Nontargeted analysis has become a defining capability in modern separation science. As chromatography and mass spectrometry platforms generate larger, richer outputs, the challenge has shifted from signal acquisition to interpretation. High-dimensional data promise discovery, but they also introduce ambiguity, redundancy, and analytical burden—especially when no predefined analyte list guides interpretation.
Caitlin Cain is a postdoctoral research fellow at the University of Michigan. Her research develops chemometric techniques for nontargeted chromatographic analysis, encompassing feature discovery, redundancy reduction, and machine-learning-driven prioritization. In this discussion, she outlines how informatics tools reshape nontargeted workflows, from data reduction to defensible use in regulated environments.
Why Nontargeted Chromatography Data Are Hard to Analyze
“Chromatographic separations coupled to mass spectrometers can produce high-dimensional data that can become onerous to manually analyze, especially if there are matrix effects, retention time misalignment, or co-elutions,” explains Cain. “And the challenge intensifies in nontargeted analyses where there is no predefined list of analyte targets”.
Rather than accepting this challenge as inevitable, she positions informatics as a way to restore analytical focus. “Alignment or feature detection algorithms can help us efficiently analyze these complex data by teasing out meaningful chemical differences from the background noise.” In practice, the choice of alignment strategy, peak-picking thresholds, and preprocessing order can strongly influence downstream statistics, making method transparency as important as algorithm selection.
Working in analytical science?
Register for a FREE Separation Science account to subscribe to the Separation Science Newsletter.
Tile-Based Data Analysis for GC×GC and Nontargeted Separations
One area in which Cain has made notable contributions is tile-based analysis for comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC×GC–TOF-MS) datasets, which reframes the evaluation of chromatographic space when retention-time drift undermines peak-based alignment.
“Tiling is beneficial for mitigating retention time misalignment while simultaneously discovering chemically significant signals in a data set,” describes Cain. “Instead of relying on precise peak correspondence, a tile-based analysis bins the chromatogram in small rectangular sections that can be evaluated using statistical calculations or machine learning algorithms to determine their significance.”
Tile dimensions are determined with chromatographic behavior in mind. “The tile size is selected to account for the average peak width plus any shifting observed in the chromatogram, overcoming alignment errors rather than amplifying them,” advises Cain.
This framework adapts well to real-world datasets that rarely conform to ideal experimental balance. For unbalanced class sizes, Cain points to ensemble methods as a practical solution. “Machine learning algorithms such as Random Forests can be applied within this tile-based framework to discover chemical differences,” she notes. Cain adds that careful cross-validation and inspection of variable importance scores are critical to avoid tiles driven by noise or batch effects rather than chemistry from overfitting.
Reducing Redundant Features in Nontargeted LC–MS and GC Data
Feature inflation remains a persistent obstacle in nontargeted liquid chromatography–mass spectrometry (LC–MS) and gas chromatography (GC)–based workflows, in which multiple ions can represent the same underlying chemical signal, complicating interpretation.
Cain has addressed this issue through a computational strategy known as lack-of-fit clustering. “This method is aimed at reducing the number of redundant features detected in a chromatographic data set,” she explains.
The approach relies on peak-shape comparison rather than mass spectral heuristics alone. Cain advises that lack-of-fit clustering calculates peak shape similarity between co-eluting features. “If those features are similar and have a low lack-of-fit score, then we can cluster those redundancies together and reduce the overall number of features for downstream analyses.”
The result is a compressed dataset that preserves chemical meaning while improving statistical tractability. Cain emphasizes that redundancy reduction must be conservative—overly aggressive clustering risks merging chemically distinct but co-eluting species, particularly in high peak-capacity separations.
Using Machine Learning to Prioritize Features in Nontargeted Analysis
Across Cain’s work, machine learning serves a targeted role. She consistently frames these tools as guides for analytical attention rather than engines for compound identification.
“Random Forest models and other machine learning techniques represent a promising approach for discovering meaningful chemical information from raw instrumental data by prioritizing features that discriminate between different sample classes, allowing researchers to focus on those regions for later identification efforts,” she observes. These models are most effective when paired with chemically informed filtering steps, rather than applied directly to fully uncurated feature tables.
Applying Nontargeted Informatics in Regulated Analytical Laboratories
For laboratories operating under regulatory constraints, exploratory informatics can raise concerns about reproducibility and defensibility. Cain argues that nontargeted approaches are best used upstream of validation.
“In regulated industries, nontargeted analyses are best positioned as hypothesis-generating rather than confirmatory,” she notes. Used this way, they allow researchers to systematically discover unexpected differences, trends, or anomalies that can be investigated further using well-established workflows.
Rigor remains essential. “Informatic workflows should be scripted, version-controlled, and have all preprocessing steps, statistics, and decision criteria documented,” emphasizes Cain. Reference materials, system suitability checks, and replicates continue to play a role.
From Offline Informatics to Real-Time Data Analysis
Most informatics workflows continue to operate after data collection concludes, a boundary Cain expects will shift.
“Currently, informatics tools are mostly used offline, but we will start to see machine learning algorithms automatically integrated into data acquisition, providing researchers with information in real time,” Cain anticipates.
Such integration could enable adaptive experiments rather than static methods, such as flagging retention time drift, abnormal feature density, or class-separating regions during acquisition rather than after the fact.
Avoiding Black-Box Informatics in Nontargeted Workflows
Despite growing adoption, misconceptions persist. Cain often encounters skepticism that frames nontargeted analysis as unfocused.
“A common misconception is that people see nontargeted analyses as a ‘fishing expedition,’ when in a well-designed experiment they can be rigorous tools to test hypotheses and generate new ones,” she observes.
Cain also cautions against uncritical automation. “I want to warn against an over-reliance on these informatic tools as a ‘black box.’ Informatics should augment, not replace, our own analytical judgment.”
Getting Started with Nontargeted Analysis and Informatics
For separation scientists new to informatics, the entry point may be simpler than expected. “The data science community mostly operates in an open access format,” Cain asserts, adding that example datasets and scripts are widely available.
“Learning the basics of a scripting language like Matlab, Python, or R will help you make sense of the code behind the informatics tools,” she notes. From there, applying those methods to one’s own data becomes a practical next step.
As nontargeted analysis continues to mature, Cain’s perspective highlights a clear takeaway. Discovery depends less on collecting more data and more on extracting meaning with intention, transparency, and chemical insight.
