A nanoparticle discrimination data processing method for laser ablation plasma mass spectrometry

Abstract

The application discloses a kind of nanoparticle distinguishing data processing methods of laser ablation plasma mass spectrometry.The method includes a set of linear interpolation between original data minimum point Dynamic baseline to separate each NPs signal, further according to whether there is maximum to distinguish high concentration ion and NPs signal data processing flow, by comparing after setting condition, determine the best scheme, to realize the data processing of actual plant original signal in LA-sp-ICP-MS nanoparticle distinguishing.This method can peak area and number statistics to almost all nanoparticle information in original data, mainly for the existence NPs local concentration position or high concentration ion and NPs exist simultaneously Actual biological sample, future can provide more comprehensive, more in-depth results for the NPs absorption, transport process and ion and NPs conversion information in animal and plant, cell and other biological samples.

Description

Technical Field

[0001] This invention relates to the field of inorganic mass spectrometry data processing, specifically to a data processing method and electronic device for distinguishing nanoparticles using laser ablation inductively coupled plasma mass spectrometry, and its application in nanoparticle analysis in plant tissues. Background Technology

[0002] Plants are constantly exposed to various metal ions and nanoparticles (NPs) during their growth. The rich interactions between NPs and plant diversity lead to different plant responses to these NPs, and some metal ions may also be bioreduced in plants, accumulating into NPs. However, the extent, causes, and mechanisms of these processes remain unclear. Understanding these responses is crucial for plant physiology, molecular biology and genetics, biochemistry, and materials chemistry. Although we can obtain partial information on the absorption, transport, or biosynthesis of some NPs in plants through techniques such as electron microscopy, X-ray fluorescence imaging, and fluorescence imaging, quantitative information on NPs in plants (concentration, particle size, distribution), NP particle size and location within plants, and their impact on plants remain unresolved issues. This is the foundation for the rational and sustainable utilization of NPs and the construction of a harmonious environment between organisms and NPs. (Su, Y.; Ashworth, V.; Kim, C.; Adeleye, AS, et al. Delivery, uptake, fate, and transport of engineered nanoparticles in plants: a critical review and data analysis [J]. Environmental Science-Nano, 2022.).

[0003] Single-particle inductively coupled plasma mass spectrometry (sp-ICP-MS), derived from ICP-MS, is used to characterize metallic nanoparticles (NPs) in solution. By introducing a highly diluted NP suspension, individual discrete NPs are introduced, allowing for the detection of individual NP signals. The signal intensity corresponds to the size of the ion cloud generated by the NP, and the signal is proportional to the mass of the element present in the target analyte. The number of detected events is proportional to the NP concentration in the suspension. In analyzing actual plants, methods such as acid digestion can lose information about NPs within plant tissues. Therefore, a method based on a ionizing enzyme containing cellulose, hemicellulose, and pectinase was developed to digest plant tissues and extract AuNPs. Furthermore, a method for measuring AuNP size and particle concentration was developed to understand the AuNP uptake behavior in tomato plants. (Dan, Y.; Zhang, W.; Xue, R., et al. Characterization of Gold Nanoparticle Uptake by TomatoPlants Using Enzymatic Extraction Followed by Single-Particle InductivelyCoupled Plasma-Mass Spectrometry Analysis. [J]. Environmental Science & Technology, 2015.).

[0004] Although sp-ICP-MS can accurately quantify the size and number concentration of nanoparticles (NPs), conventional nebulization systems have low sample introduction efficiency, and complex pretreatment processes can lead to the loss of NP location information in plants. To simultaneously obtain NP location, particle size, and concentration information, combining sp-ICP-MS with laser ablation technology can solve these problems. In laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), ions and NPs of the same element are analyzed simultaneously. Based on their different signal characteristics in the plasma and detector, they are separated in post-processing (Metarapi, D, Sala, M, Vogel-Mikus, K, et al. Nanoparticle Analysis in Biomaterials Using Laser Ablation-Single Particle-Inductively Coupled Plasma Mass Spectrometry[J]. Analytical Chemistry, 2019).

[0005] However, the currently developed laser ablation combined with single-particle inductively coupled plasma mass spectrometry (LA-sp-ICP-MS) method is mainly aimed at analyzing discrete NPs in samples, and data processing is primarily based on the baseline and threshold calculation methods of sp-ICP-MS. The NP environment in real samples is relatively complex, often exhibiting NP aggregation at certain locations or the coexistence of discrete NPs with high concentrations of ions. Since the spatial resolution of LA-sp-ICP-MS is only at the micrometer level, signal processing of complex NPs in real samples is an unavoidable problem for the practical application of LA-sp-ICP-MS. Unlike the mixing of NP and ion signals or excessively high NP concentrations in solution samples, these situations in LA-sp-ICP-MS are unpredictable before analysis and cannot be resolved by dilution, unlike in solution-based sp-ICP-MS. In many cases, the raw data from LA-sp-ICP-MS cannot distinguish the signals of NPs and ions using a fixed ion baseline, especially in local locations where the concentration of NPs is too high or the concentration of Au ions is high. The ion baseline will be lower than the local signal minimum point, and multiple NPs will be treated as one NP, which will significantly affect the number of NPs and the particle size results obtained.

[0006] To address this issue, this invention proposes a data processing procedure that constructs a dynamic baseline composed of linear interpolation between the minimum points of the original data to separate the signal of each NP, and further distinguishes between high-concentration ion and NP signals based on the presence or absence of maxima. This procedure is then programmed into a computer program to ultimately achieve data processing for distinguishing nanoparticles in the original LA-sp-ICP-MS signal. Summary of the Invention

[0007] To address the aforementioned technical problems, the present invention aims to construct a dynamic baseline composed of linear interpolation between the minimum points of the original data to separate the signal of each NP, and further distinguish the high-concentration ion and NP signals based on the existence of maxima. The optimal scheme is determined by comparing the set conditions to achieve data processing for distinguishing nanoparticles in the actual plant original signals of LA-sp-ICP-MS.

[0008] The objective of this invention is achieved through the following technical solution:

[0009] In a first aspect, the present invention provides a data processing method for distinguishing nanoparticles by laser ablation inductively coupled plasma mass spectrometry, comprising the following steps:

[0010] (1) Raw signal intensity-time data of inductively coupled plasma mass spectrometry of plant tissue were obtained by combining laser ablation system with inductively coupled plasma mass spectrometry.

[0011] (2) By finding local minima in the original signal strength-time data, the minima are connected by a straight line to construct a dynamic baseline composed of linear interpolation;

[0012] (3) Subtract the dynamic baseline from the original data to obtain data A. Iteratively calculate the ion baseline by using the multiple standard deviation of data A.

[0013] (4) Subtract the ion baseline value from the data A, determine whether there are positive points in the continuous width range of the difference, and determine whether there are maximum values ​​for each positive point in the continuous width range.

[0014] (5) Take the maximum point that meets the typical characteristics of the NPs signal in step (4) as the NPs signal, and integrate the signal value in the original data in step (1) according to the corresponding time to calculate the total signal strength of NPs.

[0015] Furthermore, in step (1), the method for obtaining the original data through the laser ablation system is as follows:

[0016] 1.1 The root, stem, and leaf organs of actual plant samples containing NPs were isolated, and the samples were processed according to the objects to be analyzed;

[0017] 1.2 Place a glass slide containing a plant sample in the ablation cell of the laser ablation system, and control the parameters of the laser ablation system and inductively coupled plasma mass spectrometry to ablate the plant.

[0018] 1.3 Record the raw data of "signal intensity-time" in count for inductively coupled plasma mass spectrometry obtained after ablation.

[0019] Furthermore, in step 1.1, the method for processing the sample includes fixing the stems and leaves of the plant with paraformaldehyde, embedding and slicing them in paraffin, and then air-drying the sample before pasting it onto a glass slide.

[0020] Furthermore, in step (2), the method for finding the minimum point is: by judging whether each data point is less than the signal value of the previous or next four points, it is determined whether the data point belongs to the minimum point.

[0021] Furthermore, in step (2), the linear interpolation method is as follows: the found minimum points are connected by line segments between every two points to form a dynamic baseline composed of linear interpolation.

[0022] Furthermore, in step (3), the method for iteratively calculating the ion baseline using multiple standard deviations is as follows: after subtracting the dynamic baseline from the original data, calculate the average value and multiple standard deviations of all signal intensity points. Extract all data in the previous step that are within the range of the average value plus or minus the multiple standard deviations. Repeat the above steps until two sets of selected data within the range of the average value plus or minus the multiple standard deviations are completely identical. Use the average value calculated at this time as the calculated ion baseline.

[0023] Furthermore, in step (3), the standard deviation of the multiple is 5 times the standard deviation, and the width range is 0.4-1.2ms.

[0024] Furthermore, in step (5), the maximum point requirement that meets the typical characteristics of the NPs signal is: after subtracting the ion baseline obtained by iteration from the original data, the data A corresponding to the original data has a continuous positive value with a width greater than or equal to 0.4ms and less than or equal to 1.2ms. At the same time, there is only one maximum value among these continuous positive value points (that is, the data of the maximum value point is higher than the data value of any other point in the positive value interval). When the above requirements are met, the signal is regarded as an NPs signal.

[0025] Secondly, the present invention provides the application of the data processing method described in the first aspect in analyzing the uptake, accumulation and transformation of gold nanoparticles in plants.

[0026] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the program, it implements the data processing method for distinguishing nanoparticles by laser ablation inductively coupled plasma mass spectrometry as described in the first aspect.

[0027] Fourthly, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the data processing method for distinguishing nanoparticles by laser ablation inductively coupled plasma mass spectrometry as described in the first aspect.

[0028] This invention takes into account the presence of localized concentrations of nanoparticles (NPs) in actual plant samples, which may coexist with high concentrations of metal ions. Based on the characteristics of the original data, a dynamic baseline consisting of linear interpolation between the minimum points of the original data is constructed to separate the signal of each NP. Furthermore, a data processing flow is used to distinguish between high-concentration ions and NP signals based on the presence or absence of maxima, ultimately achieving data processing for distinguishing nanoparticles in the original LA-sp-ICP-MS signal.

[0029] By applying this method to analyze the uptake and accumulation of gold nanoparticles (AuNPs) in plants and the process of AuNP synthesis from ions in plants and within the body, this method can also be extended to provide more comprehensive and in-depth information on NP absorption, transport processes, and ion-NP conversion in biological samples such as animals, plants, and cells, thereby obtaining broader and more practical insights in the fields of life sciences, environmental sciences, and chemistry.

[0030] The present invention has the following advantages over the prior art:

[0031] (1) This invention can perform peak area and number statistics on almost all nanoparticle information in the raw data of LA-sp-ICP-MS, mainly for actual samples with local concentration of NPs or high concentration of metal ions.

[0032] (2) The algorithm proposed in this invention can directly analyze the transport and accumulation of NPs in biological tissues, as well as the process of metal ions being reduced and aggregated into NPs. Compared with other methods, the processing flow is simple and easy to understand, and the operating environment requirements are low. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 Schematic diagram of nanoparticle signal extraction by laser ablation inductively coupled plasma mass spectrometry: (a) Raw data acquired by the mass spectrometer detector and the directly iterated ion baseline; (b) Dynamic baseline obtained by linear interpolation using the minimum value; (c) The result after subtracting the dynamic baseline from the raw data and the iterated new ion baseline; (d) Comparison of the raw data, dynamic baseline and new data with the local magnified data in (a) box; (e) Nanoparticle judgment process after iteration of the new data.

[0035] Figure 2 Statistical graphs of NPs information obtained by applying different data processing conditions to different erosion layers when high concentrations of AuNPs exist in plant roots; where (a) and (b) are the comparison results of NP count and median value under different NPs signal width extraction ranges, respectively, using the same caption; (c) and (d) are the comparison results of NP count and median value obtained under different maximum and minimum value judgments, synchronous data extraction and integration object conditions, respectively, using the same caption;

[0036] Figure 3Statistical graphs of NP information obtained by applying different data processing conditions to different erosion layers of plant leaf veins when high concentrations of ions and NPs coexist; where (a) and (b) are the comparison results of NP number and median value under different NP signal width extraction ranges, respectively, using the same caption; (c) and (d) are the comparison results of NP number and median value obtained under different maximum and minimum value judgments, synchronous data extraction and integration object conditions, respectively, using the same caption;

[0037] Figure 4 Comparison of NP information obtained by algorithm and NP information obtained by direct iteration in different denudation layers when high concentrations of AuNPs and high concentrations of ions and NPs coexist in plants; where (a) and (b) are the comparison results of NP number and average value when high concentrations of AuNPs are present, respectively, and (c) and (d) are the comparison results of NP number and average value when high concentrations of ions and NPs coexist. Detailed Implementation

[0038] The present invention will be described in detail below with reference to specific embodiments and examples, thereby making the advantages and various effects of the present invention more clearly apparent. Those skilled in the art should understand that these specific embodiments and examples are for illustrative purposes only and are not intended to limit the present invention.

[0039] Throughout this specification, unless otherwise specified, the terminology used herein should be understood as having the meaning commonly used in the art. Therefore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In the event of any conflict, this specification shall prevail.

[0040] Unless otherwise specified, all raw materials, reagents, instruments and equipment used in this invention can be obtained by purchasing them from the market or by existing methods.

[0041] This invention constructs a dynamic baseline composed of linear interpolation between the minimum points of the original data to separate the signal of each NP, and further distinguishes the high-concentration ion and NP signals based on the presence or absence of maxima. This data processing flow is written into a computer program to ultimately achieve the data processing of distinguishing nanoparticles in the original signal of LA-sp-ICP-MS.

[0042] The present invention will be further described below with reference to the accompanying drawings and embodiments. However, these embodiments are only for illustrating the present invention and do not limit the scope of the present invention.

[0043] Example 1: Data Processing Flow for Nanoparticle Differentiation by Laser Ablation Inductively Coupled Plasma Mass Spectrometry I. Processing Method and Steps are as follows:

[0044] 1. Obtain data from actual plant samples using laser ablation inductively coupled plasma mass spectrometry (ICP-MS).

[0045] (1) Arabidopsis thaliana plants were cultured for 14 days using AuNPs modified with 50 ppm sodium citrate at 50 nm and KAuCl4 at 100 ppm, respectively. The Arabidopsis thaliana plants were cultured for 7 days at 20 °C, 60% humidity, and a 18 / 6 light / dark cycle. The culture apparatus and culture medium were referenced in the literature (Conn, SJ; Hocking, B.; Dayod, M., et al. Protocol: optimizing hydroponic growth systems for nutritional and physiological analysis of Arabidopsis thaliana and other plants. [J]. Plant Methods, 2013.).

[0046] (2) The roots and leaves of Arabidopsis plants cultured with AuNPs and KAuCl4 were separated by scalpel, and the two were sandwiched in filter paper and air-dried at room temperature. After 7 days, they were attached to a glass slide with double-sided tape.

[0047] (3) Place a glass slide containing the above-mentioned plant sample in the ablation pool of the laser ablation system and ablate the plant under the conditions listed in Tables 1 and 2 for the laser ablation system and inductively coupled plasma mass spectrometry.

[0048] (4) Record the raw data of "signal intensity-time" of the inductively coupled plasma mass spectrometry after ablation in count units;

[0049] Table 1. Operating parameters for inductively coupled plasma mass spectrometry

[0050]

[0051] Table 2 Operating parameters of the laser ablation system

[0052]

[0053] 2. By finding local minima in the original signal strength-time data, the minima are connected by straight lines to construct a dynamic baseline composed of linear interpolation;

[0054] As a specific implementation, the method for finding the minimum point is as follows: determine whether the data point belongs to the minimum point by judging whether each data point is less than the signal value of the previous or next four points.

[0055] As a specific implementation, the linear interpolation method is as follows: the found minimum points are connected by line segments between every two points to form a dynamic baseline composed of linear interpolation.

[0056] 3. Subtract the dynamic baseline from the original data to obtain data A. Iterate through data A using multiples of standard deviation to calculate the ion baseline.

[0057] As a specific implementation, the method for iteratively calculating the ion baseline using multiple standard deviations is as follows: After subtracting the dynamic baseline from the original data, calculate the average value and multiple standard deviations of all signal intensity points. Extract all data points in the previous step that fall within the range of the average value plus or minus the multiple standard deviations. Repeat the above steps until two sets of selected data points that fall within the range of the average value plus or minus the multiple standard deviations are exactly the same. Use the average value calculated at this point as the calculated ion baseline.

[0058] As an improved embodiment, the multiple standard deviation is 5 times the standard deviation, and the width range is 0.4-1.2 ms.

[0059] 4. Subtract the ion baseline value from data A, determine whether there are positive points within a continuous width range of the difference, and determine whether there are maxima for each positive point within a continuous width range.

[0060] 5. Treat the maximum point in step 4 as the NPs signal, and integrate the signal value in the original data in step 1 according to the corresponding time to calculate the total signal strength of NPs.

[0061] II. A brief explanation of the theoretical basis of the data processing method for distinguishing nanoparticles.

[0062] Observations revealed a significant increase in the ion baseline caused by localized high concentrations of NPs or ions in the raw data. A typical raw signal was characterized by... Figure 1 For example, in case a, the ion baseline can only identify two distinct NPs signals. Figure 1 Detailed observation and analysis under magnification suggest that the main reason for the local signal minimum being higher than the ion baseline is that the ion cloud signal generated by the previous NP or high-concentration ions has not completely ended in the detector before the next ion cloud signal enters the detector. Despite the signal overlap, the outline of the NP signal at a specific width can still be observed in the original data. Therefore, it is considered to separate the NP signal from the original data and construct a dynamic baseline by simulating the overlapping part of the signals, such as... Figure 1 As shown in b.

[0063] The dynamic baseline, composed of lines connecting minimum points, corresponds to the overlapping signal portion in the original data. Subtracting the dynamic baseline from the original data yields data A, which is then used to correct the signals to the same level, thus initially filtering out low-concentration ion signals. The results are as follows: Figure 1 As shown in c, the newly iterated ion baseline can completely separate NP signals. Further, from the perspective of NP signal duration, the width of consecutive positive points in the data minus the iterated baseline (data A minus the ion baseline value) is determined, along with the presence of a maximum value at a correlated peak within the consecutive positive signal, to determine whether the consecutive positive signal belongs to NPs. This avoids false positives caused by high-concentration ion signals masquerading as NPs. The results of the discrimination are as follows: Figure 1 As shown in d and e (the hollow peak portion is not considered as NPs).

[0064] For example, with Figure 1 (e) For example, the hollow peak shown in the figure has a total continuous positive width between 0.4 and 1.2 ms, but it has two maxima within the continuous positive values. This does not meet the typical characteristics of an NPs signal, so it is treated as a fluctuation of a high-concentration ion signal and is not included in the statistics and calculation of NPs peaks. Other solid peaks, however, meet the requirements of this differentiation method in terms of continuous positive width and the number of maxima, and are therefore considered as NPs signals for further calculation.

[0065] The maximum point requirement that conforms to the typical characteristics of an NPs signal is as follows: after subtracting the ion baseline from the original data, the data A corresponding to the original data has a continuous positive value with a width greater than or equal to 0.4 ms and less than or equal to 1.2 ms. At the same time, there is only one maximum value among these continuous positive value points (i.e., the data of the maximum value point is higher than the data value of any other point in the positive value interval). When the above requirements are met, the signal is regarded as an NPs signal.

[0066] The new data corresponding to the time points of the identified NPs signals are further integrated to obtain the peak area information of the NPs. Although this processing method will reduce the integrated area of ​​the obtained NPs, the reduced part belongs to the beginning and end of the NPs signal, and its corresponding area is negligible compared with the main body of the peak.

[0067] Example 2 optimizes the conditions of the processing algorithm for separating NPs signals.

[0068] To obtain more accurate results, some parameters and discrimination conditions used in the processing flow were compared and optimized. The optimization primarily focused on actual plant sample data containing locally high concentrations of AuNPs signals and mixtures of high concentrations of Au ions and AuNPs. During ablation, the roots and leaf veins of the plants were ablated layer by layer from the surface downwards. The main conditions set included: the width requirement for continuous positive values, whether a maximum value existed within the width of the NPs signal, and the object of integration.

[0069] The main problem with high-concentration NPs models is the overlap of NPs signals. The number and area of ​​NPs obtained under different conditions are as follows: Figure 2 As shown, different NP recognition widths significantly affect the NP count concentration and integral area results. Figure 2 As shown in (a)-(b), widths of 0.4-1.2ms and 0.3-1.2ms can identify a larger number of NPs, and the separation effect of overlapping NP peaks is good. However, as the width increases, the number of identified NPs gradually increases. A wider identification width should be selected to avoid the loss of NP data. The effects of changing other conditions are as follows... Figure 2 As shown in (c) and (d), whether the extracted raw data contains blank data due to sample stage movement and whether there is a discrimination condition for NPs maxima have almost no significant impact on the number of NPs and the integration area. This is mainly because the NPs signal of larger particle sizes is not affected by small changes in the ion baseline division. However, changing the data integration object, i.e., integrating the data after subtracting the dynamic baseline and the iterative baseline, will lead to a lower NPs signal intensity. The strict 5-point minimum discrimination condition requires that the 5 points near the minimum point meet the strict requirement of first decreasing and then increasing, which will cause some NPs signals with imperfect peak shapes to be missed. Under this model, this embodiment initially determined the discrimination width and some discrimination conditions for NPs, excluding the discrimination widths of 0.4-0.6ms and 0.4-0.9ms, and rejecting the integration of new data and the strict 5-point minimum discrimination condition.

[0070] Models corresponding to NPs coexisting with high-concentration ions are mainly affected by significant changes in the ion baseline and the situation where high-concentration ion signals impersonate NPs. Figure 3This is a comparison result under the corresponding conditions. For sp-ICP-MS, the average signal duration of 15nm AuNPs is approximately 322μs (Fuchs, J.; Aghaei, M.; Schachel, TD, et al. Impact of the Particle Diameter on Ion Cloud Formation from Gold Nanoparticles in ICPMS[J]. Analytical Chemistry, 2018.). To avoid signal loss from small-diameter NPs, this embodiment selected 0.3 or 0.4ms as the lower limit for the width discrimination. From Figure 3 Results (a) and (b) show that although the 0.3-1.2ms requirement yields more NPs, the average signal strength is significantly lower compared to the 0.4-1.2ms requirement, potentially indicating that a considerable number of ion signals may be misrepresenting NP signals. For the maxima determination, at least three data points are required; using only three data points to determine peaks can easily lead to disordered fluctuations in ion signals being misrepresented as NP signals. Figure 3 As shown in (c) and (d).

[0071] Example 3 compares the results of the algorithm for separating NPs signals from data obtained from actual plants with those obtained using a method with a fixed ion baseline.

[0072] The number of NPs obtained in Example 2 was compared with the integral information and the results obtained by extracting NPs from a fixed ion baseline using an iterative method with the mean ± 5 times the standard deviation. The results are as follows: Figure 4 As shown in (a) and (b), after incubating plants with 50 nm NPs, the number of NPs obtained by the algorithm is greater than that of the fixed baseline method for different erosion layers of the root. The average NPs integral area remains stable between layers, which is more consistent with the actual transport and accumulation of NPs in plants. It obtains accurate and complete NPs information to a great extent, without the influence of signal overlap on the results.

[0073] After incubating plants with 100 ppm KAuCl4, the different erosion layers of leaf veins were observed. Figure 4The results in (c) and (d) show that the signal integral distribution of AuNPs synthesized in plant leaf veins using 100 ppm KAuCl4 conforms to a log-normal distribution. Compared with the results obtained by extracting NPs from a fixed ion baseline using an iterative method with a mean ± 5 standard deviation, the number of NPs obtained using the algorithm is greater than that from the fixed baseline, and the average particle integral area remains stable across layers. This confirms that AuNPs do not undergo particle size changes after transport in plants, and supports the hypothesis that metallic NPs are synthesized in living plants.

[0074] Finally, it should be noted that the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0075] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0076] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0077] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the invention.

Claims

1. A method for processing nanoparticle differentiation data using laser ablation plasma mass spectrometry, characterized in that, Includes the following steps: (1) Raw signal intensity-time data of inductively coupled plasma mass spectrometry of plant tissue were obtained by combining laser ablation system with inductively coupled plasma mass spectrometry. (2) By finding local minima in the original signal strength-time data, the minima are connected by a straight line to construct a dynamic baseline composed of linear interpolation; (3) Subtract the dynamic baseline from the original data to obtain data A. Iteratively calculate the ion baseline by using the multiple standard deviation of data A. (4) Subtract the ion baseline value from the data A, determine whether there are positive points in the continuous width range of the difference, and determine the maximum value of the positive point data in each continuous width range. (5) Take the maximum point that meets the typical characteristics of the NPs signal in step (4) as the NPs signal, and integrate the signal value in the original data in step (1) according to the corresponding time to calculate the total signal strength of NPs.

2. The data processing method according to claim 1, characterized in that: In step (1), the method for obtaining the original data through the laser ablation system is as follows: 1.1 The root, stem, and leaf organs of actual plant samples containing NPs were isolated, and the samples were processed according to the objects to be analyzed; 1.2 Place a glass slide containing a plant sample in the ablation cell of the laser ablation system, and control the parameters of the laser ablation system and inductively coupled plasma mass spectrometry to ablate the plant. 1.3 Record the raw "signal intensity-time" data in count for the inductively coupled plasma mass spectrometry obtained after ablation.

3. The data processing method according to claim 2, characterized in that: In step 1.1, the method for processing the sample includes fixing the stems and leaves of the plant with paraformaldehyde, embedding and slicing them in paraffin, and then air-drying the sample and attaching it to a glass slide.

4. The data processing method according to claim 1, characterized in that: In step (2), the method for finding the minimum point is to determine whether the data point belongs to the minimum point by judging whether each data point is less than the signal value of the previous or next four points.

5. The data processing method according to claim 4, characterized in that: In step (2), the linear interpolation method is to connect the minimum points with line segments between every two points to form a dynamic baseline composed of linear interpolation.

6. The data processing method according to claim 1, characterized in that: In step (3), the method for iteratively calculating the ion baseline using multiple standard deviations is as follows: after subtracting the dynamic baseline from the original data, calculate the average value and multiple standard deviations of all signal intensity points; then extract all data within the range of the average value plus or minus the multiple standard deviations from the aforementioned data, repeat the above steps until two sets of selected data within the range of the average value plus or minus the multiple standard deviations are completely identical, and use the average value calculated at this time as the calculated ion baseline.

7. The data processing method according to claim 6, characterized in that: In step (3), the standard deviation of the multiple is 5 times the standard deviation; the width range is 0.4-1.2ms.

8. The application of the data processing method as described in any one of claims 1-7 in analyzing the uptake, accumulation and transformation of gold nanoparticles in plants.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the data processing method for distinguishing nanoparticles by laser ablation inductively coupled plasma mass spectrometry as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the data processing method for distinguishing nanoparticles by laser ablation inductively coupled plasma mass spectrometry as described in any one of claims 1 to 7.

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