Complexity in Analytical Science: Should We Fight It or Embrace It?

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Spookily enough, the word “complexity” has surfaced repeatedly in various contexts of my work life over the past two years, almost as if it were an omen. Inevitably, the recurrence of the theme piqued my curiosity, and I started to wonder about the essence of complexity itself and its implications for our field as analytical scientists. For someone whose love for data analytics is rooted in unravelling complexity through statistical tools and data visualisation, this felt like a sign I couldn’t ignore. 

As I navigated through the various stages of the analytical workflow, I couldn’t help but ask, “What if complexity isn’t just a hurdle but a gateway for discovery and understanding?” In our quest to master analytical science, complexity often appears as both a challenge and an opportunity. It’s like trying to decipher a grand tapestry where every thread intertwines, creating a pattern that’s both intricate and revealing. 

The Etymology of “Complexity” 

Funnily enough, the tapestry metaphor is indeed the truest to the real meaning of the word.  

Did you know that the word “complexity” originates from the Latin word “complexus“, which means “embraced” or “entwined“? The Latin root is composed of two parts: “com-” (together) and “plectere” (to braid or twine). The idea of intertwining or braiding captures the essence of complexity—multiple elements coming together to form a whole. Over time, it has evolved and, unfortunately, ended up acquiring common negative connotations such as difficulty, inaccessibility and unpredictability, which can translate into user alienation, wasted resources and lack of control.  

In reality, though, complexity often implies a sophisticated richness of detail and depth that can be intriguing and intellectually stimulating. The challenge lies in unravelling how the diverse elements work together to understand the multiple pathways and their response to change.  

Complexity in our Analytical Workflow

In the intricate world of analytical science, complexity has somehow become our constant companion—whether we are grappling with the diversity of sampling matrices, the intricacy of sample preparation, the sophistication of instrumental analysis, or the puzzles of data processing. Each stage involves its own complexities that we must navigate to ensure success. 

Sampling

Complexity in sampling often arises from the need to obtain a representative aliquot from a very diverse and heterogeneous matrix. The difficulty arises in ensuring that the sample truly reflects the composition of the entire bulk material, e.g. multiple sub-samples combined and homogenised together to create the final analytical sample. 

Sample Preparation 

Sample preparation involves converting the collected sample into a form suitable for instrumental analysis. The process often requires multi-stage procedures to isolate the analytes of interest and remove interferences, with each additional step introducing potential variability. Optimising these steps to maximise recovery and minimise loss or degradation of the analytes is where complexity impacts the most and why careful method development and validation are crucial requirements for reliable results. 

Instrumental Analysis

Instrumental analysis introduces the prepared sample into the selected instrumentation for analytes detection and quantification. Added layers of complexity emerge not only in the choice of the most suitable system for the task at hand but also in the fine-tuning of the hardware and software settings to provide the best performance in terms of selectivity, sensitivity and precision, which are required to detect and quantify the analytes accurately. 

Data Processing

Data processing requires treatment and interpretation of the raw data generated by the analytical instrument. This stage is inherently complex due to the need to convert vast amounts of data into meaningful and actionable information. Advanced software tools and algorithms are used to handle these tasks, but the complexity remains in ensuring that the processed data accurately reflects the chemical space targeted by the analysis and, most importantly, provides meaningful insights into the analytical question we set to answer. 

Embracing Simplicity to Harness Complexity

Everyone has their favourite quote, which they live by. This is mine. 

“Out of complexity, find simplicity.”  – Albert Einstein. 

Whilst common conception might make this sound like an oxymoron, simplicity is not complexity’s opposite. Instead, it is a route to harness and unravel the intricate details hidden within complex processes, a sophisticated approach to distilling complexity into its components and extracting meaningful insights without losing the essence of the underlying intricacies.  

Simplicity in analytical science is about making the complex understandable and manageable without sacrificing depth or accuracy.  It involves designing streamlined workflows, employing accurate methodologies, and using robust tools to handle complex data efficiently. This is precisely why automation and data analytics resonate so powerfully with my ethos.  

Join Us at Future Focus III

Understanding and managing complexity in analytical science is not just a speculative exercise—it’s a practical necessity that drives innovation and discovery. However, achieving this balance requires continuous learning and collaboration within the analytical community. 

It is with this philosophy in mind that our yearly in-person symposium, Future Focus, was born three years ago. And it must come as no surprise to you that the theme for this third edition is “Exploring Complexity in Analytical Science”.  

The complexities we face in analytical science require a collective effort to understand and overcome. Future Focus III is all about offering a unique platform for the academia and industry community to share their perspectives and learnings, helping each other on our journey from complexity to clarity.  

Here is the agenda if you are curious to know more about it.

You can also click here to register.

I hope to see you there. 

In the meantime, here is to your success in embracing analytical complexity.