Method, system and equipment for synchronously detecting various dopants based on mid-infrared microscopic image

Abstract

The invention discloses a method, system and equipment for synchronously detecting multiple dopants based on an intermediate infrared microscopic image, and relates to the field of mixture detection.The method comprises the steps that a microscopic infrared spectrum image of a sample to be detected is collected, and a spectrum to be detected is obtained; calculating a measurement index value between the spectrum to be measured and each representative spectrum in a dopant spectrum library; taking the dopant type corresponding to the representative spectrum with the highest measurement index value with the to-be-detected spectrum as the dopant type of the to-be-detected sample; according to a measurement index value and a judgment threshold value between the spectrum to be detected and each representative spectrum in a dopant spectrum library, determining the dopant variety abundance in the sample to be detected; the judgment threshold is determined in advance according to a threshold spectrum library; wherein the dopant spectrum library and the threshold spectrum library respectively comprise a representative spectrum of each dopant. According to the invention, the problem of synchronous detection of multiple dopants is solved, and meanwhile, the detection efficiency and precision of the dopants are improved.

Description

Technical Field

[0001] This application relates to the field of mixture detection, and in particular to a method, system and device for simultaneous detection of multiple dopants based on mid-infrared microscopic images. Background Technology

[0002] Driven by profit, unscrupulous businesses frequently adulterate products such as food, pharmaceuticals, cosmetics, and animal feed. This can range from simply lowering product quality to causing serious environmental and health problems. Therefore, efficient and accurate detection of adulterants in mixed systems is crucial. Existing methods for adulteration detection are diverse, with common wet chemical methods such as high-performance liquid chromatography (HPLC) being costly and time-consuming. Furthermore, some methods only detect characteristic components of the adulterant, failing to accurately pinpoint its source. Summary of the Invention

[0003] The purpose of this application is to provide a method, system, and device for simultaneous detection of multiple dopants based on mid-infrared microscopic images, which can solve the problem of simultaneous detection of multiple dopants and improve the detection efficiency and accuracy of dopants.

[0004] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for simultaneous detection of multiple dopants based on mid-infrared microscopic images, including: Acquire microscopic infrared spectral images of the sample to be tested to obtain the spectrum to be tested; Calculate the metric value between the spectrum to be measured and each representative spectrum in the dopant spectral library; The dopant species corresponding to the representative spectrum with the highest metric value between the spectrum to be tested is taken as the dopant species of the sample to be tested. The abundance of dopant species in the sample to be tested is determined based on the metric values ​​and judgment thresholds between the spectrum to be tested and the representative spectra in the dopant spectrum library; the judgment thresholds are determined in advance based on the threshold spectrum library. The dopant spectral library and the threshold spectral library both include representative spectra of each dopant.

[0005] Secondly, this application provides a system for simultaneous detection of multiple dopants based on mid-infrared microscopic images, including: The spectral acquisition module is used to acquire the microscopic infrared spectral image of the sample to be tested, and obtain the spectrum to be tested; The spectral measurement module is used to calculate the measurement index value between the spectrum to be measured and each representative spectrum in the dopant spectral library; The type determination module is used to identify the dopant type corresponding to the representative spectrum with the highest metric value between the spectrum to be tested and the dopant type of the sample to be tested. The abundance determination module is used to determine the abundance of dopant species in the sample to be tested based on the metric values ​​and judgment thresholds between the spectrum to be tested and each representative spectrum in the dopant spectrum library; the judgment thresholds are determined in advance based on the threshold spectrum library. The dopant spectral library and the threshold spectral library both include representative spectra of each dopant.

[0006] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for simultaneous detection of multiple dopants based on mid-infrared microscopic images.

[0007] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, system, and device for simultaneous detection of multiple dopants based on mid-infrared microscopic images. By comparing the microscopic infrared spectral image of the sample to be tested with the representative spectra in the dopant spectral library, the dopant type is determined according to the representative spectrum with the highest metric value, and the dopant type abundance is further determined according to the judgment threshold. This solves the problem of simultaneous detection of multiple dopants and improves the detection efficiency and accuracy of dopants. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A flowchart illustrating a method for simultaneous detection of multiple dopants based on mid-infrared microscopic images, provided in an embodiment of this application; Figure 2 This is an average micro-infrared spectrum of penicillin filter residue from different sources in one embodiment of this application; Figure 3 This is an average micro-infrared spectrum of oxytetracycline filter residue from different sources in one embodiment of this application; Figure 4 This is an average micro-infrared spectrum of neomycin filter residue from different sources in one embodiment of this application; Figure 5 This is the average micro-infrared spectrum of a feed sample to be tested in one embodiment of this application; Figure 6 This is a diagram showing the identification results of penicillin filter residue in soybean meal, a feed sample to be tested, in one embodiment of this application. Figure 7 This is a diagram showing the identification results of penicillin filter residue in yeast protein residue of a feed sample to be tested in one embodiment of this application; Figure 8 This is a diagram showing the identification results of penicillin filter residue in a feed sample DDGS in one embodiment of this application; Figure 9 This is a diagram showing the identification results of penicillin filter residue in the nucleotide residue of a feed sample to be tested in one embodiment of this application; Figure 10 This is a diagram showing the identification results of penicillin filter residue in a chicken compound feed sample from one embodiment of this application. Figure 11 This is a diagram showing the identification results of oxytetracycline filter residue in a soybean meal sample of feed to be tested in one embodiment of this application; Figure 12 This is a diagram showing the identification results of oxytetracycline filter residue in yeast protein residue of a feed sample to be tested in one embodiment of this application; Figure 13 This is a diagram showing the identification results of oxytetracycline filter residue in a feed sample (DDGS) in one embodiment of this application. Figure 14 This is a diagram showing the identification results of oxytetracycline filter residue in the nucleotide residue of a feed sample to be tested in one embodiment of this application; Figure 15 This is a diagram showing the identification results of oxytetracycline filter residue in a chicken compound feed sample from one embodiment of this application. Figure 16 This is a diagram showing the identification results of neomycin filter residue in soybean meal of a feed sample to be tested in one embodiment of this application; Figure 17 This is a diagram showing the identification results of neomycin filter residue in yeast protein residue of a feed sample under test in one embodiment of this application; Figure 18 This is a diagram showing the identification results of neomycin filter residue in a feed sample DDGS in one embodiment of this application; Figure 19 This is a diagram showing the identification results of neomycin filter residue in the nucleotide residue of the feed sample to be tested in one embodiment of this application; Figure 20 This is a diagram showing the identification results of neomycin filter residue in a chicken compound feed sample from one embodiment of this application. Figure 21 A schematic diagram of the functional modules of a multi-dopane simultaneous detection system based on mid-infrared microscopic images provided in an embodiment of this application; Figure 22 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0010] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0011] Mid-infrared spectroscopy, as a type of molecular fingerprint spectroscopy, can obtain molecular fingerprint information of substances, making it highly suitable for substance identification. Mid-infrared microscopy is an analytical technique that combines infrared spectroscopy and imaging technology. It has high spatial resolution and can efficiently obtain the spatial distribution of microscopic substances such as tiny particles while simultaneously acquiring their infrared fingerprint spectral information. It is particularly suitable for the precise location and compositional analysis of trace dopants in complex samples.

[0012] For physical adulteration at the particle or powder level, most adulteration studies to date using spectroscopic methods are based on qualitative or quantitative models established using chemometric methods. These methods are only applicable to specific matrices and dopants. When the matrix of a new suspected sample does not match the matrix in the model, the model becomes inapplicable. Furthermore, complex matrices, such as those found in animal feed, undoubtedly reduce the accuracy of the model. When the types of dopants increase, it is also necessary to rebuild the model.

[0013] Therefore, the purpose of this application is to provide a non-calibrated dopant detection method applicable to dopant detection in physical mixing scenarios.

[0014] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0015] In one exemplary embodiment, a method for simultaneous detection of multiple dopants based on mid-infrared microscopic images is provided. This method is executed by a computer device, specifically a terminal or server, or both. In this embodiment, the method first establishes a dopant spectral library and a threshold spectral library and determines a judgment threshold through steps 101 to 103. Both the dopant spectral library and the threshold spectral library include representative spectra of each dopant, and dopants from different sources and of different types are distinguished by markers in both libraries.

[0016] Step 101: Collect multiple micro-infrared spectral images of representative samples of each dopant.

[0017] As an optional implementation, representative samples of each dopant are collected, and if the moisture content is too high, they need to be dried to constant weight. The dried samples are then pulverized and sieved, with the sieve mesh size... d and spatial resolution p The following relationship must be satisfied: d ≥2√2 p Then, in accordance with the requirements of the micro-infrared spectrometer, its micro-infrared spectral images were acquired.

[0018] Step 102: Select multiple representative spectra of each dopant from multiple micro-infrared spectral images to obtain the dopant spectral library and threshold spectral library.

[0019] In this process, each microscopic infrared spectrum image of a representative sample of each dopant can be preprocessed (including but not limited to noise reduction), and then multiple representative spectra of each dopant can be selected from the preprocessed microscopic infrared spectrum images.

[0020] As an optional implementation method, the noise reduction process for a microscopic infrared spectral image includes: selecting the first N principal components of the microscopic infrared spectral image using principal component analysis, reconstructing the image to obtain a reconstructed spectrum. Here, N is a preset number of principal components. The reconstructed spectrum is then processed using its second derivative.

[0021] Anomalous spectra in micro-infrared spectral images were removed using chemometric methods, and spectral preprocessing methods were used to reduce noise and highlight signals in the micro-infrared spectral images, and representative spectra without anomalies were calculated.

[0022] Methods for selecting representative spectra include, but are not limited to, the Duplex method and the K-Means method. As an optional implementation, the Duplex method is used to select representative spectra as follows: For any dopant, the Euclidean distances between each pair of multiple representative spectra of the dopant are calculated. Based on the maximum value of the Euclidean distances between the pairs of representative spectra, the representative spectra are alternately divided into a dopant spectral library and a threshold spectral library.

[0023] Step 103: Determine the judgment threshold based on the threshold spectrum library.

[0024] As an optional implementation, step 103 includes: calculating the metric values ​​between the spectra in the threshold spectral library and the spectra of the corresponding types in the dopant spectral library, and determining the distribution of the metric values. A judgment threshold is then determined based on the distribution of the metric values. The metric values ​​are indicators that can measure the correlation between two spectra, such as cosine similarity, correlation coefficient, Euclidean distance, and Mahalanobis distance.

[0025] In another exemplary embodiment, when it is necessary to expand the dopant spectral library, new representative dopant samples are further collected, and steps 101 to 104 described above are repeated.

[0026] In this embodiment of the application, the method for simultaneous detection of multiple dopants based on mid-infrared microscopic images includes the following steps 104 to 107.

[0027] Step 104: Acquire microscopic infrared spectral images of the sample to be tested to obtain the spectrum to be tested. Specifically, the number of microscopic infrared spectral images of the sample to be tested is multiple.

[0028] In this application, a Fourier transform micro-infrared imaging system can be used to acquire micro-infrared spectral images of representative samples and samples to be tested.

[0029] As an optional implementation, step 104 includes: preparing the sample to be tested into a pellet to obtain the pellet to be tested; acquiring a microscopic infrared spectral image of the pellet to be tested to obtain the spectrum to be tested; correspondingly, the microscopic infrared spectral image of the pellet to be tested is preprocessed using the same preprocessing steps as in step 102.

[0030] In a specific application example, before preparing the sample to be tested into a tablet, the sample is first dried and pulverized, and the processing method is the same as that for the representative sample in step 101.

[0031] Step 105: Calculate the metric value between the spectrum to be measured and each representative spectrum in the dopant spectrum library.

[0032] Step 106: The dopant type corresponding to the representative spectrum with the highest metric value between the spectrum to be tested is taken as the dopant type of the sample to be tested.

[0033] Step 107: Determine the abundance of dopant species in the sample to be tested based on the measurement index value and judgment threshold between the spectrum to be tested and each representative spectrum in the dopant spectrum library.

[0034] As an optional implementation, step 107 includes: classifying pixels in the spectrum to be tested whose metric values ​​relative to each representative spectrum are greater than a judgment threshold as dopants, and vice versa as matrix. Based on the number of dopant pixels in the spectrum to be tested, the abundance of dopant species in the sample to be tested is calculated.

[0035] This application also provides examples of detecting penicillin filter residue, oxytetracycline filter residue, and neomycin filter residue in feed: (1) Sample collection: Representative antibiotic filter residue samples from different antibiotic manufacturers with different processing processes and different production batches were collected: 3 penicillin filter residues, 4 oxytetracycline filter residues, and 5 neomycin filter residues. The samples with higher moisture content were first dried to constant weight at 60°C, and then all samples were pulverized through a 0.425mm sieve using a cyclone mill (ZM100, Restsh GmbH, Germany).

[0036] Preparation of Test Tablets: To obtain micro-infrared spectra with good signal-to-noise ratio, test tablets need to be prepared. The tablet diameter was 13 mm, the sample mass was 200 mg, the pressure was 40 MPa, and the pressing time was 2 minutes. The tablet press used in this experiment was a BHY-81A (AZZOTA Corporation, USA). Before micro-infrared spectroscopy acquisition, the surface flatness of the prepared test tablets was checked to avoid physical defects such as dents, protrusions, and cracks that could affect infrared spectral acquisition.

[0037] Acquisition of microscopic infrared spectra: The microscopic infrared spectra of the test pellets were acquired using a Fourier transform microscopic infrared imaging system. Following the operating instructions or user manual provided by the instrument manufacturer, liquid nitrogen was added to cool the detector before acquisition, and the system was preheated for 1 hour. A self-test was then performed. After successful self-test, the microscopic infrared spectral images were acquired. The scanning parameters were as follows: wavenumber range of 4000 cm⁻¹. -1 ~750cm -1 Spectral resolution of 8cm -1 The scan was performed 16 times, the interferometer moving mirror moved at a speed of 1 cm / s, and the spatial resolution was 25 μm × 25 μm. A 4000 μm × 4000 μm region was selected from each experimental pellet for microscopic infrared spectroscopy imaging analysis, resulting in 25600 (4000 / 25 × 4000 / 25) spectra for each pellet. The average microscopic infrared spectra of the obtained penicillin, oxytetracycline, and neomycin filter residues are shown below. Figures 2 to 4 As shown, to facilitate observation of spectral differences, baseline correction and maximum / minimum value normalization were performed on the spectra.

[0038] (2) Preprocessing of microscopic infrared spectroscopy: Noise reduction was performed on the microscopic infrared spectroscopy image. Specifically, principal component analysis was used to select the first 20 principal components of the microscopic infrared spectroscopy image for reconstruction. Second derivative processing (window width of 5) was used to improve the resolution of the microscopic infrared spectroscopy image. CO2 was removed at 2422 cm⁻¹. -1 ~2263cm -1 The infrared band of the band is used to subtract the interference of CO2 signal on the analysis.

[0039] (3) Construction of dopant spectral library and threshold spectral library: Representative spectra were selected from 25,600 micro-infrared spectra of each antibiotic filter residue sample based on Euclidean distance to obtain the antibiotic filter residue spectral library and antibiotic filter residue threshold spectral library. The specific process is as follows: Calculate the Euclidean distance between each micro-infrared spectrum and the remaining micro-infrared spectra. Abnormal micro-infrared spectra are removed based on 6 times the Euclidean distance as the criterion to obtain representative spectra. Calculate the Euclidean distance between each pair of representative spectra and assign the two representative spectra with the greatest Euclidean distance to the antibiotic filter residue spectral library. Find the representative spectrum with the greatest Euclidean distance from the remaining representative spectra and assign it to the antibiotic filter residue threshold spectral library. Repeat the above steps to alternately assign representative spectra to the antibiotic filter residue spectral library and the antibiotic filter residue threshold spectral library until the number of representative spectra in the antibiotic filter residue spectral library reaches the specified number.

[0040] The number of representative spectra in the antibiotic filter residue spectral library can be determined by the user based on identification accuracy and efficiency, or a small Euclidean distance (adjusted according to the characteristics of the dataset) can be set as the basis for selecting the number of spectra. To ensure identification accuracy, 4000 representative spectra were selected as the antibiotic filter residue spectral library, and 4000 representative spectra were used as the spectral library for determining the antibiotic filter residue threshold.

[0041] (4) Calculate the judgment threshold: Cosine similarity is used to measure the similarity of representative spectra. Cosine similarity is calculated by the cosine of the angle between two spectra. The calculation formula is as follows: ;in, A and B For two different representative spectra, Representative spectrum A With representative spectrum B cosine similarity, Representative spectrum A With representative spectrum B The included angle.

[0042] The closer the cosine similarity is to 1, the more similar the two spectra are. For each type of antibiotic filter residue sample, the cosine similarity between the spectrum in the antibiotic filter residue threshold spectral library and the spectrum of the same type in the same antibiotic filter residue spectral library is calculated, and the judgment threshold is determined according to the 2.5% percentile of the cosine similarity. Here, the judgment thresholds for penicillin filter residue, oxytetracycline filter residue, and neomycin filter residue are 0.92, 0.96, and 0.92, respectively.

[0043] (5) Collect feed samples to be tested, including soybean meal, yeast protein residue, corn ethanol residue, nucleotide residue, and chicken compound feed. Each of the five feeds was mixed with antibiotic filter residue at a ratio of 5% by mass, and then crushed and sieved using the same method. The average micro-infrared spectrum of the feed samples to be tested is as follows: Figure 5 As shown. Then, the test pellet was prepared and a microscopic infrared spectrum image was acquired using the method described in step (1).

[0044] (6) Calculate the cosine similarity between the micro-infrared spectrum of the feed sample containing antibiotic filter residue and the spectra in the antibiotic filter residue spectral library. Based on the judgment threshold determined in step (4), identify the antibiotic filter residue in the feed. The identification results are as follows: Figures 6 to 20 As shown.

[0045] Figures 6 to 10 In the image on the left, the red pixels represent identified antibiotic filter residue, and the blue pixels represent feed. The spectra on the right, from top to bottom, are: the spectrum with the highest cosine similarity to the antibiotic filter residue micro-infrared spectral library; the average spectrum of the pixels identified as antibiotic filter residue; the average spectrum of the penicillin filter residue spectral library; and the average spectrum of the feed. The similarity scores of the highest similarity spectra to the spectral library are 0.97, 0.95, 0.96, 0.97, and 0.96, respectively, and the pixel abundances of the identified antibiotic filter residue are 0.39%, 0.97%, 0.50%, 1.63%, and 0.96%, respectively.

[0046] Figures 11 to 15 In the image on the left, the red pixels represent identified antibiotic filter residue, and the blue pixels represent feed. The spectra on the right, from top to bottom, are: the spectrum with the highest cosine similarity to the antibiotic filter residue micro-infrared spectral library; the average spectrum of the pixels identified as antibiotic filter residue; the average spectrum of the oxytetracycline filter residue spectral library; and the average spectrum of the feed. The similarity scores of the highest similarity spectrum to the spectral library are 0.99, 0.98, 0.98, 0.99, and 0.98, respectively, and the pixel abundances of the identified antibiotic filter residue are 2.64%, 0.52%, 0.68%, 3.50%, and 0.68%, respectively.

[0047] Figures 16 to 20 In the image on the left, the red pixels represent identified antibiotic filter residue, and the blue pixels represent feed. The spectra on the right, from top to bottom, are: the spectrum with the highest cosine similarity to the antibiotic filter residue micro-infrared spectral library; the average spectrum of the pixels identified as antibiotic filter residue; the average spectrum of the neomycin filter residue spectral library; and the average spectrum of the feed. The similarity scores of the highest similarity spectra to the spectral library are 0.98, 0.98, 0.97, 0.99, and 0.98, respectively, and the pixel abundances of identified antibiotic filter residue are 2.12%, 2.11%, 1.63%, 3.65%, and 2.04%, respectively.

[0048] The results show that the corresponding antibiotic filter residue spectra were detected in all the feed samples containing antibiotic filter residue. Furthermore, the classification of the antibiotic filter residue was correctly determined based on the highest similarity spectrum.

[0049] This application establishes a dopant spectral library, which, thanks to the imaging principle of microscopic infrared spectroscopy, can acquire the infrared spectrum of each pixel. By calculating the similarity between the microscopic infrared spectrum of the sample to be tested and the representative spectrum in the dopant spectral library, the dopant type is determined based on the representative spectrum corresponding to the spectrum with the highest similarity. The dopant distribution is determined based on the judgment threshold, and the dopant type abundance is calculated. This achieves the purpose of accurately detecting and identifying dopant at the microscopic scale, enabling accurate detection and identification of adulteration phenomena in physical mixtures, thereby assisting in product quality monitoring in various fields.

[0050] Compared to traditional wet chemical detection methods, this application reduces detection costs and environmental pollution, and the detection process is faster, simpler, and more direct. Compared to traditional infrared / near-infrared methods, this application reduces the complexity of calibration model establishment and the risk of undetectable results.

[0051] In summary, this application can solve the problem of simultaneous detection of multiple dopants, while significantly improving the detection efficiency of dopants, reducing the complexity of preliminary preparation, and greatly improving its versatility.

[0052] Based on the same inventive concept, this application also provides a system for simultaneous detection of multiple dopants based on mid-infrared microscopic images to implement the method described above. The solution provided by this system is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the system for simultaneous detection of multiple dopants based on mid-infrared microscopic images provided below can be found in the limitations of the method described above, and will not be repeated here.

[0053] In one exemplary embodiment, such as Figure 21 As shown, a system for simultaneous detection of multiple dopants based on mid-infrared microscopic images is provided, including: a spectral acquisition module 201, a spectral measurement module 202, a species determination module 203, and an abundance determination module 204.

[0054] The spectral acquisition module 201 is used to acquire the microscopic infrared spectral image of the sample to be tested, and obtain the spectrum to be tested.

[0055] The spectral measurement module 202 is used to calculate the measurement index value between the spectrum to be measured and each representative spectrum in the dopant spectral library.

[0056] The type determination module 203 is used to determine the dopant type corresponding to the representative spectrum with the highest metric value between the spectrum to be tested and the dopant type of the sample to be tested.

[0057] The abundance determination module 204 is used to determine the abundance of dopant species in the sample to be tested based on the metric values ​​and judgment thresholds between the spectrum to be tested and each representative spectrum in the dopant spectrum library. The judgment thresholds are pre-determined based on the threshold spectrum library.

[0058] The dopant spectral library and the threshold spectral library both include representative spectra of each dopant.

[0059] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 22 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores dopant spectral libraries and threshold spectral libraries. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for simultaneous detection of multiple dopants based on mid-infrared microscopic images.

[0060] Those skilled in the art will understand that Figure 22 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0061] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0062] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0063] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0064] In this application, all actions to acquire signals, information, or data are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with the authorization granted by the owner of the relevant device.

[0065] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0066] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0067] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for simultaneous detection of multiple dopants based on mid-infrared microscopic images, characterized in that, The method includes: Acquire microscopic infrared spectral images of the sample to be tested to obtain the spectrum to be tested; Calculate the metric value between the spectrum to be measured and each representative spectrum in the dopant spectral library; The dopant species corresponding to the representative spectrum with the highest metric value between the spectrum to be tested is taken as the dopant species of the sample to be tested. The abundance of dopant species in the sample to be tested is determined based on the metric values ​​and judgment thresholds between the spectrum to be tested and the representative spectra in the dopant spectrum library; the judgment thresholds are determined in advance based on the threshold spectrum library. The dopant spectral library and the threshold spectral library both include representative spectra of each dopant.

2. The method for simultaneous detection of multiple dopants based on mid-infrared microscopic images according to claim 1, characterized in that, The number of micro-infrared spectral images of the sample to be tested is multiple.

3. The method for simultaneous detection of multiple dopants based on mid-infrared microscopic images according to claim 1, characterized in that, Acquire microscopic infrared spectral images of the sample to be tested to obtain the spectrum to be tested, specifically including: The sample to be tested is prepared into a tablet to obtain the tablet to be tested; The microscopic infrared spectrum image of the tablet to be tested is acquired to obtain the spectrum to be tested.

4. The method for simultaneous detection of multiple dopants based on mid-infrared microscopic images according to claim 1, characterized in that, The construction process of the dopant spectral library and the threshold spectral library includes: Multiple micro-infrared spectral images of representative samples for each dopant were acquired; Multiple representative spectra of each dopant were selected from multiple micro-infrared spectral images to obtain a dopant spectral library and a threshold spectral library.

5. The method for simultaneous detection of multiple dopants based on mid-infrared microscopic images according to claim 1, characterized in that, The process of determining the judgment threshold includes: Calculate the metric values ​​between the spectra in the threshold spectral library and the spectra of the corresponding types in the dopant spectral library, and determine the distribution of the metric values; The judgment threshold is determined based on the distribution of the metric.

6. The method for simultaneous detection of multiple dopants based on mid-infrared microscopic images according to claim 1, characterized in that, Based on the metric values ​​and judgment thresholds between the spectrum to be tested and representative spectra in the dopant spectral library, the abundance of dopant species in the sample to be tested is determined, specifically including: Pixels in the spectrum to be tested whose metric values ​​between the spectrum and each representative spectrum are greater than the judgment threshold are identified as dopants. The abundance of dopant species in the sample to be tested is calculated based on the number of dopant pixels in the spectrum to be tested.

7. A system for simultaneous detection of multiple dopants based on mid-infrared microscopic images, characterized in that, The system is applied to the method for simultaneous detection of multiple dopants based on mid-infrared microscopic images according to any one of claims 1-6, and the system comprises: The spectral acquisition module is used to acquire the microscopic infrared spectral image of the sample to be tested, and obtain the spectrum to be tested; The spectral measurement module is used to calculate the measurement index value between the spectrum to be measured and each representative spectrum in the dopant spectral library; The type determination module is used to identify the dopant type corresponding to the representative spectrum with the highest metric value between the spectrum to be tested and the dopant type of the sample to be tested. The abundance determination module is used to determine the abundance of dopant species in the sample to be tested based on the metric values ​​and judgment thresholds between the spectrum to be tested and each representative spectrum in the dopant spectrum library; the judgment thresholds are determined in advance based on the threshold spectrum library. The dopant spectral library and the threshold spectral library both include representative spectra of each dopant.

8. The system for simultaneous detection of multiple dopants based on mid-infrared microscopic images according to claim 7, characterized in that, The number of micro-infrared spectral images of the sample to be tested is multiple.

9. The system for simultaneous detection of multiple dopants based on mid-infrared microscopic images according to claim 7, characterized in that, The system also includes: The spectral library construction module is used to acquire multiple micro-infrared spectral images of representative samples of each dopant, and select multiple representative spectra of each dopant from the multiple micro-infrared spectral images to obtain the dopant spectral library and the threshold spectral library.

10. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for simultaneous detection of multiple dopants based on mid-infrared microscopic images according to any one of claims 1-6.

Images