Unveiling the Secrets of Error-Free Calibration Curve Preparation

Unveiling the Secrets of Error-Free Calibration Curve Preparation


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  • Source: Microbioz India

  • Date: 08 Sep,2023

A calibration curve is a technique for determining the concentration of a substance in an unidentified sample. These curves can be used to identify where an unidentified substance plots using data points from identified substances at varied concentrations. The calibration curve is also used to establish the limit of detection and quantitation by determining the concentration of an unidentified sample. The curve is generated by measuring the instrumental response to a set of conventional samples at various concentrations.

A  curve calibration is used in the majority of analytical procedures. This technique has a lot of advantages. First, the calibration curve provides a valid method for calculating the ambiguity of the calibration curve-derived concentration. Secondly, the curve gives empirical relationship data. The mechanism underlying the instrument’s response to the sample can be anticipated or understood using a theoretical model, although most of these models have limited applicability to authentic samples.

There are several steps involved in calibrating an analytical instrument which is discussed below:

  1. Design the tests
  2. Take measurements;
  3. Visualize the outcomes.
  4. Conduct statistical analysis of the data to generate the calibration function
  5. Analyze the regression analysis results
  6. Apply the calibration function to estimate values for test samples
  7. Determine the uncertainty associated with the test sample values.

Schematic Presentation of Calibration Curve

How to avoid sources of errors in calibration curve preparation?

The basis of any quantitation is reliable calibration data. Nonetheless, every successful calibration curve is the result of a remarkable amount of effort. Instances such as pipetting various sampling volumes, permitting air bubbles in the samples, or preparing the samples before time, can raise the fluctuation in the findings acquired from the calibrators tested during the calibration process. Numerous possible sources of error await at every step, so it pays to be aware of the best ways to assure accuracy and preciseness. To avoid the annoyance of failed calibration curves, missed deadlines, and wasted resources and time, let’s look at some recommendations for avoiding some of the issues that can arise while developing calibration curve standards.

Also read;

HPLC calibration curve: Everything You Need to Know

Use equipment in the right way:

The first step towards achieving a decent standard curve is becoming acquainted with your equipment and learning how to utilize it correctly. It may appear simple, but understanding how the equipment works and adopting proper lab techniques should never be taken for granted. Familiarize yourself with the tools at your disposal and practice utilizing them correctly until you feel at ease with the required equipment. Just though you’ve used a similar device in the past does not guarantee that the new tool will perform similarly.

Look into the Problems of Stability

Several makers of reference standards get ISO certificates to verify that processes are in effect to ensure that the labelled concentration is correct until the item expires. But, once a reference substance is unsealed and interacts with other standards or agents, chemical reactions may take place that cause premature deterioration, making your prepared calibration standards unreliable.

Temperatures and light tolerances may also need to be taken into consideration, particularly when storing. To ensure that the new mixtures create correct calibration curves when utilized, you must determine how long they are durable. Perform stability studies ahead of time to determine how to handle and preserve working standards, as well as how long they will generate reliable and precise quantitative findings. It’s important to remember that different concentrations deteriorate at different rates.

Aside from stability, sample solubility can be a problem. If you have molecules that are not particularly soluble in the dilution solvent, you may have major calibration issues. Always do sample studies to make sure that the chemicals and their various calibration levels are soluble and stable in the prepared solutions.

Create a Fail-Safe work process

Establishing a documented approach for producing calibration curve standards in advance will allow lab work to be considerably more effective. Utilizing a sheet with pre-calculated concentration levels to capture the workflow phases guarantees that no stages are missed and avoids incorporation of inaccurate amount. It’s a terrific approach to figure out exactly how many chemicals and solvents you’ll need and to visualize the process before you start.

Using a color scheme sheet in your work is a simple approach for offering an extra layer of protection. When all looks the same, it’s simple to grab the wrong vial. You can reduce the odds of error by matching the colors of the spreadsheet cells to the labels or covers you use in the lab. Print your working procedure once you’ve finished, bring it to the lab, and keep a copy in front of you while you work.

Check the quantities of chemicals, solvents, and supplies again once you’re ready to start working in the lab to make sure you have enough of everything.

Afterward, start using the work approach you built. Include a tracking system that allows you to keep track of the job you’ve performed while executing each stage.


Standardizing your strategy, making use of decision support and prompts, using pre-existing routines and patterns, making the requested response the standard rather than the exception, and introducing redundancy can all help to reduce the chance of faults in the process.

Minimizing possible error sources while establishing calibration curve standards is critical for proper quantification. Educating yourself about equipment and building adequate techniques, knowing stability and solubility restrictions, and creating a fail-safe work process are all worthwhile investments that will improve the certainty of your analysis model.

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