus.
Fig. 5 is through the near-infrared absorption spectrum figure of the pretreated staphylococcus aureus of MSC.
Fig. 6 is through the near-infrared absorption spectrum figure of the pretreated salmonella of MSC.
Fig. 7 is through the near-infrared absorption spectrum figure of the pretreated colon bacillus of MSC.
The Clustering Effect figure of Fig. 8 after Fisher differentiates.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention will be further described.
The present invention realizes by the following technical solutions: a kind of method differentiating clinical bacteria based on principal component analysis (PCA) and Fish diagnostic method fast, is characterized in that: comprise the following steps: step S1: cultivate pathogenic bacteria sample; Step S2: sample is carried out near infrared spectrum scanning, gathers the near infrared spectrum of these three kinds of bacteriums, and the sample data of rejecting abnormalities; Step S3: choose 4003.497 ~ 12396.18cm
-1spectral information under characteristic wave bands, multiplicative scatter correction MSC is used to carry out processing spectral information, the spectroscopic data obtained after scatter correction can eliminate diffuse transmission influence effectively, enhance the spectral absorption information relevant to component content, first the use of the method requires " the desirable spectrum " of setting up a testing sample, namely in the change of spectrum and sample, the content of composition meets direct linear relationship, with this spectrum for the near infrared spectrum of standard-required to every other sample is revised, comprising baseline translation and offset correction etc.; Step S4: utilize principal component analysis (PCA) (PCA method) to carry out the dimension-reduction treatment of data the spectroscopic data after process, chooses major component number under satisfied accumulative confidence level is >98% condition; Step S5: choose corresponding major component after principal component analytical method after, set up Fisher discrimination function, sets up the discriminating model of three kinds of corresponding bacteriums, obtains discriminant function; Step S6: after Fisher discriminatory analysis, selects the number of the discriminant function of needs by eigenwert and the contribution rate of its variance; Step S7: the bacterium of various species, in the data after principal component analysis (PCA), in the discriminant function choosing out, obtains the central value of often kind of kind after each discriminant function dimensionality reduction; Step S8: by differentiating that the central value of unknown mushroom distance often kind of mushroom differentiates its classification.Schematic flow sheet is see Fig. 1.
The concrete steps of the present invention one specific embodiment are as follows:
(1) a Fourier transform near-infrared spectrometers Nicolet6700FTIR is provided
(2) cultivate three kinds of clinical common pathogenic bacteria: staphylococcus aureus, salmonella, Escherichia coli, in Colombia's blood plate, cultivate 24h, for subsequent use.
(3) use Fourier transform near-infrared spectrometers to scan these three kinds of bacteriums, gather staphylococcus aureus, salmonella, colibacillary near infrared spectrum, choose 66 strain bacteriums as qualification sample, as in Figure 2-4.
(4) near infrared spectrum collected by above-mentioned gained adopts polynary loose correction method to carry out preprocess method and processes, and used herein is multiplicative scatter correction method, as illustrated in figs. 5-7.
(4) adopt principal component analytical method to carry out dimensionality reduction the data obtained after pre-service, selected major component is choose major component number under >98% condition according to confidence level.As table 1:
| Major component | Contribution rate/% | Contribution rate of accumulative total |
| PC1 | 78.7 | 78.7 |
| PC2 | 17.1 | 95.8 |
| PC3 | 1.6 | 97.4 |
| PC4 | 1.1 | 98.5 |
Table 1
(5) choose the model foundation that 56 samples differentiate as Fisher, remaining 10 samples are prediction.
(6) set up Fisher discrimination function, the method for solving of Fisher discriminatory analysis is as follows:
Totally there is C class: y=W
tx, sample point covariance matrix in class:
every class distance central overall covariance matrix:
wherein μ
ibe all kinds of centers, μ is central overall, N
iall kinds of sample numbers, N=∑ N
i;
According to tolerance
the W obtaining projecting depends on
the eigenwert characteristic of correspondence vector solved.
And the corresponding variance contribution ratio of selected eigenwert is as table 2.
Table 2
(7) in the differentiation superior function finally obtained, can in the hope of often kind of mushroom after Fisher central value, by central value, can differentiate that the central value of unknown mushroom distance often kind of mushroom is to differentiate its classification.
The central value of its every kind is as table 3.
| The kind of bacterium | First kind discriminant function central value | Equations of The Second Kind discriminant function central value |
| Staphylococcus aureus (I) | -0.1510 | -0.0911 |
| Salmonella (II) | 0.2305 | -0.0210 |
| Escherichia coli (III) | -0.0794 | 0.1121 |
Table 3
(8) often kind of mushroom 10 samples predict the outcome as table 4, can find out and to be differentiated by Fisher, the accuracy rate of 10 samples reaches 100%.
Table 4
Fig. 8 is the Clustering Effect figure after Fisher differentiates.
In sum; above-listed preferred embodiment provided by the invention; the object, technical solutions and advantages of the present invention are further described; be understood that; the foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, within the spirit and principles in the present invention all; any amendment of doing, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (3)
1. differentiate a method for clinical bacteria based on principal component analysis (PCA) and Fish diagnostic method fast, it is characterized in that: comprise the following steps:
Step S1: cultivate clinical bacteria sample;
Step S2: sample is carried out near infrared spectrum scanning, gathers the near infrared spectrum of these three kinds of bacteriums, and the sample data of rejecting abnormalities;
Step S3: choose 4003.497 ~ 12396.18cm
-1spectral information under characteristic wave bands, uses multiplicative scatter correction MSC to carry out processing spectral information;
Step S4: utilize principal component analysis (PCA) PCA method to carry out the dimension-reduction treatment of data the spectroscopic data after process, chooses major component number under satisfied accumulative confidence level is >98% condition;
Step S5: choose corresponding major component after principal component analytical method after, set up Fisher discrimination function, sets up the discriminating model of three kinds of corresponding bacteriums, obtains discriminant function;
Step S6: after Fisher discriminatory analysis, selects the number of the discriminant function of needs by eigenwert and the contribution rate of its variance;
Step S7: the bacterium of various species, in the data after principal component analysis (PCA), in the discriminant function choosing out, obtains the central value of often kind of kind after each discriminant function dimensionality reduction;
Step S8: by differentiating that the central value of unknown mushroom distance often kind of mushroom differentiates its classification.
2. the method differentiating clinical bacteria based on principal component analysis (PCA) and Fish diagnostic method fast according to claim 1, is characterized in that: step S5 sets up Fisher discrimination function and comprises following concrete steps:
Step S51: solve Fisher discriminatory analysis: totally have C class
, sample point covariance matrix in class:
, every class distance central overall covariance matrix:
; Wherein
all kinds of centers,
central overall,
all kinds of sample numbers,
;
Step S52: according to tolerance
, the W obtaining projecting depends on
the eigenwert characteristic of correspondence vector solved.
3. the method differentiating clinical bacteria based on principal component analysis (PCA) and Fish diagnostic method fast according to claim 1, is characterized in that: described clinical bacteria is staphylococcus aureus, salmonella, colon bacillus.
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| CN107167447A (en) * | 2017-05-24 | 2017-09-15 | 福州大学 | The method for blending apple fumet content in cider is calculated using near-infrared spectrum technique |
| CN108034692A (en) * | 2018-01-23 | 2018-05-15 | 丽水市食品药品与质量技术检验检测院 | For differentiating the determination method of salmonella and Listeria monocytogenes |
| CN110346312A (en) * | 2019-07-19 | 2019-10-18 | 安徽大学 | Winter wheat fringe head blight recognition methods based on Fei Shi linear discriminant and support vector machines technology |
| CN111060476A (en) * | 2019-12-19 | 2020-04-24 | 中山大学附属第一医院 | Near-infrared spectrum system for detecting bacterial infection of wound surface or LB culture solution and detection method thereof |
| CN111665216A (en) * | 2020-06-02 | 2020-09-15 | 中南民族大学 | Method for judging pollution degree of escherichia coli and staphylococcus aureus in quick-frozen rice-flour product |
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| CN107167447A (en) * | 2017-05-24 | 2017-09-15 | 福州大学 | The method for blending apple fumet content in cider is calculated using near-infrared spectrum technique |
| CN108034692A (en) * | 2018-01-23 | 2018-05-15 | 丽水市食品药品与质量技术检验检测院 | For differentiating the determination method of salmonella and Listeria monocytogenes |
| CN110346312B (en) * | 2019-07-19 | 2021-08-10 | 安徽大学 | Winter wheat head gibberellic disease identification method based on Fisher linear discrimination and support vector machine technology |
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| CN111060476A (en) * | 2019-12-19 | 2020-04-24 | 中山大学附属第一医院 | Near-infrared spectrum system for detecting bacterial infection of wound surface or LB culture solution and detection method thereof |
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| CN112098357A (en) * | 2020-08-21 | 2020-12-18 | 南京农业大学 | Strawberry sensory quality grade evaluation method based on near infrared spectrum |
| CN112098357B (en) * | 2020-08-21 | 2021-12-10 | 南京农业大学 | Strawberry sensory quality grade evaluation method based on near infrared spectrum |
| CN114252318A (en) * | 2021-12-27 | 2022-03-29 | 浙江大学 | Method and system for detecting staphylococcus aureus in chicken |
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| CN117349664A (en) * | 2023-12-04 | 2024-01-05 | 江苏新希望生态科技有限公司 | On-line monitoring method and system for sprouting vegetable growth environment |
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