NL2035042B1 - Multi-dimensional terahertz metamaterial signal-based pathogen rapid identification method - Google Patents
Multi-dimensional terahertz metamaterial signal-based pathogen rapid identification method Download PDFInfo
- Publication number
- NL2035042B1 NL2035042B1 NL2035042A NL2035042A NL2035042B1 NL 2035042 B1 NL2035042 B1 NL 2035042B1 NL 2035042 A NL2035042 A NL 2035042A NL 2035042 A NL2035042 A NL 2035042A NL 2035042 B1 NL2035042 B1 NL 2035042B1
- Authority
- NL
- Netherlands
- Prior art keywords
- terahertz
- pathogen
- rapid identification
- metamaterial
- identification method
- Prior art date
Links
- 244000052769 pathogen Species 0.000 title claims abstract description 51
- 230000001717 pathogenic effect Effects 0.000 title claims abstract description 40
- 238000000034 method Methods 0.000 title claims abstract description 18
- 239000011159 matrix material Substances 0.000 claims abstract description 14
- 238000010239 partial least squares discriminant analysis Methods 0.000 claims abstract description 13
- 239000000523 sample Substances 0.000 claims description 23
- 230000003287 optical effect Effects 0.000 claims description 19
- 241000894006 Bacteria Species 0.000 claims description 11
- 230000001580 bacterial effect Effects 0.000 claims description 11
- 238000001228 spectrum Methods 0.000 claims description 11
- 239000000758 substrate Substances 0.000 claims description 11
- 239000000463 material Substances 0.000 claims description 9
- 239000012488 sample solution Substances 0.000 claims description 8
- 241001135265 Cronobacter sakazakii Species 0.000 claims description 7
- 241001354013 Salmonella enterica subsp. enterica serovar Enteritidis Species 0.000 claims description 7
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 claims description 7
- 241000191967 Staphylococcus aureus Species 0.000 claims description 7
- 229910052710 silicon Inorganic materials 0.000 claims description 7
- 239000010703 silicon Substances 0.000 claims description 7
- 238000010521 absorption reaction Methods 0.000 claims description 6
- 239000007864 aqueous solution Substances 0.000 claims description 6
- 235000013336 milk Nutrition 0.000 claims description 5
- 239000008267 milk Substances 0.000 claims description 5
- 210000004080 milk Anatomy 0.000 claims description 5
- 239000000843 powder Substances 0.000 claims description 5
- 238000013145 classification model Methods 0.000 claims description 4
- 238000004451 qualitative analysis Methods 0.000 claims description 4
- 238000007605 air drying Methods 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract description 8
- 230000002906 microbiologic effect Effects 0.000 abstract description 2
- 239000000243 solution Substances 0.000 description 24
- 238000010586 diagram Methods 0.000 description 7
- 238000007621 cluster analysis Methods 0.000 description 5
- 238000000708 deep reactive-ion etching Methods 0.000 description 4
- 238000001259 photo etching Methods 0.000 description 4
- 239000012154 double-distilled water Substances 0.000 description 3
- 239000002184 metal Substances 0.000 description 3
- 241000588914 Enterobacter Species 0.000 description 2
- 241000607142 Salmonella Species 0.000 description 2
- 241000191940 Staphylococcus Species 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 229920001817 Agar Polymers 0.000 description 1
- 238000002965 ELISA Methods 0.000 description 1
- 241001139947 Mida Species 0.000 description 1
- 239000008272 agar Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 235000013350 formula milk Nutrition 0.000 description 1
- 239000000383 hazardous chemical Substances 0.000 description 1
- 210000004251 human milk Anatomy 0.000 description 1
- 235000020256 human milk Nutrition 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000003757 reverse transcription PCR Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 230000001954 sterilising effect Effects 0.000 description 1
- 238000004659 sterilization and disinfection Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Classifications
-
- 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
- G01N21/3581—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using far infrared light; using Terahertz radiation
- G01N21/3586—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using far infrared light; using Terahertz radiation by Terahertz time domain spectroscopy [THz-TDS]
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
- C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Organic Chemistry (AREA)
- Analytical Chemistry (AREA)
- Toxicology (AREA)
- General Health & Medical Sciences (AREA)
- Zoology (AREA)
- Immunology (AREA)
- Biochemistry (AREA)
- Wood Science & Technology (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Engineering & Computer Science (AREA)
- Pathology (AREA)
- Microbiology (AREA)
- Molecular Biology (AREA)
- Biotechnology (AREA)
- Biophysics (AREA)
- General Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Engineering & Computer Science (AREA)
- Genetics & Genomics (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The present invention provides a multi-dimensional terahertz metamaterial signal-based pathogen rapid identification method, and relates to the technical field of microbiological detection and identification. According to the present invention, by integrating characteristic signals generated by pathogen samples on multiple different terahertz metamaterials, a difference in response is amplified, so that differential information of the samples is increased, and classification and identification of the samples are realized. Experimental results show that after being integrated into a three-dimensional signal matrix dataset of D(M1M2M3)‚ signals generated by various samples on three different terahertz metamaterials can be significantly distinguished by a partial least squares-discriminant analysis (PLS-DA) model to realize clear identification, R2 of a training dataset and a test dataset is greater than 0.98, and an identification accuracy rate of the model can reach 97.78%.
Description
MULTI-DIMENSIONAL TERAHERTZ METAMATERIAL SIGNAL-BASED
PATHOGEN RAPID IDENTIFICATION METHOD
[01] The present invention belongs to the technical field of microbiological detection and identification, and particularly, relates to a multi-dimensional terahertz metamaterial signal-based pathogen rapid identification method.
[02] Powdered infant formula (PIF) can be used as an alternative when breastfeeding is insufficient. However, some nutrients added to PIF for mimicking breast milk are susceptible to high temperature, so high temperature sterilization (>70°C) is performed for a few seconds only during production of PIF. It means that it is impossible to produce completely sterile PIF in the prior art, and residual trace pathogens can still make infants seriously ill. Therefore, PIF products need to comply with extremely strict biological quality and safety standards. Conventional detection methods, such as PCR, RT-PCR, and ELISA, recommended by food administrations of various countries require long detection time and experienced operators. Therefore, a novel intelligent pathogen detection method has been attracting attention.
[03] Currently, bioelectronics and biosensors have become a novel foodborne hazardous substance diagnosis technology. A terahertz wave band is a 0.1-10 THz electromagnetic wave. However, terahertz signals generated by trace pathogens are easily submerged in signal noise and cannot provide a clear and reliable result for clinical detection. Moreover, a sample terahertz signal from a single terahertz metamaterial contains limited shift information, which is insufficient to recognize individual pathogens from a mixed pathogen sample. Therefore, it is urgent to develop a highly sensitive terahertz technology for trace pathogen detection.
[04] In view of this, an objective of the present invention is to provide a multi-dimensional terahertz metamaterial signal-based pathogen rapid identification method, which can rapid and accurately identify foodborne Gram bacteria.
[05] In order to achieve the foregoing objective, the present invention provides the following technical solutions.
[06] The present invention provides a multi-dimensional terahertz metamaterial signal-based pathogen rapid identification method, which includes: inoculating a pathogen in an aqueous solution of a product for multiplication culture to obtain a bacterial sample solution, dropwise adding onto surfaces of three different terahertz metamaterials, air-drying, acquiring metamaterial surface signals to obtain a multi-dimensional dataset matrix of an optical spectrum, constructing a classification model for samples according to partial least squares-discriminant analysis (PLS-DA), and performing qualitative analysis.
[07] Preferably, a substrate material of the terahertz metamaterial is a heavily doped silicon wafer with a thickness of 300 um.
[08] Preferably, an optical grating height of the terahertz metamaterial is 30-60 um, and an interval is 15-40 um.
[09] Preferably, the pathogen includes foodborne Gram bacteria.
[10] Preferably, the pathogen is Staphylococcus aureus, Enterobacter sakazakii, and
Salmonella enteritidis.
[11] Preferably, a concentration of the bacterial sample solution is 2-375 CFU/uL.
[12] Preferably, the product includes milk powder, and a concentration of the aqueous solution of the product is 90-110 mg/mL.
[13] Preferably, the signals are 0.1-5 THz signals.
[14] Preferably, the dataset matrix is De(M:…Mp=(MiAo.1, ..., Mida, ..., Mido, ...,
MA), M is the terahertz metamaterial, & is the serial number of the terahertz metamaterial, c is the serial number of the pathogen sample, w is a frequency of a terahertz optical spectrum, and 4 is an absorption coefficient corresponding to the frequency.
[15] Compared with the prior art, the present invention has the following beneficial effects.
[16] According to the present invention, by integrating characteristic signals generated by pathogen samples on multiple different terahertz metamaterials, a difference in response is amplified, so that differential information of the samples is increased, and the classification and identification of the pathogen samples in realized.
Experimental results show that after being integrated into a three-dimensional signal matrix dataset of D(M;,M2,M5), signals generated by various samples on three different terahertz metamaterials can be significantly distinguished by a PLS-DA model to realize clear identification, R? of a training dataset and a test dataset of the model is greater than 0.98, which indicates excellent quality of the model, and an identification accuracy rate of the model can reach 97.78%.
[17] FIG. 1 is a diagram of surface structures of three different terahertz metamaterials, which are METAL, META2, and META3, respectively, from left to right.
[18] FIG. 2 is a diagram of characteristic signals and responses to pathogen samples on terahertz metamaterials META1, META2, and META3.
[19] FIG. 3 is a diagram of influences of pathogens on characteristic peak offsets of terahertz metamaterials, wherein Sta, Ent, and Sal respectively represent
Staphylococcus aureus, Enterobacter sakazakii, and Salmonella enteritidis, and
META2-I and METAZ2-II respectively represent two characteristic peaks of META2 at 0.769 THz and 0.172 THz.
[20] FIG. 4 is a diagram of PLS-DA cluster analysis results of terahertz metamaterial signals of pathogen samples, wherein (a) is a cluster analysis result of a pathogen sample on METAL, (b) is a cluster analysis result of a pathogen sample on
META2, (c) 1s a cluster analysis result of a pathogen sample on META3, and (d) is a cluster analysis result of a three-dimensional terahertz metamaterial signal of a pathogen sample.
[21] FIG. 5 is a diagram of explained variances of dataset principal component analysis in a PLS-DA model, from which it can be seen that the first three factors are qualified that they describe more than 95% of sample's spectral characteristics.
[2] FIG. 61s a diagram of a distribution difference between dataset real values and model prediction values in a PLS-DA model, from which it can be seen that R? is greater than 0.98, indicating good quality of the model.
[23] FIG. 7 is a diagram of a confusion matrix, from which accuracy reaches 97.78%, indicating a good prediction of model.
[24] The present invention provides a multi-dimensional terahertz metamaterial signal-based pathogen rapid identification method, which is characterized by including the following steps: a pathogen is introduced into an aqueous solution of a product for multiplication culture to obtain a bacterial sample solution, the bacterial sample solution is dropwise added onto surfaces of three different terahertz metamaterials and air-dried, metamaterial surface signals are acquired to obtain a multi-dimensional dataset matrix of an optical spectrum, a classification model for samples is constructed according to PLS-DA, and qualitative analysis is performed.
[25] In the present invention, a substrate material of the terahertz metamaterial is preferably a heavily doped silicon wafer with a thickness of 300 um, an optical grating height of the terahertz metamaterial is 30-60 um, and an interval is 15-40 um. In the present invention, a heavily doped silicon wafer is used as a metamaterial substrate of the terahertz metamaterial, and an optical structure is manufactured by photoetching and deep reactive ion etching. A carrier concentration is 2.9110" cm™, and the electron mobility is 174 cm?/(Vs). An optical height of an individual resonator is 30-60 um, an interval is 15-40 pm, and a substrate thickness is 300 um. FIG. 1 shows surface structures of the three different terahertz metamaterials of the present invention, which are META 1, META2, and META3, respectively, from left to right.
[26] Different pathogens include different chemical components, and responses of a terahertz metamaterial to different pathogens are also different. Surface resonance unit structures of different terahertz metamaterials are different, and responses of ditferent terahertz metamaterials to the same pathogen are also different. According to the 5 present invention, the three different terahertz metamaterials are used for detection of different pathogens to amplify a difference in response, so that difference information of samples is increased, and the classification and identification of the samples are realized.
[27] An instrument for acquiring terahertz metamaterial surface signals is not specified in the present invention, and an instrument commonly used in the art is adopted. In a possible implementation, the instrument for acquiring terahertz metamaterial surface signals 1s a terahertz time-domain optical spectrum instrument.
[28] In the present invention, the pathogen is preferably foodborne Gram bacteria, the foodborne Gram bacteria preferably include Staphylococcus aureus, Enterobacter sakazakii, and Salmonella enteritidis, and a concentration of the bacterial sample solution is preferably 2-375 CFU/uL.
[29] In the present invention, the product preferably includes milk powder.
[30] In the present invention, the signals are preferably 0.1-5 THz absorption coefficient signals.
[31] In the present invention, the concentration of the bacterial sample solution is 2-375 CFU.
[32] In the present invention, the multi-dimensional optical spectrum matrix dataset is preferably Dc(M,,... M)=(MiAo1, ..., Mide, ..., Mido, ..., Mids), M is the terahertz metamaterial, & is the serial number of the metamaterial, c is the serial number of the pathogen sample, © is a frequency of a terahertz optical spectrum, and A is an absorption coefficient corresponding to the frequency.
[33] After being integrated into a three-dimensional signal matrix dataset of
DM M2 Ms), signals of various samples on the three different terahertz metamaterials can be significantly distinguished by the PLS-DA model to realize clear identification.
Moreover, R? of a training set and a test set is greater than 0.98, which indicates good quality of the model, and an identification accuracy rate of the model can reach 97.78%.
[34] Sources of raw materials are not specified in the present invention, and commercially available products in the art are adopted.
[35] The technical solutions provided in the present invention will be described in detail below with reference to examples, but these examples shall not be understood as limiting the scope of protection of the present invention.
[36] Example 1
[37] Three different terahertz metamaterials
[38] METAL: a heavily doped silicon wafer was taken as a substrate material, a resonance unit 1s formed on the surface of the substrate material by photoetching and deep reactive ion etching, a thickness of an optical grating structure of the resonance unit was 30 um, and an interval was 15 um, as shown in FIG. IA.
[39] META2: a heavily doped silicon wafer was taken as a substrate material, a resonance unit is formed on the surface of the substrate material by photoetching and deep reactive ion etching, a thickness of an optical grating structure of the resonance unit was 45 pm, and an interval was 15-30 um, as shown in FIG. 1B.
[40] METAS: a heavily doped silicon wafer was taken as a substrate material, a resonance unit is formed on the surface of the substrate material by photoetching and deep reactive ion etching, a thickness of an optical grating structure of the resonance unit was 60 pm, and an interval was 15-30 um, as shown in FIG. IC.
[41] Specific structures of the three different terahertz metamaterials are shown in
FIG. 1.
[42] Example 2
[43] Identification of pure bacterial solution samples
[44] Three different pathogens (Staphylococcus aureus, Enterobacter sakazakii, and
Salmonella enteritidis) commonly found in milk powder were selected and respectively introduced into a 100 mg/mL qualified milk powder aqueous solution for multiplication culture at 37°C for 48 h. 10 ul of each of the foregoing bacteria-containing solutions was taken and distributed over the surface of an agar medium by the spread plate method for multiplication culture, the medium surface was rinsed with 2 mL of double distilled water, 1 mL of solution was collected as an original bacteria solution, 1 uL of original bacteria solution was distributed over the surface of a counting medium by the streak plate method for culture, a concentration of the original bacteria solution was obtained, the original bacteria solution was diluted to 100-400 CFU/uL with double distilled water, and the diluted solution was defined as a 10° solution of the bacteria solution and denoted as a 3 solution.
[45] According to the foregoing operation, three pathogen solutions shown in Table 1 were cultured and prepared, the 3x solution was diluted with double distilled water to prepare a 2.0 solution, a 2= solution, and a 1* solution, and concentrations of the 3% solution, the 2.6% solution, and the 2x solution were respectively 1000 times, 398 times, and 100 times that of the 1x solution.
[46] Table 1 Parameters of pathogen samples
Average concentration (CFU/uL)
Pathogen TTT 1x 2x 2.6% 3x
Staphylococcus 2 16 63 158 aureus
Enterobacter 3 27 104 262 sakazakii
Salmonella 4 38 150 375 enteritidis
[47] After all the samples were dropwise added to surfaces of the terahertz metamaterials META 1, META2, and META3 (whose surface structures were as shown in FIG. 1) and naturally air-dried, a terahertz time-domain optical spectrum instrument (THz-TDS) was used to acquire 0.1-5 THz absorption coefficient signals of the terahertz metamaterial surfaces, each sample was repeatedly tested 100 times to obtain a multi-dimensional optical spectrum matrix dataset Dc(M, … Mi)=(MiAo1, ...,
Mido, ..., MiAor, ..., MA), M was the terahertz metamaterial, & was the serial number of the metamaterial, ¢ was the serial number of the pathogen sample, © was a frequency of a terahertz optical spectrum, and 4 was an absorption coefficient corresponding to the frequency.
[48] A classification model for the samples was constructed according to PLS-DA in combination with a classification function, and was used for performing qualitative analysis on a mixed bacterial sample and its components. Obtained signal responses are shown in FIG. 2.
[49] It can be seen from FIG. 2 that the terahertz metamaterial META has a characteristic peak at 0.926 THz, and the peak shifts to higher frequencies (blue shift) as the concentration of the sample increases; the terahertz metamaterial META2 has two characteristic peaks at 0.769 THz and 0.172 THz, respectively, the peaks shift to lower frequencies (red shift) as the concentration of the sample increases; and the terahertz metamaterial META3 has a characteristic peak at 1.401 THz, and the peak shows red shift as the concentration of the sample increases. As the concentration of the pathogen sample increases, the intensity of the peak diminishes whereas the offset increases because more structure units come into contract with the sample.
[50] It can be seen from FIG. 3 that offsets of the terahertz metamaterial characteristic peaks enable META1 to identify Staphvlococeus aureus at concentrations of 2 CFU/uL, 16 CFU/uL, 63 CFU/uL, and 158 CFU/uL, to identify
Enterobacter sakazakii at concentrations of 3 CFU/uL, 27 CFU/uL, and 262 CFU/uL, and to identify Salmonella enteritidis at concentrations of 4 CFU/uL, 38 CFU/uL, and 375 CFU/uL; enable a peak I of META2 to identify Staphylococcus aureus at concentrations of 2 CFU/uL, 16 CFU/uL, 63 CFU/uL, and 158 CFU/uL; and enable a peak II of META2 and METAS to identify Staphylococcus aureus at concentrations of 2 CFU/uL, 16 CFU/uL, 63 CFU/uL, and 158 CFU/uL, to identify Enterobacter sakazakii at concentrations of 3 CFU/uL, 27 CFU/uL, 104 CFU/uL, and 262 CFU/uL, and to identify Salmonella enteritidis at concentrations of 4 CFU/uL, 38 CFU/uL, 150
CFU/uL, and 375 CFU/uL. [S1] However, some terahertz metamaterials have non-significantly different characteristic peak offset responses (responses of META1 and META2-I to Sal at concentrations of 38 CFU/uL, 150 CFU/uL, and 375 CFU/uL) to the increase of concentrations of some pathogen samples; and some different pathogens at different concentrations may cause non-significantly different characteristic peak offsets (responses of METAI to Sta at the concentration of 16 CFU/uL, Ent at the concentration of 27 CFU/uL, and Sal at the concentration of 150 CFU/uL) of the same terahertz metamaterial. Therefore, as shown in FIG. 4a, FIG. 4b, and FIG. 4c, a single terahertz metamaterial signal contains insufficient information, and it cannot identify different pathogens at different concentrations based on the PLS-DA model. However, as shown in FIG. 4d, after being integrated into a three-dimensional signal matrix dataset of D(M;M> M3), the signals of the samples on the three different terahertz metamaterials can be significantly distinguished by the PLS-DA model to realize clear identification.
[52] Example 3
[53] Identification of mixed bacterial solution samples
[54] The pathogen solutions shown in Table 1 were prepared into mixed pathogen samples shown in Table 2.
[55] Table 2 Components of mixed pathogen samples
Serial number Component content (x) of sample Staphylococcus Enterobacter Salmonella aureus sakazakii enteritidis
I 2 2 2
II 0 2 2
III 0 2.6 2.6
IV 2 0 2
Vv 2 2 2.6
VI 2 2.6 0
VII 2.6 0 2.6
VIII 2.6 2 0
IX 2.6 2.6 2
CK 0 0 0
[56] 200 duplications of each sample were prepared, terahertz signals of the sample on the three different terahertz metamaterials were acquired and integrated into a three-dimensional signal matrix dataset of D(M; A> Mz). Samples in the dataset D were divided into a training set and a test set according to a ratio of 4: 1, and a
PLS-DA model was constructed by programming in Matlab.
[57] As shown in FIG. 5, the first three factors can explain more than 95% of sample characteristics. As shown in FIG. 6, R? of the training set and the test set is greater than 0.98, indicating good quality of the model. As a confusion matrix shown in FIG. 7, the identification accuracy of the model can reach 97.78%.
[58] The above are merely preferred embodiments of the present invention. It should be pointed out that those of ordinary skill in the art may further make several improvements and modifications without departing from the principles of the present invention, and these improvements and modifications shall still fall within the scope of protection of the present invention.
Claims (9)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
NL2035042A NL2035042B1 (en) | 2023-06-08 | 2023-06-08 | Multi-dimensional terahertz metamaterial signal-based pathogen rapid identification method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
NL2035042A NL2035042B1 (en) | 2023-06-08 | 2023-06-08 | Multi-dimensional terahertz metamaterial signal-based pathogen rapid identification method |
Publications (2)
Publication Number | Publication Date |
---|---|
NL2035042A NL2035042A (en) | 2023-07-13 |
NL2035042B1 true NL2035042B1 (en) | 2024-02-05 |
Family
ID=87202464
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
NL2035042A NL2035042B1 (en) | 2023-06-08 | 2023-06-08 | Multi-dimensional terahertz metamaterial signal-based pathogen rapid identification method |
Country Status (1)
Country | Link |
---|---|
NL (1) | NL2035042B1 (en) |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105602840B (en) * | 2016-01-28 | 2017-11-24 | 中国人民解放军第三军医大学第一附属医院 | Rolling circle amplification Terahertz Meta Materials biology sensor and the method for quick detection multiple-drug resistance tuberculosis bacillus |
CN109211833A (en) * | 2018-08-30 | 2019-01-15 | 中国人民解放军陆军军医大学第附属医院 | A kind of reproducible universal Terahertz Meta Materials sensor |
CN115015159A (en) * | 2022-06-21 | 2022-09-06 | 济南微生态生物医学省实验室 | Method for detecting living cell sample based on terahertz technology |
-
2023
- 2023-06-08 NL NL2035042A patent/NL2035042B1/en active
Also Published As
Publication number | Publication date |
---|---|
NL2035042A (en) | 2023-07-13 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ferone et al. | Microbial detection and identification methods: Bench top assays to omics approaches | |
Bhunia | One day to one hour: how quickly can foodborne pathogens be detected? | |
Hochel et al. | Occurrence of Cronobacter spp. in retail foods | |
CA2262923C (en) | Article and method for detection of enterotoxigenic staphylococci | |
CN109557014B (en) | Method for rapidly detecting number of lactic acid bacteria in fermented milk | |
Maity et al. | Effects of gamma radiation on fungi infected rice (in vitro) | |
Wei et al. | Paper chip-based colorimetric assay for detection of Salmonella typhimurium by combining aptamer-modified Fe 3 O 4@ Ag nanoprobes and urease activity inhibition | |
NL2035042B1 (en) | Multi-dimensional terahertz metamaterial signal-based pathogen rapid identification method | |
Alvarez et al. | Using a porous silicon photonic crystal for bacterial cell‐based biosensing | |
Zheng et al. | Spinyhead croaker (Collichthys lucidus) quality determination using multi-walled carbon nanotubes gas-ionization sensor array | |
AL‐HOLY et al. | Classification of foodborne pathogens by Fourier transform infrared spectroscopy and pattern recognition techniques | |
Vaňhara et al. | Intact cell mass spectrometry for embryonic stem cell biotyping | |
Moreirinha et al. | MIR spectroscopy as alternative method for further confirmation of foodborne pathogens Salmonella spp. and Listeria monocytogenes | |
Yang et al. | Volatile metabolic markers for monitoring Pectobacterium carotovorum subsp. carotovorum using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry | |
CN107202880A (en) | A kind of microorganism detection method analyzed based on electrochemical impedance phase angle | |
Tan et al. | Major fungal postharvest diseases of papaya: Current and prospective diagnosis methods | |
CN103217398A (en) | Classification and identification upon 13 pathogenic bacteria by using Fourier transform infrared spectroscopic technology | |
Lin et al. | Detection of Maize Mold Based on a Nanocomposite Colorimetric Sensor Array under Different Substrates | |
Shaheen et al. | Microbial assessment of pathogenic bacterial growth in ice cream and kulfa | |
Falasconi et al. | Electronic nose and its application to microbiological food spoilage screening | |
Lin et al. | DETECTION AND DISCRIMINATION OF ENTEROBACTER SAKAZAKII (CRONOBACTER SPP.) BY MID‐INFRARED SPECTROSCOPY AND MULTIVARIATE STATISTICAL ANALYSES | |
Pečinka et al. | Intact Cell Mass Spectrometry for Embryonic Stem Cell Biotyping | |
Cranz et al. | Carbon dioxide as a novel indicator for bacterial growth in milk | |
Bramwell et al. | The food microbiological analyst: pairing tradition with the future | |
Audu et al. | BACTERIOLOGICAL ASSESSMENT OF FAST FOODS SOLD A |