Pretreatment for fluid transfer quality evaluation

Abstract

A system is provided for evaluating the quality of a fluid process performed on a fluid, the fluid process including one of fluid suction and fluid dispensing. The system includes a pump; a probe; a connection, wherein a fluid path is formed at least partially through the connection between the pump and the probe; a pressure sensor in fluid communication with the fluid path and configured to sense a pressure of the pump during a fluid process, where pressure measurement data includes the pressure of the pump sensed during the fluid process; and a processor; and a memory including instructions executed by the processor.

Description

[0001] Cross-references to related applications This application claims the benefit of U.S. Provisional Patent Application No. 63 / 517,861, filed August 4, 2023, entitled “PRE-PROCESSING FOR FLUID TRANSFERQUALITY ASSESSMENT,” the disclosure of which is incorporated herein by reference in its entirety for all purposes. Technical Field

[0002] This disclosure generally relates to methods for pretreatment prior to quality assessment of fluid processes, and particularly to quality assessment of fluid aspiration or dispensing in laboratory diagnostic instruments. Background Technology

[0003] In laboratory diagnostic instruments, human samples are mixed with chemical reagents, and the chemical reactions are studied / measured to predict the health condition of the analyte or patient. Reagents are stored in small plastic reagent packets, while samples are stored in test tubes. Probes connected to a pump are used to aspirate or “draw” the required volume of reagent from the reagent packet or the required volume of sample from the test tube. Because the volume of reagent or sample drawn is crucial for a successful diagnostic test, a pressure sensor is used to prepare a pressure-time curve, referred to herein as a “pressure trace.” The shape of the pressure trace is used to predict whether the full volume has been aspirated or only a portion of the volume; in the case of only aspirating a portion of the volume, the condition is referred to as a “short” aspiration. This pressure monitoring is used to reduce the risk of reporting incorrect patient results due to incorrect reaction volumes.

[0004] Threshold-based methods for checking aspiration volume are based on studying pressure traces and determining aspiration quality by relating the calculated slope, plateaus, or other features of the pressure trace at certain points on the curve of pressure values ​​versus time to a threshold.

[0005] The various inflection points, slopes, and heights of the plateau segment are used to infer aspiration quality. Different volumes cause these points to shift along the pressure trace. This means that the same points along the x and y axes cannot be used to collect data for comparison with thresholds for different aspiration volumes. This problem is currently addressed on various instrument platforms by storing the inflection points for each aspirated volume (immunoassay module approach) or by writing functions in which the inflection points are the dependent variable and time or volume is the independent variable (clinical chemistry module approach). These solutions either create a lifelong need to maintain a set of different points along the x and y axes to collect data for comparison with thresholds for different aspiration volumes, or increase the complexity of the algorithm.

[0006] For different assays, the aspirated reagent or sample volume can be different (e.g., 0 to 1000 uL). As a result, the pressure trace has different lengths, in part due to different aspiration durations. Moreover, for larger aspiration volumes, the pump needs to operate at higher speeds to meet the instrument time period requirements. In addition to the fluid properties and the probe / tube diameter / length, the shape of the pressure trace is affected by the aspiration time (longer duration along the x-axis for larger volumes) and the aspiration speed (lower depth of the bathtub for higher speeds). Because the pressure trace can be different, it is necessary to use different points along the x-y axes to collect data to be compared against thresholds for different volumes. As described above, whether it is a traditional threshold-based algorithm or a futuristic machine learning algorithm for aspiration quality evaluation, the pressure trace can create additional lifetime work to maintain different points along the x-y axes to collect data to be compared against thresholds for different volumes.

[0007] The present disclosure is directed to overcoming these and other problems of the prior art. SUMMARY

[0008] Embodiments of the present invention address and overcome one or more of the above-referenced shortcomings and drawbacks by providing systems, methods, and computer program products for fluid process quality evaluation. Additional features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments with reference to the drawings.

[0009] In an example embodiment, a system for evaluating a quality of a fluid process performed on a fluid, the fluid process comprising one of fluid aspiration and fluid dispensing, is provided. The system comprises a pump; a probe; a connection forming a fluid path between the pump and the probe at least partially through the connection; a pressure sensor in fluid communication with the fluid path and configured to sense a pressure of the pump during the fluid process, wherein pressure measurement data comprises the pressure of the pump sensed during the fluid process; and a processor; and a memory. The memory comprises instructions executed by the processor to cause the processor to receive pressure measurement data from the pressure sensor during the fluid process, wherein the pressure measurement data comprises pressure data and time data; modify the pressure measurement data to conform to a common domain; identify a datum at a common correlation plot region of the modified pressure measurement data, wherein the common correlation plot region comprises a correlation plot region usable to determine a fluid process quality of the fluid performed on a plurality of volumes of fluid conforming to the common domain; calculate a quality value using the datum; compare the quality value to a threshold value, and determine a quality of the fluid process by the processor based on the comparison.

[0010] In some embodiments, the system further comprises an analyzer configured to analyze a mixture comprising the fluid when the quality of the fluid process is satisfactory. In some embodiments, the instructions further cause the processor to cause the pump to operate to perform a subsequent fluid process in response to determining that the quality of the fluid process is abnormal. In some embodiments, the common correlation plot region comprises a subset of the modified pressure measurement data associated with a pump phase. In some embodiments, the time data comprises one of a duration and a timestamp. In some embodiments, modifying the pressure measurement data comprises compressing the pressure measurement data in one of a time domain and a pressure domain. In some embodiments, modifying the pressure measurement data comprises reducing a number of data points. In some embodiments, modifying the pressure measurement data comprises converting the time data to an integer; and interpolating new pressure data. In some embodiments, interpolating new pressure data comprises one of linear interpolation and non-linear interpolation. In some embodiments, modifying the pressure measurement data comprises converting the pressure data to an integer; and interpolating new time data. In some embodiments, the quality of the fluid process indicates one or more of: an actual aspirated volume is less than an expected aspirated volume, an actual aspirated volume is equal to an expected aspirated volume, air is present in an actual aspirated volume, and a blockage is present in an actual aspirated volume.

[0011] In another example embodiment, a method of quality evaluation of a fluid process is provided. The method includes performing, by a fluid handling system, the fluid process on a fluid, wherein the fluid process includes one of fluid aspiration and fluid dispensing; measuring, by the fluid handling system, pressure measurement data during the fluid process, wherein the pressure measurement data includes pressure data and time data; modifying the pressure measurement data to conform to a common domain; identifying a fiducial at a common correlation plot region of the modified pressure measurement data, wherein the common correlation plot region includes a correlation plot region that can be used to determine a fluid process quality of the fluid process performed on a plurality of volumes of fluid that conform to the common domain; calculating a quality value using the fiducial; comparing the quality value to a threshold value; and determining the quality evaluation of the fluid process based on the comparison result.

[0012] In some embodiments, the method further includes analyzing a mixture including the fluid in response to determining that the quality evaluation of the fluid process is satisfactory. In some embodiments, the method further includes aspirating, by the fluid handling system, an additional fluid process in response to determining that the quality evaluation of the fluid process is abnormal. In some embodiments, the common correlation plot region includes a subset of the modified pressure measurement data associated with a pump phase. In some embodiments, the time includes one of a duration and a timestamp. In some embodiments, wherein modifying the pressure measurement data includes compressing the pressure measurement data in one of a time domain and a pressure domain. In some embodiments, modifying the pressure measurement data includes reducing a number of data points. In some embodiments, the common domain includes a common time domain, and modifying the pressure measurement data includes compressing the time data to the common time domain; and interpolating new pressure data.

[0013] In yet another example embodiment, a computer program product embodied in a computer readable storage medium is provided. The computer readable storage medium includes software that, when executed by a processor, performs a method including: receiving, from a pressure sensor, pressure measurement data during a fluid process performed on a fluid, wherein the pressure measurement data includes pressure data and time data; modifying, by the processor, the pressure measurement data to conform to a predetermined value; identifying a fiducial at a common correlation plot region of the modified pressure measurement data, wherein the common correlation plot region includes a correlation plot region that can be used to determine a fluid process quality of the fluid performed on a plurality of volumes of fluid that conform to the common domain; calculating a quality value using the fiducial; comparing the quality value to a threshold value; and determining, by the processor, a quality of the fluid process based on the comparison result.

[0014] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the DETAILED DESCRIPTION. This Summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Additional features and advantages of the disclosed technology will be BRIEF DESCRIPTION OF DRAWINGS

[0015] The foregoing aspects and other aspects of the present application are best understood with reference to the following detailed description when read in conjunction with the accompanying drawings. For the purposes of illustrating the present application, there is shown in the drawings a presently preferred embodiment, it being understood, of course, that the application is not limited to the precise arrangements and instrumentalities shown. The drawings include the following figures: Figure 1 is a graph of representative pressure traces for various aspiration volumes and at different aspiration speeds according to embodiments of the present disclosure; Figure 2 is an aspiration system with pressure monitoring according to embodiments of the present disclosure; Figure 3 is an example of short aspiration detection according to embodiments of the present disclosure; Figure 4 is a method of aspiration quality evaluation according to embodiments of the present disclosure; Figures 5A-5C is a graph showing unmodified pressure versus time data and modified pressure versus time data using linear interpolation according to embodiments of the present disclosure; Figure 6 is a table of linear interpolation modified pressure versus time data according to embodiments of the present disclosure; Figure 7 is a graph of unmodified pressure versus time data and modified pressure versus time data from Figure 6 according to embodiments of the present disclosure.

[0016] Figures 8A-8D is a pressure trace annotated with relevant plotted areas according to embodiments of the present disclosure; Figure 9 is a pressure trace annotated with examples of calculating aspiration quality values using data from the pressure trace and using them to evaluate aspiration quality according to embodiments of the present disclosure; and Figure 10 illustrates an example computing environment according to embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] Individuals with a male or female gender identity are included in the use of the grammatical term, independent of the use of the term.

[0018] The present disclosure describes systems, methods, and computer program products for quality evaluation of fluid processes. In some embodiments, the systems, methods, and computer program products described herein can be used, for example, in an IVD environment, with IVD equipment, or as part of an IVD method. For example, in some embodiments, the systems, methods, and computer program products described herein can be used with diagnostic or chemical analyzers, such as automated clinical diagnostic analyzers and automated clinical chemistry analyzers. These analyzers are capable of processing hundreds of thousands of human sample diagnostic tests per year. These tests can be ordered for patients by primary care physicians (e.g., general health checkups), specialists (e.g., cardiac, cancer), and in a hospital setting (e.g., prior to treatment or surgery).

[0019] The presently disclosed subject matter is described in the context of using the presently disclosed subject matter during fluid aspiration. However, the present disclosure is not limited to this, and can be applicable to other processes. The present disclosure may, for example but not limited to, be used during fluid dispensing. Such implementations and applications are contemplated to be within the scope of the present disclosure. Thus, when the present disclosure is described in the context of using the present disclosure during fluid aspiration, it will be understood that other implementations, such as fluid dispensing, can be used instead of those described.

[0020] Further, the presently disclosed subject matter is primarily described in the context of pressure traces, the shape of which changes according to the volume of fluid aspirated. However, as will be appreciated by one of ordinary skill in the art, the shape of the pressure trace can also change according to various other variables, including, for example, aspiration duration, aspiration speed, and probe and tubing geometry. It is to be understood that the systems, methods, and computer program products described herein work for all aspiration quality evaluation of all pressure traces, regardless of the variables driving their change in shape.

[0021] Further, the presently disclosed subject matter is primarily described in the context of plotted modified or unmodified pressure versus time data. However, as will be appreciated by one of ordinary skill in the art, the data itself, rather than a plot or graph of the data, can be analyzed according to the systems, methods, and computer program products disclosed herein. In other words, in some embodiments, the data is not plotted at all.

[0022] Determining the quality of metered sample or reagent transfer is an important function of clinical diagnostic instruments, for example, in an IVD environment. Typical failure modes that are desirable to detect include probe clogging and insufficient sample or reagent in a fluid vessel, among others. A common method for determining aspiration (or dispensing) quality is to monitor the event (such as a pump stroke) and compare the actual volume to the expected volume. This method is limited in that it only detects a failure if the event is not executed at all. For example, if a pump stroke is executed but the volume is not the expected volume, the method described above will not detect the failure. Figure 2(As illustrated in the diagram) During the period, the pressure in the suction system is monitored, analyzed, and the quality of suction / dispensing is determined. Figure 2 This is a suction system with pressure monitoring according to embodiments of the present disclosure. In some embodiments, the suction system 100 may include a pump 101, a tube 102, a pressure transducer 103, a probe 104, and a fluid vessel 105.

[0023] One method for detecting suction quality is to compare the suction pressure signal with a predetermined value for air or other fluids or gases, as disclosed in U.S. Patent No. 7,867,769, which is hereby incorporated herein by reference in its entirety. Another method is to analyze the pressure signal to detect anomalies, as disclosed in U.S. Patent Nos. 6,370,942 and 7,634,378, which are hereby incorporated herein by reference in their entirety.

[0024] Factors affecting the pressure signal can include, for example, the pressure drop during suction and the diameter and length of the probe and tube. The pressure drop during suction can be indicated by: (1) fluid or gas properties (e.g., density, dynamic viscosity, etc.), (2) flow rate, and (3) the diameter and length of the probe and tube. In one embodiment, this can be demonstrated by utilizing analysis that applies Bernoulli's equation to the steady state portion of the suction, as follows: P = Pressure = Density = Gravitational constant α = Energy correction factor v = Velocity h = Height h L = Head loss K L = Head loss factor Q = Flow rate A = Area f = Friction factor l = Length D = Hydraulic diameter Re = Reynolds number µ = Dynamic viscosity in: It is the effect of fluid density. It is the effect of the geometry of the tubes and pipettes. It is the effect of the liquid column height It is the effect of flow resistance.

[0025] Further analysis can provide more details about the effects of probe diameter and fluid viscosity. The equations below show the main influencing factors on pressure drop, demonstrating that viscosity directly affects the pressure drop during suction, and that probe diameter has a very significant effect.

[0026] .

[0027] Available Figure 1 The effect of the suction flow rate can be seen in the diagram. Figure 1 This is a graph showing representative pressure traces for various suction volumes and different suction rates according to embodiments of the present disclosure. As those skilled in the art will expect, regardless of whether fluid or air is being pumped, the pressure decreases as the flow rate increases. The difference between air or fluid pumping also increases proportionally. Note that varying volumes pumped using the same flow rate have the same pressure drop.

[0028] Conventional algorithms typically identify key points or regions on a pressure curve, referred to in this paper as “Relevant Plotted Areas” or “RPAs”. Data values ​​within these RPAs can then be directly compared to thresholds (such as minimum pressure) or used in calculations to determine values ​​that can then also be compared to one or more thresholds. For example, the best-fit slope can be calculated across regions of the curve, or differences can be compared between points (or the average of points) at two or more regions of the pressure curve. Typically, each curve is evaluated against multiple criteria, each optimized to find specific characteristics or failure modes.

[0029] For example, such as Figure 3 As illustrated in the diagram, the pressure values ​​at the RPA points marked with "evaluation points" on each line can be compared with thresholds to determine the quality of suction. Figure 3 The diagram illustrates numerous air and fluid pumping operations. During the two fluid pumping operations in these operations, insufficient fluid was available, resulting in "short" pumping. Photometric analysis of the transferred material reveals these short pumping operations to be 40% and 16%, respectively. While the 40% short pumping operation is clearly distinguishable from the norm (-140 ms) at the end of the steady-state pumping operation, the 16% short pumping operation is slightly more difficult to differentiate.

[0030] As described above, pressure traces have different lengths (i.e., along the x-axis) and heights (i.e., along the y-axis) for various reasons (including, for example, the volume of fluid being pumped). Because the length and / or height of the pressure trace can be different for each volume pumped, the RPA used to collect data for comparison with a threshold can be different for each volume pumped. For example, although in Figure 3 In this study, the RPA for determining whether a 50 µL aspiration is short is at T = 140 ms, but this RPA will be inaccurate for different aspiration volumes (e.g., 100 µL). Therefore, the RPA for each aspiration volume must be determined, stored, and maintained, or alternatively or additionally, a complex inflection point correlation algorithm can be used.

[0031] However, when the pressure trace is compressed (or expanded) to a fixed length in the time domain, the slope, plateau, and other characteristics of the pressure trace will essentially align regardless of the volume of fluid pumped. The method disclosed herein utilizes this finding by providing a way to determine pumping quality by compressing the pressure trace to a fixed length in the time domain.

[0032] Those skilled in the art may be discouraged from compressing pressure traces prior to analysis, fearing that compression might negatively impact the results of subsequent analyses. However, when a sufficiently high sampling frequency is used during aspiration / dispensing, the shape of the pressure trace is preserved after compression, and therefore the results are not negatively affected by the compression.

[0033] By adjusting the sampling rate of each suction / dispensing operation to output a fixed number of data points, a pressure trace of fixed length in the time domain or fixed height in the pressure domain can also be achieved. However, this method is impractical because the total duration of suction / dispensing is not always known in advance. Compressing the pressure trace after suction / dispensing effectively provides a variable sampling rate without having to adjust the sampling rate of each suction / dispensing operation.

[0034] Compressing and analyzing pressure traces offers several advantages. First, RPA and algorithm maintenance can be simplified. Second, the amount of data required for algorithm development can be significantly reduced. Third, it can be used to accurately analyze pressure traces for which there is insufficient data to generate accurate RPAs. Fourth, it reduces reliance on the experience of technicians interpreting pressure traces.

[0035] Compressing and analyzing the pressure trace simplifies the maintenance of the RPA and algorithm; it allows for the use of a single RPA set for any pumped fluid volume, rather than a separate RPA set for each volume, as long as the pressure trace for that volume is compressed. Therefore, there is no longer a need for complex inflection point-related functions or for developing, storing, and maintaining multiple RPA sets for multiple volumes; only a single RPA set is required.

[0036] Additionally, compressing and analyzing the pressure trace significantly reduces the amount of expensive data required for developing machine learning algorithms. Instead of training and retraining the machine learning algorithm for each fluid volume to be pumped, only one machine learning algorithm is needed for a pressure trace compressed to a predetermined width. This greatly reduces the amount of data required for training and retraining purposes.

[0037] Even more importantly, compressing and analyzing the pressure trace allows for accurate analysis of pressure traces for which insufficient data is available to generate an accurate RPA. For less common volumes (e.g., 38 µL), there may not be enough data to accurately determine the RPA. However, with this solution, the RPA works for all volumes (assuming they are compressed), including those for which insufficient data exists to determine an accurate RPA. In other words, compressing and analyzing the pressure trace makes the analysis of a specific volume, previously impossible, possible.

[0038] Furthermore, compressing and analyzing the pressure traces reduces reliance on the experience of those skilled in the art to interpret them. As those skilled in the art will appreciate, they gain experience in interpreting pressure traces across various volumes. For example, a technical expert who has reviewed hundreds of pressure traces may have the experience to know that a certain event on a pressure trace is normal for one suction volume but abnormal for another. However, using the suction quality assessment method disclosed herein, the technician only needs to review one plot, rather than reviewing a plot for each volume to gain experience.

[0039] Figure 4 This is a flowchart of an embodiment of a method for determining aspiration quality according to an embodiment. At step 401, method 400 may include aspirating fluid. The fluid may be anything that generates a pressure profile, such as a biological sample, a fluid reagent, or air. At step 402, method 400 may include measuring pressure over time while aspirating the fluid. In some embodiments, the pressure may be measured by a pressure sensor connected to a tube between the pump and the probe. The pressure may be measured using a timestamp or duration.

[0040] In some embodiments, the raw pressure-time data can be filtered to reduce or eliminate noise. In some embodiments, an outlier electrical noise pre-filter can be applied to the raw data points (i.e., a data point with an increment (delta) greater than 2500 can be replaced by a moving average of the preceding three data points). This can be limited to 10% of the raw data points. In some embodiments, a Butterworth low-pass filter can also be applied to the raw data in each of the three motion phases with different coefficients (i.e., cutoff frequencies). For example, for the acceleration delay phase, the filter can be undamped using a cutoff value of 0.05; for the sway phase, the filter can be critically damped using a cutoff value of 0.04; and for the deceleration delay phase, the filter can be underdamped using a cutoff value of 0.075. In some embodiments, the filtered or raw pressure data over time can be plotted.

[0041] At step 403, the method may include modifying the pressure-time data to conform to a common pattern. In some embodiments, the common pattern is defined by a maximum time value. For example, the maximum time value can be any value not exceeding 100 ms in terms of duration. In some embodiments, the common pattern is defined by a time period, such as 3:00 PM to 3:05 PM. In other embodiments, the common pattern is defined by a minimum pressure value. For example, the minimum pressure value can be any value not less than a count of -400.

[0042] In some embodiments, a common scheme is defined by a number of data points. For example, a common scheme may include 100 data points. Figures 5A-5C An example of this embodiment is illustrated. Figures 5A-5C This is a graph showing unmodified pressure-relative time data and pressure-relative time data modified using linear interpolation according to embodiments of the present disclosure. Figure 5A This is a graph of unmodified pressure-to-time data for pressure traces of 38µL, 50µL, and 100µL according to embodiments of this disclosure. Figure 5B It has a common timing scheme that has been modified to conform to 100 data points from 1 to 100. Figure 5A A graph of pressure relative to time. Figure 5C They are respectively possessing Figure 5A and 5B A graph showing both unmodified and modified data on pressure relative to time.

[0043] Pressure-to-time data can be conformed to common time schemes using any method known in the art. In some embodiments, pressure-to-time data can be compressed or expanded to conform to common time schemes. For example, depending on the applicable circumstances, this can be visualized as stretching or compressing a pressure trace along the x-axis (or y-axis, or both x-axis and y-axis) and stopping when the desired pressure trace length is achieved.

[0044] In some embodiments, data points may be removed from or added to the pressure-relative-time data. For example, if a common timing scheme has 100 data points and the unmodified pressure-relative-time data has 200 data points, modifying the pressure-relative-time data may include removing every other data point to reduce the pressure-relative-time data to 100 data points. For another example, if a common timing scheme has 100 data points and the pressure-relative-time data has 50 data points, modifying the pressure-relative-time data may include adding a data point between each of the original data points to increase the pressure-relative-time data to 100 data points. As those skilled in the art will appreciate, for example, interpolation may be used to determine the additional data points.

[0045] In some embodiments, interpolation can be used to generate new pressure-to-time data based on the original pressure-to-time data. For filtered data, interpolation can be performed before or after filtering. Interpolation can help maintain the shape of the pressure trace. Interpolation can be linear or non-linear. Figure 6 This is a table of linear interpolations of modified pressure-time data according to embodiments of this disclosure. Figure 6 In the table, column "38-x1" contains the original time data, and column "38-y1" contains the original pressure data. Column "38-xnew" contains the modified time data. Figure 6 In the embodiment illustrated, the new time data is an integer starting with zero and ending with seventeen. The column "38-ynew" includes the modified pressure data. The modified pressure data is calculated using linear interpolation. As those skilled in the art will appreciate, the columns "slope-m", "yintercept-c", and "ycomp" include values ​​that are helpful in calculating the modified pressure data. Figure 7 It is from the embodiments of this disclosure Figure 6 A graph of unmodified pressure relative to time data and modified pressure relative to time data.

[0046] In some embodiments, the `interpolate.interp1d(x, y)` function from the Python SciPy library can be used to generate new pressure-relative-time data. However, new pressure-relative-time data can also be generated using any other software program. To generate new pressure-relative-time data using, for example, Python SciPy's `interpolate.interp1d(x, y)`, the following steps can be performed: Map the pressure trace values ​​y to unmodified time intervals x. Next, scale x to the required length (e.g., 100). Interpolate the y values ​​to integers onto this new compressed x-scale, producing a consistent number of points for each pressure trace.

[0047] To illustrate, consider the following example. Consider the following input: aspiration volume 38uL, Original_Trace_Length = 334, Compressed_Trace_Size = 100. The Y array preserves the original, unmodified pressure trace. The length of y depends on the aspirated volume. Y array = row[0: Original_Trace_Length -1] = row[0: 334-1] = (-86, -94, -102, ...) = 334 data points.

[0048] The step size is the difference between neighboring x values ​​mapped from the original uncompressed x-scale to the compressed scale. The step size depends on the scaling ratio of the compressed pressure trace to the uncompressed pressure trace. Step_Size = Compressed_Trace_Size / Original_Trace_Length = 100 / 334 = 0.299.

[0049] The x array stores incrementing fractional numbers from zero to Compressed_Trace_Size. The length of x depends on the volume of aspiration. X array = np.arange(0, Compressed_Trace_Size, Step_Size) = (0, 0.299, 0.598, ... , 99.701) = 334 data points.

[0050] The xcomp array stores integer x values ​​of the last compressed length specified by Comp_Trace_Size. XcompArray = np.arange(0, Compressed_Trace_Size) = (0, 1, 2, ...., 99) = 100 data points.

[0051] The following function interpolates the y-values ​​at fractional x-values ​​in the mapped x-array to integers on a new compressed x-scale: `f` is a one-dimensional (1d) interpolation function from the Python SciPy library. Linear interpolation is used. However, non-linear interpolation can also be used in this application. `f = interpolate.interp1d(x, y)`

[0052] The Ycomp array contains the compressed pressure trace after applying the function f to xcomp. Ycomp = f(xcomp) = (-86, -103.333 -80, ... ) = 100 data points.

[0053] Figure 5 and Figure 6 The first 18 points of the reproduced 38µL unmodified pressure trace are shown. Neighboring points are joined by straight lines, and the slopes m and y-intercepts of these individual lines are calculated using the following formula. This calculation is done only to explain the linear interpolation. All of this can be done automatically using the interpolate.interp1d() function in the SciPy library.

[0054] The slope m = (y2-y1) / (x2-x1).

[0055] Y-intercept c = y2 — m.x2.

[0056] Ycomp is calculated manually below, and it closely matches the compressed 38-ynew obtained via function f.

[0057] ycomp = m.38-xnew + c.

[0058] In some embodiments, method 400 may include plotting modified pressure-to-time (RPA) data. As the modified RPA data is plotted, a common RPA can be applied to the plot. The RPA is a plotting area defined by x and y coordinates. Data located within the RPA is used to calculate a suction quality value. The RPA can be as small as a single data point. The suction quality value is a value that can be compared to a threshold to determine the suction quality.

[0059] Examples of suction mass values ​​include, but are not limited to, slope, viscosity increment, residual, and blockage increment. The linear regression slope of the oscillation phase is a suction mass value that can indicate the material density of the fluid. Therefore, it is important to know when the oscillation phase begins and ends. RPA 801a can be applied to regions of the graph associated with the oscillation phase, such as... Figure 8AAs illustrated in the figure, the linear regression slope of the pressure trace within the RPA can be calculated. This slope can be compared with a threshold to determine the material density of the fluid.

[0060] The viscosity increment is a pumping mass value that indicates the material viscosity of a fluid. It can be calculated by subtracting the final pressure value of the sway phase from the final pressure of the post-pumping delay phase. Therefore, knowing when the sway phase and the post-pumping delay phase begin and end is important. RPA 801b can be applied to the region of the graph associated with the sway phase, and another RPA 801c can be applied to the region of the graph associated with the post-pumping delay phase, such as... Figure 8B As illustrated in the diagram. The final pressure value of each of these RPAs can be used to calculate the viscosity increment. The viscosity increment can be compared to a threshold to make a determination about the material density of the fluid.

[0061] The residual is the suction mass value that indicates an abnormal suction distribution pattern (e.g., a short suction). Using data from the pendulum compression phase, the residual can be calculated using the following equation: Therefore, knowing when the oscillation phase begins and ends is important. RPA 801d can be applied to regions of the graph associated with the oscillation phase, such as... Figure 8C As illustrated in the diagram. The input required to calculate the residuals can be extracted from the region of the plot identified as RPA 801d. The residuals can be compared with a threshold to determine the distribution of the extraction.

[0062] The blockage increment is a suction quality value that indicates the primary obstruction. It can be calculated by subtracting the first pressure value from the last pressure value on the pressure trace. Therefore, knowing when the first and last pressure values ​​are present is important. When a common timing scheme is, for example, an integer from 0 to 100, the first pressure value would be the pressure value at x=0, and the last pressure value would be the pressure value at x=100. An exemplary RPA in... Figure 8D The figures are illustrated as 801e and 801f. The blocking increment can be compared with a threshold to determine the presence of probe obstruction.

[0063] Back Figure 4 In some embodiments, method 700 may optionally include offsetting or biasing the pressure trace so that its initial value is zero. For example, this can be done by subtracting the difference between the initial value of the pressure trace and zero for each data point in the pressure trace before or after the pressure trace conforms to a common domain. This can have the effect of removing pressure sensor bias differences, thereby removing an input "noise" source that the algorithm would otherwise need to address.

[0064] At step 404, method 400 may include calculating a suction mass value. At step 405, the method may include comparing the suction mass value to a threshold. Each suction mass value may be compared to its own threshold. For example, there may be a slope threshold, a viscosity increment threshold, a residual threshold, and a blockage increment threshold. In some embodiments, the thresholds used for comparison to evaluate pressure curves in order to produce pass / fail results are determined empirically. In other words, a large number (hundreds to thousands) of pressure curves may be generated for each condition, and a statistical distribution of the calculated values ​​may be established. These distributions can be used to determine pass / fail limits (i.e., thresholds) with appropriate consumer / producer risk levels. This determination must be performed for each set of conditions (e.g., flow rate, volume, sample type, etc.).

[0065] Figure 9 The pressure traces are according to embodiments of the present disclosure, and the pressure traces are annotated with examples of calculating suction quality values ​​using data from the pressure traces and using them to evaluate suction quality.

[0066] Figure 10 An exemplary computing environment 1000 on which embodiments of the present invention may be implemented is illustrated. For example, the computing environment 1000 may be configured to perform a method of placing an item with irregular dimensions. The computing environment 1000 may include a computer system 1010, which is an example of a computing system on which embodiments of the present invention may be implemented. Computers and computing environments (such as computer system 1010 and computing environment 1000) are known to those skilled in the art and are therefore briefly described herein.

[0067] like Figure 10 As shown, the computer system 1010 may include communication mechanisms, such as bus 1005 or other communication mechanisms for transmitting information within the computer system 1010. The computer system 1010 also includes one or more processors 1020 coupled to bus 1005 for processing information. Processor 1020 may include one or more central processing units (CPUs), graphics processing units (GPUs), or any other processor known in the art.

[0068] Computer system 1010 also includes system memory 1030 coupled to bus 1005 for storing information and instructions to be executed by processor 1020. System memory 1030 may include computer-readable storage media in the form of volatile and / or non-volatile memory, such as read-only memory (ROM) 1031 and / or random access memory (RAM) 1032. System memory RAM 1032 may include one or more other dynamic storage devices (e.g., dynamic RAM, static RAM, and synchronous DRAM). System memory ROM 1031 may include one or more other static storage devices (e.g., programmable ROM, erasable PROM, and electrically erasable PROM). Additionally, system memory 1030 may be used to store temporary variables or other intermediate information during instruction execution by processor 1020. Basic input / output system (BIOS) 1033 contains basic routines such as those that help transfer information between elements within computer system 1010 during startup; these basic routines may be stored in ROM 1031. RAM 1032 may contain data and / or program modules that are readily accessible to and / or currently being operated on by processor 1020. System memory 1030 may additionally include, for example, operating system 1034, application programs 1035, other program modules 1036, and program data 1037.

[0069] Computer system 1010 also includes a disk controller 1040 coupled to bus 1005 to control one or more storage devices for storing information and instructions, such as hard disks 1041 and removable media drives 1042 (e.g., floppy disk drives, optical disk drives, tape drives, and / or solid-state drives). Storage devices can be added to computer system 1010 using appropriate device interfaces such as Small Computer System Interface (SCSI), Integrated Device Electronics (IDE), Universal Serial Bus (USB), or FireWire.

[0070] Computer system 1010 may also include a display controller 1065 coupled to bus 1005 to control display 1066, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user. Computer system 1010 includes an input interface 1060 and one or more input devices, such as a keyboard 1062 and a pointing device 1061, for interacting with the computer user and providing information to processor 1020. Pointing device 1061 may be, for example, a mouse, trackball, or pointing stick, for transmitting directional information and command selections to processor 1020 and for controlling cursor movement on display 1066. Display 1066 may provide a touchscreen interface that allows input to supplement or replace the directional information and command selections transmitted by pointing device 1061.

[0071] In response to processor 1020 executing one or more sequences of instructions contained in memory (such as system memory 1030), computer system 1010 may perform some or all of the processing steps of embodiments of the present invention. Such instructions may be read into system memory 1030 from another computer-readable medium (such as hard disk 1041 or removable media drive 1042). Hard disk 1041 may contain one or more data storage devices and data files used by embodiments of the present invention. The contents of the data storage devices and data files may be encrypted to improve security. Processor 1020 may also be employed in a multiprocessing arrangement to execute one or more sequences of instructions contained in system memory 1030. In alternative embodiments, hardwired circuitry may be used instead of or in combination with software instructions. Therefore, embodiments are not limited to any particular combination of hardware circuitry and software.

[0072] As stated above, computer system 1010 may include at least one computer-readable medium or memory for storing instructions programmed according to embodiments of the present invention and for containing data structures, tables, records, or other data described herein. As used herein, the term "computer-readable medium" refers to any medium involved in providing instructions to processor 1020 for execution. Computer-readable media can take many forms, including but not limited to non-volatile media, volatile media, and transmission media. Non-limiting examples of non-volatile media include optical discs, solid-state drives, magnetic disks, and magneto-optical disks, such as hard disk 1041 or removable media drive 1042. Non-limiting examples of volatile media include dynamic memory, such as system memory 1030. Non-limiting examples of transmission media include coaxial cables, copper wires, and optical fibers, including conductors constituting bus 1005. Transmission media may also take the form of acoustic waves or light waves (such as those generated during radio wave and infrared data communication).

[0073] The computing environment 1000 may also include a computer system 1010 operating in a networked environment using a logical connection to one or more remote computers, such as remote computer 1080. Remote computer 1080 may be a personal computer (laptop or desktop), mobile device, server, router, network PC, peer-to-peer device, or other common network node, and typically includes many or all of the elements described above with respect to computer system 1010. When used in a networked environment, computer system 1010 may include a modem 1072 for establishing communication over a network 1071, such as the Internet. Modem 1072 may be connected to bus 1005 via user network interface 1070 or via another suitable mechanism.

[0074] Network 1071 can be any network or system generally known in the art, including the Internet, intranet, local area network (LAN), wide area network (WAN), metropolitan area network (MAN), direct connection or a series of connections, cellular telephone network, or any other network or medium capable of facilitating communication between computer system 1010 and other computers (e.g., remote computer 1080). Network 1071 can be wired, wireless, or a combination thereof. Wired connections can be implemented using Ethernet, Universal Serial Bus (USB), RJ-11, or any other wired connection generally known in the art. Wireless connections can be implemented using Wi-Fi, WiMAX and Bluetooth, infrared, cellular networks, satellite, or any other wireless connection method generally known in the art. Additionally, several networks can operate independently or communicate with each other to facilitate communication within network 1071.

[0075] Embodiments of this disclosure can be implemented using any combination of hardware and software. Additionally, embodiments of this disclosure can be included in an article of manufacture having, for example, a computer-readable, non-transitory medium (e.g., one or more computer program products). This medium embodies computer-readable program code therein, for example, mechanisms for providing and facilitating embodiments of this disclosure. This article of manufacture can be included as part of a computer system or sold separately.

[0076] While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for illustrative purposes and are not intended to be limiting; the true scope and spirit are indicated by the following claims.

[0077] As used herein, an executable application includes code or machine-readable instructions for conditioning a processor to perform predetermined functions, such as those of an operating system, contextual data acquisition system, or other information processing system, for example, in response to a user command or input. An executable program is a segment of code or machine-readable instructions, subroutines, or other distinct sections of code or executable application used to perform one or more specific procedures. These procedures may include receiving input data and / or parameters, performing operations on received input data and / or performing functions in response to received input parameters, and providing output data and / or parameters.

[0078] As used herein, a graphical user interface (GUI) includes one or more display images generated by a display processor and enables user interaction with the processor or other devices, as well as associated data acquisition and processing functions. A GUI also includes an executable program or executable application. The executable program or executable application modulates the display processor to generate signals representing the GUI display images. These signals are supplied to a display device, which displays the images for the user to view. Under the control of the executable program or executable application, the processor manipulates the GUI display images in response to signals received from input devices. In this way, the user can interact with the display images using input devices, enabling user interaction with the processor or other devices.

[0079] The functions and procedures described herein may be executed automatically, or in whole or in part in response to user commands. Automatically executed activities (including steps) are performed in response to one or more executable instructions or device operations without direct user initiation.

[0080] While various illustrative embodiments incorporating the principles of this teaching have been disclosed, this teaching is not limited to the disclosed embodiments. Rather, this application is intended to cover any variations, uses, or adaptations of this teaching and its general principles. Furthermore, this application is intended to cover such deviations from this disclosure within the scope of known or customary practice in the fields to which these teachings pertain. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0081] In the above detailed description, reference has been made to the accompanying drawings, which form a part thereof. In the drawings, similar symbols generally identify similar components unless the context indicates otherwise. The illustrative embodiments described in this disclosure are not intended to be limiting. Other embodiments may be used, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that, as generally described herein and illustrated in the accompanying drawings, the various features of this disclosure can be arranged, replaced, combined, separated, and designed in a wide variety of different configurations, all of which are expressly contemplated herein.

[0082] This document describes aspects of the present technical solution with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the technical solution. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0083] These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create components for implementing the functions / actions specified in the flowchart and / or block diagram boxes. These computer-readable program instructions may also be stored in a computer-readable storage medium that can instruct a computer, programmable data processing apparatus, and / or other device to function in a particular manner, such that the computer-readable storage medium storing the instructions includes an article of writing comprising instructions for implementing aspects of the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0084] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device, thereby producing a computer-implemented process, such that the instructions that execute on the computer, other programmable apparatus or other device perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0085] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present technical solution. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing one or more specified logical functions. In some alternative implementations, the functions indicated in the blocks may occur in a non-consecutive order. For example, two blocks shown consecutively may actually execute substantially concurrently, or these blocks may sometimes execute in reverse order, depending on the functions involved. It will also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a system based on dedicated hardware that performs the specified functions or actions or executes a combination of dedicated hardware and computer instructions.

[0086] The second action can be described as "responsive to" the first action, independent of whether the second action is directly or indirectly caused by the first action. The second action can occur much later than the first action and still be responsive to the first action. Similarly, the second action can be described as responsive to the first action even if an intervention occurs between the first and second actions, and even if one or more of the intervention actions directly cause the second action to be executed. For example, if the first action sets a flag, the second action can be responsive to the first action, and the second action is initiated after the third action, regardless of when the flag is set.

[0087] Regarding the use of virtually any plural and / or singular terms in this document, those skilled in the art can convert plural to singular and / or from singular to plural in a manner appropriate for the context and / or application. For clarity, various singular / plural permutations may be explicitly described herein.

[0088] It will be understood by those skilled in the art that, in general, the terms used herein are intended to be “open” terms (e.g., the term “comprising” should be interpreted as “comprising but not limited to,” the term “having” should be interpreted as “having at least,” the term “including” should be interpreted as “including but not limited to,” etc.). While various components, methods, and apparatuses are described by way of “comprising” various components or steps (which is interpreted as “comprising, but not limited to”), these components, methods, and apparatuses may also be “substantially composed of various components and steps” or “composed of various components and steps,” and such terms should be interpreted as defining a substantially closed group of members.

[0089] As used in this document, the singular forms “a,” “an,” and “the” include plural references unless the context clearly specifies otherwise. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. Nothing in this disclosure should be construed as an admission that the embodiments described herein do not have rights prior to such disclosure by means of existing inventions.

[0090] Additionally, even when specific numbers are explicitly listed, those skilled in the art will recognize that such a listing should be interpreted as meaning at least the listed numbers (e.g., a simple listing of "two listings" without other modifiers means at least two listings, or two or more listings). Furthermore, in cases where conventions such as "at least one of A, B, and C" are used, generally, such a construction is intended in the sense that those skilled in the art would understand the convention (e.g., "a system having at least one of A, B, and C" will include, but is not limited to, systems having A alone, having B alone, having C alone, having A and B together, having A and C together, having B and C together, and / or having A, B, and C together, etc.). In cases where conventions such as "at least one of A, B, or C" are used, generally, such a construction is intended in the sense that those skilled in the art would understand the convention (e.g., "a system having at least one of A, B, or C" will include, but is not limited to, systems having A alone, having B alone, having C alone, having A and B together, having A and C together, having B and C together, and / or having A, B, and C together, etc.). Those skilled in the art will further understand that, whether in the specification, sample embodiments, or drawings, any extractive word and / or phrase that substantially presents two or more alternative terms should be understood as potentially including one, any one, or both of the terms. For example, the phrase “A or B” will be understood as potentially including “A” or “B” or “A and B”.

[0091] Additionally, in the context of describing the features of this disclosure in accordance with the Markush Group, those skilled in the art will recognize that this disclosure is therefore also described in accordance with any individual member or subgroup of the Markush Group.

[0092] As those skilled in the art will understand, for any and all purposes, such as for the purpose of providing a written description, all scopes disclosed herein also encompass any and all possible subscopes and combinations thereof. Any listed scope can be readily considered sufficiently descriptive and allows the same scope to be decomposed into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each scope discussed herein can be readily decomposed into a lower third, a middle third, and an upper third, etc. As will also be understood by those skilled in the art, all language, such as “at most,” “at least,” and the like, includes the listed numbers and refers to scopes that can subsequently be decomposed into subscopes as described above. Finally, as will be understood by those skilled in the art, a scope includes each individual member. Thus, for example, a group having 1-3 components refers to a group having 1, 2, or 3 components. Similarly, a group having 1-5 components refers to a group having 1, 2, 3, 4, or 5 components, and so on.

[0093] Non-limiting illustrative examples The following is a list of numbered non-limiting illustrative embodiments of the inventive concepts disclosed herein: Illustrative Example 1. A system for evaluating the quality of a fluid process performed on a fluid, the fluid process including one of fluid suction and fluid dispensing, the system comprising: a pump; a probe; a connection, wherein a fluid path is formed between the pump and the probe at least partially via the connection; a pressure sensor fluidly communicating with the fluid path and configured to sense pressure of the pump during the fluid process, wherein pressure measurement data includes the pressure of the pump sensed during the fluid process; a processor; and a memory including instructions executed by the processor to cause the processor to: receive pressure measurement data from the pressure sensor during the fluid process, wherein the pressure measurement data includes pressure data and time data; modify the pressure measurement data to conform to a common domain; identify a reference at a common correlation plotting region of the modified pressure measurement data, wherein the common correlation plotting region includes a correlation plotting region capable of determining the quality of the fluid process performed on a plurality of volumes of fluid conforming to the common domain; calculate a quality value using the reference; compare the quality value with a threshold; and determine the quality of the fluid process by the processor based on the comparison result.

[0094] Illustrative Example 2. The system according to the foregoing embodiments, wherein the system further includes: an analyzer configured to analyze a mixture comprising the fluid when the quality of the fluid process is satisfactory.

[0095] Illustrative Example 3. According to one of the foregoing embodiments, the system, wherein the instructions further cause the processor to: in response to determining the quality anomaly of the fluid process, cause the pump to operate in order to perform a subsequent fluid process.

[0096] Illustrative Example 4. According to the system of one of the foregoing embodiments, wherein the common related plotting area includes a subset of the modified pressure measurement data associated with the pump stage.

[0097] Illustrative Example 5. In the system according to one of the foregoing embodiments, the time data includes one of duration and timestamp.

[0098] Illustrative Example 6. A system according to one of the foregoing embodiments, wherein modifying the pressure measurement data includes compressing the pressure measurement data in one of the time domain and the pressure domain.

[0099] Illustrative Example 7. In the system according to one of the foregoing embodiments, modifying the pressure measurement data includes reducing the number of data points.

[0100] Illustrative Example 8. In the system according to one of the foregoing embodiments, modifying the pressure measurement data includes: converting the time data into an integer; and interpolating the new pressure data.

[0101] Illustrative Example 9. The system according to one of the foregoing embodiments, wherein interpolation of new pressure data includes one of linear interpolation and nonlinear interpolation.

[0102] Illustrative Example 10. According to one of the foregoing embodiments, modifying pressure measurement data includes: converting the pressure data into an integer; and interpolating the new time data.

[0103] Illustrative Example 11. According to one of the foregoing embodiments, the quality indication of the fluid process is one or more of the following: the actual volume drawn is less than the expected volume drawn, the actual volume drawn is equal to the expected volume drawn, air is present in the actual volume drawn, and there is a blockage in the actual volume drawn.

[0104] Illustrative Example 12. A method for quality assessment of a fluid process, the method comprising: performing the fluid process on a fluid by a fluid handling system, wherein the fluid process includes one of fluid suction and fluid distribution; measuring pressure measurement data by the fluid handling system during the fluid process, wherein the pressure measurement data includes pressure data and time data; modifying the pressure measurement data to conform to a common domain; identifying a reference at a common correlation plotting region of the modified pressure measurement data, wherein the common correlation plotting region includes a correlation plotting region capable of determining the fluid process quality of the fluid process performed on a plurality of volumes of fluid conforming to the common domain; calculating a quality value using the reference; comparing the quality value with a threshold; and determining the quality assessment of the fluid process based on the comparison result.

[0105] Illustrative Example 13. The method according to one of the foregoing embodiments further includes: in response to determining that the quality assessment of the fluid process is satisfactory, analyzing a mixture comprising the fluid.

[0106] Illustrative Example 14. The method according to one of the foregoing embodiments further includes: in response to determining the quality assessment anomaly of the fluid process, the fluid process being pumped by the fluid handling system.

[0107] Illustrative Example 15. According to the method of one of the foregoing embodiments, the common related plotting area includes a subset of modified pressure measurement data associated with the pump stage.

[0108] Illustrative Example 16. The method according to one of the foregoing embodiments, wherein time includes one of duration and timestamp.

[0109] Illustrative Example 17. The method according to one of the foregoing embodiments, wherein modifying the pressure measurement data includes compressing the pressure measurement data in one of the time domain and the pressure domain.

[0110] Illustrative Example 18. The method according to one of the foregoing embodiments, wherein modifying the pressure measurement data includes reducing the number of data points.

[0111] Illustrative Example 19. According to the method of one of the foregoing embodiments, wherein the common domain includes a common time domain, and modifying the pressure measurement data includes: compressing the time data into the common time domain; and interpolating the new pressure data.

[0112] Illustrative Example 20. A computer program product embodied in a computer-readable storage medium, the computer-readable storage medium including software, which, when executed by a processor, performs a method comprising: receiving pressure measurement data from a pressure sensor during a fluid process performed on a fluid, wherein the pressure measurement data includes pressure data and time data; modifying the pressure measurement data by the processor to conform to a predetermined value; identifying a reference at a common correlation plotting region of the modified pressure measurement data, wherein the common correlation plotting region includes a correlation plotting region capable of determining the quality of a fluid process performed on a plurality of volumes of fluid conforming to the common region; calculating a quality value using the reference; comparing the quality value with a threshold; and determining the quality of the fluid process by the processor based on the comparison result.

[0113] The various features and functions disclosed above, as well as other features and functions or alternatives thereof, can be combined with many other different systems or applications. Those skilled in the art can then make various alternatives, modifications, variations, or improvements therein that are not currently foreseen or anticipated, each of which is also intended to be covered by the disclosed embodiments.

Claims

1. A system for evaluating the quality of a fluid process performed on a fluid, said fluid process including one of fluid suction and fluid dispensing, said system comprising: Pump; probe; A connection, wherein a fluid path is formed at least partially between the pump and the probe via the connection; A pressure sensor that communicates fluidly with the fluid path and is configured to sense the pressure of the pump during the fluid process, wherein pressure measurement data includes the pressure of the pump sensed during the fluid process; as well as Processor; and The memory includes instructions that are executed by the processor to cause the processor to: Pressure measurement data, including pressure data and time data, is received from the pressure sensor during the fluid process. The processor modifies the pressure measurement data to conform to the common domain. A reference is established at a common correlation plotting region of the modified pressure measurement data, wherein the common correlation plotting region includes a correlation plotting region capable of determining the quality of the fluid process performed on multiple volumes of fluid conforming to the common domain. The mass value is calculated using the aforementioned benchmark. The quality value is compared with a threshold, and The processor determines the quality of the fluid process based on the comparison results.

2. The system according to claim 1, wherein the system further comprises: An analyzer configured to analyze a mixture of fluids when the quality of the fluid process is satisfactory.

3. The system of claim 1, wherein the instructions further cause the processor to: In response to the determination of the quality anomaly in the fluid process, the pump is operated to perform a subsequent fluid process.

4. The system of claim 1, wherein the common related plotting area includes a subset of the modified pressure measurement data associated with the pump stage.

5. The system of claim 1, wherein the time data includes one of duration and timestamp.

6. The system of claim 1, wherein modifying the pressure measurement data includes compressing the pressure measurement data in one of the time domain and the pressure domain.

7. The system of claim 1, wherein modifying the pressure measurement data includes reducing the number of data points.

8. The system of claim 1, wherein modifying the pressure measurement data comprises: Convert the time data into an integer; as well as Interpolate the new pressure data.

9. The system of claim 8, wherein interpolation of the new pressure data includes one of linear interpolation and nonlinear interpolation.

10. The system of claim 1, wherein modifying the pressure measurement data comprises: Convert the pressure data into integers; as well as Interpolate the new time data.

11. The system of claim 1, wherein the quality indication of the fluid process is one or more of the following: the actual pumped volume is less than the expected pumped volume, the actual pumped volume is equal to the expected pumped volume, air is present in the actual pumped volume, and a blockage is present in the actual pumped volume.

12. A method for quality evaluation of a fluid process, the method comprising: The fluid process is performed on the fluid by a fluid handling system, wherein the fluid process includes one of fluid suction and fluid dispensing; The fluid handling system measures pressure measurement data during the fluid process, wherein the pressure measurement data includes pressure data and time data; Modify the pressure measurement data to conform to the public domain; A reference is established at a common correlation plotting region of the modified pressure measurement data, wherein the common correlation plotting region includes a correlation plotting region that can be used to determine the fluid process quality of the fluid process performed on multiple volumes of fluid conforming to the common domain; The quality value is calculated using the aforementioned benchmark; The quality value is compared with a threshold. as well as The quality assessment of the fluid process is determined based on the comparison results.

13. The method of claim 12, further comprising: In response to determining that the quality assessment of the fluid process is satisfactory, the analysis includes a mixture of the fluids.

14. The method of claim 12, further comprising: In response to the determination of the quality assessment anomaly in the fluid process, the fluid process is pumped out by the fluid handling system.

15. The method of claim 12, wherein the common related plotting area includes a subset of modified pressure measurement data associated with the pump stage.

16. The method of claim 12, wherein time includes one of duration and timestamp.

17. The method of claim 12, wherein modifying the pressure measurement data includes compressing the pressure measurement data in one of the time domain and the pressure domain.

18. The method of claim 12, wherein modifying the pressure measurement data includes reducing the number of data points.

19. The method of claim 12, wherein the common domain includes a common time domain, and modifying the pressure measurement data includes: Compress the time data into the common time domain; as well as Interpolate the new pressure data.

20. A computer program product embodied in a computer-readable storage medium, the computer-readable storage medium comprising software, which, when executed by a processor, performs a method, the method comprising: During a fluid process performed on a fluid, pressure measurement data is received from a pressure sensor, wherein the pressure measurement data includes pressure data and time data; The processor modifies the pressure measurement data to conform to a predetermined value; A reference is established at a common correlation plotting region of the modified pressure measurement data, wherein the common correlation plotting region includes a correlation plotting region that can be used to determine the quality of the fluid process performed on multiple volumes of fluid conforming to the common domain; The quality value is calculated using the aforementioned benchmark; The quality value is compared with a threshold. as well as The processor determines the quality of the fluid process based on the comparison results.

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