CN101556242B - Method for discriminating microorganism by utilizing Fourier infrared spectrum - Google Patents
Method for discriminating microorganism by utilizing Fourier infrared spectrum Download PDFInfo
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Abstract
The invention relates to a method for discriminating microorganism by utilizing Fourier infrared spectrum, comprising the following steps of: a) culturing control microorganism; b) collecting infraredspectrum of the control microorganism; c) establishing a discriminating model of the microorganism in one or a plurality of spectral sections within the interval between 3000-2800cm<-1> and 1800-700c m<-1>; d) culturing the microorganism to be detected under the same condition with the step a); e) collecting the infrared spectrum of the microorganism to be detected under the same condition with the step b); and f) substituting the infrared spectrum obtained in the step e) into the discriminating model of the microorganism and determining the attribution of the microorganism to be detected. Thediscriminating model of the microorganism can realize fast discrimination and does not need to carry out pre-selection to the classification of the microorganism to be detected by using other taxonom ic methods; the method has strong specificity, can carry out classification according to the units of families, classes, species and the like, and even can discriminate to the level of subspecies and serological types; and once the discriminating model is established, the cost for discriminating microorganism is extremely low.
Description
Technical field
The present invention relates to use method for discriminating microorganism by utilizing Fourier infrared spectrum.More particularly, relate to and a kind ofly with Fourier infrared spectrum, set up microorganism and differentiate model and use this model to differentiate the microorganism in medicine, the method for for example contaminated bacteria.
Background technology
In medicine, the calibrating of microorganism is the big event of quality control on pharmaceuticals, and the accuracy of this assay directly determines its credibility.Wherein, first the check of microbial contamination should be able to be confirmed whether to exist pathogenic microorganisms.Many times, also need further to determine the kind of existing pathogenic microorganisms.
Generally, pharmacopeia, for example Chinese Pharmacopoeia, requires medicine to carry out limit test of microbe (comprising contaminated bacteria number mensuration and Control bacteria examination), and the Control bacteria examination of regulation comprises Escherichia coli, coliform, detection of Salmonella, pseudomonas aeruginosa, staphylococcus aureus and clostridium.In pharmacopeia, for the above six kinds calibration methods of controlling bacterium, be mainly the reaction of selective medium color combining and biochemical reaction is differentiated.This authentication method, namely so-called classic method exists that nutrient culture media complexity, evaluation program are loaded down with trivial details, the defect in discriminating cycle long (needing 3-7 days or longer), and qualification result is with certain subjectivity.In addition, because these class methods there is no general method for the evaluation of each bacterium, be difficult to realize robotization and computerize.Other authentication methods also have molecular biology method, such as PCR, DNA (G+C) mol% value, DNA-DNA homology analysis, 16S rRNA sequence homology analysis etc.The specificity of these methods is good, highly sensitive, but needs professional to complete, and is difficult to promote in common lab.Whether in the case, needs are a kind of can to existing contaminating microorganisms to differentiate fast and accurately and discrimination method easy and simple to handle in medicine.
Summary of the invention
The object of the invention is to overcome the defect existing in prior art, a kind of microorganism discrimination method quick, accurate, easy and simple to handle is provided.
The invention provides a kind of Fourier infrared spectrum (FTIR) of using and differentiate method of microorganism, the method comprises the steps:
A) cultivate contrast microorganism;
B) gather the infared spectrum of contrast microorganism;
C) at 3000-2800cm
-1with 1800 to 700cm
-1one or more spectral coverages in interval are set up microorganism and are differentiated model;
D) under identical condition, cultivating microorganism to be measured with step in a);
E) with step b) in gather the infared spectrum of microorganism to be measured under identical condition;
F) by step e) in the infared spectrum substitution microorganism that obtains differentiate the ownership of determining microorganism to be measured in model.
Method of the present invention not only can identify for contaminating microorganisms in medicine, also can be for the microbial identification such as other fields such as food.The microorganism that can be differentiated include but not limited to above-mentioned pharmacopeia regulation six kinds of bacterium and with these bacterial classifications close bacterial classification on taxonomy, the microorganism of for example mentioning in following embodiment.
Method of the present invention can realize quick discriminating, and the classification of microorganism to be identified is not needed to be undertaken preselected by other taxonomy methods; Method high specificity, the units such as classification Neng An section, genus and species carry out, and even can distinguish subspecies, serotype level; Once set up discriminating model, the cost of Identifying micro-organisms is extremely low.
Accompanying drawing explanation
Fig. 1 shows the DNA correlativity of enterobacteriaceae, the digitized representation % that is correlated with;
Fig. 2 is cluster analysis discrimination method and the design parameter adopting in the embodiment of the present invention 1;
Fig. 3 is the maximum matching value histogram of the inside collection of illustrative plates in the qualitative analysis discrimination method of the embodiment of the present invention 1;
Fig. 4 is a kind of specific procedure and the result determination methods that simultaneously adopts cluster analysis discrimination method of the present invention and qualitative analysis discrimination method in embodiment 2;
Fig. 5 is another kind of specific procedure and the result determination methods that simultaneously adopts cluster analysis discrimination method of the present invention and qualitative analysis discrimination method in embodiment 2;
Fig. 6 shows the differentiation that the inventive method is carried out below kind salmonella.
Embodiment
The term of mentioning in the present invention has implication well known by persons skilled in the art.
" approximately " in the present invention means that the numerical value in described numerical value ± 5% scope is also included in the present invention.
The microorganism of using in this method is from CMCC: Chinese medicine bacterium management preservation center (National Center for medical culture collections), CSAR: national Surveillance of antibiotic resistance in bacterial isolates center (National Center for Surveillance of Antimicrobial Resistance) and CGMCC: Chinese common micro-organisms culture presevation administrative center (China General Microbiological Culture Collection Center).
1. cultivation and the processing of the selection of contrast microorganism and contrast microorganism, microorganism to be measured
The selection of 1.1 contrast microorganisms
The kind that comprises microorganism to be measured for setting up the kind of the contrast microorganism of differentiating model, and other microbe species that are easy to obscure with it erroneous judgement in infra-red sepectrometry discrimination process, for example, in systematics, with nearer other microorganisms of microorganism sibship to be measured, and its kind ownership is clear and definite.After determining contrast microbe species, select in this microbe sort the microorganism of multiple types as far as possible, gather its ir data for setting up discriminating model.
The alleged microorganism of the present invention comprises bacterium and fungi.
Can choose contrast microorganism according to the kind of the control bacterium of pharmacopeia defined, as selected enterobacteriaceae, staphylococcus, pseudomonas and fusobacterium etc.
For example, when sample to be checked is medicine, if control bacterium (referring to < < Pharmacopoeia of People's Republic of China > > 2005 editions, Control bacteria examination one chapter in two appendix XI J limit test of microbe methods) for adhering to enterobacteriaceae (Escherichia coli separately, coliform, salmonella), pseudomonadaceae pseudomonas, the Escherichia coli of micrococcaceae staphylococcus and Bacillaceae fusobacterium, coliform, salmonella, pseudomonas aeruginosa, when staphylococcus aureus and clostridium, can select following bacterial classification microorganism in contrast:
(1) enterobacter cloacae, Fei Shi citric acid bacillus, Friedlander's bacillus and Escherichia coli (enterobacteriaceae);
(2) Bacterium enteritidis, salmonella typhi, moscow' paratyphi B, Salmonella paratyphi A, salmonella typhimurium and Salmonella choleraesuls (Salmonella);
(3) pseudomonas aeruginosa, Pseudomonas fluorescens and pseudomonas putida (pseudomonas);
(4) staphylococcus aureus and Staphylococcus epidermidis (staphylococcus);
(5) clostridium tetani, clostridium sporogenes, C.perfringens and septicopyemia clostridium (fusobacterium).
The preparation of the cultivation of 1.2 microorganisms and infrared spectrum sample
The present invention contrasts the cultivation of microorganism and microorganism to be measured need use identical nutrient culture media.Its cultural method can be used any cultural method well known in the prior art to carry out, for example, referring to < < medicine microbiological Test handbook > > Ma Xurong, Su Demo work, Hua Ling publishing house, the method for recording in 2007 1 month.
The nutrient culture media preferably using is such as commercially available TSA(pancreas junket soy agar), common nutrition agar-agar nutrient culture media, Colombia's agar medium, peptone dusty yeast agar medium etc.Preferably when enforcement is of the present invention, use same nutrient culture media; More preferably use the nutrient culture media from the same batch of same provider; Or when using the nutrient culture media of same provider different batches, with the nutrient culture media of different lot numbers, cultivate same bacterial strain, by gathering the mode that many group spectroscopic datas are got its mean value accordingly, eliminate the infrared spectrum difference that may bring because of nutrient culture media difference.
Microorganism can be cultivated according to its upgrowth situation at 25-55 ℃, but for same microorganism, preferably under identical cultivation temperature, cultivates, and more preferably adopts identical incubation time, as 18-30 hour, and preferred 22-24 hour.
For carrying out the sample of ir data collection, according to method well known in the prior art, prepare, for example, referring to Microbiological characterizations by FT-IR spectroscopy, D.Naumann, D.Helm & H.Labischinski, Nature, Vol.351, No.6321, the method for recording in pp.81-82 preparation.
Contrast microorganism of the present invention and microorganism to be measured should be cultivated under identical condition, and use and microbial to be measured with condition and method prepare it for carrying out the sample of ir data collection.
2. the collection of ir data
In the present invention, the instrument and the material that carry out infrared spectrum measurement use can be any known instrument and materials in this area.
The acquisition condition of infrared spectrum of the present invention is condition well known in the prior art, can according to the experiment of limited number of time, be obtained by those skilled in the art, for example use the high-throughout microorganism special module of Bruker HTS-XT, and with the measurement of OPUS/LAB software control sample.The wavelength coverage of collection of illustrative plates collection is 4000 to 500cm
-1in infrared spectral coverage.
With regard to every kind of microorganism, preferably gained testing sample is carried out repeatedly to (being m time), preferably 2 to 10 times, more preferably 2 to 5 times, most preferably three panel data collections, using the baseline results of the many piece of data of gained and average result as for setting up the spectroscopic data of identifying model or detection.
3. the foundation of microorganism infrared spectrum discriminating model and perfect
The parametrization of 3.1 ir datas is processed
Before setting up described discriminating model, first need spectroscopic data to carry out parametrization processing.Described parametrization disposal route can be in prior art for setting up any means of model, for example measured infared spectrum is carried out to smoothing processing and single order or second derivative is processed.
The selection of 3.2 spectral coverages and the foundation of model
Microorganism infrared spectrum of the present invention differentiates that the type of model can be the infrared spectrum discriminating model of any known type in this area, and for example model (Cluster Analysis) or qualitative analysis (Identity test) discriminating model are differentiated in cluster analysis.
Cluster analysis differentiates that model refers to the sample group that use is identical, according to predefined step, change successively cluster analysis parameter, by the cluster analysis of one or many, determine the method for sample ownership classification, its essence is based on unsupervised mode identification method, is conventionally applicable to, the in the situation that of the unknown of sample classification, sample be classified according to the close degree of its character.First the basic thought of hierarchial-cluster analysis is to define distance between sample and the distance between class and class, two nearest classes of chosen distance are merged into a new class, and calculate the distance between new class and other class, again two nearest classes are merged, repeat this process, so each minimizing one class, until all samples all become a class, finally forms dendrogram or dendrogram.Can use for example OPUS software of Bruker company to set up this model.
Qualitative analysis differentiates that model is by the ir data of microorganism to be measured is compared with the data that contrast in the ir data storehouse (hereafter spectrum storehouse) of microorganism, in evaluation contrast microorganism ir data storehouse, which kind of the contrast spectrum of microorganism and the spectroscopic data of microorganism to be measured mate most qualitatively, to determine the ownership of microorganism to be measured.Its essence is the mode identification method based on there being supervision.The method is used the infrared spectrum of one group of known ownership, for example contrast the infrared spectrum of microorganism, as training set, and by this training set by training or learn to obtain to differentiate accordingly rudimentary model, this rudimentary model is 100% for the discrimination of training set microorganism; Again by another known its ownership of group but the infrared spectrum that is not included in the contrast microorganism in above-mentioned training set as checking collection, the discriminating model of gained is verified, thereby obtain the discrimination of model (, all checking light harvesting bands of a spectrum are entered to compose storehouse, and resulting matching value is less than all spectrum numbers of critical matching value and all brings the ratio of spectrum number into).Then using the satisfactory discriminating model of discrimination as qualified qualitative analysis, differentiate that model is used for differentiating the classification that unknown infrared spectrum (as microorganism infrared spectrum to be measured) should belong to.Should be understood that discrimination is higher, loss is less.Can use for example OPUS software of Bruker company to set up this qualitative analysis model.The checking of qualitative analysis model can be undertaken by method well known in the prior art, for example, referring to OPUS-IDENT, and User Manual, the 6th edition, 77-103 page.
When adopting cluster analysis to differentiate model, the selection of setting up model spectral coverage is can for example, carrying out the infared spectrum of bacterium of the same race not required classification by known kind and be as the criterion dendrogram (, seeing accompanying drawing 6) is upper.Preferably, select 3000-2800cm
-1with 1800 to 700cm
-1in interval, preferably select 3000-2800cm
-1with 1800 to 900cm
-1one or more spectral coverages in interval are set up a layer of model, and as required, select one or more layer, until can be determined classification evaluation for setting up the contrast microorganism of model.
For example, in a specific embodiments, while setting up the ground floor of model, adopt following three spectral coverages: about 1200cm simultaneously
-1-900cm Yue
-1, about 3000cm
-1-2800cm Yue
-1, and about 1500cm
-1-1200cm Yue
-1.While setting up the second layer of model, adopt following three spectral coverages: about 1200cm simultaneously
-1-900cm Yue
-1, about 1500cm
-1-1230cm Yue
-1, and about 3000cm
-1-2800cm Yue
-1.While setting up the 3rd layer of model, can choose following spectral coverage: about 1500cm
-1-1200cm Yue
-1, about 940cm
-1-760cm Yue
-1and about 3100cm
-1-2900cm Yue
-1; Or about 1480cm
-1-1310cm Yue
-1, and about 920cm
-1-870cm Yue
-1; Or about 1500cm
-1-1300cm Yue
-1, and about 950cm
-1-800cm Yue
-1, or their combination.
When adopting qualitative analysis to differentiate model, the difference that contrasts microorganism infrared spectrum by observation is selected spectral coverage region, selects collection of illustrative plates difference spectral coverage little and that between classification, collection of illustrative plates difference is large in microbe sort to be identified to combine as spectral coverage.The spectral coverage of preferably setting up model employing is about 1200cm
-1-900cm Yue
-1, about 3000cm
-1-2800cm Yue
-1and about 1500cm
-1-1400cm Yue
-1.
The correct resolution that microorganism discriminating model of the present invention is treated micrometer biology is higher than 70%, and in one embodiment, correct resolution is higher than 80%; In another embodiment, correct resolution is higher than 90%; In another embodiment, correct resolution is higher than 95%.
When finding not belong to the new microorganism of setting up the contrast microorganism of differentiating model, particularly representational microorganism (for example passes through additive method, as use API to identify bar, or other molecular biology reactions are differentiated as objective microbe, but in the time of the infrared microorganism that is accredited as non-aimed strain), it can be carried out to spectrum data gathering and processing as new contrast microorganism, and by its with set up before this model and be used from and set up new discriminating model with the spectroscopic data one that contrasts microorganism, also (be for example about to its original spectrum and averaged spectrum, the spectrum average function carrying in OPUS software obtains) join in the spectrum storehouse of microorganism infrared spectrum discriminating model of the present invention, re-establish microorganism and differentiate model, so that microorganism is differentiated to model is optimized.
4. the discriminating of microorganism to be measured
According to contrast microbial with condition of culture, sample preparation methods and spectra collection condition process microorganism to be measured and carry out spectrum data gathering, then the testing sample spectroscopic data on the identical spectral coverage of spectral coverage when setting up model is updated to microorganism differentiates in model and differentiate.
In the present invention, can use respectively one or many cluster analysis discriminating model or qualitative analysis discriminating model to treat micrometer biology and differentiate, also can use the two to differentiate simultaneously.
When adopting cluster analysis to differentiate that model and qualitative analysis differentiate that model is differentiated, the spectral coverage that the spectral coverage of employing adopts while differentiating model and qualitative analysis discriminating model with above-mentioned independent use cluster analysis is identical simultaneously.
Preferably adopt above-mentioned two kinds of discriminating models to differentiate simultaneously.
The judgment criterion of identification result is as follows:
(1) while using cluster analysis to differentiate model
While using this model to carry out microorganism discriminating to be measured, first use the averaged spectrum and the unknown microorganism spectrum that respectively contrast microorganism together to carry out cluster analysis, calculate spectrum intervals D value, carry out cluster analysis, and belong to judgement according to D value.
When Pearson correlation coefficient r >=0.95, think that two variablees exist conspicuousness relevant; Now, corresponding D≤50; Therefore determine that critical value is 50, D≤50 o'clock two strain bacterium are considered to have high similarity.
Wherein, the account form of Pearson correlation coefficient r is as follows:
Wherein X, Y represent the original spectrum intensity of response frequency scope;
,
it is average spectrum intensity;
According to equation (2), related coefficient is converted into D value (spectrum intervals).
D=(1.0-r)*1000.0 （2）
In the present invention, conventionally select several spectral coverages of measured spectrum to carry out distance calculating, in other words, similarity between two spectrum is calculated with the weight (generally each weight coefficient is chosen as 1) of each spectral coverage similarity, and represents average or overall spectral similarity with this result of calculation.For example can use in the OPUS of Bruker company program Scaling to1st range or Normal to reprolevel operational method to carry out this calculating.
As ground floor, the ownership of microorganism to be measured is can not determine in cluster analysis, then presses successively the second layer, the 3rd strata alanysis, until determine the ownership of all microorganisms to be measured.
(2) while using qualitative analysis model
The distance D of each spectrum in calculating microorganism infrared spectrum to be measured and contrasting microorganism ir data storehouse
nR(matching value), critical matching value by itself and this model corresponding to a certain Identification rate compares, if parallel having carried out m time of collection to the data of each contrast microorganism, microorganism to be measured is arranged from small to large with the matching value that contrasts microorganism, at least front n matching value is all less than critical matching value, and the microorganism corresponding to these matching values belongs to same classification, this microorganism to be measured is included in the corresponding microbe sort of a front n matching value.If the corresponding microbe sort of a front n matching value is not quite similar, thinks and can not correctly differentiate, thereby can not belong to the classification of microorganism.This process can be by being used for example OPUS software to complete, particularly, if in the analysis report of OPUS software at least before the capable matching value of n be all less than critical matching value and corresponding to same microbe sort, can praise and differentiate microorganism to be measured, and microorganism to be measured be included in analysis report before in the microbe sort of n shown in capable; If front n is capable of different microbe sorts, thinks and can not correctly differentiate, thereby can not belong to the classification of microorganism.
Described critical matching value is determined in the following manner: the contrast microorganism spectrum of evenly choosing some in spectrum storehouse is brought spectrum storehouse into and carried out qualitative analysis, obtain a series of matching values of arranging from small to large, choose the smallest match value while correctly identifying corresponding to each selected microorganism to be measured, gather, draw histogram and cumulative percentage rate table.The maximum matching value that can correctly identify selected contrast microorganism collection of illustrative plates is set as critical matching value.This critical matching value can be about 0.1-2, preferably 0.1 to 1.2, more preferably 0.1 to 1.0, and 0.1-0.5 most preferably.
Described spectrum intervals D
nRthe Normal to reprolevel disposal route that can provide by OPUS software, that is, the horizontal normalization method of reappearance is calculated as follows:
In formula, the spectrum intervals that Di is i spectral coverage, calculates according to formula (1) (2) above; N is the number of selected spectral coverage.Reprolevel is reappearance level value,
In formula,
with σ value be respectively repeatedly measure in the infrared spectrum of same microorganism the range averaging value and the standard deviation that according to formula (1) (2), calculate.
When infrared spectrum database while being independently comprised of as one each spectrum, in order to embody the SPECTRAL DIVERSITY of this microorganism itself, is selected the horizontal normalization method calculating of this reappearance spectrum intervals by mentioned above.
(3) while adopting above two kinds of models simultaneously
When adopting cluster analysis to differentiate that model is differentiated in model and qualitative analysis, when the identification result of two kinds of discriminating models is identical, be correct identification result simultaneously.
When adopting cluster analysis to differentiate that model is differentiated in model and qualitative analysis simultaneously, another judgment criterion of identification result is: will adopt cluster analysis to differentiate that the positive result of model is judged as correct discriminating, and will adopt cluster analysis to differentiate that model is negative but employing qualitative analysis differentiates that the positive result of model is also judged as correct discriminating.But the result when credibility of this result is identical lower than two kinds of discriminating conclusions of differentiating models.
Below by instantiation, the present invention is described in detail, but the invention is not restricted to these examples.
Embodiment 1
Selection and the cultivation of step 1 contrast microorganism
For differentiating that six kinds of stipulating in Chinese Pharmacopoeia control bacterium, selected altogether 191 strain bacteriums that in 4 sections 9 belong to 23 kinds in contrast microorganism build with reference to spectrum storehouse.Concrete bacterial classification and quantity are in Table 1.
Table 1
The spectroscopic assay of step 2 contrast microorganism
Above-mentioned contrast microbial bacteria purifying on TSA nutrient culture media (U.S. company BD) flat board is formed to single bacterium colony, then select single bacterium colony streak inoculation on this flat board, in 35 ℃, cultivate 24 ± 2h.
The vibration platinum oese that is 1mm with the diameter through overcorrect (German Braun company product) bacterium colony junction from TSA nutrient culture media is got 1-2 ring lawn, be placed in 100 μ l aqua sterilisas, Vibration on Start-up switch makes uniform bacteria suspension, measure 15 μ l and be placed in the load sample place of 96 hole Si sheets, be placed in immediately the vacuum drying apparatus that silica gel is housed, drying under reduced pressure makes bacteria suspension become transparent film.Take out Si sheet, be placed in immediately HTS-XT(Germany Braun company product) sample cavity, with TENSOR27 type FTIR spectrophotometric determination, FTIR absorbs light collection of illustrative plates.Measurement parameter: wave-number range 4000-500cm
-1, scanning times 16 times, resolution 4cm
-1.
The foundation of model is differentiated in step 3.1 cluster analysis
With selected bacterial classification in table 1 in contrast microbial bacteria set up spectrum storehouse.Because many bacterial classifications wherein have nearer sibship, by a cluster analysis, all bacterial classifications cannot be distinguished, adopt cluster analysis analytical approach for this reason.In this step, by three cluster analyses, distinguish each bacterial classification.Idiographic flow and parameter setting are seen Fig. 2.
The foundation of model is differentiated in step 3.2 qualitative analysis
Determining and spectral coverage selection of reappearance level value
Above-mentioned bacterial classification collection of illustrative plates is at 1200-900cm
-1, 3000-2800cm
-1, 1500-1400cm
-1three spectral coverage differences are obvious, therefore choose the combination of these three spectral coverages and set up model.
Determine the collection of illustrative plates of m collection of following three strain bacterium the reappearance level value of different spectral coverage (referring to " Working with the IFS28/B and B-module " in " Sample Preparation; Measurement and Evaluation of the FT-IR Spectra of Microorganisms " part, the 3rd edition, 54-56 page).
Table 2
Reappearance level value is got respectively 10,5,20(and is rounded for ease of calculating herein).
Determining of critical matching value
It is inner collection of illustrative plates that the original infrared spectrum that extracts the difference contrast microbial strains sample of equal number from every group of bacterium amounts to 140 conducts, use OPUS software to carry out qualitative analysis, obtain respectively a series of matching values of arranging from small to large, choose the maximum matching value that can correctly identify spectrum, gather, draw histogram and cumulative percentage rate table.Result is as shown in Fig. 3 and following table.
Table 3
As seen from the above table, the maximum matching value that inner collection of illustrative plates can correctly be identified is all less than or equal to 1.50(100% contrast microbial bacteria and is correctly validated), therefore choose critical matching value, be 1.5.
Step 4 is differentiated the correct resolution of model
Choose altogether 411 infrared spectrograms of 90 strain bacterium, comprising: pseudomonas aeruginosa: 13 61 of strains infrared spectrums; Escherichia coli: 32 141 of strains infrared spectrums; Staphylococcus aureus: 12 54 of strains infrared spectrums; Staphylococcus epidermidis: 13 63 of strains infrared spectrums; Salmonella: 20 92 of strains infrared spectrums.
The correct resolution of Clustering Model
Pseudomonas aeruginosa, staphylococcus aureus, Staphylococcus epidermidis and salmonella all can correctly differentiate have 5 strains to be included into other classes in 32 strain Escherichia coli.Correct resolution is: 95.5%(85/90).
The correct resolution of qualitative analysis model
(spectral coverage is 3000-2800,1500-1400 and 1200-900 to bring one by one all original spectrums of above-mentioned 90 bacterial strains and averaged spectrum into set up model, reappearance level is respectively 5,20,10) in, calculate, by determined judgment criterion, judge, correct resolution is: 93.4%(384/411).
Embodiment 2
Use method and the step identical with embodiment 1 to test, difference is to treat the method that two kinds of discriminating models of the biological employing of micrometer are combined use, to improve discriminating accuracy.
Specific procedure and result determination methods are as shown in Figure 5.
The first step is differentiated to the original collection of illustrative plates of correct bacterial classification and average collection of illustrative plates bring respectively second step discrimination method into, and correct resolution is 92.2%(379/411).The bacterial strain of authentication error and the results are shown in Table 4.As shown in Table 4, differentiate that wrong mainly occurring in differentiates to be the microorganism of other enterobacteriaceaes by E.coli, presents false-negative result.
For former qualitative analysis and identification model, cause false-negative result, the collection of illustrative plates of failing above correctly to differentiate is added in modeling spectrum storehouse, make to compose in storehouse the coverage rate of collection of illustrative plates wider, can represent the biological variability that selected bacterial classification shows, the spectrum storehouse based on new re-establishes microorganism and differentiates model.The correct resolution of the qualitative test after adjustment is 100%, the results are shown in Table 4.
A table 4 liang step discrimination method is optimized front and back identification result
Now, use cluster analysis analytical model to carry out first step discriminating, the sample collection of illustrative plates that is classified as non-control bacterium during the first step is differentiated re-uses qualitative analysis model and carries out second step discriminating, as still differentiated as non-control bacterium, assert that this identification result is final identification result.Use art methods to check assay and show, the correct resolution that adopts above two-step approach is 100%.
For further evaluating the accuracy that is classified as control bacterium result in two step differential methods mentioned above, by differentiating as controlling the original collection of illustrative plates of bacterial classification of bacterium and average collection of illustrative plates, differentiate that model and qualitative analysis differentiate that model differentiates again with cluster analysis, specific procedure and result determination methods are as shown in Figure 4 simultaneously.When differentiating that model draws identical conclusion for two kinds, this bacterial strain is control bacterium certainly; When differentiating that model draws different conclusions for two kinds, this bacterial strain is considered to suspicious control bacterium.
Embodiment 3 distinguishes below kind
For bacterial classification is distinguished below kind, as serotype level.In the present embodiment, choose a series of salmonella, and measured their serotype and IR collection of illustrative plates, carried out cluster analysis.Detail is shown in Fig. 6, wherein serum type comprises O9, O4, O7, and selected spectral coverage is 1202-899,901-698, and reappearance value level value is 1, figure spectrum processing method is First derivative+Vector normalization, and algorithm is Ward ' s algorithm.As can be seen from the figure, IR collection of illustrative plates is classified well by serotype type.
Claims (29)
1. utilize infrared spectrum to differentiate a method of microorganism, comprising:
A) cultivate contrast microorganism;
B) gather the infared spectrum of contrast microorganism;
C) at 3000-2800cm
-1with 1800 to 700cm
-1one or more spectral coverages in interval are set up microorganism and are differentiated model;
D) under identical condition, cultivating microorganism to be measured with step in a);
E) with step b) in gather the infared spectrum of microorganism to be measured under identical condition;
F) by step e) in the infared spectrum substitution microorganism that obtains differentiate the ownership of determining microorganism to be measured in model,
Step c wherein) the discriminating model described in is that model is differentiated in qualitative analysis, in described qualitative analysis, differentiate in model, the ownership of microorganism to be measured is carried out as follows: the critical matching value of determining microorganism discriminating model according to the discrimination of model, parallel the carrying out m time of collection to the data of each contrast microorganism, microorganism to be measured is arranged from small to large with the matching value that contrasts microorganism, at least front n matching value is all less than critical matching value, and the microorganism corresponding to these matching values belongs to same classification, this microorganism to be measured is included in the corresponding microbe sort of a front n matching value, described m is the integer that is selected from 2-10.
2. the process of claim 1 wherein that described m is the integer that is selected from 2-5.
3. the process of claim 1 wherein that described m is 3.
4. utilize infrared spectrum to differentiate a method of microorganism, comprising:
A) cultivate contrast microorganism;
B) gather the infared spectrum of contrast microorganism;
C) at 3000-2800cm
-1with 1800 to 700cm
-1one or more spectral coverages in interval are set up microorganism and are differentiated model;
D) under identical condition, cultivating microorganism to be measured with step in a);
E) with step b) in gather the infared spectrum of microorganism to be measured under identical condition;
F) by step e) in the infared spectrum substitution microorganism that obtains differentiate the ownership of determining microorganism to be measured in model,
Step c wherein) the discriminating model described in is for used cluster analysis to differentiate that model and qualitative analysis differentiate model simultaneously, and described qualitative analysis is differentiated model as claimed in claim 1, and described cluster analysis differentiates that the spectral coverage that the ground floor of model adopts is about 1200cm
-1-900cm Yue
-1, about 3000cm
-1-2800cm Yue
-1and about 1500cm
-1-1200cm Yue
-1, the spectral coverage that the second layer adopts is about 1200cm
-1-900cm Yue
-1, about 1500cm
-1-1230cm Yue
-1and about 3000cm
-1-2800cm Yue
-1, the spectral coverage of the 3rd layer of employing is about 1500cm
-1-1200cm Yue
-1, about 940cm
-1-760cm Yue
-1and about 3100cm
-1-2900cm Yue
-1; Or about 1480cm
-1-1310cm Yue
-1and about 920cm
-1-870cm Yue
-1; Or about 1500cm
-1-1300cm Yue
-1and about 950cm
-1-800cm Yue
-1, or their combination in any,
The numerical value that " approximately " in above-mentioned numerical range is illustrated in respective value ± 5% scope is also included in this numerical range.
5. claim 1 or 4 method, the kind of wherein said contrast microorganism comprises the kind of microorganism to be measured and other microbe species that are easy to obscure with it erroneous judgement in infra-red sepectrometry discrimination process, and described contrast microbe species ownership is clear and definite; Described microorganism comprises bacterium or fungi.
6. claim 1 or 4 method, wherein said contrast microorganism is selected from enterobacteriaceae, staphylococcus, pseudomonas or fusobacterium; Or their combination in any.
7. claim 1 or 4 method, the cultivation of wherein said contrast microorganism or microorganism to be measured is used pancreas junket soy agar nutrient culture media, ordinary nutrient agar nutrient culture media, Colombia's agar medium, peptone dusty yeast agar medium to carry out.
8. claim 1 or 4 method, the cultivation of wherein said contrast microorganism or microorganism to be measured is carried out at 25-55 ℃, and incubation time is 18-30 hour.
9. the method for claim 8, wherein said incubation time is 22-24 hour.
10. the method for claim 8 for same microorganism, is cultivated under identical cultivation temperature.
The method of 11. claims 10, for same microorganism, also adopts identical incubation time to cultivate.
The method of 12. claims 4 is being wherein 4000 to 500cm to the infared spectrum acquisition mode of described contrast microorganism or microorganism to be measured
-1in carry out m panel data collection in infrared spectral coverage, and using the average result of the collection of illustrative plates of above multi collect as for setting up the spectroscopic data of identifying model or detection, described m is the integer that is selected from 2-10.
The method of 13. claims 12, wherein said m is the integer that is selected from 2-5.
The method of 14. claims 12, wherein said m is 3.
15. the process of claim 1 wherein that to the infared spectrum acquisition mode of described contrast microorganism or microorganism to be measured be 4000 to 500cm
-1in carry out m panel data collection in infrared spectral coverage, and using the average result of the collection of illustrative plates of above multi collect as for setting up the spectroscopic data of identifying model or detection.
16. methods claimed in claim 4, is characterized in that differentiating in model in described cluster analysis described step c) in spectral coverage be selected from 3000-2800cm
-1and 1800-900cm
-1one or more spectral coverages in interval.
17. methods claimed in claim 4, in described cluster analysis, differentiate in model, the ownership of microorganism to be measured is carried out as follows: two nearest classes of spectrum intervals are merged into a new class, and calculate the distance between new class and other class, again two nearest classes are merged, repeat this process, so each minimizing one class, until all samples are all included in the classification of contrast microorganism.
Method described in 18. claims 1 or 4, differentiates in model described step c in described qualitative analysis) in spectral coverage be 1200-900cm
-1, 3000-2800cm
-1and 1500-1400cm
-1.
Method described in 19. claims 1 or 4, wherein differentiates in model in described qualitative analysis, uses the infrared spectrogram of one group of known ownership to set up discriminating rudimentary model.
Method described in 20. claims 19, wherein for differentiating rudimentary model, the infrared spectrogram of using one group of known its ownership but being different from the contrast microorganism of infrared spectrogram described in claim 18 is verified the discriminating rudimentary model of gained, obtains the discrimination of model.
Method described in 21. claims 20, is wherein greater than the discrimination of model 95% discriminating rudimentary model and differentiates model as qualitative analysis.
Method described in 22. claims 21, is wherein greater than the discrimination of model 96% discriminating rudimentary model and differentiates model as qualitative analysis.
Method described in 23. claims 21, is wherein greater than the discrimination of model 97% discriminating rudimentary model and differentiates model as qualitative analysis.
Method described in 24. claims 21, is wherein greater than the discrimination of model 98% discriminating rudimentary model and differentiates model as qualitative analysis.
Method described in 25. claims 21, is wherein greater than the discrimination of model 99% discriminating rudimentary model and differentiates model as qualitative analysis.
The discriminating rudimentary model that method described in 26. claims 21 is wherein 100% using the discrimination of model is differentiated model as qualitative analysis.
The method of 27. claims 4, when two kinds of identification results of differentiating models are identical, is correct identification result.
The method of 28. claims 4, wherein will adopt cluster analysis to differentiate that the positive result of model is judged as correct discriminating, to adopt cluster analysis to differentiate that model is negative but adopt qualitative analysis to differentiate that the positive result of model is also judged as correct discriminating, but the result of the credibility of this result when identical lower than two kinds of discriminating conclusions of differentiating models.
29. claims 1 or 4 method, further comprising the steps of: the original spectrum of the new bacterial strain of identifying and averaged spectrum are added in spectrum storehouse, and the spectrum storehouse based on new is optimized microorganism and differentiated model.
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| CN102564995A (en) * | 2011-12-31 | 2012-07-11 | 威海出入境检验检疫局检验检疫技术中心 | Classification and determination of Salmonella parayphi A, Salmonella parayphi B and Salmonella parayphi C through Fourier transform infrared spectrum technology |
| CN102590128B (en) * | 2012-01-10 | 2014-03-19 | 上海市兽药饲料检测所 | Method for discriminating adulterated raw and fresh milk by using near infrared spectrum |
| CN103115891A (en) * | 2013-01-18 | 2013-05-22 | 浙江大学 | Method for detecting plasmodiophora brassicae in Chinese cabbages |
| DE102013022016B4 (en) | 2013-12-20 | 2015-07-09 | Bruker Daltonik Gmbh | Microbial identification by mass spectrometry and infrared spectrometry |
| CN103792204A (en) * | 2014-01-20 | 2014-05-14 | 郑州市中食农产品加工研究院 | Rapid microbiological detection technology based on terahertz time-domain spectroscopy |
| DE102017108278B4 (en) * | 2017-04-19 | 2019-03-07 | Bruker Daltonik Gmbh | Microbial test standard for use in infrared spectrometry |
| CN108535214A (en) * | 2018-04-08 | 2018-09-14 | 浙江大学 | A method of Trichoderma is identified based on hyperspectral technique |
| CN109811032B (en) * | 2019-01-04 | 2022-03-08 | 山东省科学院海洋仪器仪表研究所 | Spectral detection method for seawater microbial biomass |
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