Determination of nanoparticle baseline and particle detection threshold by iterative exclusion of outliers

Abstract

A system and method for repeatedly removing outlier data from spectroscopic measurement data and determining one or more of a particle baseline and a detection threshold for nanoparticles will be described. Ion signal intensity values that exceed an outlier threshold related to the sum of a first multiple of the mean of the count distribution of the ion signal intensity and a first multiple of the standard deviation of the count distribution of the ion signal intensity are repeatedly removed from the raw data set until no outliers remain, providing a background data set. The baseline intensity value of the nanoparticles is set as the sum of a second multiple of the mean of the background data set and a second multiple of the standard deviation of the background data set in order to distinguish between signal intensity values related to background interference and signal intensity values related to the presence of nanoparticles in the sample.

Description

Technical Field

[0001] (Related Applications) This application claims the benefit of 35 U.S.C. § 119(e) of U.S. Provisional Application No. 63 / 335,510, filed on April 27, 2022, 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, entitled "NANOPARTICLE DETECTION THRESHOLD DETERMINATION THROUGH LOCAL MINIMUM ANALYSIS"; and U.S. Provisional Application No. 63 / 335,523, filed on April 27, 2022, entitled "MULTI DATA PROCESS SWITCHING FOR NANOPARTICLE BASELINE AND DETECTION THRESHOLD DETERMINATION". The entire contents of U.S. Provisional Application Nos. 63 / 335,510, 63 / 335,516, and 63 / 335,523 are hereby incorporated by reference into this specification.

Background Art

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

[0003] To introduce a liquid sample into an ICP mass spectrometer (such as an inductively coupled plasma mass spectrometer (ICP / ICPMS), an inductively coupled plasma optical emission spectrometer (ICP-AES), etc.) for analysis, a sample introduction system may be employed. For example, the sample introduction system may take an aliquot of the liquid sample from a container, and then convey the aliquot to a nebulizer, converting it into a polydisperse aerosol suitable for ionization in the plasma by the ICP mass spectrometer. Thereafter, the aerosol is sorted in a spray chamber, and larger aerosol particles are removed. The aerosol exiting the spray chamber is introduced into an ICPMS or ICPAES device for analysis. In many cases, the introduction of samples is automated so that a large number of samples can be efficiently introduced into the ICP mass spectrometer. Summary of the Invention

[0004] Systems and methods are described for analyzing spectroscopic measurement data to determine nanoparticle factors including one or more of a nanoparticle baseline and a nanoparticle detection threshold. In one aspect, method embodiments include transporting a fluid sample containing nanoparticles to a spectroscopic sample analyzer; generating, via the spectroscopic sample analyzer, a set of spectroscopic measurement data related to ion signal intensity detected over time; generating, via one or more computer processors, from the set of spectroscopic measurement data, a raw data set including a count distribution of counts of ion signal intensity and a frequency of ion signal intensity for each count; iteratively removing ion signal intensity values that exceed a threshold value of outliers until there are no count values that exceed a threshold value of outliers associated with a sum of a first multiple of an average of the count distribution of ion signal intensity and a first multiple of a standard deviation of the count distribution of ion signal intensity, via one or more computer processors, to provide a background data set; and setting a baseline intensity value of the nanoparticles as a sum of a second multiple of an average of the background data set and a second multiple of a standard deviation of the background data set, via one or more computer processors, where the first multiple of the standard deviation of the count distribution of ion signal intensity is different from, but not limited to, the second multiple of the standard deviation of the background data set.

[0005] In one aspect, method embodiments include transporting 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, forming, via one or more computer processors, a histogram of the spectroscopic measurement data set, where the histogram is associated with the count frequency of integrated ion signal intensity values, incrementing, via one or more computer processors, a window spanning multiple counts of the histogram along the histogram to determine candidates for minimum frequency values within the window, verifying, via one or more computer processors, whether a candidate for a minimum frequency value is a minimum value of the histogram and providing the verified minimum value, and assigning, via one or more computer processors, the verified minimum value as a detection threshold for nanoparticles in the spectroscopic measurement data set, but not limited thereto.

[0006] In one aspect, method embodiments include transporting 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, analyzing, via one or more computer processors, the spectroscopic measurement data set with a first data process and determining at least one of a first nanoparticle baseline or a first nanoparticle detection threshold with the first data process, automatically switching to a second data process, analyzing, via one or more computer processors, the spectroscopic measurement data set and determining at least one of a second nanoparticle baseline or a second nanoparticle detection threshold with the second data process, and determining, via one or more computer processors, whether results from the first data process converge with or diverge from results from the second data process, but not limited thereto.

[0007] This summary is a simplified introduction to some of the concepts described later in the "Detailed Description". This summary is not intended to identify the main 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 be made with reference to the accompanying drawings.

Brief Description of the Drawings

[0009]

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DETAILED DESCRIPTION OF THE INVENTION

[0010] The research on nanoparticles has grown to cover applications from the medical industry to the environmental industry. Such applications can focus on the function of detecting nanoparticles (e.g., particles with a diameter less than 1000 nm) and calculating the size of the nanoparticles present in a sample. However, when analyzing spectroscopic measurement data, it is accompanied by many difficulties to determine what is a nanoparticle and what is not. For example, spectroscopic measurement data such as ICPMS data includes information related to ionized samples and background interference, such as that caused by the plasma gas introduced into the ICP torch, and may overlap with data related to small nanoparticles. For example, as the size of the nanoparticles decreases, the spectroscopic measurement data of the nanoparticles begins to converge to the data related to the ion species generated by the ICP torch. This overlap and the issues related to removing background interference while avoiding removing nanoparticle data lead to continuous problems in providing reliable data related to nanoparticles, including but not limited to the identification of nanoparticles, the determination of the number of nanoparticles, and the associated size distribution.

[0011] Accordingly, in one aspect, the present disclosure is directed to a system and method for repeatedly removing outlier data from spectroscopic measurement data to determine one or more of a particle baseline and a detection threshold for nanoparticles. The spectroscopic measurement data is converted into a raw data set having a count distribution of counts of ion signal intensities and a frequency of the ion signal intensities for each count. Ion signal intensity values that exceed an outlier threshold related to the sum of a first multiple of the mean of the count distribution of ion signal intensities and a first multiple of the standard deviation of the count distribution of ion signal intensities are repeatedly removed from the raw data set until no outliers remain, providing a background data set. The baseline intensity value of the nanoparticles is set as the sum of a second multiple of the mean of the background data set and a second multiple of the standard deviation of the background data set to distinguish between signal intensity values related to background interference and signal intensity values related to the presence of nanoparticles in the sample.

[0012] (Example) Generally referring to FIGS. 1A through 19, a process for utilizing multiple data processes for determining a nanoparticle baseline and a nanoparticle detection threshold in accordance with an embodiment of the present disclosure is shown. The process switches between multiple data processes to analyze one or more characteristics of a spectroscopic data set, compares the results of the multiple data processes, and can determine the probability that one or more of the multiple data processes provides a reliable result for determining the nanoparticle baseline and the nanoparticle detection threshold. The present disclosure provides an explanation of an exemplary system for nanoparticle analysis in FIGS. 1A and 1B, an explanation of exemplary data processes in FIGS. 2 through 14, an iterative decision data process described with respect to FIGS. 2 through 9B, an explanation of a minimum value data process described with respect to FIGS. 10 through 15, and an explanation of an exemplary switching between multiple data processes with respect to FIGS. 16 through 19.

[0013] Referring to FIGS. 1A and 1B, a system 100 for the analysis of nanoparticles contained in a fluid sample is shown in accordance with an embodiment of the present disclosure. 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 supplies 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 handling 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 the nanoparticles of interest, a diluent, a sample matrix component, a component for generating a calibration curve (e.g., a standard fluid, standard nanoparticles, etc.), or a combination thereof. In an embodiment, the controller 108 facilitates the control of one or more aspects of the 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 the fluid transfer line 116) and transfers a fluid sample containing nanoparticles to the ICP torch 104 for ionization of the sample for analysis by the sample analyzer 106. In embodiments, 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 receive a fluid sample from an autosampler 110 and include a nebulizer that 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 quenching of the plasma generated by the ICP torch 104.

[0015] An example of an ICP torch 104 is shown in FIG. 1B, and 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 maintain 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 (intermediate) tube 132, and a third (injector) tube 136. The plasma torch 126 is attached by the housing 124 to be positioned at the center of the RF induction coil 120 such that the end of the first (outer) tube 130 is adjacent to the interface 122 (e.g., about 10 - 20 mm from the interface 122). The interface 122 can be included in the sample analyzer 106 or can be included as a separate component thereof, and generally includes a sampler cone 138 disposed adjacent to the plasma and a skimmer cone 140 disposed opposite the plasma and adjacent to the sampler cone 138. Small-diameter openings 142, 144 are formed in each of the cones 138, 140 at the apexes of the cones 138, 140 to allow passage of ions from the inductively coupled plasma for analysis by the sample analyzer 106.

[0016] The flow of gas (e.g., plasma-forming gas) used to form plasma (e.g., plasma 146) is passed between the first (outer) tube 130 and the second (intermediate) tube 132. A second flow of gas (e.g., auxiliary gas) is passed between the second (intermediate) tube 132 of the injector assembly 134 and the third (injector) tube 136. The second flow of gas can be used to change the position of the base of the plasma relative to the ends of the second (intermediate) tube 132 and the third (injector) tube 136. In an example, the plasma-forming gas and the auxiliary gas include argon (Ar), but in a particular example, other gases may be used instead of, or in addition to, argon (Ar). The 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 within the coil 120. Due to the oscillation of this alternating current (e.g., 27 MHz, 40 MHz, etc.), an electromagnetic field is formed in the plasma-forming gas within the first (outer) tube 130 of the plasma torch 126, and an ICP discharge is formed by inductive coupling. Next, the carrier gas is introduced into the third (injector) tube 136 of the injector assembly 134. The carrier gas passes through the center of the plasma, forming a channel with a lower temperature than the surrounding plasma there. The sample to be analyzed is introduced into the carrier gas for transport into the plasma region, and within the plasma region, the sample can be formed into a liquid aerosol by passing the liquid sample from the sample source 102 through a nebulizer. When the droplets of the nebulized sample enter the central flow path of the ICP, the droplets evaporate, and the solids that were dissolved or carried in the liquid vaporize and decompose into atoms. In an example, the carrier gas includes argon (Ar), but in a particular example, other gases may be used instead of, or in addition to, argon (Ar).

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

[0018] An example of a spectral data set from the controller 108 is shown in FIG. 2, where the spectral measurement data set 200 is shown together with a normal distribution curve 202. The ion content in the sample analyzed by ICPMS is generally uniform. The ion signal can proceed with the processes described herein as if it resembles a normal distribution centered around the average signal. For example, in the example of the spectral measurement data set 200, the ion signal within one standard deviation from the average value (i.e., μ±σ) accounts for 68.27% of the whole, while the ion signal within two standard deviations from the average value (i.e., μ±2σ) accounts for 95.45% of the whole, and the ion signal within three standard deviations from the average value (i.e., μ±3σ) accounts for 99.73% of the whole. The outliers of the distribution are nanoparticles potentially present in the sample analyzed by ICPMS. However, since the outliers may distort the standard deviation of the spectrometry data set, the outliers are repeatedly removed from the data set in the process described herein. For example, in accordance with the embodiments of the present disclosure, an exemplary process for repeatedly removing outlier data from the spectral measurement data set to determine the particle baseline and detection threshold of the nanoparticles is described herein.

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

[0020] Next, in block 306, process 300 removes outliers from the data set based on the previous threshold calculation (i.e., 1μ + 5σ) to approach a data set that has no outliers (i.e., only ion data that does not contain nanoparticle data). Next, the remaining data set (i.e., the raw data set excluding the outlier data) is processed to determine a second iteration of the mean and standard deviation of the remaining data set and to determine outlier data points (e.g., those that exceed the threshold). For example, process 300 proceeds to block 308 and determines whether there are any remaining outliers based on a new threshold calculation using the remaining data set after removing the outlier data points from block 306. If there are remaining outlier data points (i.e., "yes" in block 308), process 300 can recognize, in block 310, a data set in which non-particle data still exists for further iterative removal of particle data. Process 300 continues to iteratively remove outlier data from the data set until no further outliers are identified. For example, process 300 can return to block 304 and process the data from block 310 instead of the raw data set from block 302. If no further outliers are identified, the process, in block 312, establishes the resulting data set as the data background and determines a nanoparticle baseline based on the data background. An example is that the data background is determined using a baseline calculation having one or more multiples different from those used for the iterative threshold calculation. For example, the threshold is shown as aμ + bσ, but the baseline calculation is xμ background + yσ background as shown. An example of process 300 is described with respect to FIGS. 4 through 8.

[0021] Referring to FIGS. 4 to 6, an initial iterative step for processing the raw dataset is shown. FIG. 5 shows an example of the raw dataset 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 exceeding the cut-off value of 3.7 is identified as an outlier and removed from the dataset for subsequent iterations. For example, in block 306, replicates shown beyond the threshold line of 3.7 (i.e., replicates extending near 5) are removed from the dataset. FIG. 7 shows the second iteration showing the dataset with data points exceeding the threshold line of 3.7 removed. In the second iteration, based on the same threshold calculation of 1μ + 5σ, a new outlier threshold is determined to be 2.0. Since there are no data points exceeding 2.0, the data points are not determined to be outliers.

[0022] If no outliers are present, process 300 determines that the outliers of the nanoparticles have been removed from the dataset so that nanoparticle baseline determination can be performed. Thereafter, process 300 proceeds to block 312 to determine the nanoparticle baseline based on the data background. For example, FIG. 8 shows the resulting data providing the data background and determines the nanoparticle baseline based on the data background. The calculation formula for the particle baseline is 1*avg background + 3.3*standarddeviation backgroundAlthough it is included, the process is not limited to such values, and different multiples of the mean and standard deviation can be used. For example, in an embodiment, the multiple of the mean and standard deviation is a function selectable by the user. In an embodiment, the multiples of the mean and standard deviation may be different from the multiples of the mean and standard deviation during the iterative removal step of process 300 (e.g., during block 304). For example, the multiples of the mean and standard deviation for particle-based baseline calculation are 1 times and 3.3 times respectively, while the multiples of the mean and standard deviation for iterative removal calculation are 1 times and 5 times respectively. If the multiples of the standard deviation are different, different levels of strictness can be provided when determining which data is considered below or above the particle-based baseline after the determination of the background data set. In an embodiment, process 300 can delete zero values of the data set.

[0023] Referring to FIGS. 9A and 9B, an example of a data set of an iterative step including the calculation formula of 1*avg + 5*standard deviation and the particle-based baseline calculation formula of 1*avg background + 1*standard deviation background is shown. FIG. 9B shows a subset of the data of FIG. 9A in which the determined particle-based baseline 900 is shown above the spectroscopic measurement data 902.

[0024] Referring to FIGS. 10 to 15, the minimum value data processing for determining the detection threshold of nanoparticles is described. FIG. 10 shows an exemplary spectroscopic measurement data set having an approximate background determination for the detection of nanoparticles in which a portion of the data set due to signal background (shown as 1000 and resulting from background interference such as due to an ionized plasma gas from an ICP torch, for example) 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 the nanoparticle detection threshold provides a data boundary such that 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 flowchart of process 1100 for determining a minimum value for determining a detection threshold of nanoparticles and validating a spectroscopic measurement dataset in accordance with an embodiment of the present disclosure is shown. The flowchart begins with the raw dataset being manipulated to remove background at block 1102 and integrate consecutive data points. In an embodiment, the raw dataset includes an intensity-versus-time dataset provided by ICPMS, and the background removed from the raw dataset is determined through data processing such as the iterative determination E described with respect to FIGS. 2 through 9B, a user-selected feature, or a combination thereof. In an embodiment, the dataset is integrated after removing the background from the raw dataset. For example, summing temporally consecutive non-zero data points of the detected intensity, where the data points can be considered temporally consecutive if no intervening zero values are detected by the ICPMS at a given time detection interval such as a 0.01-second detection interval. By integrating after background removal, background removal can filter out lower non-zero data points from the raw dataset to provide zero values, allowing for more zero data points to be present.

[0026] Thereafter, process 1100 continues to block 1104 where a histogram of the manipulated dataset is formed. In an embodiment, the histogram is formed by rounding all integrated 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 truncated to the next lower integer count value (e.g., 3.2 and 3.7 are each truncated to a value of 3). In an implementation, the data is rounded up to the next higher integer count value (e.g., 3.2 and 3.7 are each rounded up to a value of 4). The histogram can be formed from the rounded points based on how many points there are for each (e.g., the frequency of occurrence of each count). An example of a histogram of a simplified dataset is shown with respect to FIGS. 12A through 14.

[0027] Process 1100 further includes, at block 1106, examining the histogram frequency based on the window size of the count to determine a candidate for the minimum count value. In an example, the window size is odd (e.g., a window covering 5 counts), the center value of the window is compared to the values on the left and right of the center position on the histogram, and it is determined whether there is a minimum count (e.g., based on the window size, whether the frequency of the count in the center of the window is less than the frequencies of the counts on the left and right of the center count). For counts at the start of the histogram (e.g., count 0, 1, 2, etc.), the window may be shrunk by not expanding it across the entire window size. For example, FIG. 11A shows a window 1200 covering counts from 1 to 4 (i.e., window size of 4), where the frequency of 26 for count 2 is reviewed to determine whether it is a candidate for the minimum value of a given window. The window may be considered to cover count 0 on the left of count 1 such that count 2 is at the center of window 1200 with a window size of 5 counts, but since there is no data for count 0, the window covers those counts that exist in the histogram. Similarly, before considering count 2, count 1 is reviewed for a candidate for the minimum value, and if count 1 were at the center position of window 1200, the frequency of 51 would be compared to the frequency of 26 for count 2 and the frequency of 12 for count 3, and it would be determined whether 51 is the minimum value, and it is determined that it is not the minimum value.

[0028] In block 1108, process 1100 determines whether the value of the frequency at the center of the window is a minimum value. If the value of the central frequency is not a minimum value, process 1100 proceeds to block 1110, where the window is further incremented to the right of the histogram and the additional count range is reexamined (e.g., via blocks 1106 and 1108) to determine whether the new value of the central frequency is a minimum value. For example, since the frequency of 12 at count 3 and the frequency of 5 at count 4 are each less than 26, the frequency of 26 at count 2 in FIG. 12A is not a minimum value. Thereafter, window 1200 is moved above count 3, and compared with the frequencies of counts 1, 2, 4, and 5 to evaluate whether the frequency of 12 is a minimum value. Since each of the frequency of 5 at count 4 and the frequency of 2 at count 5 is less than 12, the frequency of 12 at count 3 is probably not a minimum value. Thereafter, 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 value.

[0029] Process 1100 continues to evaluate each new iteration of the placement of window 1200. For example, FIGS. 12B and 13 show window 1200 at a further advanced position on the histogram to determine whether the frequency of 2 becomes a candidate for the minimum value of a given window, as compared with FIG. 12A, where window 1200 covers counts 3 through 7 (i.e., window value 5). Since the window placement includes frequency values of 0 for counts 6 and 7 within window 1200, process 1100 does not identify the frequency of 2 as a candidate for the minimum value. This process 1100 continues until a minimum value candidate is identified. For example, FIG. 13 shows the window increasing to the right of the histogram until the frequency of 0, identified as a candidate for the minimum value, is centered above count 6.

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

[0031] Thereafter, process 1100 determines whether the candidate for the minimum value has been verified. In an embodiment, to determine whether the minimum value has been verified, the average value of all frequencies within the window is calculated, and it is determined whether the candidate for the minimum value is within one standard deviation of the window average. In an embodiment, verification can include determining whether the candidate for the minimum value is within a multiple of the standard deviation from the window average. If the candidate for the minimum value is more than one standard deviation from the window average, the candidate for the minimum value does not hold as the minimum value. For example, referring to FIG. 14, the 2 of the frequency value of 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, since 2 + 8.65 is less than 14.25, the frequency value 2, count 2 does not hold.

[0032] Continuing with the example shown in FIG. 14, the window location is increased below the histogram until the center comes above count 6, where the frequency of 0 is determined to be a candidate for the minimum value. The frequency of 0 is within one standard deviation (6.34) of the window average (3.8), which verifies that count 6, frequency 0 is a local minimum. The verified local minimum is used as the detection threshold for the nanoparticles. Intensities greater than the detection threshold are treated as nanoparticles, and intensities less than the detection threshold are treated as background (e.g., ion sample, mass spectrometry interference, nanoparticles of a size below the detection threshold). FIG. 15 shows an example of a dataset analyzed according to the local minimum analysis process, with a detection threshold of 1500 illustrated for separating the background (i.e., intensity values preceding the detection threshold of 1500) from the intensity values corresponding to the nanoparticles present in the sample (i.e., intensity values following the detection threshold of 1500).

[0033] Referring to FIGS. 16 through 19, in accordance with an embodiment of the present disclosure, a process 1600 for utilizing multiple data processes for the determination of nanoparticle baselines and nanoparticle detection thresholds is shown. System 100 can analyze a single data set from sample analyzer 106 in accordance with multiple data processes (e.g., via controller 108) to compare results against each other to determine converging or diverging data results from the multiple data processes. Process 1600 can include using multiple data processes (e.g., data processes 1604, 1606, 1608) to analyze spectroscopic measurement data set 1602 and achieving multiple data results that are automatically switched from one data process to the next in the data process having multiple data processes for analyzing the same spectroscopic measurement data set in accordance with the multiple data processes. For example, after automating the analysis of spectroscopic measurement data set 1602 in accordance with one data process, controller 108 can switch to analyze the first spectroscopic measurement data set 1602 in accordance with one or more different analyses to determine whether the result of the data process is reliable or too divergent. For example, process 1600 is shown with a first data process 1604, a second data process 1606, up to a maximum of 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 process, where n can be any number greater than 2. In an embodiment, controller 108 of system 100 facilitates the analysis of spectroscopic measurement data set 1602 in accordance with one or more of the data processes of process 1600. For example, controller 108 can 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 in accordance with one or more of the data processes of process 1600.

[0034] Data processing (e.g., data processing 1604, 1606, 1608) can include, but is not limited to, iterative decision data processing as described with respect to FIGS. 2 through 9B, minimum value data processing as described with respect to FIGS. 10 through 15, device-specific data analysis processes (e.g., ICPMS analyzer software), user-definable functions and / or user-defined data processing having user-defined values (e.g., where a user can select a reference material and determine a particle-based line and detection threshold to use with that reference material and apply the particle-based line and detection threshold to future samples), or other data processing. Each data processing generates a data result after processing the original spectroscopic measurement data set. For example, first data processing 1604 is shown generating a first set 1610 of data results, second data processing 1606 is shown generating a second set 1612 of data results, and data processing 1608 is shown generating a third set 1614 of data results. Exemplary data results include, but are not limited to, particle-based line 1616, nanoparticle detection threshold 1618, particle count 1620, particle size and standard deviation 1622, etc., and combinations thereof.

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

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

[0037] Referring to FIG. 19, the data results from multiple data processes are shown as being mixed between the converged data result 1700 and the deviated data result 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 deviated data results (e.g., data results 1906, 1908). When the results are mixed such that converged data results and deviated data results are provided from multiple data processes, a statistical model can be used to determine whether there are sufficient converged results to discard or otherwise ignore the deviated results, or whether there are too many deviated results such that the reliability of the data process is low, for example, to determine whether to exclude the results of the data process from the standard deviation analysis of all the results, or to use another model. For example, a simple majority of the converged results can indicate a satisfactory reliability of the converged data results. In an embodiment, if there are a threshold number of deviated data results, the entire data result can be flagged as not reliable by the system 100. For example, the system 100 can be configured such that when two different data processes deviate from the rest of the converged data processes, the controller 108 can identify all of the overall data results as not reliable. Alternatively or additionally, one or more data processes can be used as benchmark data results, and the results of one or more additional data processes can be compared with the benchmark data results to determine the range of deviation from the benchmark.

[0038] This process can include automatically reporting the results of multiple data processes. For example, this process can automatically identify and report which data processes provided reliable (e.g., converging with other data results) or unreliable (e.g., deviating from other data results) 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 one or more data processes. For example, one or more communication signals can be transmitted to a user interface for a laboratory person to review.

[0039] Electromechanical devices (e.g., electric motors, servos, actuators, etc.) may be coupled to the components of System 100 or embedded within the components of System 100 to facilitate autonomous operation via control logic embedded within System 100 or control logic driving System 100 from an external source. The electromechanical devices can be configured to move devices or fluids according to various procedures, such as the procedures described herein. System 100 may include 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.), or may be controlled by a computing system. The computing system can be connected to various components of System 100 directly or via 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 can be communicatively coupled to a system controller, an ICP torch, a carriage motor, a fluid processing system (e.g., valves, pumps, etc.), other components described herein, components that direct their control, or combinations thereof. When executed by the processor or other controller, the program instructions can cause the computing system to control System 100 according to one or more operating modes as described herein.

[0040] It should be recognized that the various functions, control operations, processing blocks, or steps described throughout this disclosure may be executed by any combination of hardware, software, or firmware. In some embodiments, the various steps or functions are executed by one or more of an electronic circuit, logic gate, multiplexer, programmable logic device, application specific integrated circuit (ASIC), controller / microcontroller, or computing system. The computing system may include, but is not limited to, a personal computing system, a mobile computing device, a mainframe computing system, a workstation, an image computer, a parallel processor, or other devices known in the art. Generally, the term "computing system" is broadly defined to include any device having one or more processors or other controllers that execute instructions from a carrier medium.

[0041] Program instructions implementing functions, control operations, processing blocks, or steps as disclosed by the embodiments described herein may be transmitted via a carrier medium or stored on a carrier medium. The carrier medium may be a transmission medium such as a wire, cable, wireless transmission link, etc., but is not limited thereto. The carrier medium may also include, but is not limited to, a non-transitory signal-carrying or storage medium such as read-only memory, random access memory, magnetic disk or optical disk, solid state or flash memory device, or magnetic tape.

[0042] (Conclusion) Although the 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 exemplary forms for carrying out the claims.

Claims

Claim 1 A method for iterative determination of outlier data from a spectroscopic measurement dataset, comprising: transporting a fluid sample containing nanoparticles to a spectroscopic sample analyzer; generating, via the spectroscopic sample analyzer, a spectroscopic measurement dataset related to ion signal intensities detected over time; generating, via one or more computer processors, a raw dataset from the spectroscopic measurement dataset, the raw dataset including a count distribution of counts of ion signal intensities and a frequency of ion signal intensities for each count; iteratively removing ion signal intensity values that exceed a threshold for outliers via the one or more computer processors until no count values exceed a threshold for outliers associated with the sum of a first multiple of the mean of the count distribution of ion signal intensities and a first multiple of the standard deviation of the count distribution of ion signal intensities, thereby providing a background dataset; setting, via the one or more computer processors, a baseline intensity value for the nanoparticles as the sum of a second multiple of the mean of the background dataset and a second multiple of the standard deviation of the background dataset; A method, wherein a first multiple of the standard deviation of the count distribution of ion signal intensities is different from a second multiple of the standard deviation of the background dataset. Claim 2 The method according to claim 1, wherein the spectroscopic sample analyzer is an inductively coupled plasma mass spectrometer (ICP-MS). Claim 3 The method according to claim 2, wherein transporting a fluid sample containing nanoparticles to the spectroscopic sample analyzer includes transporting the fluid sample from a fluid source to an inductively coupled plasma torch and then to the ICP-MS. Claim 4 The method according to claim 3, wherein transporting the fluid sample from the fluid source to the inductively coupled plasma torch includes transporting the fluid sample from the fluid source to the inductively coupled plasma torch via autosampler control of a sample probe. Claim 5 The method according to claim 1, further comprising removing, via the one or more computer processors, data values smaller than the baseline intensity value of the nanoparticles so as to remove a portion of the ion signal intensity values due to background interference from the spectroscopic measurement dataset. Claim 6 The method according to claim 1, wherein a first multiple of the mean of the count distribution of the ion signal intensity is the same as the first multiple of the mean of the background data set.

7. A system for the iterative determination of outlier data from a spectroscopic measurement data set, 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 the ion signal intensity detected over time; one or more computer processors; wherein the one or more computer processors generate, via the one or more computer processors, a raw data set from the spectroscopic measurement data set, the raw data set including a count distribution of the counts of the ion signal intensity and the frequency of the ion signal intensity of each count; iteratively remove ion signal intensity values that exceed a threshold value of outliers via the one or more computer processors until there are no count values that exceed the threshold value of outliers, the threshold value of outliers being associated with the sum of a first multiple of the mean of the count distribution of the ion signal intensity and a first multiple of the standard deviation of the count distribution of the ion signal intensity, to provide a background data set; set a baseline intensity value of the nanoparticles as the sum of a second multiple of the mean of the background data set and a second multiple of the standard deviation of the background data set via the one or more computer processors, wherein the first multiple of the standard deviation of the count distribution of the ion signal intensity is different from the second multiple of the standard deviation of the background data set; a non-transitory computer-readable medium storing one or more instructions for execution by the one or more computer processors to cause the one or more computer processors to perform the above. A system comprising.

8. The system according to claim 7, wherein the spectroscopic sample analyzer is an inductively coupled plasma mass spectrometer (ICPMS).

9. The system according to claim 8, further comprising an inductively coupled plasma torch fluidly coupled between the sample source and the ICPMS.

10. The system according to claim 9, further comprising an autosampler that instructs control of a sample probe to introduce a fluid sample into the inductively coupled plasma torch.

11. The system according to claim 7, wherein the one or more instructions further comprise one or more instructions for execution by the one or more computer processors to cause the one or more computer processors to remove data values smaller than the baseline intensity value of the nanoparticles from the spectroscopic measurement data set to remove the portion of the ion signal intensity value due to background interference.

12. The system according to claim 7, wherein a first multiple of the mean of the count distribution of the ion signal intensity is the same as a first multiple of the mean of the background data set.

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