Secondary statistical cut-off methodology for gas-liquid diffusion integrity test

Abstract

To provide a method for increasing the sensitivity and reducing background noise in an integrity test of a defective film filter to effectively process the defective film filter.SOLUTION: There is provided an integrity test methodology which can maintain the convenience of a gas-liquid integrity test, while suppressing influence of a background noise variable as much as possible which can reduce the capability of a test for detecting defects and can rise the possibility of determining a filter without defects to be defective by mistake.SELECTED DRAWING: Figure 1A

Description

Technical Field

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 131,850, filed Dec. 30, 2020, the entire content of which is incorporated herein by reference in its entirety.

[0002] The embodiments described herein relate to a method for a gas-liquid integrity test for a membrane filter. More specifically, certain embodiments of the present technology relate to a methodology for gas-liquid diffusion integrity test data analysis that uses normalization of a population subset to reduce background noise and thereby prepare integrity test data for operating conditions such as temperature and pressure for the purpose of improving the defect detection signal-to-noise ratio.

Background Art

[0003] High-purity filtration of aqueous media used in the fields of biotechnology, chemistry, electronics, pharmaceuticals, and the food and beverage industries is achieved using high-performance membrane filter modules capable of high-level separation. These membrane filters also prevent contamination of the environment, the media being filtered, and the resulting filtrate in order to prevent undesirable and often dangerous organisms such as bacteria or viruses, as well as environmental contaminants such as dust and dirt, from entering the process stream and the final product.

[0004] To ensure that the sterility and / or retention capacity of the membrane filter is not impaired, integrity testing is a requirement during important process filtration applications. The manufacture of membrane filters for critical applications also always applies integrity testing to the finished filter elements as a lot release or 100% test criterion. Integrity testing detects the presence of pores that are too large or defects that could impair the retention capacity of the porous material. Integrity testing methods include particle challenge tests, liquid-liquid porometry tests, bubble point tests, gas-liquid diffusion tests, and diffusion tests that measure trace components. Some of these tests, such as particle challenge tests, are destructive. Therefore, these tests cannot be used as pre-use tests. Liquid-liquid porometry and bubble point tests are useful for ensuring that membranes with the appropriate nominal pore size are installed, but this method lacks the sensitivity to identify small defects in small numbers.

[0005] Gas-liquid diffusion tests are widely used to evaluate filter integrity. A common gas-liquid pair for integrity testing is air-water due to the safety, resistance stoichiometry, and environmentally friendly properties of the pair. Diffusion tests measure the rate of gas movement through the filter. At different gas pressures below the bubble point, gas molecules move through the water-filled pores of the wet membrane according to Fick's law of diffusion as follows.

Number

[0006] Measured gas flow rates that exceed those predicted by Fick's law or are higher than the empirically established flow rates for a perfect membrane are signals of defects. The sensitivity of this test is limited by the minimum detectable excess flow rate. Due to differences in membrane area, membrane thickness, membrane porosity, and pore tortuosity, there is a possibility of significant variation between filters in the gas diffusion flow rate of a perfect membrane filter device.

[0007] Variations in the hardware and instruments of the test system, as well as operating conditions such as pressure and temperature that affect the diffusibility and solubility of gases in liquids, also contribute to background noise. Background noise can compete with or interfere with defect signals, which are excess gas flow rates due to convection through one or more defects. When faced with an increase in noise fluctuations, in a conventional air diffusion integrity test, it is necessary to either expand the acceptable specification window (i.e., reduce the defect detection ability) or tolerate more false rejects.

[0008] The main factors contributing to this noise fluctuation are membrane properties that vary depending on the lot (referred to herein as the "input date") and the test position on the equipment performing the integrity test cycle (referred to herein as the "bowl"). The sensitivity of the gas-liquid diffusion test for detecting defects is also directly limited by background noise. High background noise can cause perfect filters to be incorrectly failed in the test, resulting in a waste of additional costs. When establishing test pass criteria, including upper and lower limits of diffusion specifications, a trade-off decision is necessary between test sensitivity (reducing risks for products and end-users) and the ability to reliably supply products (yield of supply to the market, costs, and safety). Summary of the Invention Problems to be Solved by the Invention

[0009] Therefore, there is a need for a integrity test methodology that maintains the convenience of the gas-liquid integrity test while minimizing the impact of inherent background noise variables that can reduce the ability of the test to detect defects and increase the probability of misclassifying a perfect filter as failing.

Means for Solving the Problems

[0010] The disadvantages of the prior art are overcome by the embodiments described herein, including a method of an embodiment that reduces background noise and improves the signal-to-noise ratio of the integrity test for processing membrane filters, the method comprising performing a gas-liquid diffusion analysis on the membrane filter, identifying whether the membrane filter is an outlier compared to an initial fixed cutoff during the gas-liquid diffusion analysis, normalizing the value of at least one characteristic of at least one membrane filter that affects background noise in the gas-liquid diffusion analysis if the membrane filter is not an outlier relative to the initial fixed cutoff, identifying whether the membrane filter is an outlier compared to a secondary statistical cutoff based on values from the gas-liquid diffusion analysis of a plurality of membrane filters, and processing the membrane filter as perfect or failing.

[0011] In certain embodiments, the gas-liquid diffusion method is air-water. In certain embodiments, at least one characteristic is selected from the group consisting of lot, test location of the equipment, water temperature, air temperature, and atmospheric pressure. In certain embodiments, the values of at least two characteristics are normalized. Certain embodiments of the method further include the step of normalizing the pressures of two or more membrane filters. Certain embodiments of the method further include the step of detecting at least one defect in two or more membrane filters. In certain embodiments, at least one defect is selected from the range consisting of between about 4 μm and about 9 μm. In certain embodiments, the defect is greater than 4 μm. In certain embodiments, the defect is selected from the group consisting of about 4 μm, about 5 μm, about 6 μm, about 7 μm, about 8 μm, and about 9 μm. In certain embodiments, the initial upper cut-off maintains a safety margin of at least 5% from the end-user's diffusion specification criteria. In certain embodiments, the initial upper cut-off maintains a safety margin of at least 5% from the quality control (QC) lot release diffusion criteria.

[0012] In certain embodiments, the initial upper cut-off maintains a safety margin selected from the range consisting of about 5% to about 15% from the end-user's diffusion specification criteria or the quality control (QC) lot release diffusion criteria. In certain embodiments, the initial upper cut-off maintains a safety margin selected from the group consisting of about 5%, about 6%, about 7%, about 8%, about 9%, about 10%, about 11%, about 12%, about 13%, about 14%, and about 15%. In certain embodiments, the membrane filter is an outlier with respect to the initial cut-off and is treated as non-conforming. In certain embodiments, the membrane filter is an outlier with respect to the secondary cut-off and is treated as non-conforming. In certain embodiments, the membrane filter is not an outlier with respect to either the initial cut-off or the secondary cut-off and is treated as perfect.

[0013] In certain embodiments, a plurality is a statistically significant number of membrane filters. In certain embodiments, the membrane filter and the plurality of membrane filters are of the same type of filtration device. In certain embodiments, the filtration device is selected from the group consisting of a sterilization grade filter, a virus filter, a clarification filter, and an ultrafiltration filter. In certain embodiments, the secondary statistical cut-off is established by setting a statistical upper limit 2-5 standard deviations above the normalized median of the values. In certain embodiments, the secondary statistical cut-off is established by setting a statistical upper limit approximately 2, approximately 3, approximately 4, and approximately 5 standard deviations above the normalized median of the values. In certain embodiments, the secondary statistical cut-off is established by setting a statistical lower limit 4-6 standard deviations below the normalized median of the values. In certain embodiments, the secondary statistical cut-off is established by setting a statistical upper limit approximately 2, approximately 3, approximately 4, and approximately 5 standard deviations above the normalized median of the values.

Brief Description of the Drawings

[0014]

Figure 1A

Figure 1B

Figure 2A

Figure 2B

Figure 3A

Figure 3B

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[0015] The accompanying drawings illustrate certain embodiments of the present disclosure and, accordingly, the present invention should not be considered limited in scope since it may admit of other equally effective embodiments. Elements and features of any one embodiment may be found in other embodiments without further recitation, and it should be understood that the same reference numbers are used, where appropriate, to indicate similar elements common to the figures.

DETAILED DESCRIPTION OF THE INVENTION

[0016] The disclosure herein describes certain embodiments of a method for integrity testing.

[0017] Certain embodiments of the integrity testing method described herein use normalization of air diffusion data by bowl and insertion date to increase the signal-to-noise ratio and enhance defect detection capabilities. Further, the method better absorbs membrane and equipment variations than conventional integrity testing to provide more rigorous and consistent processing of the product without sacrificing yield or reducing sensitivity.

[0018] Certain embodiments of the integrity testing method described herein normalize air diffusion data by the test location (bowl) of the equipment as well as by the insertion date (variations in materials and upstream processes). The resulting variations can be explained using normalization by understanding the variations that these sub-populations add to the process in a predictable manner. The normalized dataset of the integrity testing method described herein has a significantly lower background noise level without affecting the defect signal, resulting in an increased signal-to-noise ratio and a higher ability to find outliers (defects) within the normal population.

[0019] Certain embodiments of the methods described herein can detect defects as small as 5 microns (μm) in a 10-inch filtration device, compared to 10 - 15μm in conventional air diffusion tests.

[0020] Certain embodiments of the methods of the integrity test described herein comprise the following procedure for setting upper and lower cutoffs of a normalized dataset. Certain embodiments of the method include a two-cutoff system with an initial fixed cutoff that identifies all defective products at the time of the test and a secondary statistical cutoff that is applied to the normalized dataset at the end of the lot.

[0021] Certain embodiments of the method of the integrity test include the following steps for setting initial fixed specification upper and lower limits. Obtaining past air diffusion data from a wide range of input dates and determining the degree of diffusion variation; Determining a pressure adjustment value that can be directly compared to the integrity specifications before and after end-user use and the QC lot release diffusion criteria; Determining an upper cutoff that has a minimal impact from the end-user's diffusion specifications and the QC lot release diffusion criteria and maintains a safety margin of at least 15 percent; Determining a lower cutoff that has a minimal impact but also provides an appropriate boundary that triggers a process re-evaluation if exceeded.

[0022] I. Cutoff Setting In certain embodiments, the initial cutoff is placed sufficiently far from the current air diffusion values that can withstand a large amount of input date variation without affecting the yield. In certain embodiments, the initial cutoff maintains a safety margin from the integrity tests before and after end-user use as well as the quality control (QC) lot release test specifications. In certain embodiments, the QC lot release test specifications are adjusted according to the pressure. Certain embodiments of the integrity test are more sensitive than both the end-user specifications and the QC lot release test specifications.

[0023] In certain embodiments, the diffusion specification is set at a reference test pressure (i.e., less than 30.0 sccm at 40 psi). In certain embodiments, if the integrity test pressure is not equivalent to the existing reference pressure of the end user specification or the QC specification, a correction factor is defined to compare values from different test pressures.

[0024] In certain embodiments, the secondary cut-off is applied to lot data normalized by bowl and / or lot date to significantly reduce two of the largest sources of variation from the data, enabling easier placement of the cut-off for optimal separation of outliers from the normal population.

[0025] Certain embodiments of the method for integrity testing include the following steps to set secondary statistical specification upper and lower limits. Selecting a dataset of integrity test data representing a statistically significant number of membrane filter devices, lot date, and any other tests or filter production variables that may affect the air diffusion test background noise; Testing the filter using air diffusion and removing any gross failures; Performing a Ryan-Joiner (RJ) normality test; Removing outliers until the RJ score reaches an acceptable value; Performing normalization of the air diffusion data according to the methodology described herein; Calculating a proposed specification upper limit 3 - 4 standard deviations above the normalized median and a proposed specification lower limit 4 - 6 standard deviations below the normalized median; Evaluating the bacterial retention performance of units below, exactly at, and above the available upper cut-off; Considering the defect mode, defect size, and log reduction value (LRV) of retention failures below the cut-off, as well as the sample size; Reviewing the retention data and determining the final upper cut-off; Steps for evaluating proximity to the lower cut-off of the diffusion test results, and Steps for reconsidering the results and determining the final specification lower limit.

[0026] In certain embodiments, the integrity test methods described herein enable the identification of incorrect raw materials used in a manufacturing process that cannot be identified by existing techniques during in-process testing. Certain embodiments of the integrity test method have identified improperly processed materials (e.g., misoriented films) that cannot be identified by existing techniques during in-process testing.

[0027] Certain embodiments of the normalization method adjust for test location (bowl) and lot (date of input) variations. Certain embodiments include additional sources of variation such as water temperature and air temperature, date of input of the second film layer, support material roll, and actual achieved prestress or test pressure. A trade-off for statistical reliability must be recognized when evaluating the inclusion of additional factors due to a reduction in the sample size of the subgroup.

[0028] Certain embodiments of the integrity test method can be used in any filtration device that employs a gas-liquid diffusion test, including but not limited to sterilizing grade filters, virus filters, clarification filters, and ultrafiltration filters. Certain embodiments of the integrity test methods described herein maintain appropriate yields and improve defect detection.

[0029] II. Equipment Certain embodiments of the integrity test method are performed on a control system constructed to operate 24 hours a day and 365 days a year. In certain embodiments, the combination of equipment and software infrastructure enables the automatic collection of both structural and integrity test material and process data. In certain embodiments, process and material information is collected simultaneously by a data acquisition system.

[0030] In certain embodiments, the integrity test of the membrane filter is performed in a single-piece flow. Alternatively, batch processing of the test may be performed.

[0031] In certain embodiments of the integrity test method, the automated system uses the initial (overall) diffusion specification limit to process non-conforming products in real time. In certain embodiments, a lot typically comprises from about 1000 to about 1800 ten-inch membrane filters.

[0032] Certain embodiments of the integrity test method include flagging any test bowl with an abnormal result to reexamine the potential impact from factors exceeding the median and / or standard deviation results expected compared to the recipe setting or grand lot population. Certain embodiments of the integrity test method include flagging any membrane roll (date of input) with an abnormal result to reexamine the potential impact from factors exceeding the median and / or standard deviation results expected compared to the recipe setting or grand lot population.

[0033] III. Definitions Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0034] As used herein, the singular forms "a", "an" and "the" include the plural unless specifically indicated otherwise.

[0035] As used herein, "CF1" refers to a correction factor for normalization of a data set based on a first characteristic. In certain embodiments, CF1 is a correction factor for a normalized data set by a bowl near a median of zero.

[0036] As used herein, "CF1MF" refers to a correction factor for normalization of mass flow.

[0037] As used herein, "CF2" refers to a correction factor for the normalization of a dataset based on a second characteristic. In certain embodiments, CF2 is a correction factor for a dataset normalized by a master roll (date of input) near a median of 0.

[0038] As used herein, "CF2MF" refers to a correction factor for the normalization of mass flow rate.

[0039] As used herein, "initial fixed cutoff" refers to defects at the time of testing, similar to a conventional integrity test cutoff. The initial fixed cutoff is the upper specification limit (USL) and lower specification limit (LSL) applied at the time of testing to identify medium to overall non-conforming products. Units that fail the initial cutoff are rejected and removed from the lot during the test process.

[0040] As used herein, "perfect" refers to a membrane filter without defects.

[0041] As used herein, "non-conforming" refers to a membrane filter with defects. In certain embodiments, a non-conforming membrane filter is alternatively referred to as "imperfect".

[0042] As used herein, "secondary statistical cut-off" refers to the use of normalized test data that is applied at the end of a lot to identify additional defective parts that are outliers with respect to a normal population. The secondary statistical cut-off is a fixed USL and LSL that is applied to the data set at the end of the lot after being normalized by the bowl and master roll. Units that fail the secondary statistical cut-off are rejected and removed from the lot during the responsibility step. The secondary statistical cut-off is established by setting the USL such that it falls within the range of 2 to 4 standard deviations above the normalized median. In certain embodiments, the USL is at least 2 standard deviations or more above the normalized median. In certain embodiments, the USL is approximately 2 standard deviations or more above the normalized median. In certain embodiments, the USL is at least 3 standard deviations or more above the normalized median. In certain embodiments, the USL is approximately 3 standard deviations or more above the normalized median. In certain embodiments, the USL is at least 4 standard deviations or more above the normalized median. In certain embodiments, the USL is approximately 4 standard deviations or more above the normalized median.

[0043] In certain embodiments, the secondary statistical cut-off is established by setting the LSL such that it falls within the range of 4 to 6 standard deviations below the normalized median. In certain embodiments, the LSL is at least 4 standard deviations or less below the normalized median. In certain embodiments, the LSL is approximately 4 standard deviations or less below the normalized median. In certain embodiments, the LSL is at least 5 standard deviations or less below the normalized median. In certain embodiments, the LSL is approximately 5 standard deviations or less below the normalized median. In certain embodiments, the LSL is at least 6 standard deviations or less below the normalized median. In certain embodiments, the LSL is approximately 6 standard deviations or less below the normalized median. The standard deviation calculation may be from the characteristics of the membrane filter used in development, such as the input date variation lot or bowl.

[0044] As used herein, "MRMinQty" refers to the minimum sample size per master roll required for standard processing. If the minimum quantity is not met, alternative processing rules apply. This is a recipe definition parameter.

[0045] As used herein, "BowlMinQty" refers to the minimum sample size per bowl per lot required for standard processing. If the minimum quantity is not met, alternative processing rules apply. This is a recipe definition parameter.

[0046] As used herein, "MaxBowlVar" refers to the allowable limit of variation between the bowl median and the average lot bowl median for standard processing. This is a recipe definition parameter.

[0047] As used herein, "MaxMRVar" refers to the allowable limit of variation between the master roll and the average lot master roll median for standard processing. This is a recipe definition parameter.

[0048] As used herein, "MaxMRStDev" refers to the allowable limit of standard deviation of the normalized data for each master roll for standard processing. Sample standard deviation is used in this calculation. This is a recipe definition parameter.

Example

[0049] Example 1. Experimental Procedure In the in-line integrity test in additional examples, the initial fixed cutoff and secondary statistical cutoff processing procedures for air diffusion data were performed as follows. The calculations may be performed manually and / or using a database.

[0050] The variables of mass flow rate USL, LSL, and CF2MF were rounded to one-tenth of a decimal point. The variables of CF1, CF1MF, CF2, and any standard deviation (StdDev) were rounded to one-hundredth of a decimal point. Normalization was used to align the medians of sub-populations of the dataset. This process normalized the dataset by a bowl near the median of 0. The modified dataset was then normalized by a master roll (input date) near the median of 0.

[0051] Air diffusion data was collected at the end of the lot. Data from membrane filters that did not pass the first cutoff, including aerosol defects, wetting failures, and wetting retests, was removed. One dataset was used for each membrane filter. When a membrane filter had more than one air diffusion test value, the final diffusion test value was used. The sample size N and the mass flow rate median (BOWL_MEDIAN) were calculated for each bowl from the remaining datasets. Then, for each bowl B, a correction factor CF1 for normalization was calculated.

[0052] CF1 B = 0 - BOWL_MEDIAN B

[0053] Example: CF1 343 = 0 - BOWL_MEDIAN 343

[0054] The previous steps were completed for all bowls.

[0055] If the N of any bowl (B) is less than BowlMinQty, the following equation was used instead to calculate CF1 for the (one or more) low sample size bowls. BOWL_MEDIAN AVG is the average bowl median of all bowls with a sample size exceeding BowlMinQty.

[0056] CF1 B = 0 - BOWL_MEDIAN AVG

[0057] Example: The bowl 346 was N = 20. This was less than 24 of BowlMinQty.

[0058] CF1 346 = 0 - BOWL_MEDIAN AVG

[0059] Example: Four bowls were used in the test and MaxBowlVar = 0.9 sccm (standard cubic centimeter).

[0060] BOWL_MEDIAN 343 = 12.8 sccm

[0061] BOWL_MEDIAN 344 = 14.4 sccm

[0062] BOWL_MEDIAN 345 = 13.8 sccm

[0063] BOWL_MEDIAN 346 = 14.2 sccm

[0064] Calculated BOWL_MEDIAN AVG = (12.8 + 14.4 + 13.8 + 14.2) / 4 = 13.8

[0065] For each membrane filter x in the dataset having a mass flow rate value (MF X ) tested in bowl (B), the correction factor for normalization of the mass flow rate (CF1MF) value was calculated using the following equation.

[0066] CF1MF X = MF X + CF1 B

[0067] Example: When 1001 consecutive ones were tested in bowl 345, it had a recorded mass flow rate value of 14.3 sccm. CF1 345 was calculated to be -13.8 sccm.

[0068] CF1MF 1001 =14.3 + (-13.8 sccm) = 0.5 sccm

[0069] The CF1MF dataset was reexamined, and for each master roll (MR_MEDIAN) used within the lot, the sample size N and the median CF1MF value were calculated. Subsequently, the correction factor CF2 for normalization was calculated for each master roll (R) using the following: CF2 R = 0 - MR_MEDIAN R 。

[0070] Example: CF2 3135UE = 0 - MR_MEDIAN 3135UE

[0071] CF2 3004UD = 0 - MR_MEDIAN 3004UD

[0072] The previous steps were completed for all master rolls.

[0073] Limit verification was performed on the master roll sample size and the master roll median. If the N of any master roll (R) was less than MRMinQty, then the following formula was used instead to calculate CF2 for the (one or more) master rolls with low sample sizes: CF2 R = 0 - MR_MEDIAN AVG 。MR_MEDIAN AVG is the average master roll median of the remaining master rolls having a sample size exceeding MRMinQty.

[0074] Example: Master roll 3162UE had N = 15. This is less than the MRMinQty of 24.

[0075] CF2 3162UE = 0 - MR_MEDIAN AVG

[0076] Example: Four master rolls were used in one lot, and MaxMRVar = 1.5 sccm.

[0077] The data is as follows.

[0078] MR_MEDIAN 5240UE = 0.0 sccm

[0079] MR_MEDIAN 5241UE = -1.0 sccm

[0080] MR_MEDIAN 5242UE = 1.7 sccm

[0081] MR_MEDIAN 5243UE = -0.7 sccm

[0082] Calculated MR_MEDIAN AVG =(0.0 + -1.0 + 1.7 + -0.7) / 4 = 0.0

[0083] For each group of membrane filters (x) in the dataset, using the CF1MF value (CF1MF X ) having a membrane from the master roll (R), the CF2MF value is calculated using the following formula.

[0084] CF2MF X = CF1MF X + CF2 R

[0085] Example: Group 2001 included a membrane from master roll 3452UE and had a calculated CF1MF value of 1.1 sccm. CF23452UE was calculated to be -0.8 sccm.

[0086] CF2MF 2001 = 1.1 + (-0.8 sccm) = 0.3 sccm

[0087] Limit verification was performed on the master roll standard deviation of the normalized mass flow rate values. Outliers were removed from the CF2MF dataset, and then the standard deviation of CF2MF was calculated for each master roll. The CF2MF values of each unit were compared with the secondary statistical cutoffs USL and LSL. Any unit exceeding the USL or falling below the LSL was treated as a non-conforming unit. Defective products from the secondary statistical cutoffs were excluded from the production lot.

[0088] Example 2. Comparing Standard Data with Normalized Data Figure 1A (CGEP = 10-inch SHF filter, CSTVARLT1 = lot name) is a box plot of air diffusion values from a lot consisting of complete filters (0.2 μm PES (polyethersulfone) sterilization grade 10-inch cartridge membrane filter; diffusion specification; less than 30.0 sccm at 40 psi) from 11 input dates. The standard diffusion data showed a population with a median of 13.4 sccm and a standard deviation of 0.45 sccm. Potential test specification limits, typically within 3 - 4 standard deviations, were placed at 14.8 - 15.2 sccm. An upper cutoff was established at 15.0 sccm, which is 1.6 sccm (3.5 standard deviations) from the population median. Devices with a nominal defect signal of 1.7 sccm or more were treated as defective. At a test pressure of 35 psi, this excess flow corresponded to a single cylindrical defect size of 7 - 8 μm for orifice type defects and was experimentally confirmed.

[0089] Figure 1B (CF2MF = normalized mass flow rate) is a box plot of the same data from the same membrane filter, normalized by the test bowl and membrane input date using the methods of the integrity test herein. The data was normalized to have a median of 0, and the standard deviation of the population was 0.16 sccm. In the normalized dataset, the upper cutoff for 3 standard deviations was placed at 0.5 sccm from the population median. Devices with a nominal defect signal of 0.6 sccm or more were treated as defective. At a test pressure of 35 psi, this excess flow corresponded to a defect size of 4 - 5 μm.

[0090] It was observed that the normalization of the dataset significantly reduced the overall variation of the complete filter diffusion population. Therefore, the method of the integrity test in this specification provides a high degree of sensitivity, and since its final processing is determined with respect to the subpopulation median rather than the absolute diffusion value, a constant cut-off is applied to each device.

[0091] Example 3: Treatment of Sterilization Grade Filters It was observed that the air diffusion integrity test using the secondary cut-off can appropriately treat sterilization grade filters as defective products, which fail the bacterial retention test but may pass the conventional air diffusion integrity test. The lots shown in Figure 2A have air diffusion data plotted at the conventional upper and lower cut-offs of this product, which are 9.5 sccm and 5 sccm respectively. A complete filter was observed as a normal distribution population located between the upper cut-off and the lower cut-off, and filters with increasing diffusion (i.e., outliers with respect to the normal population) were treated as non-conforming units. Figure 2B shows the conforming filters from this lot with all non-conforming units removed.

[0092] Example 4: Comparison of Normalization of the Populations of Test Bowls and Membrane Insertion Dates Looking at the dataset of the previous example by the subpopulations of the test bowl (Figure 3A) and the membrane insertion date (Figure 3B), additional outliers were observed. The CVGL lot C0BB87266 represents a 0.2 μm PVDF (polyvinylidene fluoride) sterilization grade 10-inch DURAPORE(R) cartridge membrane filter.

[0093] Example 5: Treatment of Membrane Filters Failing the Retention Test The whisker plot of Figure 4 shows the same data set from the previous example normalized for both the test location and the membrane insertion date. Certain additional outliers were observed for the complete population, all of which passed the conventional air diffusion integrity test. One of these additional outliers also failed the retention test, which demonstrates the benefit to product quality (product and end user risk) by application of this method of integrity testing.

[0094] equivalent All ranges of formulations recited herein include the ranges therebetween and can include or exclude the endpoints. Optionally included ranges are from integer values therebetween (or including one of the original endpoints) of the recited magnitude, or the next lesser magnitude. For example, if the lower limit value is 0.2, optionally included endpoints can be 0.3, 0.4, ... 1.1, 1.2, etc., as well as 1, 2, 3, etc., and if the higher range is 8, optionally included endpoints can be 7, 6, etc., as well as 7.9, 7.8, etc. One-sided boundaries such as 3 or more similarly include a consistent boundary (or range) starting from the recited magnitude or an integer value one lower. For example, 3 or more includes 4 or 3.1 or more.

[0095] Throughout this specification, references to "one embodiment", "a particular embodiment", "one or more embodiments", "an embodiment", or "an embodiment" indicate that the described feature, structure, material, or characteristic is included in at least one embodiment of the present disclosure. Thus, appearances of the phrases "in one or more embodiments", "in a particular embodiment", "in one embodiment", "in an embodiment", or "in an embodiment" throughout this specification are not necessarily referring to the same embodiment.

[0096] Patent applications and patent publications, as well as other non-patent documents, cited herein are hereby incorporated by reference in their entirety, as if each individual publication or reference were specifically and individually set forth as being incorporated by reference in its entirety, in the portions thereof that are cited. Any patent application for which this application claims priority is also hereby incorporated by reference in its entirety, as described above for publications and references.

Claims

1. 1. A method for reducing background noise and improving the signal to noise ratio of an integrity test for treating a membrane filter, comprising: performing a gas-liquid diffusion analysis on the membrane filter; Identifying whether the membrane filter is an outlier by comparing with an initial fixed cutoff during the gas-liquid diffusion analysis; normalizing the value of at least one characteristic of the at least one membrane filter that contributes to background noise in the gas-liquid diffusion analysis if the membrane filter is not an outlier relative to an initial fixed cutoff; based on values ​​from the gas-liquid diffusion analysis of the plurality of membrane filters, comparing to a secondary statistical cutoff to identify whether the membrane filter is an outlier; processing the membrane filter as full or rejected; A method for providing the above.

2. The method of claim 1, wherein the gas-liquid diffusion method is air-water.

3. 3. The method of claim 1, wherein the at least one characteristic is selected from the group consisting of lot, equipment test location, water temperature, air temperature, and air pressure.

4. The method according to any one of claims 1 to 3, wherein the values ​​of at least two properties are normalized.

5. The method of any one of claims 1 to 4, further comprising normalizing the pressure of two or more membrane filters.

6. The method of any one of claims 1 to 5, further comprising detecting at least one defect in two or more membrane filters.

7. The method of any one of claims 1 to 6, wherein the membrane filter is a 10 inch sterilizing grade filter.

8. The method of claim 7, wherein the at least one defect is selected from the range consisting of between about 4 μm and about 9 μm.

9. 9. The method of claim 7, wherein the defects are greater than 4 μm.

10. The method of any one of claims 7 to 9, wherein the defects are selected from the group consisting of about 4 μm, about 5 μm, about 6 μm, about 7 μm, about 8 μm, and about 9 μm.

11. The method of any one of claims 1 to 10, wherein the initial upper cutoff maintains at least a 5% safety margin from the end user's diffusion specification criteria.

12. The method of any one of claims 1 to 10, wherein the initial upper cutoff maintains at least a 5% safety margin from quality control (QC) lot release dispersion criteria.

13. 13. The method of any one of claims 11 and 12, wherein the initial upper cutoff maintains a safety margin selected from the range consisting of about 5% to about 15% from an end user diffusion specification standard or a quality control (QC) lot release diffusion standard.

14. 14. The method of any one of claims 11-13, wherein the initial upper cutoff maintains a safety margin selected from the group consisting of about 5%, about 6%, about 7%, about 8%, about 9%, about 10%, about 11%, about 12%, about 13%, about 14%, and about 15%.

15. The method of any one of claims 1 to 14, wherein the membrane filter is an outlier relative to the initial cutoff and is treated as a failure.

16. The method of any one of claims 1 to 15, wherein the membrane filter is an outlier relative to the secondary cutoff and is treated as a failure.

17. The method according to any one of claims 1 to 16, wherein the membrane filter is treated as being perfect and not an outlier for either the initial or secondary cutoff.

18. The method of any one of claims 1 to 17, wherein the plurality is a statistically significant number of membrane filters.

19. The method of any one of claims 1 to 18, wherein the membrane filter and the plurality of membrane filters are the same type of filtration device.

20. 20. The method of any one of claims 1 to 19, wherein the filtration device is selected from the group consisting of a sterilizing grade filter, a viral filter, a clarification filter, and an ultrafiltration filter.

21. 21. The method of any one of claims 1 to 20, wherein the secondary statistical cutoff is established by setting an upper statistical limit 2 to 5 standard deviations above the normalized median of the values.

22. 22. The method of any one of claims 1 to 21, wherein the secondary statistical cutoffs are established by setting upper statistical limits at about 2, about 3, about 4, and about 5 standard deviations above the normalized median of the values.

23. 23. The method of any one of claims 1 to 22, wherein the secondary statistical cutoff is established by setting a lower statistical limit 4 to 6 standard deviations below the normalized median of the values.

24. 24. The method of any one of claims 1 to 23, wherein the secondary statistical cutoffs are established by setting upper statistical limits at about 2, about 3, about 4, and about 5 standard deviations above the normalized median of the values.

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