CN113390853B - Method and system for identifying bacteria and fungi by using Raman spectrum - Google Patents
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Abstract
The invention discloses a method for identifying bacteria and fungi by using Raman spectrum, which comprises the following steps: 1) Constructing a Raman spectrum database of bacteria and fungi, and acquiring a plurality of characteristic peaks of the bacteria and the fungi and Raman shifts corresponding to the characteristic peaks, wherein the Raman spectrum database comprises Raman spectrum data of the bacteria containing cytochrome C; 2) Collecting a Raman spectrum of a sample to be detected; 3) And (3) identifying bacteria and fungi of the sample to be detected by adopting one or the combination of the methods A and B. The bacteria and fungi identification method provided by the invention detects cells at the single cell level, can reflect data results from the single cell level, has high accuracy and does not generate false positive data; the present invention successfully distinguishes cytochrome C-containing bacteria from fungi and, further, in some embodiments, from cytochrome C-free bacteria.
Description
Technical Field
The invention relates to the technical field of microorganism identification, in particular to a method and a system for identifying bacteria and fungi by using Raman spectrum.
Background
Invasive fungal diseases pose a great threat to human health, especially to immunocompromised persons, such as patients with solid organ and bone marrow transplants, cancer patients, aids infected persons, persons receiving immunomodulator therapy, and the like. Fungal infections cause over 150 million deaths each year, affecting over 10 million people. The clinical manifestations of patients with fungal infections are often similar to those of patients with bacterial infections, but the treatment strategies are completely different. Since bacterial infections are more common in clinical pathogen infections than in fungi, microbiological diagnostic methods usually first consider diagnosing bacterial infections. Empirical therapy also typically employs broad spectrum antibiotics against bacterial infections before a microbiological diagnostic result is given. This situation causes the diagnosis and treatment of patients with fungal infection to be greatly delayed, and seriously threatens the life health of the patients.
Existing methods for detecting fungi include traditional methods and some emerging rapid detection methods. The traditional detection methods comprise culture, direct microscopic observation and pathological observation. The methods of microscopic and pathological observation are simpler, but both methods require personnel with higher levels of specific fungal training. At present, the culture method is still the gold standard of fungal infection, and can comparatively detect a very accurate result, but the method needs long culture time, and some filamentous fungi need culture for even several days, so that the early diagnosis of invasive fungi can be delayed, and the treatment of fungal infection can be influenced. Emerging methods include molecular diagnostic methods based on PCR and antigen detection. These methods are not based on traditional phenotypic analysis and allow rapid detection of invading fungi in a short time. However, these methods still have the disadvantages of high technical requirements and extreme sensitivity to external contamination, and are very easy to cause false positives, which affect judgment. To overcome these problems, methods based on raman detection have also appeared in the prior art, for example patent 202010531059.9, which discloses a method for rapid identification of bacteria and fungi by raman spectroscopy, which is performed by taking a raman spectrum of a sample. However, the method has the following disadvantages: 1. the identification of bacteria containing cytochrome C is not considered in the method, and the method cannot obviously distinguish the bacteria containing cytochrome C from fungi after the bacteria containing cytochrome C is introduced; 2. it is not strict to directly judge the peak shape by naked eyes; 3. the selected sample is from antibiotic research institute in Huashan hospital, and the specific source of the strain makes it difficult for others to repeatedly implement the result.
Therefore, there is a need to provide a more reliable solution.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method and a system for identifying bacteria and fungi by raman spectroscopy, aiming at the above-mentioned deficiencies in the prior art.
In order to solve the technical problems, the invention adopts the technical scheme that: a method for identifying bacteria and fungi using raman spectroscopy, comprising the steps of:
1) Constructing a Raman spectrum database of bacteria and fungi, and acquiring a plurality of characteristic peaks of the bacteria and the fungi and Raman shifts corresponding to the characteristic peaks, wherein the Raman spectrum database comprises Raman spectrum data of the bacteria containing cytochrome C;
2) Collecting a Raman spectrum of a sample to be detected;
3) Identifying bacteria and fungi of the sample to be detected by adopting one or a combination of the following methods A and B:
the method A comprises the following steps:
a1, performing peak searching operation on a Raman spectrum of a sample to be detected by adopting an automatic peak searching algorithm to obtain all peaks in the Raman spectrum of the sample to be detected, and forming a peak set Q;
a2, raman bit shifts of all peaks in the statistical peak set Q appear at 750 + -D, 1128 + -D, 1240 + -D, 1480 + -D cm -1 Identifying the sample to be tested according to the conditions in the four intervals;
the method B comprises the following steps:
b1, extracting Raman shift of 781cm in Raman spectrum of sample to be detected -1 、1240cm -1 Height of peaks and valleys at location and at 1450cm -1 Peak height at position, 781cm -1 The heights of the wave crest and the wave trough are respectively marked as H 781 And L 781 ，1240cm -1 The heights of the wave crest and the wave trough are respectively marked as H 1240 And L 1240 ，1450cm -1 The height of the peak is recorded as H 1450 ；
B2, calculating the relative peak height H Δ ，
B3, comparison of relative peak height H Δ And comparing the standard sample with a preset demarcation threshold value T so as to identify the sample to be detected.
Preferably, the step 1) specifically includes:
1-1) respectively acquiring Raman spectrum data of a plurality of bacteria and fungi by adopting a micro-Raman spectrometer;
1-2) for each piece of Raman spectrum data, carrying out normalization treatment after baseline calibration to obtain a Raman spectrum image;
1-3) selecting a plurality of characteristic peaks in a Raman spectrogram of bacteria and fungi, and counting Raman shifts corresponding to the characteristic peaks, wherein the positions of the Raman shifts at least comprise 750, 781, 1128, 1240, 1480 and 1450cm -1 。
Preferably, the method a specifically comprises the following steps:
a1, performing baseline calibration on the Raman spectrum of a sample to be measured, and taking the lowest point of the whole Raman spectrum curve as a baseline y value;
performing peak searching operation on the Raman spectrum of the sample to be detected by adopting an automatic peak searching algorithm to obtain all peaks in the Raman spectrum of the sample to be detected, and forming a peak set Q;
a2, raman bit shifts of all peaks in the statistical peak set Q appear at 750 + -D, 1128 + -D, 1240 + -D, 1480 + -D cm -1 The four intervals are specifically as follows:
if the Raman position shift of the wave crest occurs at 750 + -D cm -1 If so, recording the score of the interval as 1, otherwise, recording the score as 0;
if the Raman position shift with wave peak appears in 1128 +/-D cm -1 If so, recording the score of the interval as 1, otherwise, recording the score as 0;
if the Raman position shift of the peak exists at 1240 +/-D cm -1 If so, recording the score of the interval as 0, otherwise, recording the score as 1;
if the Raman position shift of the peak appears at 1480 +/-D cm -1 If so, recording the score of the interval as 0, otherwise, recording the score as 1;
calculating the total score S of the sample to be detected, S =0.25a +0.25b +0.25c +0.25d, wherein a, b, c and D are 750 + -D, 1128 + -D, 1240 + -D, 1480 + -D cm in sequence -1 Scores of four intervals;
and if the total score is S =1, judging that the sample to be detected is fungi, otherwise, judging that the sample to be detected is bacteria.
Preferably, the sample is determined to be a bacterium containing no cytochrome C when the total score S =0, and the sample is determined to be a bacterium containing cytochrome C when the total score S = 0.5.
Preferably, D =10.
Preferably, the automatic peak searching algorithm is as follows: on the Raman spectrum curve, taking continuous M number value points as a unit area, and finding out a peak corresponding to the highest y value in the unit area as a peak of the unit area; setting the peak searching region as the wavelength range of the whole Raman spectrum curve, finding out the peak of each unit region, and recording the rough peak set Q 0 The preparation method comprises the following steps of (1) performing; screening out rough peak set Q 0 Highest peak q in (1) max Collecting the rough selected wave peak to Q 0 Middle peak height is not less than the highest peak q max * And reserving N% of wave crests, and removing the rest wave crests to obtain a screened wave crest set, namely the wave crest set Q.
Preferably, M =10;
preferably, N =20.
Preferably, the cut-off threshold T =0.3 when the relative peak height H Δ And when the value is more than T, judging that the sample to be detected is bacteria, otherwise, judging that the sample to be detected is fungi.
The invention also provides a system for identifying bacteria and fungi by using the Raman spectrum, which comprises a Raman spectrum acquisition module and an identification module, wherein the Raman spectrum acquisition module is used for acquiring the Raman spectrum of a sample to be detected, and the identification module is used for identifying the bacteria and the fungi of the sample to be detected according to the method.
The beneficial effects of the invention are:
the bacteria and fungi identification method provided by the invention detects cells at the single cell level, can reflect data results from the single cell level, has high accuracy and does not generate false positive data;
the identification method of the invention does not need to culture the cells for a long time, saves the culture time, and directly shortens the detection time of 18 hours to several minutes;
the identification detection method does not need any complicated steps, and can obtain the identification result only by collecting the Raman spectrum of the sample and carrying out automatic data analysis;
the present invention successfully distinguishes cytochrome C-containing bacteria from fungi, and further, in some embodiments, distinguishes cytochrome C-containing bacteria from cytochrome C-free bacteria;
according to the invention, the peak of the Raman spectrum is obtained by adopting an automatic peak searching algorithm, so that the accuracy and the efficiency are higher;
the strains selected for the present invention are all strains with specific ATCC numbers, which are readily available and which are capable of repeated implementation of the protocol of the present invention.
Drawings
FIG. 1 is a Raman spectrum of several bacteria and fungi obtained in example 1 of the present invention;
FIG. 2 shows the identification results of several bacteria and fungi in example 1 of the present invention.
Detailed Description
The present invention is further described in detail below with reference to examples so that those skilled in the art can practice the invention with reference to the description.
It will be understood that terms such as "having," "including," and "comprising," as used herein, do not preclude the presence or addition of one or more other elements or groups thereof.
Example 1
The embodiment provides a method for identifying bacteria and fungi by using Raman spectrum, which comprises the following steps:
1) Constructing a Raman spectrum database of bacteria and fungi, and acquiring a plurality of characteristic peaks of the bacteria and the fungi and Raman shifts corresponding to the characteristic peaks, wherein the Raman spectrum database comprises Raman spectrum data of the bacteria containing cytochrome C;
the step 1) specifically comprises the following steps:
1-1) respectively acquiring Raman spectrum data of a plurality of bacteria and fungi by adopting a micro-Raman spectrometer;
1-2) for each piece of Raman spectrum data, carrying out normalization treatment after baseline calibration to obtain a Raman spectrum image;
in this embodiment, after each piece of raman spectrum data is subjected to baseline correction, the raman data is normalized by using an R language software construction function. And selecting a get _ line drawing average value and a get _ ribbon drawing standard deviation in the ggplot2 function package to obtain the spectrogram shown in the figure 1.
1-3) selecting a plurality of characteristic peaks in a Raman spectrogram of bacteria and fungi, and counting Raman shifts corresponding to the characteristic peaks, wherein the positions of the Raman shifts comprise 750, 781, 1128, 1240, 1480 and 1450cm -1 . Referring to FIG. 1, raman spectra of several bacteria and fungi are shown, and it can be seen that 750 and 1128cm -1 Characteristic peaks of cytochrome C, 781 and 1240cm -1 Is a characteristic peak of nucleic acid, the cytochrome C peak of fungi is obvious, the nucleic acid peak of bacteria is obvious, and the four bacterial peaks of cytochrome C are obvious. 750 and 1128cm -1 Has characteristic peaks at 1240 and 1480cm -1 The non-characteristic peak can be judged as fungus, 750 and 1128cm -1 Has no characteristic peak, and 1240 and 1480cm -1 The bacteria containing no cytochrome C can be determined as bacteria containing 750, 1128, 1240 and 1480cm -1 All the bacteria have characteristic peaks and can be judged as bacteria containing cytochrome C. Therefore, fungi and bacteria can be distinguished from each other on the basis of the expression of these characteristic peaks.
In this example, 7 kinds of bacteria and 6 kinds of fungi were selected to establish a raman spectrum database. The 7 bacteria are acinetobacter baumannii (a. Baumannii atcc @19606, a. Bauu.), staphylococcus epidermidis (s. Epidemidis atcc @12228, s. Epi.), staphylococcus aureus (s. Aureus atcc @29213 and 25923, s. Aur), enterococcus faecalis (e.faecalalis atcc @29212, e.fae.), escherichia coli (e.coli atcc @25922, e.col), pseudomonas aeruginosa (p.aeruginosa atcc @27853, p.aer) and klebsiella pneumoniae (k.pneumoni atcc @700603, k.pne.), respectively. The 6 fungi include candida albicans (c.albicans ATCC @10231, c.alb), candida kluyveri (c.krusei ATCC @14242, c.kru.), candida tropicalis (c.tropicalis ATCC @38292, c.tro.), candida (c.parapsilosis ATCC @22019, c.par), and candida glabrata (c.glabrata ATCC @15126, c.gla.), cryptococcus neoformans ATCC 14116 (c.neoformans ATCC @14116, c.neo.). In addition, a cytochrome C-containing bacterium (S.oneidensis MR-1ATCC @700550, S.oMR-1) was selected in this example, and all of the bacteria and fungal bacteria were purchased from the American type culture Collection.
The culture method of the bacteria and the fungi comprises the following steps: bacteria were cultured on LB agar plates and fungi were cultured on YPD agar plates. The plates were incubated for 24h at 37 ℃. Then, a single clone was inoculated from the plate by using an inoculating loop, inoculated into 5mL of LB liquid medium, and cultured at 180rpm at 37 ℃ for 16 hours to a plateau stage. After that, 1mL of the medium was centrifuged at 7000g for 2 minutes, and the supernatant was discarded. The resulting cell pellet was resuspended in 1mL of sterile water and centrifuged at 7000g for 2 minutes. This washing step was performed 3 times in total, and finally the cells were resuspended in 1mL of sterile water, 2. Mu.L of the suspension was pipetted onto an aluminum-plated slide glass, and dried at room temperature.
The Raman collection method of the bacteria and the fungi comprises the following specific steps:
raman spectra of single cells were collected by microscopic Raman spectroscopy (Alpha 300R, WITec, germany). The parameters of the Raman spectrometer comprise 532nm of laser wavelength, 100 times of objective lens, 100 times of sample on the objective lens (100 x/NA =0.9, germany Zeiss company), about 7mw of laser power on the surface of the sample, 1200 lines/mm of a grating of the spectrometer and 5-20 s of exposure time. Spectral resolution of about 2cm -1 Selecting 280-2186 cm -1 The wavenumber range of (2).
2) Collecting a Raman spectrum of a sample to be detected;
3) Identifying bacteria and fungi of the sample to be detected by adopting one or a combination of the following methods A and B:
the method A comprises the following steps:
the method A specifically comprises the following steps:
a1, performing baseline calibration on the Raman spectrum of a sample to be measured, and taking the lowest point of the whole Raman spectrum curve as a baseline y value;
performing peak searching operation on the Raman spectrum of the sample to be detected by adopting an automatic peak searching algorithm to obtain all peaks in the Raman spectrum of the sample to be detected, and forming a peak set Q;
in a preferred embodiment, the automatic peak-finding algorithmComprises the following steps: on a Raman spectrum curve, taking continuous 10 numerical points as a unit area, and finding out a peak corresponding to the highest y value in the unit area as a peak of the unit area; setting the peak searching region as the wavelength range of the whole Raman spectrum curve, finding out the peak of each unit region, and recording the rough peak set Q 0 The preparation method comprises the following steps of (1) performing; screening out rough peak set Q 0 Highest peak q in (1) max Collecting the rough selected wave peak to Q 0 Peak height of not less than the highest peak q max * And (4) reserving 20% of wave crests, and removing the rest wave crests to obtain a screened wave crest set, namely the wave crest set Q.
A2, raman bit shifts of all peaks in the statistical peak set Q appear at 750 + -D, 1128 + -D, 1240 + -D, 1480 + -D cm -1 The four intervals are specifically as follows:
if the Raman position shift of the wave crest occurs at 750 + -D cm -1 If so, recording the score of the interval as 1, otherwise, recording the score as 0;
if the Raman position shift with wave peak appears at 1128 +/-D cm -1 If so, recording the score of the interval as 1, otherwise, recording the score as 0;
if the Raman position shift of the peak exists at 1240 +/-D cm -1 If so, recording the score of the interval as 0, otherwise, recording the score as 1;
if the Raman position shift of the peak appears at 1480 +/-D cm -1 If so, recording the score of the interval as 0, otherwise, recording the score as 1;
calculating the total score S of the sample to be detected, S =0.25a +0.25b +0.25c +0.25d, wherein a, b, c and D are 750 + -D, 1128 + -D, 1240 + -D, 1480 + -D cm in sequence -1 Scores of four intervals;
and if the total score is S =1, judging that the sample to be detected is fungi, otherwise, judging that the sample to be detected is bacteria.
When the total score S =0, the test sample is determined to be a bacterium containing no cytochrome C, and when the total score S =0.5, the test sample is determined to be a bacterium containing cytochrome C.
The classification of the discrimination results is shown in table 1 below:
TABLE 1
The x values obtained by peak finding have a certain deviation, not necessarily exactly the position of the above four numbers, so the x lateral deviation is set to ± D, in a preferred embodiment, D =10. For example 740-760cm -1 The characteristic peaks appeared in between are all considered to be 750cm -1 The peak of (2).
It can be seen that method A is capable of distinguishing not only fungi and bacteria, but also bacteria not containing cytochrome C and bacteria containing cytochrome C.
The method B comprises the following steps:
b1, extracting Raman shift of 781cm in Raman spectrum of sample to be detected -1 、1240cm -1 Height of peaks and valleys at location and at 1450cm -1 Peak height at position, 781cm -1 The heights of the wave crest and the wave trough are respectively marked as H 781 And L 781 ，1240cm -1 The heights of the wave crest and the wave trough are respectively marked as H 1240 And L 1240 ，1450cm -1 The height of the peak is recorded as H 1450 ；
B2, calculating the relative peak height H Δ ，
B3, comparison of relative Peak height H Δ And comparing the standard sample with a preset demarcation threshold value T so as to identify the sample to be detected.
In a preferred embodiment, the demarcation threshold T =0.3, when the relative peak height H Δ And when the value is more than T, judging that the sample to be detected is bacteria, otherwise, judging that the sample to be detected is fungi. Referring to FIG. 2, to identify several bacteria and fungi for constructing a Raman spectra database using method B, it can be seen that a cut-off threshold of 0.3 was usedDifferentiation between fungi and bacteria, and differentiation of cytochrome C-containing bacteria (differentiation of cytochrome C-containing bacteria into bacteria can be successfully achieved), 0.3 or more is a bacterium, and 0.3 or less is a fungus.
Wherein, the method A and the method B can be independently used and can realize the identification of bacteria and fungi. Certainly, in order to further improve the reliability of the identification result, the method a and the method B may be used in combination, and when the identification results of the method a and the method B are the same, the identification result is output; and when the identification results of the method A and the method B are different, repeating the identification for 1-5 times, and if the identification results are still different due to some unknown reasons, adopting other methods for identification (such as nucleic acid sequencing).
Example 2
In this example, the method of example 1 was used to identify the sample 1,
the identification result of the method A is as follows:
| raman shift/ |
750 | 1128 | 1240 | 1480 |
| Score of | 1 | 1 | 1 | 1 |
The total score of S, S =0.25 × 1+ 1, is judged as fungi.
The identification result of the method B is as follows:
relative peak height
If the ratio is less than 0.3, the fungus is still judged.
And (3) carrying out nucleic acid sequencing on the sample 1 to be detected, wherein the sequencing result is saccharomycete and accords with the detection result.
Example 3
In this example, the method of example 1 was used to identify the sample 2,
the identification result of method a is as follows:
| raman shift/ |
750 | 1128 | 1240 | 1480 |
| Score of | 0 | 0 | 0 | 0 |
The total score of S, S =0.25 + 0+0.25 + 0=0, which is judged as bacteria.
The identification result of the method B is as follows:
relative peak height
If it is greater than 0.3, it is judged to be a bacterium.
The sample 2 to be tested was subjected to nucleic acid sequencing, and the sequencing result was Escherichia coli, which was consistent with the above-mentioned detection result.
Example 4
In this example, the method of example 1 was used to identify the sample 2,
the identification result of method a is as follows:
| raman shift/ |
750 | 1128 | 1240 | 1480 |
| Score of | 0 | 0 | 0 | 0 |
The total score of S, S =0.25 + 0+0.25 + 0=0, which is judged as bacteria.
The identification result of the method B is as follows:
relative peak height
If it is greater than 0.3, it is judged to be a bacterium.
The sample 2 to be tested is subjected to nucleic acid sequencing, and the sequencing result is Klebsiella pneumoniae which accords with the detection result.
Example 5
The embodiment provides a system for identifying bacteria and fungi by using a raman spectrum, which comprises a raman spectrum acquisition module and an identification module, wherein the raman spectrum acquisition module is used for acquiring the raman spectrum of a sample to be detected, and the identification module is used for identifying the bacteria and the fungi of the sample to be detected according to the method in the embodiment 1.
While embodiments of the invention have been disclosed above, it is not intended to be limited to the details shown in the description and the examples, which are set forth, but are fully applicable to various fields of endeavor as are suited to the particular use contemplated, and further modifications will readily occur to those skilled in the art, since the invention is not limited to the details shown and described without departing from the general concept as defined by the appended claims and their equivalents.
Claims (9)
1. A method for identifying bacteria and fungi by raman spectroscopy, comprising the steps of:
1) Constructing a Raman spectrum database of bacteria and fungi, and acquiring a plurality of characteristic peaks of the bacteria and the fungi and Raman shifts corresponding to the characteristic peaks, wherein the Raman spectrum database comprises Raman spectrum data of the bacteria containing cytochrome C;
2) Collecting a Raman spectrum of a sample to be detected;
3) Identifying bacteria and fungi of the sample to be detected by adopting one or a combination of the following methods A and B:
the method A specifically comprises the following steps:
a1, performing baseline calibration on the Raman spectrum of a sample to be measured, and taking the lowest point of the whole Raman spectrum curve as a baseline y value;
performing peak searching operation on the Raman spectrum of the sample to be detected by adopting an automatic peak searching algorithm to obtain all peaks in the Raman spectrum of the sample to be detected, and forming a peak set Q;
a2, counting Raman bit shifts of all peaks in the peak set Q to be 750 +/-D, 1128 +/-D, 1240 +/-D, 1480 +/-D cm -1 The four intervals are specifically as follows:
if the Raman position shift of the wave crest occurs at 750 + -D cm -1 If so, recording the score of the interval as 1, otherwise, recording the score as 0;
if the Raman position shift with wave peak appears in 1128 +/-D cm -1 If so, recording the score of the interval as 1, otherwise, recording the score as 0;
if the Raman position shift with wave peak is 1240 +/-D cm -1 If so, recording the score of the interval as 0, otherwise, recording the score as 1;
if the Raman position shift of the wave crest occurs at 1480 +/-D cm -1 If so, recording the score of the interval as 0, otherwise, recording the score as 1;
calculating the total score S of the sample to be tested, wherein S =0.25a +0.25b +0.25c +0.25d, and a, b, c and D are 750 +/-D, 1128 +/-D, 1240 +/-D and 1480 +/-D cm in sequence -1 Scores of four intervals;
if the total score S =1, judging that the sample to be detected is fungi, otherwise, judging that the sample to be detected is bacteria;
the method B comprises the following steps:
b1, extracting Raman shift of 781cm from Raman spectrum of sample to be detected -1 、1240cm -1 Height of peaks and valleys at location and at 1450cm -1 Peak height at position, 781cm -1 The heights of the wave crest and the wave trough are respectively marked as H 781 And L 781 ，1240cm -1 The heights of the wave crest and the wave trough are respectively marked as H 1240 And L 1240 ，1450cm -1 The height of the peak is recorded as H 1450 ；
B2, calculating the relative peak height H Δ ，
B3, relative peak height H Δ And comparing with a preset demarcation threshold value T, thereby identifying the sample to be detected.
2. The method for identifying bacteria and fungi by raman spectroscopy according to claim 1, wherein said step 1) comprises in particular:
1-1) respectively acquiring Raman spectrum data of a plurality of bacteria and fungi by adopting a micro-Raman spectrometer;
1-2) for each piece of Raman spectrum data, carrying out normalization treatment after baseline calibration to obtain a Raman spectrum image;
1-3) selecting a plurality of characteristic peaks in Raman spectrogram of bacteria and fungi, and counting Raman shifts corresponding to the characteristic peaks, wherein the positions of the Raman shifts at least comprise 750, 781, 1128, 1240, 1480 and 1450cm -1 。
3. The method for distinguishing a bacterium from a fungus by a Raman spectrum according to claim 1, wherein the sample is determined as a bacterium not containing cytochrome C if the total score is S =0, and the sample is determined as a bacterium containing cytochrome C if the total score is S = 0.5.
4. The method for identifying bacteria and fungi by raman spectroscopy of claim 1, wherein D =10.
5. The method for identifying bacteria and fungi using raman spectroscopy according to claim 1, wherein said automatic peak finding algorithm is: on a Raman spectrum curve, taking continuous M number value points as a unit area, and finding out a peak corresponding to the highest y value in the unit area as a peak of the unit area; setting the peak searching region as the wavelength range of the whole Raman spectrum curve, finding out the peak of each unit region, and recording the rough peak set Q 0 Performing the following steps; screening out a coarse selection peak set Q 0 Highest peak q in (1) max Collecting the rough selected wave crest into a set Q 0 Middle peak height is not less than the highest peak q max * And reserving N% of wave crests, and removing the rest wave crests to obtain a screened wave crest set, namely the wave crest set Q.
6. The method for identifying bacteria and fungi by raman spectroscopy according to claim 5 wherein M =10.
7. The method for identifying bacteria and fungi by raman spectroscopy of claim 5, wherein N =20.
8. The method for discriminating between bacteria and fungi by raman spectroscopy according to claim 1 wherein the cut-off threshold T =0.3 when the relative peak height H Δ And when the value is more than T, judging that the sample to be detected is bacteria, otherwise, judging that the sample to be detected is fungi.
9. A system for identifying bacteria and fungi by using Raman spectrum, which is characterized by comprising a Raman spectrum acquisition module and an identification module, wherein the Raman spectrum acquisition module is used for acquiring the Raman spectrum of a sample to be detected, and the identification module is used for identifying the bacteria and the fungi of the sample to be detected according to the method of any one of claims 1-8.
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| WO2010062351A1 (en) * | 2008-10-31 | 2010-06-03 | Biomerieux, Inc. | Methods for separation, characterization, and/or identification of microorganisms using raman spectroscopy |
| CN107505304A (en) * | 2017-07-26 | 2017-12-22 | 中国人民解放军第二军医大学 | A kind of quick method for judging drug-resistant type Candida albicans |
| TW202045913A (en) * | 2019-06-13 | 2020-12-16 | 雲陽科技有限公司 | Method for rapid identification of bacteria by portable Raman system which has advantages of requiring no bacterial culture of patient specimens and quickly obtaining identification results in 0-15 minutes |
| CN111624190A (en) * | 2020-06-11 | 2020-09-04 | 复旦大学附属华山医院 | Method for rapidly identifying bacteria and fungi by using Raman spectrum |
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