A method for extracting and detecting nanoparticle fingerprints based on co-occurring isotope combinations

Abstract

The present invention discloses a method for extracting and detecting nanoparticle fingerprints based on co-occurring isotope combinations, wherein a co-occurring isotope combination containing at least two isotopes is defined as a nanoparticle fingerprint; the frequent item extraction algorithm Apriori and the keyword weighting algorithm TF-IDF are used to extract the nanoparticle fingerprints of different samples respectively; the nanoparticles to be detected and the obtained nanoparticle fingerprints are encoded using a bitmap index-based encoding method, and the encoding of the nanoparticles to be detected and the nanoparticle fingerprints are ANDed, and it is determined whether the nanoparticles to be detected belong to a certain nanoparticle fingerprint according to the operation result. The present invention also discloses the application of the above method in the isotope detection representation of nanoparticles of unknown samples.

Description

Technical Field

[0001] The present invention belongs to the technical field of nanoparticle analysis and processing in the ecological field, and specifically relates to a nanoparticle fingerprint extraction and detection method based on co-occurring isotope combinations. Background Art

[0002] In the era of rapid development of information technology, more and more disciplines are beginning to combine with artificial intelligence technology to solve unique problems in the field. In the field of ecology, with the rise and development of nanotechnology, the objects of research by scientific researchers are often extremely small molecules or particles. The types of these substances are usually very complicated, and it is impossible to conduct in-depth research on the composition and structure of all substances. This has led to the fact that although the types of substances discovered are increasing, it is difficult to extract valuable information from the characterization data of these substances by manpower alone. This contradiction has become an obstacle to the further development of this discipline. Therefore, the use of various algorithms to extract the characteristics of nanoparticles and the use of convenient and efficient data management technology to manage nanoparticle data have become the key research direction in this field in recent years.

[0003] In existing research, most of the research on sample nanoparticles is to screen and analyze nanoparticles containing a single isotope. However, nanoparticles in the real environment usually contain multiple trace elements, which reflect the important characteristics of the sampling environment. However, due to the large number of nanoparticles contained in the sample, many isotopes, and complex composition, there is currently no method that can simultaneously represent the multiple isotopes contained in the nanoparticles. Summary of the invention

[0004] In order to represent the trace elements in nanoparticles, the present invention uses nanoparticle fingerprints based on co-occurring isotopes to extract the isotopic characteristics of nanoparticles, and designs a bitmap index-based encoding method to encode and represent nanoparticles and nanoparticle fingerprints to achieve efficient matching between the two.

[0005] The present invention provides a complete set of nanoparticle fingerprint extraction and analysis methods for the ecological field, provides a new processing idea for the analysis of environmental samples, and gives full play to the potential value of the isotope mass ratio sequence data of nanoparticles in environmental samples.

[0006] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is as follows:

[0007] Step (1), collecting multiple samples, reading the nanoparticle isotope mass ratio sequence data of each sample, setting the isotope with a content greater than zero in each nanoparticle to True, otherwise set to False, to obtain a Boolean sequence; the samples include pure samples, prepared samples, and real samples; the pure samples refer to samples composed of a single substance, and the substances corresponding to the pure samples are all substances involved in the samples of the present invention; the prepared samples refer to samples obtained by mixing different pure substances (pure samples) in a preset ratio; the real samples refer to samples collected from the natural environment and composed of multiple substances;

[0008] In the present invention, when constructing the sequence, 34 isotopes are included in the sequence, including 20 metal elements commonly found on the surface of the earth and in industrial production (Al, Ti, V, Cr, Fe, Mn, Co, Ni, Cu, Zn, Sr, Y, Mo, Ag, Cd, Sn, Sb, Ba, Tl, Pb), and 14 rare earth elements with extremely low contents (La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu);

[0009] Step (2), using the frequent item extraction algorithm Apriori to extract the co-occurring isotope combination that satisfies the minimum support and minimum confidence constraints of the Apriori algorithm from the nanoparticle isotope mass ratio sequence data of the above three types of samples. When the sample is not a pure sample, the co-occurring isotope combination is directly used as the nanoparticle fingerprint spectrum of the impure sample. When the sample is a pure sample, the subsequent step (3) is required to obtain the nanoparticle fingerprint spectrum, which is used together for the subsequent preparation of samples and real samples. Among them, support refers to the probability of multiple isotopes appearing at the same time; confidence refers to the probability that other isotopes will also appear when a certain isotope appears;

[0010] The frequent item extraction algorithm Apriori is a data mining algorithm used to filter out high-frequency data combinations from a large amount of data. When mining frequent item sets, the minimum support and minimum confidence can be set as constraints to filter out frequent item sets and association rules with sufficient support and confidence. The support and confidence need to be calculated in the Apriori algorithm, and the formula is as follows:

[0011] support(A,B)=P(A,B)=P(AB),

[0012]

[0013] Among them, A and B represent two isotopes; support(A,B) represents the probability that isotopes A and B appear in the co-occurring isotope combination at the same time; confidence(A,B) represents the probability that isotope B also appears in the co-occurring isotope combination when isotope A appears in the co-occurring isotope combination.

[0014] Minimum support refers to the minimum proportion or absolute number of frequent item sets that appear in a data set (i.e., the support of the item set is greater than or equal to the minimum support). Support refers to the frequency of an item set appearing in a data set, which can be calculated by the ratio of the number of times the item set appears in the data set to the total number of transactions. Minimum support is used to control the number of frequent item sets. Setting a smaller minimum support can generate more frequent item sets, while setting a larger minimum support can filter out fewer but more meaningful frequent item sets.

[0015] Minimum confidence refers to the ratio or probability of the condition part and the result part of the association rule appearing at the same time, that is, the confidence of the rule. The confidence can be calculated by the ratio of the number of times the association rule appears in the data set to the number of times the condition part appears in the data set. The minimum confidence is used to control the quality of the association rules. Setting a smaller minimum confidence can generate more association rules, while setting a larger minimum confidence can screen out more reliable association rules.

[0016] By setting the minimum support and minimum confidence constraints, the results of frequent item set mining can be controlled according to specific needs. It is usually necessary to perform multiple iterative calculations and gradually adjust the values ​​of the minimum support and minimum confidence to obtain frequent item sets and association rules that meet the requirements. This ensures that the mined frequent item sets and association rules have certain meaning and credibility, which helps to deeply understand the association relationship in the data set.

[0017] In the present invention, the frequent item extraction algorithm Apriori can be used to select the co-occurring isotope combination that appears multiple times from a large number of isotope mass ratio sequences of sample data as the nanoparticle fingerprint spectrum of the sample.

[0018] Step (3), for the co-occurring isotope combination obtained by the Apriori algorithm for the pure sample, the keyword weighted algorithm TF-IDF is used to obtain the co-occurring isotope combination unique to each pure sample as the nanoparticle fingerprint of the pure sample;

[0019] The TF-IDF algorithm is a weighted technology used for information retrieval, which is used to evaluate the importance of a word to a document in a document collection.

[0020] TF (Term Frequency) refers to the frequency of a word appearing in a document. It indicates the importance of the word in the document and is usually expressed by word frequency (number of occurrences). The more times a word appears in a document, the higher its TF value.

[0021] IDF (Inverse Document Frequency) refers to inverse document frequency. It is used to evaluate the importance of a word in the entire document collection. The IDF value is calculated by taking the logarithm of the ratio of the number of documents in the entire document collection to the number of documents containing the word. If a word appears commonly in the entire document collection, its IDF value will be small, indicating that the word has a low ability to distinguish different documents; if a word appears in a few documents, its IDF value will be large, indicating that the word has a strong ability to distinguish different documents.

[0022] The TF-IDF value is calculated by multiplying TF and IDF. A high TF-IDF value means that a word appears frequently in the current document, but relatively rarely in the entire document collection, and has a certain uniqueness and importance.

[0023] In text mining and information retrieval, the TF-IDF algorithm is often used to extract keywords, calculate similarity, and sort documents. It can help determine the important words in a document, thereby realizing applications such as text classification, clustering, and search.

[0024] In the present invention, the TF-IDF algorithm is used to screen the unique nanoparticle fingerprint of each pure substance. The TF-IDF algorithm includes the calculation of two indicators, namely, term frequency and inverse document frequency; the formulas of TF and IDF of TF-IDF are as follows:

[0025]

[0026]

[0027] Among them, cnt i Indicates the number of times a certain co-occurring isotope combination appears in all co-occurring isotope combinations contained in a sample, and total indicates the total number of co-occurring isotope combinations contained in the sample. D indicates the number of all pure samples, doc i Represents the number of pure samples containing a certain co-occurring isotope combination.

[0028] In the present invention, the term refers to a co-occurring isotope combination; the document refers to all co-occurring isotope combinations contained in a pure sample; the term frequency refers to the number of times a co-occurring isotope combination appears in a sample; the inverse document frequency refers to the natural logarithm of the ratio of the total number of pure samples to the number of pure samples containing a certain co-occurring isotope combination;

[0029] Step (4), the isotopes appearing in the studied sample are arranged in ascending order according to the relative atomic mass, and are represented by binary codes, with the codes starting from 1 and increasing by the power of 2 to obtain an isotope code table. In the sample nanoparticle isotope mass ratio sequence data used in the present invention, a total of 34 isotopes are included, among which the three isotopes with the smallest atomic mass are Al, Ti, and V, respectively, and the corresponding codes of these isotopes are 1, 2, and 4, respectively. The codes of other isotopes are similar, and the isotope code corresponding to Pb with the largest atomic mass is 2. 33 ;

[0030] Step (5): According to the isotope coding table obtained in step (4), the Boolean sequence of nanoparticles in step (1) is bitmap indexed to obtain the coding of the nanoparticles; the sample nanoparticle fingerprint obtained in step (2) and step (3) is bitmap indexed to obtain the coding representation of the sample nanoparticle fingerprint. For example, a nanoparticle fingerprint is [V, Fe, Ce], where the isotope code corresponding to V is 4, the isotope code corresponding to Fe is 16, and the isotope code corresponding to Ce is 2. 19 , adding these three codes together, we can get the code representation of the nanoparticle fingerprint as 524308; the code representation of the nanoparticle is the same;

[0031] Bitmap indexes use bit vectors to represent the presence or absence of data. Each bit vector corresponds to a unique attribute value, and the bits in it represent the presence or absence of the attribute value in the data set. If an attribute value exists in a row of data, the corresponding position of the corresponding bit vector is set to 1, otherwise it is set to 0.

[0032] Step (6), according to the defined matching rules, the code of the nanoparticle fingerprint is "ANDed" with the code of the nanoparticle one by one. If the result after the operation is equal to the code of the nanoparticle fingerprint, it means that the nanoparticle belongs to the nanoparticle fingerprint, and finally all the nanoparticles contained in the nanoparticle fingerprint are obtained.

[0033] For example, a nanoparticle fingerprint consists of [V, Fe, Ce], and its nanoparticle fingerprint code is 524308, and a nanoparticle fingerprint consists of [V, Fe, Co, Cu, Zn, Ba, Ce] is coded as 656212. The result of the "AND" operation of the two codes is still 524308, indicating that the nanoparticle belongs to the nanoparticle fingerprint [V, Fe, Ce].

[0034] Based on the above method, the present invention also proposes an application of a nanoparticle fingerprint extraction and detection method in the isotope detection of nanoparticles of unknown samples.

[0035] Compared with the prior art, the advantages of the present invention are:

[0036] 1. According to the characteristics of different types of samples, the present invention uses the frequent item extraction algorithm Apriori and the keyword weighting algorithm TF-IDF to design a corresponding nanoparticle fingerprint extraction method for each sample, and extracts the nanoparticle fingerprint of the sample, thereby extracting richer isotope information from the sample;

[0037] 2. The present invention encodes isotopes based on the isotope atomic mass sorting and binary coding, and on this basis, encodes the nanoparticles and the nanoparticle fingerprint based on the bitmap index, and matches the nanoparticles contained in the nanoparticle fingerprint by performing an "AND" operation on the two codes. The matching method based on isotope coding performs matching through bit operations, which greatly improves the matching efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of the method of the present invention.

[0039] Figure 2 It is the nanoparticle isotope mass ratio sequence data used in the present invention.

[0040] Figure 3 The present invention relates to a coding table of isotopes.

[0041] Figure 4 It is the process by which nanoparticles generate coded representations.

[0042] Figure 5 It is the process of matching the nanoparticle fingerprint to the corresponding nanoparticle.

[0043] Figure 6 It is the process of matching nanoparticles according to the priority of their nanoparticle fingerprints. DETAILED DESCRIPTION

[0044] The invention is further described in detail with reference to the following specific embodiments and drawings. The process, conditions, experimental methods, etc. for implementing the present invention, except for the contents specifically mentioned below, are all common knowledge and common common sense in the art and are not particularly limited by the present invention.

[0045] The present invention proposes a method for extracting and detecting nanoparticle fingerprints based on co-occurring isotope combinations, which realizes the extraction of nanoparticle fingerprints and the detection of nanoparticles for the first time in the field of nanoparticle research. A co-occurring isotope combination containing at least two isotopes is defined as a nanoparticle fingerprint; the frequent item extraction algorithm Apriori and the keyword weighting algorithm TF-IDF are used to extract the nanoparticle fingerprints of different samples respectively; the nanoparticles to be detected and the obtained nanoparticle fingerprints are encoded using a bitmap index-based encoding method, and the encoding of the nanoparticles to be detected and the nanoparticle fingerprints are ANDed, and it is determined whether the nanoparticles to be detected belong to a certain nanoparticle fingerprint based on the operation result.

[0046] The method comprises the following steps:

[0047] Step 1: Collect multiple samples and read the nanoparticle isotope mass ratio sequence data of each sample. Figure 2 As shown ( Figure 2 , which contains 6845 nanoparticles). Set the isotope with a content greater than zero in each nanoparticle to True, otherwise set it to False, and obtain a Boolean sequence;

[0048] Step 2: Use the frequent item extraction algorithm Apriori to extract the co-occurring isotope combinations that meet the minimum support and minimum confidence constraints of the algorithm from the nanoparticle isotope mass ratio sequence data of three types of environmental samples: pure samples, prepared samples, and real samples;

[0049] Step 3: For the co-occurring isotope combination obtained by the Apriori algorithm for the pure sample, the keyword weighted algorithm TF-IDF is used to obtain the co-occurring isotope combination unique to each pure sample as the nanoparticle fingerprint of each pure sample; for the prepared sample and the real sample, the nanoparticle fingerprint spectrum obtained in step 2 is its nanoparticle fingerprint;

[0050] Step 4: Arrange the isotopes appearing in all samples in ascending order according to their relative atomic mass, and represent them with binary codes, starting from 1 and increasing by powers of 2 to obtain an isotope code table, such as Figure 3 As shown;

[0051] Step 5: According to the isotope coding table obtained in step 4, the Boolean sequence representing the isotope combination of the nanoparticles in step 1 is bitmap indexed to obtain the coded representation of the nanoparticles; the sample nanoparticle fingerprint obtained in step 2 and step 3 is bitmap indexed to obtain the coded representation of the sample nanoparticle fingerprint;

[0052] Step 6: According to the defined matching rules, the codes of the nanoparticle fingerprint are ANDed with the codes of the nanoparticles one by one. If the result of the operation is equal to the code of the nanoparticle fingerprint, it means that the nanoparticle belongs to the nanoparticle fingerprint, and finally the nanoparticles contained in the nanoparticle fingerprint are obtained.

[0053] The original data used in the present invention is the isotope mass spectrum intensity sequence of nanoparticles in environmental samples, which is obtained after analyzing the samples by a single particle inductively coupled plasma time-of-flight mass spectrometry (sp-ICP-TOF-MS).

[0054] The samples involved in the present invention include pure samples, prepared samples, and real samples. Pure samples are samples composed of a single substance, such as soil; prepared samples are samples that are made by experimenters mixing different pure substances (pure samples) in a certain proportion; and real samples are samples collected from the natural environment and composed of multiple substances.

[0055] The "nanoparticle fingerprint" mentioned in the present invention is expressed as a unique isotope co-occurrence combination in the sample nanoparticles. For example, the three elements (Ti, As, Pb) in the soil sample always appear together in the nanoparticles contained in the soil sample, and this combination is not included in the element combination with a higher occurrence frequency in the other two pure samples, coal ash and tail gas, so Ti-As-Pb is regarded as a nanoparticle fingerprint of the soil.

[0056] In step (2), the frequent item extraction algorithm Apriori is used to extract the co-occurring isotope combination from the nanoparticle isotope mass ratio sequence of the sample, and the Apriori algorithm is set with constraint rules to extract the frequent items that meet the conditions. The constraint rules are mainly determined by two variables: minimum support and minimum confidence. Support represents the probability of an event occurring; confidence is a conditional probability, which represents the probability of the event occurring under certain conditions. The formulas for support and confidence are as follows, where A and B represent two independent events, and in the present invention, two isotopes are represented. For example, the Fe element appearing in the co-occurring isotope combination is recorded as event A, and the Co element appearing in the co-occurring isotope combination is recorded as event B, then support (A, B) represents the probability that the two elements Fe and Co appear in the co-occurring isotope combination at the same time; confidence (A, B) represents the probability that when the Co element appears in the co-occurring isotope combination, the Fe element also appears in the co-occurring isotope combination. In the present invention, the minimum support is set to 0.1 and the minimum confidence is set to 0.3.

[0057] support(A,B)=P(A,B)=P(AB)

[0058]

[0059] The co-occurring isotope combination obtained by the frequent item extraction algorithm Apriori in step (2) is the nanoparticle fingerprint of the prepared sample and the real sample. In the present invention, the pure sample is a sample containing only one substance, so it is necessary to deduplicate the co-occurring isotope combination obtained by Apriori for the pure sample to obtain the nanoparticle fingerprint of the pure sample. In step (3), the keyword weighted algorithm TF-IDF is used to obtain the co-occurring isotope combination unique to each pure sample as the nanoparticle fingerprint of the sample. The algorithm includes the calculation of two indicators: term frequency (TF) and inverse document frequency (IDF). The formulas for TF and IDF of word i are as follows.

[0060]

[0061]

[0062] cnt i Indicates the number of times word i appears in the document, and total indicates the total number of words in the document. D indicates the number of all documents, doc iRepresents the number of documents containing word i. In the present invention, the word refers to a co-occurring isotope combination; the document refers to all co-occurring isotope combinations contained in a sample; the word frequency refers to the frequency of a co-occurring isotope combination in a sample; the inverse document frequency refers to the natural logarithm of the ratio of the total number of pure samples to the number of pure samples containing a certain co-occurring isotope combination. The basic principle of the TF-IDF algorithm is: if a co-occurring isotope combination appears more frequently in a pure sample, that is, the tf value is larger; but at the same time, the co-occurring isotope combination appears less frequently in other pure samples, that is, the corresponding idf value is larger. The tfidf value is the multiplication of the tf and idf of the co-occurring isotope combination. The larger the tfidf value, the more unique this co-occurring isotope combination is to the pure sample. For the co-occurring isotope combination i, the calculation formula of TF-IDF is as follows. Therefore, those co-occurring isotope combinations with the highest tfidf values ​​in each pure substance are the nanoparticle fingerprints of the pure sample.

[0063] tfidf i =tf i *idf i

[0064] In the stage of encoding and representing the nanoparticle fingerprints and nanoparticles in step (5), the present invention adopts a bitmap index-based encoding method to binary encode all isotopes contained in the three types of samples under study in order of increasing atomic mass, and the encoding starts from 1 and increases according to the power of 2. After all isotopes are encoded, a coding dictionary corresponding to the isotopes is established to ensure that the codes generated for all samples in a batch of data are consistent. All nanoparticles and nanoparticle fingerprints can obtain their own coded representations based on the isotopes contained therein. The introduction of coding can not only vectorize the nanoparticles or co-occurring isotope combinations, but also reduce the time overhead in the subsequent particle matching process of the nanoparticle fingerprints. Taking nanoparticles as an example, the process of encoding them using an isotope coding dictionary is as follows. Figure 4 As shown. A nanoparticle composed of [V,Fe,Co,Cu,Zn,Ba,Ce], Figure 3 The codes corresponding to these isotopes are [4,16,64,2 8 ,2 9 ,2 17 ,2 19 ], adding up each isotope code gives the particle code 656212.

[0065] When matching the nanoparticle fingerprint with the nanoparticle in step (6), the present invention has formulated the following matching rules based on the background knowledge of the ecological field: 1) The nanoparticle fingerprint containing more isotopes can better reflect the uniqueness of the sample. Therefore, the nanoparticle fingerprint containing more isotopes is matched first; 2) For the nanoparticle fingerprint containing the same number of isotopes, the fingerprint with a large code value generated in step (5) is matched first. This is because the isotope coding designed by the present invention is assigned in ascending order according to the atomic mass, resulting in a smaller code value corresponding to the surface elements (Al, Fe, etc.). Trace elements (such as heavy metal elements, rare earth elements, etc.) that can better reflect the uniqueness of the sample have a larger atomic mass and therefore a larger corresponding code value.

[0066] The matching rule can avoid conflicts caused when a nanoparticle is matched by multiple nanoparticle fingerprints at the same time, thereby achieving more accurate matching.

[0067] In step (6), when matching the nanoparticles contained in the nanoparticle fingerprint according to the defined matching rules, the nanoparticle fingerprint of the sample and the nanoparticles contained therein have been generated using the method in step (5), so the present invention uses an "AND" operation for matching. If the result of the "AND" operation of the code of nanoparticle fingerprint i and the code of nanoparticle j is equal to the code of nanoparticle fingerprint i, it means that nanoparticle j belongs to nanoparticle fingerprint i; otherwise, it does not belong to nanoparticle fingerprint i. The matching principle is as follows: Figure 5 As shown. If the nanoparticle fingerprint i is composed of [Fe, Co, Cu, Zn, Ba], the nanoparticle fingerprint code obtained according to the method in step 5 is 131920. A nanoparticle j composed of [V, Fe, Co, Cu, Zn, Ba, Ce] 1 The code is 656212, and another nanoparticle composed of [Al, Co, Ag, Sn] 2 The code of the two nanoparticles is 41025. The codes of the two nanoparticles are ANDed with the code of the nanoparticle fingerprint i, and the results are 131920 and 0 respectively, which means that nanoparticle j 1 belongs to nanoparticle fingerprint i, while nanoparticle j 2 It does not belong to the nanoparticle fingerprint i.

[0068] In summary, the present invention proposes a complete set of nanoparticle fingerprint extraction and detection methods based on co-occurring isotope combinations for the isotope mass ratio sequence data of environmental samples, which provides a new idea for fully mining nanoparticle data and is of great significance for the monitoring and analysis of the ecological environment.

[0069] Example 1

[0070] A method for extracting and detecting nanoparticle fingerprints from pure samples based on co-occurring isotope combinations. The specific process is as follows:

[0071] Step 1: Obtain the isotopic composition of the nanoparticles. Read the isotopic mass ratio sequence of the nanoparticles of the sample to be processed, such as Figure 2 Each row represents a nanoparticle, and the isotope with a content greater than zero in each nanoparticle is set to True, otherwise it is set to False, and a Boolean sequence representing the isotopic composition of the nanoparticle is obtained.

[0072] Step 2, extraction of nanoparticle fingerprints. If the sample to be processed is a prepared sample and / or a real sample, the Apriori algorithm is used to extract the co-occurring isotope combination as the nanoparticle fingerprint of the prepared sample and / or the real sample when the minimum support is 0.1 and the minimum confidence is 0.3; if the sample to be processed is a pure sample, after extracting the co-occurring isotope combination of each pure sample under the same conditions, the TF-IDF algorithm is used to calculate the co-occurring isotope combination of all pure samples, and the co-occurring isotope combination with the highest TFIDF value in each pure sample is the nanoparticle fingerprint of the pure sample.

[0073] Step 3, matching of nanoparticles. First, the isotopes appearing in all samples are arranged in ascending order according to their relative atomic mass, and each isotope is represented by a binary code, starting from 1 and increasing by the power of 2. The obtained isotope code table is as follows: Figure 3 According to the isotope coding table, the nanoparticles and nanoparticle fingerprints obtained in the first two steps are bitmap indexed, and the codes corresponding to all isotopes in each nanoparticle or nanoparticle fingerprint are added together to obtain a unique coding representation of the nanoparticle or nanoparticle fingerprint, as shown in Figure 4 As shown. According to the defined matching rules, the codes of the nanoparticle fingerprint are “ANDed” with the codes of the nanoparticles one by one. If the result is equal to the code of the nanoparticle fingerprint, it means that the nanoparticle belongs to the nanoparticle fingerprint, such as Figure 5 As shown, the nanoparticles contained in each nanoparticle fingerprint are obtained by this method.

[0074] Example 1 shows the extraction of the nanoparticle fingerprint of the pure sample mentioned in the present invention and the matching of the nanoparticles to be detected. In this example, the code of the nanoparticle fingerprint [Fe, Co, Cu, Zn, Ba] and the code of the nanoparticle [V, Fe, Co, Cu, Zn, Ba, Ce] are successfully matched with the nanoparticle fingerprint after AND operation. There may be multiple nanoparticle fingerprints and nanoparticles in the sample, so there will be a priority order when the nanoparticle fingerprint matches the nanoparticles. Next, Example 2 is provided to show the determination of the priority order of nanoparticle fingerprint matching and an example of matching nanoparticles according to the priority order.

[0075] Example 2

[0076] Step 1: For two nanoparticles in the same sample 1 [V,Fe,Co,Cu,Zn,Ba,Ce],j 2 [Al, Co, Ag, Sn], whose codes are 656212 and 41025 respectively; and the three nanoparticle fingerprints extracted from the sample i 1 [Fe, Co, Cu, Zn, Ba], i 2 [Co,Sn],i 3 [Al,Sn], their codes are 131920, 32832, and 32769 respectively.

[0077] Step 2, determination of the priority order of nanoparticle fingerprint matching. According to the rule of nanoparticle fingerprint matching of nanoparticles proposed in the present invention, because i 1 Contains the most isotopes, so i 1 The matching priority is the highest. 2 and i 3 , the number of isotope species contained in the two is the same, and the code size of the two is compared. 2 The code is larger, so i 2 The matching priority is higher than i 3 Therefore, the final priority order of nanoparticle fingerprint matching nanoparticles is i 1 >i 2 >i 3 .

[0078] Step 3: Nanoparticle fingerprint matching of nanoparticles. According to the priority order determined in step 2, i 1 First, the nanoparticles are matched. 1 Coding and Nanoparticles 1 , j 2 After performing AND operations on the codes of 1 With j 1 The result of the encoding operation is equal to i 1 The encoding of j 1 Nanoparticle fingerprint 1 , and j 2 Not belonging to i 1 , j 1 Remove from the nanoparticles to be matched, and there are still j nanoparticles left to be matched 2 . Secondly, i 2 The code and the nanoparticle to be matched 2 The result of AND operation is equal to j 2 The encoding of j2 Nanoparticle fingerprint 2 , j 2 Remove from the nanoparticle to be matched, and the nanoparticle to be matched is empty. The last remaining nanoparticle fingerprint j 3 No matching is required.

[0079] The protection content of the present invention is not limited to the above embodiments. Without departing from the spirit and scope of the inventive concept, changes and advantages that can be thought of by those skilled in the art are included in the present invention and are protected by the attached claims.

Claims

1. A method for extracting and detecting nanoparticle fingerprints based on co-occurring isotope combinations, characterized in that: A co-occurring isotope combination containing at least two isotopes is defined as a nanoparticle fingerprint; a bitmap index-based encoding method is used to encode the nanoparticle to be detected and the obtained nanoparticle fingerprint, and an AND operation is performed on the encoding of the nanoparticle to be detected and the nanoparticle fingerprint, and whether the nanoparticle to be detected belongs to the nanoparticle fingerprint is determined according to the operation result; The number of isotopes is 34, including 20 metal elements and 14 rare earth elements; the metal elements include Al, Ti, V, Cr, Fe, Mn, Co, Ni, Cu, Zn, Sr, Y, Mo, Ag, Cd, Sn, Sb, Ba, Tl, Pb, and the rare earth elements include La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu; The method comprises the following steps: Step 1: Collect one or more samples to be tested, read the nanoparticle isotope mass ratio sequence data of each sample, set the isotope with a content greater than zero in each nanoparticle to True, otherwise set it to False, and obtain a Boolean sequence; Step 2: Obtain a Boolean sequence of the isotope content of a known sample, and use a frequent item extraction algorithm Apriori and / or a keyword weighted algorithm TF-IDF to obtain the nanoparticle fingerprint of a pure sample and / or a prepared sample and / or a real sample; Step 3: Arrange all isotopes appearing in the pure sample and / or prepared sample and / or real sample in ascending order of relative atomic mass, and represent the sorted isotopes with binary codes, with the codes starting from 1 and increasing in powers of 2 to obtain an isotope code table; Step 4: bitmap indexing is performed on the Boolean sequence of the nanoparticles to be detected in step 1 and the fingerprint of the nanoparticles in step 2 to obtain the coded representation of the nanoparticles and the fingerprint of the nanoparticles respectively; Step 5: performing AND operations on the codes of multiple nanoparticle fingerprints and the code of the nanoparticle respectively according to the matching rules to determine whether the nanoparticle belongs to a certain nanoparticle fingerprint; Among them, in the step 2, the pure sample obtains co-occurring isotopes through the frequent item extraction algorithm Apriori, and the keyword weighted algorithm TF-IDF is used to obtain the co-occurring isotope combination unique to each pure sample as the nanoparticle fingerprint of the pure sample; the prepared sample and / or the real sample extracts the co-occurring isotope combination that meets the minimum support and minimum confidence constraints of the Apriori algorithm from the nanoparticle isotope mass ratio sequence data as the nanoparticle fingerprint of the prepared sample and / or the real sample.

2. The method for extracting and detecting nanoparticle fingerprints according to claim 1, characterized in that: The Apriori algorithm needs to calculate support and confidence. The formula is as follows: support(A,B)=P(A,B)=P(AB), Among them, A and B represent two isotopes; support(A,B) represents the probability that isotopes A and B appear in the co-occurring isotope combination at the same time; confidence(A,B) represents the probability that isotope B also appears in the co-occurring isotope combination when isotope A appears in the co-occurring isotope combination.

3. The method for extracting and detecting nanoparticle fingerprints according to claim 1, characterized in that: The minimum support is set to 0.1, and the minimum confidence is set to 0.

3.

4. The method for extracting and detecting nanoparticle fingerprints according to claim 1, characterized in that: After obtaining the co-occurring isotope combination of the pure sample by Apriori, the co-occurring isotope combination unique to the pure sample is calculated by using the keyword weighted algorithm TF-IDF as the nanoparticle fingerprint of the pure sample; Among them, the formulas of TF and IDF of TF-IDF are as follows: Among them, cnt i Indicates the number of times a co-occurring isotope combination appears in all co-occurring isotope combinations contained in a sample, total indicates the total number of co-occurring isotope combinations contained in the sample, D indicates the number of all pure samples, doc i Represents the number of pure samples containing a certain co-occurring isotope combination.

5. The method for extracting and detecting nanoparticle fingerprints according to claim 1, characterized in that: In step 4, according to the Boolean sequence of the nanoparticles and whether the nanoparticle fingerprint contains one or more isotopes, the codes of the isotopes contained are obtained from the isotope coding table in step 3, and the codes of the isotopes contained are added.

6. The method for extracting and detecting nanoparticle fingerprints according to claim 1, characterized in that: In step 5, if the result of the "AND" operation between the code of the nanoparticle fingerprint i and the code of the nanoparticle j to be detected is equal to the code of the nanoparticle fingerprint i, it means that the nanoparticle j to be detected belongs to the nanoparticle fingerprint i; otherwise, the nanoparticle j to be detected does not belong to the nanoparticle fingerprint i; The matching rule refers to preferentially matching the fingerprints of nanoparticles containing more isotopes and / or preferentially matching the fingerprints of nanoparticles containing the same number of isotopes with the fingerprints having a larger fingerprint code value.

7. Application of the nanoparticle fingerprint extraction and detection method according to any one of claims 1 to 6 in the isotope detection of nanoparticles in unknown samples.

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