Determination of nanoparticle detection thresholds by local minimum analysis

Abstract

Systems and methods are described for analyzing local minimum data from spectroscopic measurement data to determine a nanoparticle detection threshold. In embodiments, a histogram of the spectroscopic measurement data is used to search for candidate local minimum values, which are then verified to establish a nanoparticle detection threshold for the spectroscopic measurement data, and ion intensity values ​​below the nanoparticle detection threshold are due to signal background.

Description

[Technical field]

[0001] (Related Applications) This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Application No. 63 / 335,510, filed on April 27, 2022, and entitled "NANOPARTICLE BASELINE AND PARTICLE DETECTION THRESHOLD DETERMINATION THROUGH ITERATIVE OUTLIER REMOVAL," U.S. Provisional Application No. 63 / 335,516, filed on April 27, 2022, and entitled "NANOPARTICLE DETECTION THRESHOLD DETERMINATION THROUGH LOCAL MINIMUM ANALYSIS," and U.S. Provisional Application No. 63 / 335,523, filed on April 27, 2022, and entitled "MULTI DATA PROCESS SWITCHING FOR NANOPARTICLE BASELINE AND DETECTION THRESHOLD DETERMINATION." The entireties of U.S. Provisional Application Nos. 63 / 335,510, 63 / 335,516, and 63 / 335,523 are incorporated herein by reference. [Background technology]

[0002] Inductively Coupled Plasma (ICP) mass spectrometry is a commonly used analytical technique for measuring trace element concentrations and isotope ratios in liquid samples. ICP mass spectrometry uses an electromagnetically generated, partially ionized argon plasma, which reaches temperatures of approximately 7000 K. When a sample is introduced into the plasma, the high temperature causes the sample atoms to ionize or emit light. Each chemical element produces a characteristic mass or emission spectrum, and by measuring this spectrum the elemental composition of the original sample can be determined.

[0003] A sample introduction system may be employed to introduce a liquid sample into an ICP mass spectrometer (such as an inductively coupled plasma mass spectrometer (ICP / ICPMS), an inductively coupled plasma atomic emission spectrometry (ICP-AES)) for analysis. For example, the sample introduction system may remove an aliquot of the liquid sample from a container and then transport the aliquot to a nebulizer to convert it into a polydisperse aerosol suitable for ionization in a plasma by the ICP mass spectrometer. The aerosol is then sorted in a spray chamber to remove larger aerosol particles. The aerosol exiting the spray chamber is introduced into the ICPMS or ICPAES instrument for analysis. Sample introduction is often automated to allow for efficient introduction of large numbers of samples into the ICP mass spectrometer. Summary of the Invention

[0004] Systems and methods are described that analyze spectroscopic measurement data to determine nanoparticle factors including one or more of a nanoparticle baseline and a nanoparticle detection threshold. In one aspect, a method embodiment includes conveying a fluid sample containing nanoparticles to a spectroscopic sample analyzer, generating, via the spectroscopic sample analyzer, a spectroscopic measurement data set related to ion signal intensities detected over time, generating, via one or more computer processors, a raw data set from the spectroscopic measurement data set including a count distribution of ion signal intensity counts and a frequency of ion signal intensity for each count, iteratively removing, via the one or more computer processors, ion signal intensity values ​​that exceed an outlier threshold associated with a sum of a first multiple of a mean of the ion signal intensity count distribution and a first multiple of a standard deviation of the ion signal intensity count distribution to provide a background data set, and setting, via the one or more computer processors, a nanoparticle baseline intensity value as a sum of a second multiple of a mean of the background data set and a second multiple of a standard deviation of the background data set, where the first multiple of the standard deviation of the ion signal intensity count distribution and the second multiple of a standard deviation of the background data set are different.

[0005] In one aspect, a method embodiment includes, but is not limited to, conveying a fluid sample containing nanoparticles to a spectroscopic sample analysis device; generating, via the spectroscopic sample analysis device, a spectroscopic measurement dataset related to detected ion signal intensities over time; forming, via one or more computer processors, a histogram of the spectroscopic measurement dataset, the histogram related to the count frequency of integrated ion signal intensity values; incrementing, via the one or more computer processors, a window along the histogram spanning a plurality of counts of the histogram to determine a candidate minimum frequency value within the window; verifying, via the one or more computer processors, whether the candidate minimum frequency value is a minimum value of the histogram and providing a verified minimum value; and assigning, via the one or more computer processors, the verified minimum value as a detection threshold for nanoparticles in the spectroscopic measurement dataset.

[0006] In one aspect, a method embodiment includes, but is not limited to, delivering a fluid sample containing nanoparticles to a spectroscopic sample analysis device; generating, via the spectroscopic sample analysis device, a spectroscopic measurement data set related to detected ion signal intensities over time; analyzing, via one or more computer processors, the spectroscopic measurement data set with a first data process and determining, in the first data process, at least one of a first nanoparticle baseline or a first nanoparticle detection threshold; automatically switching to a second data process and analyzing, via one or more computer processors, the spectroscopic measurement data set and determining, in the second data process, at least one of a second nanoparticle baseline or a second nanoparticle detection threshold; and determining, via the one or more computer processors, whether results from the first data process converge or diverge from results from the second data process.

[0007] This summary is provided to introduce in a simplified form some of the concepts described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0008] The detailed description will now be made with reference to the accompanying drawings. [Brief description of the drawings]

[0009] [Figure 1A] 1 is a schematic diagram of a system for analyzing nanoparticles according to an embodiment of the present disclosure. [Figure 1B] 1B is a partial schematic diagram of the system of FIG. 1A according to an embodiment of the present disclosure; [Diagram 2] FIG. 1 is a schematic diagram of a spectroscopic measurement data set shown with a normal distribution curve according to an embodiment of the present disclosure; [Diagram 3] FIG. 1 is a flow diagram of a process for iterative determination of outlier data from a spectroscopic measurement dataset to determine a particle baseline and detection threshold for nanoparticles, according to an embodiment of the present disclosure. [Figure 4] A flow diagram of the process in Figure 3 showing an example of iterative steps [Diagram 5] Schematic of an example data set for the iterative steps of the process in Figure 3 [Figure 6] 4 is a schematic diagram of a first iteration of the process of FIG. 3 to remove a first portion of outlier data. [Figure 7] Schematic of the second iteration of the process in Figure 3 to determine whether any outlier data remain. [Figure 8] Schematic of particle baseline determination for the process in Figure 3 after outlier removal [Figure 9A] FIG. 1 illustrates an example of a dataset analyzed according to an embodiment of the present disclosure. [Figure 9B] The determined particle baseline is shown above the spectroscopic data, a close-up of the data in FIG. 9A. [Figure 10]FIG. 1 is a schematic diagram of a spectroscopic data set showing an approximated background determination for nanoparticle determination according to an embodiment of the present disclosure; [Figure 11] FIG. 1 is a flow diagram of a process for minimum determination and validation for determining a detection threshold for nanoparticles from a spectroscopic measurement dataset, according to an embodiment of the present disclosure. [Figure 12A] Schematic of an example window used to determine candidate minimum data points from a histogram of spectroscopic data. [Figure 12B] Schematic of an example window used to determine candidate minimum data points from a histogram of spectroscopic data. [Figure 13] FIG. 1 is a schematic diagram of determining whether a data point from a histogram of spectroscopic measurement data is a candidate data point for a local minimum for an exemplary window size; [Figure 14] FIG. 1 is a schematic diagram of verifying that a candidate minimum data point from a histogram of spectroscopic measurement data is a candidate minimum data point for an exemplary window size. [Figure 15] FIG. 1 is an example of a data set analyzed according to an embodiment of the present disclosure, showing the detection threshold of nanoparticles. [Figure 16] 1 is a schematic diagram of a switching method for multi-data processing according to an embodiment of the present disclosure; [Figure 17] 17 is a schematic diagram of the method of FIG. 16 with multiple data processing having converging data results. [Figure 18] 17 is a schematic diagram of the method of FIG. 16 with multiple data processing having divergent data results. [Figure 19] 17 is a schematic diagram of the method of FIG. 16 with multiple data processing having convergent data results and multiple data processing having divergent data results. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Nanoparticle research has grown to encompass applications ranging from the medical industry to the environmental industry. Such applications can focus on the ability to detect nanoparticles (e.g., particles less than 1000 nm in diameter) and calculate the size of the nanoparticles present in a sample. However, determining what is and what is not a nanoparticle when analyzing spectroscopic data can pose many challenges. For example, spectroscopic data, such as ICPMS data, contains information related to the ionized sample and background interferences, such as those resulting from the plasma gas introduced to the ICP torch, and can overlap with data related to small nanoparticles. For example, as nanoparticles become smaller in size, spectroscopic data for nanoparticles begins to converge with data related to the ion species generated by the ICP torch. This overlap and the challenges associated with removing background interferences while avoiding the removal of nanoparticle data lead to ongoing problems in providing reliable data related to nanoparticles, including but not limited to nanoparticle identification, determining the number of nanoparticles and associated size distribution.

[0011] Thus, in one embodiment, the present disclosure is directed to a system and method for analyzing local minimum data from spectroscopic measurement data to determine a nanoparticle detection threshold.A histogram of the spectroscopic measurement data is used to search for candidate values ​​of local minimums, and then the local minimums are verified to establish a nanoparticle detection threshold of the spectroscopic measurement data, where ion intensity values ​​less than the nanoparticle detection threshold are due to signal background (e.g., due to background interference, such as ionized plasma gas from an ICP torch), and values ​​greater than the nanoparticle detection threshold are due to the presence of nanoparticles in the sample.In an embodiment, the histogram is formed by subtracting background intensity values ​​from the spectroscopic measurement data set and integrating non-zero data points that are consecutive in time for the detected intensity following subtraction of the background intensity values.

[0012] (Example) 1A-19, a process for utilizing multiple data processes for nanoparticle baseline and nanoparticle detection threshold determination according to an embodiment of the present disclosure is shown. The process can switch between the multiple data processes to analyze one or more characteristics of a spectroscopic data set and compare results of the multiple data processes to determine a probability that one or more of the multiple data processes are providing reliable results for nanoparticle baseline and nanoparticle detection threshold determination. The present disclosure provides a description of an exemplary system for analysis of nanoparticles in FIG. 1A and FIG. 1B, a description of exemplary data processes in FIG. 2-14, an iterative decision data process described with respect to FIG. 2-9B, a description of local minimum data process described with respect to FIG. 10-15, and a description of an exemplary switching between multiple data processes with respect to FIG. 16-19.

[0013] 1A and 1B, a system 100 for analysis of nanoparticles contained in a fluid sample is shown according to an embodiment of the present disclosure. The system 100 generally includes a sample source 102, an inductively coupled plasma (ICP) torch 104, a sample analyzer 106, and a controller 108. The sample source 102 provides a fluid sample containing nanoparticles for analysis by the sample analyzer 106 and can include, for example, an autosampler (e.g., autosampler 110 shown in FIG. 1B) to automate the fluid processing of the sample. For example, the autosampler 110 operates a sample probe 112 to aspirate a fluid sample held in a fluid container 114 (e.g., a sample vial, a sample bottle, etc.) and transfers the fluid sample from the autosampler to other parts of the system through vacuum transfer, pump transfer, etc. The sample can include a fluid containing nanoparticles of interest, a diluent, sample matrix components, components for generating a calibration curve (e.g., standard fluids, standard nanoparticles, etc.), etc., or a combination thereof. In an embodiment, the controller 108 facilitates control of one or more aspects of fluid transfer from the autosampler 110. In an embodiment, the controller 108 includes a computer processor communicatively coupled to a computer memory to access control programming associated with one or more processes described herein for execution by the computer processor.

[0014] The sample source 102 is fluidly coupled to the ICP torch 104 (e.g., via fluid transfer line 116) and transfers a fluid sample including nanoparticles to the ICP torch 104 for ionization of the sample for analysis by the sample analyzer 106. In an embodiment, the sample source 102 includes one or more sample conditioning systems that prepare the fluid sample for introduction to the ICP torch 104. For example, the sample source 102 can include a nebulizer that receives the fluid sample from the autosampler 110 and aerosolizes the fluid sample, and a spray chamber that receives the aerosolized sample from the nebulizer and removes larger aerosol components by impact against the spray chamber walls. The sample source 102 can thus condition the fluid sample to facilitate substantially continuous operation of the ICP torch 104 for ionization of the sample, such as by aerosolizing the sample and removing larger aerosol components to prevent extinction of the plasma generated by the ICP torch 104.

[0015] An example of an ICP torch 104 is shown in FIG. 1B, where the system is shown to include a plasma torch assembly 118, a radio frequency (RF) induction coil 120 coupled to an RF generator (not shown), and an interface 122. The plasma torch assembly 118 includes a housing 124 that receives a plasma torch 126 configured to sustain a plasma. The plasma torch 126 is shown to include an injector assembly 134 that includes a torch body 128, a first (outer) tube 130, a second (middle) tube 132, and a third (injector) tube 136. The plasma torch 126 is mounted by the housing 124 for positioning in the center of the RF induction coil 120 such that an end of the first (outer) tube 130 is adjacent the interface 122 (e.g., about 10-20 mm from the interface 122). The interface 122 may be included in the sample analyzer 106 or may be included as a separate component thereof and generally includes a sampler cone 138 positioned adjacent to the plasma and a skimmer cone 140 positioned opposite the plasma and adjacent to the sampler cone 138. A small diameter opening 142, 144 is formed in each cone 138, 140 at the apex of the cones 138, 140 to allow passage of ions from the inductively coupled plasma for analysis by the sample analyzer 106.

[0016] A flow of gas (e.g., plasma-forming gas) used to form the plasma (e.g., plasma 146) is passed between the first (outer) tube 130 and the second (middle) tube 132. A second flow of gas (e.g., auxiliary gas) is passed between the second (middle) tube 132 and the third (injector) tube 136 of the injector assembly 134. The second flow of gas can be used to change the location of the base of the plasma relative to the ends of the second (middle) tube 132 and the third (injector) tube 136. In an embodiment, the plasma-forming gas and auxiliary gas include argon (Ar), although other gases may be used in place of or in addition to argon (Ar) in certain embodiments. An RF induction coil 120 surrounds the first (outer) tube 1130 of the plasma torch 126. RF power (e.g., 750-1500 W) is applied to the coil 120, generating an alternating current in the coil 120. This alternating current (e.g., 27 MHz, 40 MHz, etc.) oscillation creates an electromagnetic field in the plasma-forming gas in the first (outer) tube 130 of the plasma torch 126, which creates an ICP discharge by inductive coupling. A carrier gas is then introduced into the third (injector) tube 136 of the injector assembly 134. The carrier gas passes through the center of the plasma, where it forms a channel that is cooler than the surrounding plasma. The sample to be analyzed is introduced into the carrier gas for transport into the plasma region, where the sample can be formed into a liquid aerosol by passing the liquid sample from the sample source 102 through a nebulizer. As the nebulized sample droplets enter the central flow passage of the ICP, the droplets evaporate and any solids dissolved or carried in the liquid are vaporized and broken down into atoms. In an embodiment, the carrier gas includes argon (Ar), although other gases may be used in place of or in addition to argon (Ar) in certain embodiments.

[0017] The sample analyzer 106 generally includes a mass analyzer 148 and an ion detector 150 to analyze the ions received from the ICP torch 104. For example, the sample analyzer 106 can guide the ions received from the plasma of the ICP torch 104 and directed through the cones 138, 140 to the mass analyzer 148. The sample analyzer 106 can include various ion conditioning components, including but not limited to ion guides, vacuum chambers, reaction cells, etc., suitable for operation of an ICPMS system. The mass analyzer 148 separates the ions according to differences in mass-to-charge ratio (m / z). For example, the mass analyzer 148 can include a quadrupole mass analyzer, a time-of-flight mass analyzer, etc. The ion detector 150 receives the separated ions from the mass analyzer 148, detects and counts the ions according to the separated m / z ratio, and outputs a detection signal. The controller 108 can receive detection signals from the ion detector 150 and adjust data for determining the concentration of components contained in the ionized sample and data for determining nanoparticle characteristics (e.g., nanoparticle size, nanoparticle amount, etc.) of the nanoparticles contained in the fluid sample according to the signal intensity of each ion detected by the ion detector 150.

[0018] An example of a spectroscopic data set from the controller 108 is shown in FIG. 2, where a spectroscopic data set 200 is shown with a normal distribution curve 202. The ion content in a sample analyzed by an ICPMS is generally uniform. The ion signals can proceed as described herein as if they resemble a normal distribution centered around a mean signal. For example, in the example spectroscopic data set 200, ion signals within one standard deviation (i.e., μ±σ) of the mean account for 68.27% of the total, while ion signals within two standard deviations (i.e., μ±2σ) of the mean account for 95.45% of the total, and ion signals within three standard deviations (i.e., μ±3σ) of the mean account for 99.73% of the total. Outliers in the distribution are nanoparticles potentially present in the sample analyzed by the ICPMS. However, outliers can distort the standard deviation of a spectrometry data set, and therefore the process described herein iteratively removes outliers from the data set. For example, an exemplary process is described herein for iteratively removing outlier data from a spectroscopic measurement dataset to determine a particle baseline and detection threshold for nanoparticles, in accordance with embodiments of the present disclosure.

[0019] Referring to FIG. 3, a flow diagram of a process 300 for iterative determination of outlier data from a spectroscopic data set to determine a particle baseline and detection threshold for nanoparticles according to an embodiment of the present disclosure is shown. The flow diagram begins with a raw data set provided through spectroscopic analysis of a sample (e.g., via ICPMS) at block 302. For example, for a spectroscopic data set including ion signal intensities detected by the ion detector 108 as a function of time, the raw data set may include a count distribution of the ion signal intensities and a frequency of the ion signal intensities. The raw data set is processed to determine a first iteration of the mean and standard deviation to determine outlier data points (e.g., those that are above a threshold) at block 304. In the illustrated example process, the outlier data points are identified as exceeding a threshold of 1*avg+5*standarddeviation (1μ+5σ). It should be noted that the process described herein is not limited to the scaling factors provided in the threshold calculation (i.e., 1*avg or 5*standarddeviation). For example, in an embodiment, the mean and standard deviation scaling factors are user selectable functions. For example, a user may select particular multiples for the mean and standard deviation through interaction with a user interface communicatively coupled to the system 100 .

[0020] Next, the process 300 removes outliers from the dataset based on the previous threshold calculation (i.e., 1μ+5σ) in block 306 to approximate a dataset without outliers (i.e., only ion data with no nanoparticle data). The remaining dataset (i.e., the raw dataset with the outlier data removed) is then processed to determine a second iteration of the mean and standard deviation of the remaining dataset to determine outlier data points (e.g., those that exceed the threshold). For example, the process 300 proceeds to block 308 to determine whether outliers remain based on a new threshold calculation with the remaining dataset after removal of the outlier data points from block 306. If outlier data points remain (i.e., “yes” at block 308), the process 300 can admit the dataset in which non-particle data still exists in block 310 for further iterative removal of particle data. The process 300 continues to iterate through the dataset to remove outlier data until no more outliers are identified. For example, the process 300 can return to block 304 to work with the data from block 310 instead of the raw dataset from block 302. If no further outliers are identified, the process establishes the resulting data set as a data background and determines a nanoparticle baseline based on the data background at block 312. In an embodiment, the data background is determined using a baseline calculation having one or more multiples different than those used for the iterative threshold calculation. For example, the threshold is shown as aμ+bσ, but the baseline calculation is determined using a multiple of xμ, as further described herein. background +yσ background An example of the process 300 is described with respect to Figures 4 through 8.

[0021] 4-6, initial iterative steps for processing a raw data set are shown, with FIG. 5 showing an example of a raw data set from block 302 provided in a simplified form for initial outlier removal. As shown in FIG. 6, the outlier threshold is determined to be 3.7 based on a threshold calculation of 1μ+5σ. Data above the cutoff value of 3.7 are identified as outliers and removed from the data set for subsequent iterations. For example, in block 306, replicates shown beyond the 3.7 threshold line (i.e., replicates extending around 5) are removed from the data set. FIG. 7 shows a second iteration showing the data set with data points beyond the 3.7 threshold line removed. In the second iteration, based on the same threshold calculation of 1μ+5σ, a new outlier threshold is determined to be 2.0, and since there are no data points above 2.0, no data points are determined to be outliers.

[0022] If no outliers are present, the process 300 determines that the nanoparticle outliers have been removed from the data set so that a nanoparticle baseline determination can be made. The process 300 then proceeds to block 312 to determine a nanoparticle baseline based on the data background. For example, FIG. 8 shows result data providing a data background and determining a nanoparticle baseline based on the data background. The particle baseline calculation formula includes 1*avg background +3.3*standarddeviation background, but the process is not limited to such values, and different scaling factors for the mean and standard deviation can be utilized. For example, in an embodiment, the scaling factors for the mean and standard deviation are user selectable features. In an embodiment, the mean and standard deviation scaling factors can be different than the mean and standard deviation scaling factors during the iterative removal step of process 300 (e.g., in block 304). For example, the mean and standard deviation scaling factors for the particle baseline calculation are 1× and 3.3×, respectively, while the mean and standard deviation scaling factors for the iterative removal calculation are 1× and 5×, respectively. Different standard deviation scaling factors can provide different levels of strictness in determining which data are considered below or above the particle baseline after the background data set is determined. In an embodiment, process 300 can remove zero values ​​in the data set.

[0023] Referring to Figures 9A and 9B, the formula for 1*avg+5*standarddeviation and the formula for 1*avg background +1*standarddeviation background 9B shows a subset of the data of FIG. 9A with the determined particle baseline 900 plotted above the spectroscopic measurement data 902.

[0024] With reference to Figures 10 to 15, the local minimum data processing for determining the detection threshold of nanoparticles is described. Figure 10 shows an exemplary spectroscopic measurement data set with approximate background determination for the detection of nanoparticles, where the portion of the data set due to the signal background (shown as 1000, due to background interference, such as due to ionized plasma gas from an ICP torch) is separated from the data set due to nanoparticles (shown as 1002). The nanoparticle detection threshold represents the transition from the background portion 1000 to the nanoparticle portion 1002, and provides a data boundary where data points with intensities greater than the nanoparticle detection threshold can be treated as originating from nanoparticles present in the sample.

[0025] Referring to FIG. 11, a flow diagram of a process 1100 for determining the minimum and validating a spectroscopic data set to determine a detection threshold for nanoparticles according to an embodiment of the present disclosure is shown. The flow diagram begins with a raw data set being manipulated to remove background and integrate consecutive data points at block 1102. In an embodiment, the raw data set includes a time-lapse intensity data set provided by an ICPMS, and the background removed from the raw data set is determined through data processing such as the iterative determination E described with respect to FIG. 2 through FIG. 9B, or is a user-selected feature, or a combination thereof. In an embodiment, the data set is integrated after removing the background from the raw data set. For example, consecutive non-zero data points in time of detected intensity are summed, and the data points can be considered consecutive in time if no intervening zero values ​​are detected by the ICPMS in a given time detection interval, such as a detection interval of 0.01 seconds. By integrating after background removal, more zero data points can be present because background removal can filter lower non-zero data points from the raw data set to provide zero values.

[0026] Process 1100 then continues at block 1104 where a histogram of the manipulated data set is formed. In an embodiment, the histogram is formed by rounding all integral data points to the nearest integer count value and determining the frequency of each rounded point (e.g., a value of 3.2 is rounded to a value of 3 and a value of 3.7 is rounded to 4). In an embodiment, the data is rounded down to the next integer count value (e.g., 3.2 and 3.7 are each rounded down to a value of 3). In an implementation, the data is rounded up to the next integer count value (e.g., 3.2 and 3.7 are each rounded up to a value of 4). A histogram can be formed from the rounded points based on how many of each point are present (e.g., the frequency of occurrence of each count). Examples of histograms for simplified data sets are shown with respect to FIGS. 12A-14.

[0027] The process 1100 further includes, at block 1106, examining the frequency of the histogram based on a window size of the counts to determine candidates for a local minimum count value. In an embodiment, the window size is an odd number (e.g., a window covering 5 counts), and the center value of the window is compared to the values ​​to the left and right of the center location on the histogram to determine if a local minimum count exists (e.g., whether the frequency of the count in the center of the window is less than the frequency of the counts to the left and right of the center count based on the window size). For counts at the beginning of the histogram (e.g., counts 0, 1, 2, etc.), the window may be reduced by not extending the entire window size. For example, FIG. 11A shows a window 1200 covering counts 1 to 4 (i.e., a window size of 4), where the frequency of count 2, 26, is reviewed to determine if it is a candidate for the minimum value for the given window. A window may be considered to cover count 0 to the left of count 1 such that count 2 is centered in window 1200 with a window size of 5 counts, but since there is no data for count 0, the window covers those counts that are present in the histogram. Similarly, before considering count 2, count 1 is reviewed for potential minimums, and if count 1 were centered in window 1200, the frequency of 51 would be compared to the frequency of count 2, 26, and the frequency of count 3, 12, to determine if 51 is a minimum and is not.

[0028] At block 1108, process 1100 determines whether the median frequency value of the window is a local minimum. If the median frequency value is not a local minimum, process 1100 proceeds to block 1110, where the window is expanded further to the right of the histogram and additional count ranges are reviewed (e.g., via blocks 1106 and 1108) to determine whether the new median frequency value is a local minimum. For example, the frequency of 26 for count 2 in FIG. 12A is not a local minimum because the frequency of 12 for count 3 and the frequency of 5 for count 4 are each less than 26. The window 1200 is then moved over count 3 and compared to the frequencies of counts 1, 2, 4, and 5 to evaluate whether the frequency of 12 is a local minimum. The frequency of 12 for count 3 would not be a local minimum because the frequency of 5 for count 4 and the frequency of 2 for count 5 are each less than 12. Then, window 1200 is moved above count 4 and compared with the frequencies of counts 2, 3, 5, and 6 to evaluate whether the frequency of 5 is a minimum.

[0029] Process 1100 continues to evaluate each new iteration of the placement of window 1200. For example, Figures 12B and 13 show window 1200 at a location further along the histogram compared to Figure 12A, where window 1200 covers counts 3 through 7 (i.e., window value 5) to determine whether a frequency of 2 is a candidate minimum for the given window. Because the window placement includes a frequency value of 0 at counts 6 and 7 within window 1200, process 1100 does not identify a frequency of 2 as a candidate minimum. This process 1100 continues until a local minimum candidate is identified. For example, Figure 13 shows a window that increases to the right of the histogram until it is centered over count 6, a frequency of 0, which is identified as a candidate minimum.

[0030] If a candidate minimum is identified at block 1108, process 1100 proceeds to block 1112 where process 1100 verifies whether the candidate minimum is a verified minimum. If the candidate minimum is not verified, process 1100 returns to block 1110 and increases the window to center on the next count. If a candidate minimum is verified at block 1112, process 1100 identifies the local minimum as the threshold for nanoparticle detection at block 1114. For example, referring to FIG. 14, a frequency value of 2 in the histogram is first identified as a candidate minimum because 2 is less than the frequency values ​​of the remaining values ​​in window 1200 (i.e., 2 is less than 22, 18, and 15).

[0031] The process 1100 then determines whether the candidate minimum has been verified. In an embodiment, to determine whether a local minimum has been verified, the average of all frequencies within the window is calculated and it is determined whether the candidate minimum is within one standard deviation of the window average. In an embodiment, the verification may include determining whether the candidate minimum is within a multiple of a standard deviation of the window average. If the candidate minimum is more than one standard deviation above the window average, the candidate minimum fails as a minimum. For example, referring to FIG. 14, the frequency value of 2 for count 2 is not verified because it is not within one standard deviation (shown as 8.65) of the window average (14.25). For example, frequency value 2, count 2 fails because 2+8.65 is less than 14.25.

[0032] Continuing with the example shown in FIG. 14, the location of the window is increased down the histogram until it is centered over count 6, where a frequency of 0 is determined to be a candidate minimum. The frequency of 0 is within one standard deviation (6.34) of the window mean (3.8), thereby verifying that count 6, frequency 0 is a local minimum. The verified local minimum is used as a detection threshold for nanoparticles, and intensities above the detection threshold are treated as nanoparticles, and intensities below the detection threshold are treated as background (e.g., ion samples, mass spectrometry interference, nanoparticles of size below the detection threshold). FIG. 15 shows an example of a data set analyzed according to the local minimum analysis process, illustrating a detection threshold 1500 to separate background (i.e., intensity values ​​preceding the detection threshold 1500) from intensity values ​​corresponding to nanoparticles present in the sample (i.e., intensity values ​​following the detection threshold 1500).

[0033] 16-19, a process 1600 for utilizing multiple data processes for nanoparticle baseline and nanoparticle detection threshold determination is shown according to an embodiment of the present disclosure. The system 100 can utilize the process 1600 to analyze a single data set from the sample analyzer 106 according to the multiple data processes to compare the results against each other (e.g., via the controller 108) to determine converging or diverging data results from the multiple data processes. The process 1600 can include analyzing a spectroscopic measurement data set 1602 with multiple data processes (e.g., data processes 1604, 1606, 1608) to achieve multiple data results where the multiple data processes are automatically switched from one data process to the next to analyze the same spectroscopic measurement data set according to the multiple data processes. For example, the controller 108 can automate the analysis of the spectroscopic measurement data set 1602 according to one data process and then switch to analyze the first spectroscopic measurement data set 1602 according to one or more different analyses to determine whether the results of the data processes are reliable or too divergent. For example, process 1600 is shown with a first data process 1604, a second data process 1606, and up to n data processes 1608 used to process the same spectroscopic measurement data set (e.g., spectroscopic measurement data set 1602) to provide an indication of the reliability of one or more results of the data processes, where n can be any number greater than two. In an embodiment, controller 108 of system 100 facilitates analysis of spectroscopic measurement data set 1602 according to one or more of the data processes of process 1600. For example, controller 108 may include, or be communicatively coupled to, a computer memory device that stores one or more data algorithms, programs, or other processes for generating data results according to one or more of the data processes of process 1600.

[0034] The data processes (e.g., data processes 1604, 1606, 1608) can include, but are not limited to, iterative decision data processes as described with respect to Figures 2-9B, local minimum data processes as described with respect to Figures 10-15, device specific data analysis processes (e.g., ICPMS analyzer software), user defined data processes having user configurable features and / or user defined values ​​(e.g., facilitating a user to select a reference material, determine a particle baseline and detection threshold to use for that reference material, and apply the particle baseline and detection threshold to future samples), or other data processes. Each data process produces a data result after processing the original spectroscopic data set. For example, a first data process 1604 is shown producing a first set of data results 1610, a second data process 1606 is shown producing a second set of data results 1612, and data process 1608 is shown producing a third set of data results 1614. Exemplary data results include, but are not limited to, particle baseline 1616, nanoparticle detection threshold 1618, particle count 1620, particle size and standard deviation 1622, and the like, and combinations thereof.

[0035] In an embodiment, each data process can provide one or more categories of data results, which may be in the same category as the other data processes used to analyze the spectroscopic data set, or in different categories. For example, a first data process (e.g., data process 1604) can provide data results associated with a particle baseline and a detection threshold, a second data process (e.g., data process 1606) can provide data results associated with a detection threshold (but not a particle baseline), a third data process (e.g., a data process between data processes 1606 and 1608) can provide data results associated with a particle baseline, a detection threshold, and a number of particles, and a fourth data process (e.g., data process 1608) can provide data results associated with a particle baseline, a detection threshold, a number of particles, and a particle size and standard deviation. The data results are useful for establishing information related to mass spectrometer interferences, ionic material measurements, particles below a detection threshold, etc.

[0036] The multiple data processes can be utilized to determine the probability that any one or more data results of the data processes are reliable or unreliable results. For example, referring to FIG. 17, each data process is shown to provide a convergent data result (e.g., shown as 1700), providing a high level of confidence that the results of each data process can be trusted. A determination of whether the results of the data process are convergent or divergent (e.g., shown as 1702) can be made through a statistical model, such as determining whether the results of the data process are excluded from a standard deviation analysis of all results, or through another model. For example, each data process can output a data result related to a nanoparticle detection threshold, the detection threshold of each data process being within a range of statistical similarity indicating a high probability of a reliable nanoparticle detection threshold. Referring to FIG. 18, each of the data processes is shown to provide a divergent data result 1702, providing a high level of confidence that the results of the data process cannot be authenticated or are otherwise unreliable (e.g., analysis of an incorrect sample).

[0037] With reference to FIG. 19, data results from multiple data processes are shown as being intermixed between converged data results 1700 and divergent data results 1702. For example, three data results are shown as converged data results (e.g., data results 1900, 1902, 1904) and two data results are shown as divergent data results (e.g., data results 1906, 1908). When results are intermixed such that multiple data processes provide converged and divergent data results, a statistical model can be utilized to determine whether there are enough converged results to discard or otherwise ignore the divergent results, or whether there are too many divergent results such that the data processes are unreliable, e.g., the results of the data processes are excluded from the standard deviation analysis of the overall results, or another model can be utilized. For example, a simple majority of converged results can indicate a satisfactory reliability of the converged data results. In an embodiment, if a threshold number of divergent data results are present, the entire data results can be flagged by the system 100 as unreliable. For example, the system 100 may be configured such that if two different data processes deviate from the remainder of the converged data processes, the controller 108 may identify all of the overall data results as unreliable. Alternatively or additionally, one or more data processes may be used as a benchmark data result, and the results of the one or more additional data processes may be compared to the benchmark data result to determine the extent of deviation from the benchmark.

[0038] The process can include automatically reporting the results of the multiple data processes. For example, the process can automatically identify and report which data processes provided reliable (e.g., convergent with other data results) or unreliable (e.g., divergent from other data results). In an embodiment, the controller 108 of the system 100 generates one or more communication signals in response to the generation of data results from the one or more data processes. For example, the one or more communication signals can be transmitted to a user interface for review by laboratory personnel.

[0039] Electromechanical devices (e.g., electric motors, servos, actuators, etc.) may be coupled to or embedded within components of system 100 to facilitate automated operation via control logic embedded within system 100 or externally driving system 100. The electromechanical devices may be configured to move devices or fluids according to various procedures, such as those described herein. System 100 may include or be controlled by a computing system having a processor or other controller configured to execute computer-readable program instructions (i.e., control logic) from a non-transitory carrier medium (e.g., a storage medium such as a flash drive, hard disk drive, solid state disk drive, SD card, optical disk, etc.). The computing system may be connected to the various components of system 100 via a direct connection or one or more network connections (e.g., local area networking (LAN), wireless area networking (WAN or WLAN), one or more hub connections (e.g., USB hub), etc.). For example, the computing system may be communicatively coupled to a system controller, an ICP torch, a carriage motor, a fluid handling system (e.g., valves, pumps, etc.), other components described herein, components that direct their control, or combinations thereof. The program instructions, when executed by a processor or other controller, can cause the computing system to control system 100 according to one or more operating modes as described herein.

[0040] It should be appreciated that the various functions, control operations, processing blocks, or steps described throughout this disclosure may be performed by any combination of hardware, software, or firmware. In some embodiments, the various steps or functions are performed by one or more of electronic circuits, logic gates, multiplexers, programmable logic devices, application specific integrated circuits (ASICs), controllers / microcontrollers, or computing systems. Computing systems may include, but are not limited to, personal computing systems, mobile computing devices, mainframe computing systems, workstations, image computers, parallel processors, or other devices known in the art. In general, the term "computing system" is broadly defined to encompass any apparatus having one or more processors or other controllers that execute instructions from a carrier medium.

[0041] Program instructions that implement a function, control operation, processing block, or step as manifested by the embodiments described herein may be transmitted over or stored on a carrier medium. The carrier medium may be a transmission medium such as, but not limited to, a wire, cable, or wireless transmission link. The carrier medium may also include a non-transitory signal-bearing medium or storage medium such as, but not limited to, a read-only memory, a random access memory, a magnetic or optical disk, a solid-state or flash memory device, or a magnetic tape.

[0042] (Conclusion) Although the present subject matter has been described in language specific to structural features and / or process operations, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. 1. A method for determination of a nanoparticle detection threshold of a fluid sample, comprising: conveying a fluid sample containing nanoparticles to a spectroscopic sample analysis device; generating a spectroscopic measurement data set relating to ion signal intensities detected over time via the spectroscopic sample analyzer; forming, via one or more computer processors, a histogram of the spectroscopic data set, the histogram being related to a count frequency of integrated ion signal intensity values; via the one or more computer processors, incrementing a window along the histogram that spans a plurality of counts of the histogram to determine candidates for minimal frequent values ​​within the window; verifying, via the one or more computer processors, whether the candidate minimum frequency value is a minimum value of the histogram and providing a verified minimum value; and assigning, via the one or more computer processors, the verified minimum as a detection threshold for nanoparticles in the spectroscopic measurement dataset.

2. The method of claim 1 , wherein the spectroscopic sample analysis instrument is an inductively coupled plasma mass spectrometry instrument (ICPMS).

3. 3. The method of claim 2, wherein delivering the fluid sample containing nanoparticles to the spectroscopic sample analysis device comprises delivering the fluid sample from a fluid source to an inductively coupled plasma torch and thereafter to an ICPMS.

4. 4. The method of claim 3, wherein conveying a fluid sample from the fluid source to the induction coupled plasma torch comprises conveying a fluid sample from the fluid source to the induction coupled plasma torch via an autosampler control of a sample probe.

5. The method of claim 1 , further comprising subtracting a background intensity value from the spectroscopic measurement data set prior to forming the histogram.

6. 6. The method of claim 5, wherein the histogram comprises frequency counts of integrated ion signal intensity values ​​formed by subtracting the background intensity value followed by adding temporally consecutive non-zero data points for detected intensity.

7. The method of claim 6 , further comprising rounding sums of temporally consecutive non-zero data points for detected intensities following subtraction of the background intensity value.

8. The method of claim 7 , wherein the sum of temporally consecutive non-zero data points of detected intensity following subtraction of the background intensity value is truncated to the next integer count value.

9. The method of claim 7 , wherein the sum of temporally consecutive non-zero data points of detected intensity following subtraction of the background intensity value is rounded up to the next integer count value.

10. 2. The method of claim 1 , wherein verifying, via the one or more computer processors, whether the candidate minimum frequency value is a minimum value of the histogram and providing the verified minimum value comprises determining whether the candidate minimum frequency value is within one standard deviation of a mean value of frequency values ​​within the window.

11. 2. The method of claim 1, wherein, via the one or more computer processors, increasing the window along the histogram spanning a plurality of counts of the histogram to determine candidates for the minimum frequent value within the window comprises determining whether a frequency of a count at a central position of the window is less than each of the other counts in the window.

12. 1. A system for determining a nanoparticle detection threshold of a fluid sample, comprising: a spectroscopic sample analyzer configured to receive a fluid sample containing nanoparticles from a sample source and generate a spectroscopic measurement data set related to detected ion signal intensities over time; one or more computer processors; the one or more computer processors: forming a histogram of the spectroscopic data set, the histogram relating to count frequencies of integrated ion signal intensity values; Incrementing a window along the histogram that spans a number of counts of the histogram to determine candidates for minimal frequent values ​​within the window; verifying whether the candidate minimum frequency value is a minimum value of the histogram and providing the verified minimum value; assigning the verified minimum as a detection threshold for nanoparticles in the spectroscopic data set; and a non-transitory computer-readable medium storing one or more instructions for execution by the one or more computer processors to cause the system to perform the above-mentioned steps.

13. The system of claim 12 , wherein the spectroscopic sample analysis device is an inductively coupled plasma mass spectrometry device (ICPMS).

14. 14. The system of claim 13, further comprising an inductively coupled plasma torch fluidly coupled between the sample source and the ICPMS.

15. 15. The system of claim 14, further comprising an autosampler that directs control of a sample probe to introduce a fluid sample into the inductively coupled plasma torch.

16. 13. The system of claim 12, wherein the one or more instructions further include one or more instructions for execution by the one or more computer processors to cause the one or more computer processors to perform subtracting a background intensity value from the spectroscopic measurement data set prior to forming the histogram.

17. 17. The system of claim 16, wherein the histogram comprises frequency counts of integrated ion signal intensity values ​​formed by subtracting the background intensity value followed by adding temporally consecutive non-zero data points for detected intensity.

18. 20. The system of claim 17, wherein the one or more instructions further include one or more instructions for execution by the one or more computer processors to cause the one or more computer processors to perform rounding a sum of temporally consecutive non-zero data points for detected intensities following subtraction of the background intensity value.

19. 13. The system of claim 12, wherein verifying whether the candidate minimum frequency value is a minimum value of the histogram and providing the verified minimum value comprises determining whether the candidate minimum frequency value is within one standard deviation of a mean value of frequency values ​​within the window.

20. 13. The system of claim 12, wherein increasing the window across a plurality of counts of the histogram to determine candidates for the minimum frequent value within the window comprises determining whether a frequency of a count at a central position of the window is less than each of the other counts in the window.

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