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How can you optimize analytical techniques for more accurate results?

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Analytical techniques are essential for biotechnology, as they allow you to measure and characterize biomolecules and their interactions. However, analytical methods are not always perfect, and they can be affected by various factors that reduce their accuracy and reliability. In this article, you will learn how to optimize analytical techniques for more accurate results, by following some practical tips and best practices.

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1 Choose the right technique

The first step to optimize your analytical results is to choose the most suitable technique for your research question and sample type. There are many analytical techniques available for biotechnology, such as chromatography, mass spectrometry, spectroscopy, microscopy, and bioinformatics. Each technique has its own advantages and limitations, and you should consider factors such as sensitivity, specificity, resolution, speed, cost, and sample preparation. For example, if you want to analyze the structure and function of a protein, you might use a combination of techniques, such as circular dichroism, nuclear magnetic resonance, and molecular docking.

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Dr. Osvaldo Daniel R.
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Molecular docking simulations helped predict how these proteins interacted with other molecules or substrates, shedding light on their functional mechanisms. Each technique contributes unique facets of information that, when amalgamated, painted a comprehensive picture. However, it's crucial to acknowledge the limitations and nuances of each method. While NMR provided high-resolution details, it is time-consuming and required purified samples. On the other hand, circular dichroism offered quick insights but lacks the depth of structural data provided by NMR. Factors like cost, sample preparation, and instrument availability weighs in on decision-making. Balancing these factors while maximizing the synergy between techniques is essential.

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2 Validate your method

The second step to optimize your analytical results is to validate your method, which means to verify that it meets the criteria of accuracy, precision, linearity, range, robustness, and reproducibility. Validation is important to ensure that your method is reliable and consistent, and that it can detect and quantify your analyte of interest. To validate your method, you should perform experiments with different concentrations, replicates, and standards, and calculate the relevant parameters, such as accuracy, precision, limit of detection, limit of quantification, and coefficient of variation.

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Dr. Osvaldo Daniel R.
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Validating a method, such as one for quantifying a biomarker, involved experiments with varied concentrations and replicates to confirm accuracy and precision. Analyzing linearity across concentrations ensured consistent quantification within the specified range. Robustness tests assessed its resilience to varied conditions. Determining parameters like limit of detection (LOD) and quantification (LOQ), and calculating coefficient of variation (CV) ensured sensitivity and measurement reliability.

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3 Control your variables

The third step to optimize your analytical results is to control your variables, which means to minimize the sources of error and variation that can affect your measurements. Variables can be classified into three types: systematic, random, and environmental. Systematic errors are caused by flaws in the equipment, calibration, or procedure, and they can be corrected by adjusting the settings or using reference materials. Random errors are caused by unpredictable factors, such as noise or human error, and they can be reduced by increasing the number of measurements or using statistical methods. Environmental variables are caused by external factors, such as temperature, humidity, or light, and they can be controlled by using stable conditions or appropriate storage.

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4 Optimize your data analysis

The fourth step to optimize your analytical results is to optimize your data analysis, which means to use the appropriate tools and techniques to process, visualize, and interpret your data. Data analysis can involve various steps, such as filtering, normalization, transformation, integration, and statistical testing. Depending on the type and complexity of your data, you might use different software or programming languages, such as Excel, R, Python, or MATLAB. You should also choose the best way to present your data, such as tables, graphs, charts, or diagrams, and use clear and consistent labels, legends, and units.

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5 Document your workflow

The fifth step to optimize your analytical results is to document your workflow, which means to record and report all the details of your method, data, and results. Documentation is essential for transparency, reproducibility, and quality assurance, as it allows you to track and verify your work, and to share and communicate it with others. You should document your workflow in a clear and organized way, using formats such as protocols, notebooks, logs, or reports. You should also include all the relevant information, such as materials, equipment, settings, parameters, calculations, and references.

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Ilya Captain, PhD

Passionate about data and technology. Organizing Data Driven Pharma events
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There have been so many high profile scientific disappointments because of poor documentation (such as LK-99). Scientists get excited about science, and writing down the exact steps becomes an afterthought. At the end of an experiment or project, it can become unclear or ambiguous how the final result was obtained. Ensuring that each simple step is described in appropriate detail is critical. However, since is no consensus on "appropriate detail," it's important to get a second opinion from colleagues that understand your project when publishing final documentation versions. This can be slow and frustrating, but it is worth it since it reduces ambiguity considerably.

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William Polk, PhD

Medical Laboratory Scientist
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Momal criteria is to compare value of control values to peer labs and establish randomized challenges on regular intervals. Establish like method and all method comparators. Run calibration on regular intervals. Run recovery of known (pooled) specimens on regular intervals.

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6 Review and improve your results

The sixth and final step to optimize your analytical results is to review and improve your results, which means to evaluate and compare your results with your objectives, expectations, and literature. Reviewing your results can help you to identify and correct any errors, anomalies, or inconsistencies, and to assess the validity and significance of your findings. Improving your results can involve repeating, refining, or expanding your experiments, or applying different or complementary techniques. You should also seek feedback and suggestions from your peers, mentors, or reviewers, and use them to improve your quality and impact.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you optimize analytical techniques for more accurate results?

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1 Choose the right technique

2 Validate your method

3 Control your variables

4 Optimize your data analysis

5 Document your workflow

6 Review and improve your results

7 Here’s what else to consider

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