The Significance of System Suitability in High-Performance Liquid Chromatography (HPLC) Analysis: Ensuring Accurate and Reliable Results

Abstract

High-Performance Liquid Chromatography (HPLC) has emerged as a fundamental technique in modern analytical chemistry, serving as a powerful tool for the separation, identification, and quantification of [1] diverse compounds. One critical aspect that ensures the validity and credibility of HPLC results is the concept of system suitability. This abstract highlights the importance of system suitability in HPLC analysis, emphasizing its role in ensuring accurate and reliable results. System suitability is a set of pre-defined criteria that evaluate the performance of the HPLC system, encompassing factors such as resolution, peak symmetry, tailing factor, retention time, and peak area reproducibility. Adhering to these criteria is essential for consistent and precise analyses, as they confirm that the instrument is operating optimally and capable of generating dependable data. This research article presents an in-depth examination of system suitability parameters and their impact on HPLC analysis. Through a comprehensive literature review and experimental study, we demonstrate how system suitability assessments play a pivotal role in detecting and rectifying potential analytical issues, such as column degradation, mobile phase fluctuations, and detector malfunctions. Moreover, the article delves into the implications of neglecting system suitability checks, leading to erroneous results and potentially compromising the reliability of analytical conclusions. The results obtained from our study affirm the significance of system suitability in HPLC analysis, underscoring its role as a crucial quality control measure that ensures consistency and accuracy in chromatographic data. By maintaining robust system suitability practices, laboratories can confidently validate their HPLC methods, establish traceability, and meet regulatory requirements in industries such as pharmaceuticals, environmental monitoring, and food analysis. In conclusion, this research emphasizes the necessity of integrating systematic system suitability assessments into HPLC analysis workflows. Adhering to these practices not only safeguards the integrity of analytical results but also enhances the overall credibility and trustworthiness of HPLC as a reliable analytical technique in the realm of modern science and industry.

Introduction

I. INTRODUCTION

High-Performance Liquid Chromatography (HPLC) has evolved into a cornerstone analytical technique, revolutionizing the field of modern analytical chemistry. Its versatility and widespread applications have made it an indispensable tool in various industries, including pharmaceuticals, environmental monitoring, and food analysis. As HPLC continues to advance, ensuring the accuracy and reliability of its results becomes increasingly critical, leading to the concept of system suitability as an indispensable quality control measure.

A. Background and Context of HPLC Analysis

HPLC, also known as high-pressure liquid chromatography, is a sophisticated chromatographic method used for separating, identifying, and quantifying diverse compounds in complex mixtures. The technique employs a high-pressure pump to push a liquid mobile phase through a chromatographic column, where the analytes interact with a stationary phase. The various components of the mixture are separated based on their affinity for the stationary phase,[2] resulting in distinct peaks in the chromatogram.

The importance of HPLC lies in its ability to handle a vast range of sample types, including small molecules, large biomolecules, and complex mixtures. Its high sensitivity, resolution, and reproducibility have made it an essential analytical technique in research, quality control, and routine analysis across a multitude of industries.

B. Widespread Applications of HPLC

The significance of HPLC becomes evident when observing its broad applications in different sectors. In the pharmaceutical industry, HPLC is employed for drug development, quality control, and purity assessment of pharmaceutical compounds. The precise separation and quantification capabilities of HPLC are vital for determining the effectiveness and safety of drugs.

In environmental monitoring, HPLC plays a crucial role in detecting and quantifying pollutants, pesticides, and other environmental contaminants. Its ability to analyze complex environmental samples ensures regulatory compliance and helps safeguard public health and ecological well-being.

The food industry also benefits significantly from HPLC, where it is utilized to detect food additives, pesticides, and natural compounds, ensuring product safety and authenticity. By providing accurate compositional information, HPLC supports food labeling and quality control practices.

C. The Role of System Suitability

System suitability is a critical aspect of HPLC analysis, ensuring the reliability and accuracy of chromatographic results. It comprises a set of predefined parameters that assess the performance of the HPLC system, such as column efficiency, peak symmetry, and reproducibility of peak areas and retention times. These criteria serve as benchmarks for evaluating the suitability and consistency of the analytical system for a particular analysis. By regularly checking system suitability, laboratories can identify potential issues that may affect the quality [3] of the analysis, such as changes in the column performance, variations in the mobile phase, or deterioration of the detector. Addressing such issues promptly ensures the generation of reliable data and minimizes the risk of erroneous conclusions.

D. Research Objectives and Questions

This research aims to comprehensively explore the significance of system suitability in HPLC analysis and its impact on the accuracy and reliability of results[4]. Specifically, the objectives of this study include: Investigating the key system suitability parameters and their individual roles in assessing the HPLC system's performance. Analyzing the consequences of neglecting system suitability assessments on the quality of HPLC data and subsequent analytical interpretations. Assessing the influence of system suitability on method validation and compliance with industry standards. Providing practical recommendations and best practices for implementing system suitability protocols in HPLC laboratories. Through this research, we aim to contribute valuable insights into the optimization of HPLC analysis by highlighting the critical role of system suitability and its significance in ensuring the reliability and accuracy of results in various industries.

II. LITERATURE REVIEW

U.S. Pharmacopeia (USP) guidelines (USP <621>, 2019) and International Conference on Harmonization (ICH) guidelines (ICH Q2(R1), 2005) emphasize the necessity of system suitability testing as an integral part of HPLC method validation. The guidelines provide clear recommendations for assessing system suitability parameters, ensuring the reliability of analytical data and adherence to regulatory standards.

III. METHODOLOGY

System suitability Evolution for Related Substances in Docetaxel Injection Formulation with HPLC

A. Experimental

In this problem, the L.C. 20AT pump and UV-Visible detector with a flexible wavelength program and Rheodyne injector are used. Zorbax C18 150mm × 4.6 mm, 5μ, is used for this chromatography analysis. From the local market, the reference sample of DTX is purchased. Acetonitrile is A.R. grade, Glacial acetic acid, and water collected and used. Mobile Phase as 1100 volumes of water, 900 volumes of Acetonitrile into 2 liters bottle and sonicated for 5 minutes. System suitability solution was prepared by taking about 2 mg Transferred into 2 mL regular flask subjected to dissolution with diluent and marked to volume with diluent. DTX Regular Stock is designed by taking 16 mg Transferred into 10 mL regular flask subjected to dissolution with 4mL of Acetonitrile and marked to volume with diluent.

B. Chromatographic Conditions

wavelength 230nm

Column : Zorbax XDB C18 150 mm × 4.6 mm, 5μm Flow rate : 1.5mL/min

Column Temperature - 30ºC

IV. RESULTS

A. System Suitability

Injected Blank (as one injection), system suitability, and Regular solution (as five injections) into chromatography and recorded different chromatograms. By using the below values, finally, it is decided that the proposed method is more suitable for the validation of the approach. Obtained results are tabulated in Table 1.

Table 1: System suitability results

V. DISCUSSION EACH SYSTEM SUITABILITY PARAMETER

A. Resolution

In High-Performance Liquid Chromatography (HPLC) analysis, resolution refers to the ability of the chromatographic system to separate and distinguish two adjacent peaks in a chromatogram. It is a crucial parameter in HPLC as it directly impacts the accuracy and reliability of the analytical results [5], especially when analyzing complex mixtures.

When two peaks of interest are well-separated, their individual characteristics, such as retention time and peak area, can be accurately determined. Conversely, if the peaks are not well-resolved and overlap significantly, it becomes challenging to quantify the individual components accurately. The resolution (Rs) between two adjacent peaks is calculated using the following formula: Rs = 2 * (tR2 - tR1) / (W1 + W2)

where:

tR1 and tR2 are the retention times of the first and second peaks, respectively. W1 and W2 are the peak widths of the first and second [6] peaks at their baseline. A resolution value greater than 1 indicates that the peaks are well-separated, whereas a resolution value less than 1 suggests that the peaks are not adequately resolved.

B. Factors Affecting Resolution in HPLC:

1. Column Efficiency: The efficiency of the HPLC column, often characterized by the number of theoretical plates (N), plays a significant role in resolution. A higher number of theoretical plates result in better peak separation.
2. Selectivity: The selectivity of the chromatographic system determines how well it can differentiate between two analytes with similar properties. A higher selectivity leads to improved resolution.
3. Mobile Phase Composition: The choice of mobile phase and its composition, including solvent type, solvent strength, and pH, can influence resolution. Optimal mobile phase conditions are crucial for achieving good separation.
4. Column Temperature: Temperature can affect the selectivity of the separation, potentially impacting resolution. Some analytes may have better resolution at elevated temperatures, while others may not.
5. Column Length and Particle Size: Longer columns and smaller particle sizes generally improve resolution by increasing the number of theoretical plates and enhancing peak sharpness.
6. Sample Dilution: If sample components are highly concentrated, it may lead to peak broadening and reduced resolution. Dilution of the sample can help improve resolution. Optimizing resolution in HPLC analysis is essential for accurate and reliable quantification of analytes in complex samples. By adjusting various chromatographic parameters and optimizing the separation conditions, chromatographers can achieve the best possible resolution and obtain precise and accurate results.
7. Tailing Factor: In High-Performance Liquid Chromatography (HPLC), the "tailing factor" is a parameter used to assess the peak shape of a chromatographic peak. HPLC is a widely used analytical technique to separate, identify, and quantify components of a mixture.[7] The peak in an HPLC chromatogram represents the elution of a particular analyte as it passes through the chromatographic column and is detected by the detector. Ideally, a well-resolved peak should have a symmetrical Gaussian shape, with the front and back sides of the peak being similar in height and slope. However, in some cases, the chromatographic peak may exhibit a tailing shape, where the trailing side of the peak is longer and shallower compared to the leading side. This tailing can be caused by various factors, including interactions between the analyte and the stationary phase, non-ideal column packing, or issues with the mobile phase.

The tailing factor (TF) is a numerical parameter used to quantify the extent of tailing in an HPLC peak. It is calculated using the following formula:

Tailing Factor (TF) = (b + c) / a where:

"a" is the distance from the peak maximum to the leading edge of the peak (measured[8] at 5% of the peak height),

"b" is the distance from the peak maximum to the trailing edge of the peak (measured [9] at 5% of the peak height),

"c" is the distance from the peak maximum to the baseline.

A tailing factor of 1 represents a perfectly symmetrical peak (ideal Gaussian shape), while values greater than 1 indicate tailing, and values less than 1 indicate fronting.

Tailing in HPLC peaks can negatively affect the accuracy and precision of quantitative analysis, so minimizing tailing is essential for obtaining reliable results. Proper column selection, mobile phase optimization, and careful sample preparation can all contribute to reducing tailing effects in HPLC peaks.

C. Several factors can affect the tailing factor in HPLC:

1. Interaction with the Stationary Phase: Tailing can occur if the analyte interacts strongly with the stationary phase in the column. This can happen when the analyte has polar functional groups that interact with residual silanol groups on the surface of the silica-based stationary phase commonly used in HPLC columns.
2. Ionic Interactions: If the analyte has ionic or charged functional groups, it can interact with oppositely charged sites on the stationary phase, leading to tailing.
3. pH of the Mobile Phase: The pH of the mobile phase can influence the degree of ionization of acidic or basic analytes. When analytes are partially ionized, they can interact more strongly with the stationary phase and cause tailing.
4. Column Chemistry: The type and composition of the HPLC column can impact tailing. Some specialized columns are designed to reduce tailing by minimizing analyte-stationary phase interactions.
5. Sample Purity: Contaminants or impurities in the sample can interact differently with the column and cause tailing of the analyte peak.
6. Sample Concentration: Very concentrated samples can cause peak broadening and tailing due to the saturation of the stationary phase.
7. Column Overloading: Injecting too much sample onto the column can lead to peak broadening and tailing.
8. Flow Rate: Inadequate flow rates or changes in flow rate during the analysis can affect peak shape and cause tailing.
9. Temperature: Elevated temperatures can reduce tailing for some analytes, but for others, it may have little effect or even exacerbate tailing.
10. Analyte Structure: The chemical structure of the analyte itself, including its hydrophobicity and size, can influence tailing. To minimize tailing and achieve symmetrical chromatographic peaks, chromatographers often employ various techniques, including using different column chemistries, adjusting the pH of the mobile phase, optimizing sample preparation, and carefully selecting the appropriate HPLC parameters. By understanding and controlling these factors, analysts can achieve better peak shapes and more accurate quantification in HPLC analyses.
11. Theoretical Plates: Theoretical plates are a fundamental concept in chromatography, including High Performance Liquid Chromatography (HPLC). They provide a measure of the efficiency of the separation process occurring within the chromatographic column.

In HPLC, the separation of components in a sample occurs as the sample passes through the [10] stationary phase (packing material) within the column and interacts with the mobile phase (solvent). The analytes in the sample distribute between the stationary and mobile phases, and as they move through the column, they repeatedly equilibrate between the two phases.

The term "theoretical plates" was originally derived from the idea that the column can be envisioned as a series of hypothetical theoretical plates stacked on top of each other. Each theoretical plate represents a single equilibrium step or interaction between the analytes and the stationary/mobile phases within the column. In other words, it's a measure of the number of equilibrium steps required for an analyte to pass through the column.

The more theoretical plates a column has, the more efficient the separation will be. Efficient separation results in sharp, well-defined peaks, and better resolution between different analytes. Conversely, a lower number of theoretical plates can lead to broad, poorly resolved peaks.

The number of theoretical plates (N) is calculated [11] using the following equation: N = 16 * (tR / W)^2

where:

tR is the retention time of the analyte [11] (the time it takes for an analyte to pass through the column).

W is the peak width at its baseline, measured in the same units as the retention time.

It is important to note that the number of theoretical plates is inversely proportional to the peak width.[12] A smaller peak width indicates a greater number of theoretical plates and a more efficient separation.

D. Factors affecting the number of theoretical plates in HPLC:

1. Column Efficiency: The quality and efficiency of the chromatographic column significantly influence the number of theoretical plates. Longer columns with smaller particle sizes generally have more theoretical plates.
2. Mobile Phase Properties: The choice of mobile phase, its composition, and flow rate can impact the number of theoretical plates achieved.
3. Temperature: Column temperature can influence analyte interactions and affect the number of theoretical plates.
4. Sample Concentration: Highly concentrated samples may experience peak broadening and reduced efficiency. In summary, theoretical plates are a measure of the efficiency of the HPLC separation, and optimizing the number of theoretical plates is crucial for achieving accurate and precise chromatographic analysis.
5. %RSD: In High-Performance Liquid Chromatography (HPLC) analysis, %RSD stands for Percent Relative Standard Deviation. It is a statistical measure used to quantify the precision or reproducibility of a set of data points. %RSD is commonly employed in HPLC to assess the consistency and variability of replicate measurements or injections of the same sample under specific conditions.

The formula for calculating %RSD is as follows:

%RSD = (Standard Deviation / Mean) * 100 where:

Standard Deviation is a measure of the spread or dispersion [13]of data points from the mean value.

Mean is the average of the data points.

%RSD is expressed as a percentage and provides an easy way to compare the variation between different datasets or to assess the repeatability of an analytical method.

E. Factors Affecting %RSD in HPLC

1. System Suitability: The performance of the HPLC system, including the column, detector, and pump, can influence %RSD. A well-maintained and calibrated system is essential for obtaining consistent results.
2. Column Quality and Stability: The quality and condition of the HPLC column can significantly impact %RSD. Columns with poor efficiency, degradation, or inconsistency in packing can lead to increased variability in results.
3. Mobile Phase Quality: The composition and preparation of the mobile phase can affect the reproducibility of the analysis. Care should be taken to ensure proper degassing and filtering of the mobile phase to minimize %RSD.
4. Sample Preparation: Inadequate or inconsistent sample preparation can introduce variability in HPLC results. Properly homogenized and representative samples should be used to minimize %RSD.
5. Injection Technique: The way samples are injected onto the column can affect the precision of the analysis. Factors such as injection volume and injection method should be optimized for reproducibility.
6. Detector Sensitivity: The sensitivity and stability of the detector can impact %RSD. Proper calibration and maintenance of the detector are crucial for obtaining consistent results.
7. Temperature Control: Temperature fluctuations can affect the separation efficiency and reproducibility of HPLC analyses. Maintaining a stable column temperature is important for reducing %RSD.
8. Sample Stability: The stability of the sample during the analysis is essential. Some analytes may degrade over time, leading to increased variability in results.
9. Operator Skill: Operator proficiency and consistency in handling the HPLC system can also influence %RSD. Adequate training and adherence to standard operating procedures are important for reducing variability.

By controlling and optimizing these factors, analysts can minimize %RSD and improve the precision and reliability of their HPLC analyses. Regular system suitability checks and validation of the method's performance are also essential to ensure accurate and reproducible results.

VI. ACKNOWLEDGMENTS

The authors are thankful to the Management of GITAM University for providing the necessary facilities to carry out this research work.[14,15]

VII. CONFLICTS OF INTEREST

There are no conflicts of interest among the authors who were done this present work.

Conclusion

Key findings regarding system suitability in HPLC analysis emphasize its crucial role in ensuring the validity and credibility of analytical results. System suitability is a set of predefined criteria that must be met before conducting an HPLC analysis. These criteria are used to assess the performance of the chromatographic system and the quality of the data generated. Here are the key findings: 1) Ensuring Reproducibility: System suitability tests help verify the consistency and reproducibility of the HPLC system. By routinely evaluating system performance, analysts can identify potential issues and maintain the integrity of the analysis over time. 2) Monitoring Column Performance: System suitability includes parameters related to the HPLC column, such as the number of theoretical plates and tailing factor. Regular checks help ensure the column is in good condition and performing optimally, leading to sharper peaks and better resolution. 3) Verifying Detector Sensitivity: System suitability tests assess the sensitivity and response of the detector. Proper detector calibration and maintenance are vital to obtain accurate and reliable quantitative results. 4) Checking Mobile Phase Quality: The system suitability parameters also evaluate the mobile phase, including solvent purity and pH. By ensuring the mobile phase meets specified criteria, analysts can avoid potential issues that may impact separation and peak shapes. 5) Validating Method Performance: System suitability tests serve as a critical validation step for HPLC methods. Adherence to these parameters verifies that the method is fit for its intended purpose and capable of delivering accurate and precise results. 6) Ensuring Data Integrity: Regularly conducting system suitability tests helps maintain data integrity by identifying any potential sources of variation or bias in the HPLC analysis. 7) Meeting Regulatory Requirements: System suitability is an essential component of good laboratory practices and analytical method validation. It is often required by regulatory agencies to ensure the reliability and credibility of analytical results. In conclusion, adhering to system suitability parameters is of utmost importance in HPLC analysis. It ensures the validity of analytical results, improves the credibility of the HPLC method, and enhances the overall quality of the data generated. By routinely evaluating system performance, analysts can confidently rely on the HPLC method to deliver accurate and precise results, leading to better-informed decisions in various scientific, industrial, and regulatory applications.

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Copyright

Copyright © 2023 Garimi Tirumala Jyothesh Kumar , B. S. A. Andrews, V. D. N. Kumar Abbaraju. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.