On the quantitative analysis of liquid chromatography - Database & Sql Blog Articles

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Quantitative analysis in liquid chromatography relies on qualitative results and requires a pure material as a reference standard. The quantification method used here is relative, meaning the amount of the sample is determined based on a known standard. This approach helps ensure accurate measurements by comparing unknown samples to calibrated standards.

Understanding the Basis for Quantitative Analysis

The amount (W) of a component is directly proportional to the response value (A), which can be either peak height or peak area. The relationship is expressed as W = f × A, where 'f' is the quantitative correction factor. This factor represents the proportionality constant between the measured response and the actual amount of the substance. It's calculated using a known standard sample and its corresponding response. Once the response of an unknown sample is measured, the amount of the component can be determined using this factor.

Key Terms in Quantitative Analysis:

• Sample: A solution containing the compound to be analyzed, which can be either a standard (known concentration) or an unknown (unknown concentration).
• Standard: A pure substance with a known concentration used for calibration.
• Unknown: A mixture whose concentration is to be determined.
• Sample weight: The original mass of the sample before dilution.
• Dilution: The process of reducing the concentration of the sample for analysis.
• Peak: A chromatographic signal representing the analyte.
• Amount: The concentration or content of the substance being tested.
• Integrity: The accuracy of the peak area measurement by the computer system.
• Calibration Curve: A linear graph showing the relationship between the concentration of a standard and its response value, used to determine unknown concentrations.

Common Quantitative Methods in Liquid Chromatography

1. External Standard Method

This is the most widely used technique in liquid chromatography. It involves preparing a series of standard solutions with known concentrations and measuring their response values (peak areas). A calibration curve is then created within a certain concentration range, assuming a linear relationship between concentration and response (W = f × A). When analyzing an unknown sample, its response is compared to the calibration curve to determine its concentration.

Advantages:

• Simple to perform and calculate.
• No need to detect and elute all components.
• Requires only standard samples.
• Consistent experimental conditions are essential.

Disadvantages:

• High sensitivity to experimental conditions like detector sensitivity, flow rate, and mobile phase composition.
• Requires precise injection volumes for reproducibility.

2. Internal Standard Method

This method involves adding a known amount of an internal standard to both the standard and unknown samples. The ratio of the analyte’s peak area to the internal standard’s peak area is used as the response value. This approach helps minimize errors caused by variations in injection volume, column performance, and detector response. It is more accurate than the external standard method but slightly more complex to implement.

Case Studies and Discussions

Case Study One:

A user was analyzing phenol hydroxylation products using an Agilent 1100 liquid chromatograph and encountered inconsistent results when using the external standard method. Selectivity values over 100% were observed, which raised concerns about possible errors in the standard curve or sample preparation. Issues such as column pressure fluctuations, dilution errors, and potential column contamination were considered. Some users suggested checking the column condition, ensuring proper rinsing, and verifying that the calculation method was correct.

Case Study Two:

An individual asked whether it was necessary to prepare a new standard curve for every batch of 20 samples. While strict protocols require a new curve daily, some users noted that if the instrument and conditions remain consistent, a single curve may suffice for several days. However, for regulated testing environments, following official guidelines is crucial to maintain data reliability.