Establishing method for raman spectrum feature database and composition determining method and system

Abstract

An establishing method for a Raman spectrum feature database includes: obtaining a Raman spectrum of at least one sample; generating a sample digital feature code according to the Raman spectrum of the sample, wherein information contained by the sample digital feature code includes information of a main peak of the Raman spectrum of the sample, and the information of the main peak includes a Raman shift of the main peak and intensity of the main peak; and storing the sample digital feature code. A composition determining method and a composition determining system are also provided.

Description

Technical Field

[0001] This invention relates to an optical measurement method and system, and more particularly to a method for establishing a Raman spectral feature database, a method for identifying components, and a system for identifying components. Prior Technology

[0002] Raman spectroscopy is a type of vibrational spectroscopy. Its principle is to use a laser light source with a fixed wavelength to excite the analyte. When the excitation light interacts with the analyte, if energy exchange occurs after the photons collide with the analyte, the photons transfer some energy to the analyte or gain some energy from the analyte, thereby changing the frequency of the light. This change is called the Raman shift.

[0003] Raman spectroscopy offers several advantages, including no need for pretreatment or damage to the analyte, and the ability to detect and obtain results instantly. Furthermore, Raman spectroscopy can be performed using microscopy, achieving sub-micron resolution for greater precision. Additionally, Raman spectroscopy boasts high selectivity, high sensitivity, and high mobility. It can be used in food testing, biomedical testing, environmental monitoring, and drug detection, among other applications.

[0004] When using Raman spectroscopy to determine the presence of a certain substance in an analyte, the conventional approach is to compare the Raman spectrum of the analyte with that of the substance. However, a complete Raman spectrum comparison often takes a considerable amount of time. Furthermore, if the substance is present in a low concentration in the analyte, the intensity of the characteristic peak corresponding to that substance in the Raman spectrum of the analyte will be much lower than the intensity of the characteristic peaks of other substances with higher concentrations. This significantly increases the difficulty of comparing the Raman spectrum of the analyte with that of the substance, and also makes the comparison time considerably longer. Summary of the Invention

[0005] This invention provides a method for establishing a Raman spectral feature database, which can establish a database for rapid comparison of Raman spectral features.

[0006] This invention provides a method for identifying components, which can quickly identify the components of a analyte.

[0007] This invention provides a component identification system that can quickly identify the components of a analyte.

[0008] An embodiment of the present invention proposes a method for establishing a Raman spectral feature database, comprising: obtaining at least one Raman spectrum of a sample; generating a sample digital feature code based on the sample's Raman spectrum, wherein the information contained in the sample digital feature code includes information about a parent peak of the sample's Raman spectrum, the parent peak information including the Raman shift and intensity of the parent peak; and storing the sample digital feature code.

[0009] One embodiment of the present invention provides a method for determining the composition of an analyte. The method includes providing a sample digital feature code, wherein the sample digital feature code contains information about a mother peak in the Raman spectrum of the sample; measuring the Raman spectrum of the analyte; generating an analyte digital feature code based on the Raman spectrum of the analyte, wherein the information contained in the analyte digital feature code includes information about a characteristic peak in the Raman spectrum of the analyte, the information of the characteristic peak including the Raman shift and intensity of the characteristic peak; and comparing the sample digital feature code with the analyte digital feature code to confirm whether the analyte contains the composition of the sample.

[0010] An embodiment of the present invention provides a component discrimination system for determining the components of an analyte. The component discrimination system includes a Raman spectral feature database, a Raman spectrometer, and a processor. The Raman spectral feature database stores a sample digital feature code for at least one sample, wherein the sample digital feature code is generated based on the Raman spectrum of the sample, and the information contained in the sample digital feature code includes information about a parent peak in the Raman spectrum of the sample, the parent peak information including the Raman shift and intensity of the parent peak. The Raman spectrometer is used to measure the Raman spectrum of the analyte. The processor is configured to perform: generating an analyte digital feature code based on the Raman spectrum of the analyte, wherein the analyte digital feature code contains information about a characteristic peak in the Raman spectrum of the analyte, the characteristic peak information including the Raman shift and intensity of the characteristic peak; and comparing the sample digital feature code in the Raman spectral feature database with the analyte digital feature code to confirm whether the analyte contains components of the sample.

[0011] In the Raman spectral feature database establishment method, component identification method, and component identification system of the embodiments of the present invention, a sample digital feature code is generated based on the Raman spectrum of the sample, and a analyte digital feature code is generated based on the Raman spectrum of the analyte. The sample digital feature code and the analyte digital feature code are then compared to confirm whether the analyte contains a component of the sample. Compared to conventional techniques that directly compare the Raman spectrum of the sample with the Raman spectrum of the analyte, the embodiments of the present invention use a comparison between the sample digital feature code and the analyte digital feature code, which significantly reduces the difficulty of comparison and greatly shortens the comparison time. Therefore, the Raman spectral feature database establishment method of the embodiments of the present invention can establish a database for rapid comparison of Raman spectral features, and the component identification method and system of the embodiments of the present invention can quickly identify the components of the analyte. Simple Explanation of the Diagram

[0012] Figure 1 is a flowchart of a method for establishing a Raman spectral feature database according to an embodiment of the present invention. Figure 2 is a flowchart of a component discrimination method according to an embodiment of the present invention. Figure 3 is a block diagram of a component discrimination system according to an embodiment of the present invention. Figure 4 shows the Raman spectrum of Escherichia coli. Figure 5 shows the Raman spectrum of methanol. Figure 6 is a Raman spectrum of a sample according to an embodiment of the present invention. Figure 7A is a schematic diagram of the one-dimensional barcode form of the sample digit feature code in Figure 1. Figure 7B is a schematic diagram of the two-dimensional barcode form of the sample digital feature code in Figure 1. Figures 8A to 8C are schematic diagrams of the preprocessing flow of the Raman spectrum of the analyte in Figure 2 before generating the digital signature of the analyte. Figure 9A is a schematic diagram comparing the Raman spectra before and after pretreatment. Figure 9B is a schematic diagram of a one-dimensional barcode generated from Raman spectroscopy. Figure 10 is a schematic diagram of one of the characteristic peaks in Figure 9A. Implementation

[0013] Figure 1 is a flowchart of a method for establishing a Raman spectral feature database according to an embodiment of the present invention, Figure 2 is a flowchart of a method for determining components according to an embodiment of the present invention, and Figure 3 is a block diagram of a system for determining components according to an embodiment of the present invention. Referring to Figures 1, 2, and 3, the method for establishing a Raman spectral feature database and the method for determining components in this embodiment can be implemented using the system for determining components 100 in Figure 3, but the present invention is not limited thereto. Furthermore, the number of characteristic peaks or reference peaks referred to in the present invention can be multiple; in other words, the number can be an integer greater than or equal to 1.

[0014] Referring to Figures 1 and 3, the component discrimination system 100 of this embodiment includes a Raman spectral feature database 110, a Raman spectrometer 120, and a processor 130. The method for establishing the Raman spectral feature database in this embodiment includes the following steps. First, step S110 is performed to obtain the Raman spectrum of at least one sample.

[0015] Figure 4 shows the Raman spectrum of *Escherichia coli*, and Figure 5 shows the Raman spectrum of methanol. In step S110, the sample can be a biological sample or a material sample. The material sample can be, for example, an organic or inorganic sample. The Raman spectrum of the sample can be, for example, the Raman spectrum of *E. coli* in Figure 4, the Raman spectrum of methanol in Figure 5, or the Raman spectrum of other samples; this invention is not limited thereto. The Raman spectrum of the sample can be obtained by measuring the sample using the Raman spectrometer 120 shown in Figure 3.

[0016] Next, step S120 is executed to generate a sample digital signature based on the Raman spectrum of the sample. The information contained in the sample digital signature includes information about a parent peak PM in the Raman spectrum (e.g., the Raman spectrum of E. coli in Figure 4), including the Raman shift D1 and the intensity N1 of the parent peak PM. Alternatively, the information contained in the sample digital signature may also include information about a parent peak PM' in the Raman spectrum of methanol as shown in Figure 5, including the Raman shift D1' and the intensity N1' of the parent peak PM'.

[0017] Then, step S130 is executed to store the above-mentioned sample digital feature code, for example, by storing the sample digital feature code in a memory to form a Raman spectral feature database 110, wherein the memory is, for example, a non-volatile memory, a magnetic disk, an optical disk, a solid-state drive, flash memory or other suitable memory that can store electronic data.

[0018] In this embodiment, the information of the parent peak PM further includes the area under the curve (A1) of the parent peak PM, the full width at half maximum (FWHM) H1 of the parent peak PM, or a combination thereof. Depending on the complexity of the analyte composition to be determined, the information contained in the sample digital feature code may selectively include information of at least one reference peak PR in the Raman spectrum. The number of selected reference peak PRs can be determined according to actual needs; it can be all reference peak PRs other than the parent peak PM, or a subset of reference peak PRs other than the parent peak PM. The information of the reference peak PR may include the Raman shift, intensity, area under the curve (A1), full width at half maximum (FWHM), or a combination thereof. The parent peak PM typically refers to the most prominent peak in the Raman spectrum of the sample, usually the strongest peak, while the reference peak PR refers to the peaks in the Raman spectrum of the sample other than the parent peak PM.

[0019] Figure 6 shows the Raman spectrum of a sample according to an embodiment of the present invention. Referring to Figure 6, in one embodiment, the information contained in the sample digital feature code further includes information about a base peak PB of the Raman spectrum, wherein the intensity of the base peak PB is used as a quantitative standard. For example, the base peak PB is the signal of the substrate carrying the sample; in Figure 6, it is the signal of a silicon substrate. Since the substrate already has a known Raman signal at the time of manufacture, the intensity of the base peak PB of the substrate can be used as a standard. The ratio of the intensity of the parent peak PM of the Raman spectrum of a component in the sample to the intensity of the base peak PB is fixed. Therefore, this ratio can be used to estimate the concentration, proportion, or content of this component in the sample. Thus, the intensity of the base peak PB can be used as a quantitative standard. In addition, in this embodiment, the ratio of the intensity of the reference peak PR to the intensity of the base peak PB is also fixed. Therefore, this ratio can also be used to estimate the concentration, proportion, or content of this component in the sample. In other embodiments, the base peak PB may also be a signal of a reagent with a known Raman signal incorporated into the sample, such as the signal of Rhodamine 6G (R6G) fluorescent dye, but the present invention is not limited thereto.

[0020] Figure 7A is a schematic diagram of the sample digital feature code in Figure 1 in the form of a one-dimensional barcode, while Figure 7B is a schematic diagram of the sample digital feature code in Figure 1 in the form of a two-dimensional barcode (quick response code, QR code). Referring to Figures 7A and 7B, in this embodiment, the method for establishing the Raman spectral feature database further includes presenting the sample digital feature code in the form of a one-dimensional barcode (as shown in Figure 7A), wherein the position of the stripes of the one-dimensional barcode (e.g., the horizontal position in Figure 7A) is related to the Raman shift of the parent peak (or at least one of the reference peak and the base peak), and the width of the stripes of the one-dimensional barcode (the width W1 in Figure 7A) is related to the intensity of the parent peak (or at least one of the reference peak and the base peak) (i.e., the intensity of the Raman spectrum). In one embodiment, the method for establishing the Raman spectral feature database further includes converting the one-dimensional barcode into a two-dimensional barcode (as shown in Figure 7B). The form of the digital feature code is not limited to a one-dimensional barcode or a two-dimensional barcode; it can also be any form of computer-storeable data.

[0021] Referring again to Figures 2 and 3, the component identification method of this embodiment is used to identify the components of a analyte. The component identification method includes the following steps. First, step S210 is executed to provide a sample digital feature code, for example, by using the Raman spectral feature database establishment method in Figure 1 to establish the sample digital feature code, wherein the sample digital feature code contains information of a mother peak of the sample's Raman spectrum. Next, step S220 is executed to measure the Raman spectrum of the analyte, for example, by using the Raman spectrometer 120 in Figure 3. Then, step S230 is executed to generate a analyte digital feature code based on the Raman spectrum of the analyte.

[0022] Figures 8A to 8C are schematic diagrams of the preprocessing flow of the Raman spectrum of the analyte in Figure 2 before generating the analyte's digital signature. Figure 9A is a comparison diagram of the Raman spectrum before and after preprocessing, and Figure 9B is a schematic diagram of the one-dimensional barcode generated from the Raman spectrum. Please refer to Figures 8A to 8C first. Figure 8A is the original Raman spectrum of the analyte measured using the Raman spectrometer 120 in Figure 3. Next, baseline correction can be performed, for example, using the processor 130 in Figure 3, to pull the peak of Figure 8A down to a position closer to the horizontal axis, resulting in the Raman spectrum shown in Figure 8B. Then, a portion of the Raman shift range to be analyzed can be selected from the entire Raman shift range in Figure 8B, resulting in the Raman spectrum shown in Figure 8C. Figure 9A shows that the Raman signal before preprocessing, after preprocessing, becomes a Raman signal closer to the horizontal axis and easier to identify. As shown in Figure 9B, the position of the stripes in a one-dimensional barcode, a form of digital feature code for the test object, (the horizontal position in Figure 9B) is related to the Raman shift of the characteristic peak PF of the test object, and the width of the stripes (e.g., the width W2 in Figure 9B) is related to the intensity of the characteristic peak PF. In one embodiment, the processor 130 can convert this one-dimensional barcode into a two-dimensional barcode.

[0023] In this embodiment, the information contained in the digital feature code of the analyte includes information about at least one characteristic peak PF of the Raman spectrum of the analyte. Figure 10 is a schematic diagram of one of the characteristic peaks PF in Figure 9A. Referring to Figures 2, 3, and 10, in this embodiment, the information of the characteristic peak PF includes the Raman shift D2 and intensity N2 of the characteristic peak PF. In one embodiment, the information of the characteristic peak PF further includes the area under the curve A2 of the characteristic peak PF, the full width at half maximum (FWHM) H2 of the characteristic peak PF, or a combination thereof. The Raman shift is related to the vibrational energy difference generated by the analyte (or sample) after the photons of the laser light emitted by the Raman spectrometer 120 collide with the analyte (or sample) and undergo energy exchange, and is related to the frequency difference between the light emitted by the analyte (or sample) after this energy exchange and the laser light.

[0024] Then, step S240 is executed, comparing the sample digital feature code with the analyte digital feature code to confirm whether the analyte contains the sample component. In this embodiment, step S240 includes determining whether the information of the characteristic peak PF matches the information of the parent peak PM. Matching can mean a similarity higher than a certain proportion or a perfect match. If, in step S240, the information of the characteristic peak PF matches the information of the parent peak PM, it can be determined that the analyte contains a sample component with this parent peak PM, and the intensity of the characteristic peak PF is related to the concentration, proportion, or content of this component. If they do not match, it can be determined that the analyte does not contain the sample component. In one embodiment, the information contained in the sample digital feature code further includes the information of the reference peak PR of the sample's Raman spectrum, and step S240 includes determining whether the information of the characteristic peak PF matches the information of the parent peak PM and the information of the reference peak PR, respectively. If they match, it can be determined that the analyte contains a sample component with this parent peak PM and the reference peak PR. If they do not match, it can be determined that the analyte does not contain the components of this sample.

[0025] In one embodiment, the information contained in the digital feature code of the analyte further includes information about the base peak of the Raman spectrum of the analyte (base peak PB in Figure 6), wherein the intensity of the base peak PB is used as a quantitative standard.

[0026] In the component discrimination system 100 of this embodiment, the Raman spectral feature database 110 is used to store the sample digital feature code of at least one sample, and the Raman spectrometer 120 is used to measure the Raman spectrum of the analyte, and can also be used to measure the Raman spectrum of the sample. The processor 130 is configured to execute steps S120 and S130 as shown in FIG1, and can execute steps S210, S230 and S240 as shown in FIG2. In one embodiment, the processor 130 and the Raman spectral feature database 110 may reside in a cloud server, and after the local Raman spectrometer 120 measures the Raman spectrum, it can upload the Raman spectrum to the cloud server, or the Raman spectrometer can transmit the Raman spectrum to a local smartphone, desktop computer, tablet computer, laptop computer or other type of computer, and the smartphone, desktop computer, tablet computer, laptop computer or other type of computer can then transmit this Raman spectrum to the cloud server through the Internet, local area network, wired network or wireless network. Then, the cloud server processes the received Raman spectrum of the analyte (or sample) into a digital signature of the analyte (or sample), and compares the digital signature of the analyte with the digital signature of the sample.

[0027] However, in other embodiments, the processor 130 and the Raman spectral feature database 110 may also reside on a local smartphone, desktop computer, tablet computer, laptop computer, or other type of computer. This local smartphone, desktop computer, tablet computer, laptop computer, or other type of computer receives the Raman spectrum output by the Raman spectrometer 120 via an electrical connection. Alternatively, in another embodiment, the processor 130 and the Raman spectral feature database 110 may be integrated into the Raman spectrometer 120, and this Raman spectrometer itself is also a computer.

[0028] In the Raman spectral feature database establishment method, component identification method, and component identification system 100 of this embodiment, a sample digital feature code is generated based on the Raman spectrum of the sample, and a analyte digital feature code is generated based on the Raman spectrum of the analyte. The sample digital feature code and the analyte digital feature code are then compared to confirm whether the analyte contains a component of the sample. Compared to conventional techniques that directly compare the Raman spectrum of the sample with the Raman spectrum of the analyte, this embodiment uses a comparison between the sample digital feature code and the analyte digital feature code, which significantly reduces the difficulty of comparison and greatly shortens the comparison time. Therefore, the Raman spectral feature database establishment method of this embodiment can establish a database for rapid comparison of Raman spectral features, and the component identification method and component identification system 100 of this embodiment can quickly identify the components of the analyte.

[0029] In some embodiments, the state of the analyte or sample can be solid, liquid, gaseous, or a combination thereof, and the analyte or sample can be a biological organism or biological tissue, organic matter, inorganic matter, pure substance, mixture, or a combination thereof. When the analyte or sample contains bacteria, the component discrimination system 100 of this embodiment can perform non-destructive detection of bacteria, measuring only the surface of the bacteria and obtaining Raman data of the bacterial surface. Based on the Raman spectrum of the bacteria, it is converted into a sample digital feature code of the bacteria or a analyte digital feature code. When the sample digital feature code of the bacteria is stored in the Raman spectral feature database 110, this sample digital feature code can be regarded as the molecular fingerprint of the bacteria, which is equivalent to the concept of the bacterial identification card. Then, during measurement, if the digital feature code of the analyte contains the digital feature code of the bacteria in the sample, it can be determined that the analyte contains the bacteria.

[0030] Furthermore, Raman spectroscopy is highly sensitive to molecular bonds and sample structure, thus each molecule or sample possesses unique Raman spectral characteristics. These characteristics can be used for chemical identification of components and other research and analysis. Additionally, the Raman signal for the same substance is independent of the frequency of the incident laser light; it is a characteristic physical quantity characterizing the vibrational-rotational energy levels of molecules, serving as the basis for qualitative and structural analysis. Moreover, Raman signals are less abundant than those from Fourier-transform infrared spectroscopy (FTIR), making them easier to interpret. Furthermore, in the field of spectroscopy, Raman spectroscopy is equivalent to a molecular barcode, a molecular identification card, capable of distinguishing even isomers of compounds. Therefore, this embodiment utilizes the conversion of the measured Raman spectrum into a digital feature code for the analyte or sample, followed by comparison of the analyte's digital feature code with the sample's digital feature code, enabling rapid and effective identification of the components within the analyte.

[0031] In one embodiment, the processor 130 may be, for example, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a programmable controller, a programmable logic device (PLD), or other similar devices or combinations thereof, and the present invention is not limited thereto. Furthermore, in one embodiment, the functions of the processor 130 may be implemented as multiple code snippets. These code snippets are stored in a memory and executed by the processor 130. Alternatively, in one embodiment, the functions of the processor 130 may be implemented as one or more circuits. The present invention does not limit the implementation of the functions of the processor 130 in software or hardware.

[0032] In summary, the Raman spectral feature database establishment method, component identification method, and component identification system of the embodiments of the present invention generate a sample digital feature code based on the Raman spectrum of the sample, generate a analyte digital feature code based on the Raman spectrum of the analyte, and compare the sample digital feature code with the analyte digital feature code to confirm whether the analyte contains the component of the sample. Compared with the conventional technology that directly compares the Raman spectrum of the sample with the Raman spectrum of the analyte, the embodiments of the present invention use the sample digital feature code to compare the analyte digital feature code, which can significantly reduce the difficulty of comparison and significantly shorten the comparison time. Therefore, the Raman spectral feature database establishment method of the embodiments of the present invention can establish a database for rapid comparison of Raman spectral features, and the component identification method and system of the embodiments of the present invention can quickly identify the component of the analyte.

[0033] 100: Component identification system 110: Raman Spectroscopic Feature Database 120: Raman spectrometer 130: Processor A1, A2: Area under the curve D1, D1', D2: Raman displacements H1, H2: Half-height width N1, N1', N2: Intensity PB: Base Peak PF: Characteristic peak PM, PM': Mother Peak PR: Reference Peak S110~S130, S210~S240: Steps

Claims

1. A method for establishing a Raman spectral feature database, comprising: Obtain the Raman spectrum of at least one sample; generate a sample digital signature based on the Raman spectrum of the sample, wherein the information contained in the sample digital signature includes information about a parent peak in the Raman spectrum of the sample, the information about the parent peak includes the Raman shift of the parent peak and the intensity of the parent peak; present the sample digital signature in the form of a one-dimensional barcode, wherein the position of the stripes of the one-dimensional barcode is related to the Raman shift of the parent peak, and the width of the stripes of the one-dimensional barcode is related to the intensity of the parent peak; and store the sample digital signature.

2. The method for establishing a Raman spectral feature database as described in claim 1, wherein the information contained in the sample digital feature code further includes information on at least one reference peak of the Raman spectrum of the sample.

3. The method for establishing a Raman spectral feature database as described in claim 1, wherein the information contained in the sample digital feature code further includes information about a base peak of the Raman spectrum, wherein the intensity of the base peak is used as a quantitative standard.

4. The method for establishing a Raman spectral feature database as described in claim 1, wherein the information of the parent peak further includes the area under the curve of the parent peak, the full width at half maximum (FWHM) of the parent peak, or a combination thereof.

5. The method for establishing a Raman spectral feature database as described in claim 1 further includes converting the one-dimensional barcode into a two-dimensional barcode.

6. A method for identifying components, used to identify components of an analyte, the method comprising: A sample digital signature is provided, wherein the sample digital signature contains information about a mother peak in the Raman spectrum of the sample; the Raman spectrum of the analyte is measured; a analyte digital signature is generated based on the Raman spectrum of the analyte, wherein the information contained in the analyte digital signature includes information about a characteristic peak in the Raman spectrum of the analyte, and the information about the characteristic peak includes the Raman shift and intensity of the characteristic peak; the analyte digital signature is presented in the form of a one-dimensional barcode, wherein the position of the stripes of the one-dimensional barcode is related to the Raman shift of the characteristic peak, and the width of the stripes of the one-dimensional barcode is related to the intensity of the characteristic peak; and the sample digital signature is compared with the analyte digital signature to confirm whether the analyte contains the components of the sample.

7. The method for identifying components as described in claim 6, wherein the step of comparing the sample digital feature code with the analyte digital feature code includes determining whether the information of the characteristic peak matches the information of the parent peak.

8. The method for identifying components as described in claim 6, wherein the information contained in the sample digital feature code further includes information of a reference peak in the Raman spectrum of the sample, and the step of comparing the sample digital feature code with the analyte digital feature code includes determining whether the information of the feature peak matches the information of the parent peak and the information of the reference peak, respectively.

9. The method for identifying components as described in claim 6, wherein the information contained in the digital feature code of the analyte further includes information about a base peak of the Raman spectrum of the analyte, wherein the intensity of the base peak is used as a quantitative standard.

10. The method for identifying a component as described in claim 6, wherein the information of the characteristic peak further includes the area under the curve of the characteristic peak, the full width at half maximum (FWHM) of the characteristic peak, or a combination thereof.

11. The method for identifying components as described in claim 6 further includes converting the one-dimensional barcode into a two-dimensional barcode.

12. A component discrimination system for identifying components of an analyte, the component discrimination system comprising: A Raman spectral feature database for storing a sample digital feature code for at least one sample, wherein the sample digital feature code is generated based on the Raman spectrum of the sample, and the information contained in the sample digital feature code includes information about a parent peak in the Raman spectrum of the sample, the information about the parent peak including the Raman shift and the intensity of the parent peak; a Raman spectrometer for measuring the Raman spectrum of the analyte; and a processor configured to perform: generating a analyte digital feature code based on the Raman spectrum of the analyte, wherein the information contained in the analyte digital feature code includes information about a characteristic peak in the Raman spectrum of the analyte, the information about the characteristic peak including the Raman shift and the intensity of the characteristic peak; The analyte digital feature code is presented in the form of a one-dimensional barcode, wherein the position of the stripes of the one-dimensional barcode is related to the Raman shift of the characteristic peak, and the width of the stripes of the one-dimensional barcode is related to the intensity of the characteristic peak; and the analyte digital feature code of the sample in the Raman spectral feature database is compared with the analyte digital feature code to confirm whether the analyte contains the component of the sample.

13. The component discrimination system as claimed in claim 12, wherein the step of comparing the sample digital feature code with the analyte digital feature code includes determining whether the information of the characteristic peak matches the information of the parent peak.

14. The component discrimination system as claimed in claim 12, wherein the information contained in the sample digital feature code further includes information of a reference peak in the Raman spectrum of the sample, and the step of comparing the sample digital feature code with the analyte digital feature code includes determining whether the information of the feature peak matches the information of the parent peak and the information of the reference peak, respectively.

15. The component discrimination system as claimed in claim 12, wherein the information contained in the analyte digital feature code further includes information about a base peak of the Raman spectrum of the analyte, wherein the intensity of the base peak is used as a quantitative standard.

16. The component discrimination system as claimed in claim 12, wherein the information of the characteristic peak further includes the area under the curve of the characteristic peak, the full width at half maximum (FWHM) of the characteristic peak, or a combination thereof.

17. A component discrimination system as described in claim 12, wherein the processor is configured to further perform: converting the one-dimensional barcode into a two-dimensional barcode.

Images