In the field of nanoparticle characterization,particle size、concentrationParameters such as potentialAccurate measurementIt is a core element to ensure the success of research and application. We have shared in detail the previous article「Common parameters in nanoparticle detectionAccurately obtaining these parameters not only helps to deeply analyze the physical and chemical properties of nanomaterials, but also provides insights into their applicationsBiomedical, Materials Science, Environmental ScienceThe practical applications in various fields provide key basis. However,Accuracy, reliability, repeatability, and reproducibility of measurement dataRelying solely on advanced instruments and equipment is not enough, a strict verification process is also needed to ensure the performance and operational standards of the instruments. This is3QCertification（IQ、OQ、PQ)The importance lies in the characterization of nanoparticles.

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3Q certificationThe content and significance of

3Q certification, which includes Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ), is an internationally recognized validation process aimed at ensuring the accuracy of analytical instrumentsInstallation compliance, operational stability, and reliability of measurement performanceThis process is clearly defined in the Good Manufacturing Practice (GMP) and widely applied in multiple fields such as pharmaceuticals, medical equipment, and materials science^[1]The 3Q certification provides a standardized framework for the use and maintenance of instruments in the field of biomedicine, ensuring that instruments consistently provide accurate and reliable data during installation, operation, and practical applications^[2].

In the field of nanoparticle characterization,3Q certification not only standardizes the installation and operation of instruments, but also ensures the scientific and repeatable nature of data through a systematic verification processThis article will provide a detailed interpretationThe core content of 3Q certification and its specific implementation in nanoparticle characterizationcontentExplore how to improve the reliability of instruments through a rigorous verification system, and contribute to the high-quality development of nanoscience and industrial applications.

Installation Qualification (IQ)

IQ is the process of systematically inspecting and verifying the installation process of nanoparticle characterization instruments.The purpose is to ensure that the installation of the instrument complies with the manufacturer's design standards and the user's usage requirements through document recording and actual inspection. This includes document review, installation environment confirmation, instrument component confirmation, and other related tasks.

• document reviewCheck whether the installation manual, user manual, maintenance manual and other documents of the instrument are complete. Verify the basic information such as the name, model, and serial number of the instrument to ensure consistency with the purchase list. Confirm whether the installation environment of the instrument meets the design requirements, including temperature, humidity, power supply, etc.
• Installation environment confirmationEnsure that the environmental conditions at the installation site (such as exhaust, temperature and humidity control, power supply, etc.) meet the operational requirements of the instrument. Check whether the electrical safety, mechanical safety, and environmental safety of the instrument comply with relevant standards.
• Instrument component confirmationCheck whether the various components of the instrument are installed correctly and whether the connections are firm. Confirm whether the calibration and debugging of the instrument have been completed, and whether all functions are normal.

Through IQ, the basic steps to ensure that the instrument is installed correctly according to design standards provide a prerequisite for subsequent operational and performance confirmation.

Operational Qualification (OQ)

OQ refers to the process of verifying whether equipment and instruments can operate normally within the specified operating range (usually under no-load or simulated operating conditions) after IQ is completed, and ensuring that their functions and performance parameters meet the manufacturer's preset technical specifications.The core objective of OQ is to confirm whether the various functions of the instrument meet the design requirements and verify its stability and consistency under standard operating conditions.

However, relying solely on OQ cannot guarantee reliable measurement of the instrument under actual experimental conditions, which highlights the necessity of PQ.

Performance Qualification (PQ)

After successfully completing the OQ, the instrument was deemed to operate stably within the specified operating range and possess the ability to perform measurement tasks. ButPQ is the process of verifying whether an instrument can achieve the expected performance under actual usage conditions. Its purpose is to ensure that the instrument can continuously and stably provide accurate measurement results under actual experimental conditions and process flow.

In daily verification work, OQ and PQ are often easily confused. For the convenience of comparison, we will use the following table to summarize the differences between the two one by one.

It is easy to understand from the above table that the main purpose of PQ is to test equipment performance under actual production conditions, ensuring that the equipment can stably meet the established performance standards and meet the requirements of product production. PQ certification is a key step in the equipment verification process, and its importance is reflected in the following aspects: 1. Risk reduction: Through PQ, ensure the safe and reliable operation of equipment in practical applications, and reduce quality risks; 2. Compliance: Comply with international regulations and industry standards, such as GMP (Good Manufacturing Practice), ISO 13485, etc., to improve the compliance of the enterprise; 3. Improving quality: Verify the functionality and performance of the equipment to ensure that the production process meets expected requirements, thereby enhancing product quality.For instruments and equipment, IQ and OQ can usually be provided by equipment suppliers in the form of product services, while PQ should be executed by equipment users or application units using real samples according to the actual process flow.

Nanoparticle detection equipment is a precision measuring instrument, and the test data is related to a series of subsequent scientific research and production work. Through rigorous PQ certification, the detection capability of the instrument can be basically determined, which has important guiding significance for how to correctly view the test data of the instrument in the future. What aspects of experimental verification are included in PQ certification for precision characterization instruments? Next, we will delve into the key experiments in PQ certification and their impact on data quality.

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concerningPQ verificationKey parameters of

Accuracy

In PQ,Accuracy isRefers to the measurement results of the instrument（Measured value)Compared to the actual value(TruevArea)Or reference value(AcceptedreferencevArea)The degree of proximity between them.According to the latest“ICH Validation of Analytical Procedures Q2(R2) ”The definition of the validation guidelines for analytical methods states that accuracy reflects the degree to which a measurement method approaches the true value, evaluates the correctness of instrument measurement results, and is one of the most core indicators for evaluating instrument performance.

In nanoparticle characterization, accuracy is typically measured through measurementCertified Reference Material (CRM) or other methodsCompare and evaluate using transmission electron microscopy (TEM) to detect particle size. In other words, accuracy reflects whether the deviation of the data obtained by the instrument when measuring target parameters such as particle size, concentration, or zeta potential of nanoparticles is within an acceptable range. Accuracy can be used in analytical chemistry and biomedical experimentsRecovery rateThis parameter is used to evaluate. It represents the proportion of the target analyte successfully obtained or detected from the sample during the experimental process. The calculation formula is:

Ideally, the recovery rate should be close to 100%, indicating that the experimental method can extract or detect the target analyte. The degree to which the recovery rate deviates from 100% reflects the systematic error (bias) of the experimental method. In practical applications, the acceptable range of recovery rate is usually determined based on experimental requirements and industry standards. For example, in biological analysis, the recovery rate is generally required to be between 80% and 120%^[6].

In addition to the aforementioned methods such as CRMComparison of reference materials（Reference material comparison)In addition to the methods used, accuracy evaluation is also commonly employedSpiking study——Artificially adding a known amount of analyte (i.e. internal standard, commonly referred to as CRM) to a test sample in a matrix that does not contain the substance, or enriching it in the test sample, and then measuring it using the analytical method to be validated, and comparing the deviation between the measured result and the theoretical addition amount (or the theoretical value of adding internal standard) (if the recovery rate deviates too much, it may indicate that the matrix components interfere with the measurement, and the method needs to be optimized or matrix effects need to be corrected); Alternatively, the accuracy of detecting components other than the internal standard in the mixed sample can be evaluated by separately evaluating the samples without and with added/enriched standards.

In addition, when it is not possible to obtain all relevant components to construct a matching matrix for spiked experiments, researchers can also useOrthogonal method（Orthogonalprocedures，Refers to using an independent analysis method that differs from the main method principle to measure the same sample and verify the reliability of the main method by comparing the results）To verify the accuracy of the main measurement methods.

Precision（Precision)

Precision refers to repeated measurements under the same experimental conditionsA set of data for the same sampleBetween thediscreteDegree refers to the consistency of measurement results.Precision measures the repeatability and reproducibility of a method, not the accuracy of data. Taking shooting and target shooting as an example (as shown in the figure below): the higher the precision, the less fluctuation the method has, and the more stable the measurement results are, but they may not necessarily be accurate (close to the target center); Similarly, data with high accuracy may not necessarily be stable, and the reproducibility of detection data may not necessarily be high. Accuracy and precision can be evaluated independently or in combination, but each requires predetermined acceptance criteria.

According to ICH Q2 (R2), precision can be divided into the following three categories:

• repetitiveness(Repeatability)The precision of the results obtained from repeated measurements taken in the same laboratory, operator, instrument, and under the same conditions within a short period of time. Taking particle concentration detection as an example, it is recommended to (a) set at least 9 tests, that is, 3 different concentrations, with each concentration repeated 3 times; Alternatively, (b) repeat the test at least 6 times for a fixed test concentration to evaluate repeatability.
• Intermediate PrecisionPrecision obtained from measurements taken in the same laboratory but with different operators, at different times, or under different instrument conditions. The specific scope of the verification experiment needs to be determined by the verifier based on the actual application scenario. In other words, it is not necessary to test each variable individually in all cases, nor is it necessary to test all variables comprehensively. Instead, it is necessary to evaluate random factors that may affect measurement precision based on actual needs, and validate experimental designs based on production development and risk assessment. According to different evaluation factors and experimental designs, intermediate precision includes daytime precision (different test dates), inter room precision (different environmental conditions: temperature and humidity changes, vibration, airflow, etc.), personnel precision (different operators), inter equipment precision (different instruments or different batches of instruments), inter batch/intra batch precision (samples between batches or within the same batch), etc.
• ReproducibilityThe precision of results measured by different personnel using the same method in different laboratories or environments. In most cases, regulatory agencies (such as FDA, EMA) typically do not mandate the submission of reproducibility testing data, but in the following situations, it is still necessary to consider conducting such testing: (a) standardizing analytical methods (such as incorporating a testing method into a pharmacopoeia). (b) When multiple laboratories use the same method together (such as across regions or institutions).

Precision is also one of the fundamental indicators for evaluating device performance in nanoparticle characterization. High precision instruments are crucial for ensuring the reliability of experimental data. For example, the precision verification of particle size is usually evaluated by measuring the same CRM standard sample (such as traceable nanoparticle size standard microspheres) multiple times and calculating the standard deviation (SD) or coefficient of variation (CV) of the measurement results to assess the precision of the instrument.

Coefficient of Variation (CV)It is a statistical indicator that measures the degree of data dispersion,Expressed as the ratio of the standard deviation of data to the meanTherefore, sometimes referred to as relative standard deviation (RSD), it can visually compare the stability of different instruments or measurement conditions. It is a dimensionless indicator, usually expressed as a percentage. The smaller the CV value and the lower the degree of dispersion, the better the repeatability and stability of the measurement results, and the higher the precision. The formula for calculating the coefficient of variation is:

Where σ is the standard deviation (SD), and μ is the average value of the measurement data.

Range of detection

The detection range refers to the effective interval within which a certain analytical method can be used for sample detection while ensuring accuracy, precision, and linearity meet the requirements.

Limit of Detection (LOD) and Limit of Quantification (LOQ)

• LOD：The analytical method or instrument can reliably detect the quantity of the target substance (particle size, concentration, potential, etc.), but does not require the signal to be accurately quantified. LOD represents the lower sensitivity limit of the detection method and is crucial for trace analysis.

• LOQ:Can quantitatively measure the lower limit of the detection range of the analyte. Analysis methods or instruments canReliable and accurateThe measurement of the quantity value of the target object (particle size, concentration, potential, etc.) requires sufficient accuracy and precision of the measurement value. When setting LOQ, it is necessary to meet the requirement of CV% ≤ 10% (or stricter).

Previous articles on this account have elaborated on the significance and importance of detection limits in the field of particle characterization in the biopharmaceutical industry. Please refer toParticle characterization LOD surface view.

Linearity Range

Linearity refers to the ability of an analytical method to exhibit a linear relationship between the detected signal (response value) and the concentration or quantity of the substance being measured within a specific concentration range.In short, when the sample concentration or particle count varies within a certain range, the measurement results should change proportionally. A method with good linearity has a high correlation coefficient (R)²)，R²The closer it is to 1, the better the linear relationship. Usually, the R of a set of data²≥ 0.8 is considered correlated; To ensure the reliability of data, R is often required²≥ 0.99 (or refer to specific industry standards).

Linearity Range or LinearRegion）It refers to the maximum range within which a detection method maintains a linear relationship within a specific concentration or particle count range.Below the lower limit of the linear interval, the signal may be below the instrument LOD and cannot be reliably detected; If it is higher than the upper limit of the linear interval, signal saturation may occur, the instrument may not respond correctly, or nonlinear effects may occur. In the PQ process, it is also necessary to verify the linear interval of the analytical method or instrument, usually using the following experimental methods:

• Calibration Curve Method：Measure using standard samples of different concentrations or particle quantities, and draw standard curves. The specific experimental steps are as follows:

1. Prepare a series of standard samples with different concentrations, which can be standard microspheres with theoretical nominal concentrations or other actual samples with known concentrations. Different concentration settings are usually prepared by continuous or fold dilution of the initial sample. ICH recommends distributing at least 5 concentrations appropriately throughout the entire range^[2].)

2. Measure each sample separately using the test method and record the detection signal (such as absorbance, fluorescence intensity, peak area, etc.) or value (direct concentration assignment, etc.).

3. Draw a scatter plot of the detection signal/value (y) versus the expected concentration (x).

4. Fit a straight line through linear regression analysis, calculate the slope (k), intercept (b), and correlation coefficient (R)²）.

5. according toR²Determine the linear interval based on the recovery verification standard (usually requiring a deviation within ± 10%).

• Linear Regression Method：If the concentration of the sample to be tested is unknown,Then use the same buffer solution system to continuously dilute (or dilute from the original solution ratio) 4-5 concentration gradients with the highest concentration of the sample solution, and implement the above standard curve method.The difference lies in adjusting the expected concentration to the reciprocal of the dilution factor of each concentration gradient sample (1/N dilution factor).Draw a linear regression fitting line between the concentration measurement value (or the concentration mean of each point in the gradient) and the corresponding dilution ratio, and evaluate itR²(as shown in the figure below).This method is one of the commonly used methods in analysis and validation.

Concentration linearity of mixed NIST standard microsphere samples

• Spiked Recovery MethodSimilar to accuracy verificationSpiked experiment,The analysis steps are as follows:

1. Add a known concentration of the target analyte to the blank matrix to prepare a series of spiked samples.

2. Measure the spiked sample using the test method, record the detection signal or direct measurement value, and calculate the recovery rate.

3. Determine the linear interval based on the recovery rate (usually requiring 80% -120%) and the results of linear regression analysis^[6].

It is worth noting that,There is a certain difference between linear intervals and detection ranges：

For example, a certain instrument can detect a concentration of 10⁶-10⁹Particles/mL of nanoparticles (detection range), but only within 2 × 10⁸-8×10⁸ Maintain a strict linear relationship between particles/mL (linear interval). The linear interval usually starts from LOQ and ends at the highest concentration or particle size where the instrument response no longer shows a linear relationship. In practical applications, the detection limit and linear interval jointly determine the measurement range and applicability of nanoparticle analysis equipment. The lower detection limit and wider linear range mean that the instrument can provide reliable measurement results over a wider range of concentrations and particle sizes.

Exclusive attribute（Specificity/Selectivity)

Specificity refers to whether the analytical method can avoid interference from other substances when determining the target object, thereby accurately identifying or quantifying the target object.Specificity validation is often one of the first validation considerations in analytical method validation, as it relates to whether an applicable detection method has been selected. The robustness verification of nanoparticle characterization is often carried out usingLabeling experimentMethods for conducting interference testing:

1. Add a certain amount of samples or impurities that are not sensitive to the current method, such as samples that are not suitable for the detection method or interference samples outside the detection range, into the matrix of the test sample. By comparing the changes in data before and after adding interference samples, test whether the addition affects the detection results. For example, if a certain device has a detection range of 50-200 nm, standard microspheres or protein particle solutions with known smaller particle sizes (20-30 nm) are added to the sample to be tested, and the particle sizes and concentrations of samples with and without additives are detected separately, in order to determine whether the device is suitable for characterizing such samples.

2. Add a known amount of internal standard to the test sample, measure the test sample, and evaluate whether the detection response of the equipment to the standard substance meets theoretical expectations, verifying whether the distribution measurement of particles of different sizes mixed is accurate. For example, adding an equal amount (with the same particle concentration) of CRM microspheres with a particle size of 150 nm to exosome samples (usually ranging from 30-150 nm), and detecting the changes in particle size and concentration of the sample after adding the internal standard compared to before adding the standard. If the corresponding internal standard signal can be detected, and the average particle size of the mixture after adding the standard is consistent with the theoretical particle size mean (the recovery rate is within the acceptable range), and the concentration value remains unchanged, it can be judged that the detection method has specificity for the extracellular vesicle sample. This experiment is particularly suitable for evaluating the detection ability of instruments for polydisperse system samples.

In addition, specialized experiments can also be compared using orthogonal methods (another method with a different measurement principle but fully validated). Meanwhile, if the principle of the analysis technique itself can ensure high specificity, there is no need for additional experimental verification, only reasonable theoretical support is needed^[2].

robustness(Robustness)

robustness(also known as durability)It refers to the analysis method changing under small-scale experimental conditions(such as temperature, pH, reagent concentration, etc.)The stability and reliability of the measurement results can still be maintained.It measures the tolerance of the method to slight environmental fluctuations. In addition, robustness testing can also be used to evaluate the stability of sample preparation and reagents, ensuring that data is not affected by changes in these factors throughout the entire experimental process. Simply put, it means assessing the "anti-interference ability" of the detection method in advance to ensure that it can work properly under real experimental conditions (non ideal conditions). The results of robustness testing can be submitted as reference data during the method development phase or provided when required by regulatory agencies.

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Conclusion

The parameters and experiments analyzed above not only involve the adaptability of the instrument under different sample conditions, but also cover core indicators such as data accuracy, repeatability, and long-term stability. Only through these rigorous verifications can we ensure that the instrument provides standardized measurement guarantees and reliable data support in practical scientific research and production environments, and provide scientific basis for researchers to correctly interpret test results.

References:

1. State Administration for Market Regulation (2020). Good Manufacturing Practice (GMP) for Drug Production

2.International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). (2023). Validation of Analytical Procedures Q2(R2).

3.U.S. Food and Drug Administration (FDA). (2011). Process Validation: General Principles and Practices.

4.International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). ICH Q7: Good Manufacturing Practice Guide for Active Pharmaceutical Ingredients[S]. 2016.

5.USP General Chapter <1058>: Analytical Instrument Qualification[S]. United States Pharmacopeia, 2023.

6.U.S. Food and Drug Administration (FDA). Bioanalytical Method Validation Guidance for Industry[S]. 2018.

7. Chinese Pharmacopoeia 2020 Edition - Guiding Principles for Validation of Analysis Methods 9101