The debate between chemometrics and machine learning in analytical science gets framed as a competition more often than it deserves. For most analytical scientists, the practical choice is not between two philosophies but between tools with different strengths, different data requirements, and different relationships with the interpretability and defensibility that regulated analytical work demands. Understanding those differences, and knowing when each approach fits the problem at hand, is more useful than loyalty to either camp.
The series overview on machine learning for MS and spectral data covers the full landscape across MS data interpretation, compound identification, proteomics, and NMR and vibrational spectroscopy. This article draws on those applications to frame when chemometrics, machine learning, or a combination of both is the right tool.
Key Takeaways
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What Chemometrics Is (and Why It Is Still Relevant)
Chemometrics is the application of mathematical and statistical methods to chemical data, developed specifically to handle the kinds of data that analytical instruments produce: high-dimensional, collinear, and often noisy. Its central methods, principal component analysis (PCA), partial least squares (PLS), and multiple linear regression (MLR), are not simple alternatives to machine learning that happened to come first; they are tools designed for the specific structure of spectroscopic and chromatographic data, and that design shows in their performance and tractability.
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Chemometrics forms the conceptual and computational basis for today's AI-assisted analytical strategies, introducing the principles of multivariate modelling, dimensionality reduction, and data-pattern interpretation that underpin machine learning algorithms. In the 2024 ACS Analytical Chemistry reviews issue, 16 of 22 papers cited machine learning or deep learning, typically built on top of chemometric preprocessing and experimental design rather than replacing it. That framing, chemometrics as infrastructure rather than competitor, is closer to how the field actually uses these tools.
What keeps chemometrics central in routine analytical work:
- Collinear spectral data. Analytical spectra are highly collinear: adjacent wavelengths carry correlated information. PLS handles this directly, using latent variable decomposition to extract the variation relevant to the property of interest. Standard regression methods fail in this regime; PLS was designed for it.
- Small to moderate dataset sizes. Many analytical calibration tasks involve tens to hundreds of samples, not tens of thousands. Machine learning approaches, particularly deep learning, need much larger datasets to deliver their accuracy advantage. On small datasets, a well-built PLS model often outperforms a more complex model that is poorly trained.
- Interpretability built in. PLS loadings show which spectral regions drive the model; PCA scores reveal sample grouping and outliers. This transparency is not just aesthetically satisfying; it is how you validate that a model is working for the right chemical reasons rather than exploiting a spurious correlation.
Chemometrics is the infrastructure that machine learning in analytical science is built on, not a legacy approach it replaces. The choice is about which layer of that infrastructure the problem requires.
PCA, PLS, and MLR: What They Do Well
A brief, honest account of each core method and where it fits best:
Method | Designed For | Best Fit In Practice |
PCA (Principal Component Analysis) | Dimensionality reduction, exploratory analysis, and outlier detection in high-dimensional data | Quality control sample profiling, batch comparisons, exploratory visualisation of spectroscopic datasets, MSPC in process monitoring |
PLS (Partial Least Squares) | Quantitative prediction of a property from correlated spectral or chromatographic variables | NIR and Raman calibrations for content, moisture, blend uniformity; concentration prediction from complex spectra |
PLS-DA (PLS Discriminant Analysis) | Classification with interpretable loadings, handling collinear spectral predictors | Spectral classification, authentication, group discrimination where loading interpretation matters |
MLR (Multiple Linear Regression) | Prediction from a small, carefully selected set of non-collinear variables | Targeted quantification when key variables are identified by domain knowledge or prior PCA/PLS variable selection |
A systematic review published in the Journal of Chemometrics, examining validation strategies for NIRS chemometric models in food analysis, found that PLS regression remains the most commonly used approach, valued specifically for its suitability in handling collinear spectral data and its well-established performance in regulated and industrial settings. That is not inertia; it reflects genuine fitness for the problem type.
When Does Machine Learning Outperform Classical Chemometrics?
Machine learning earns its place in analytical science when the conditions that limit chemometrics are genuinely in play. The critical conditions are large datasets, non-linear chemical relationships, and tasks where prediction accuracy across a broad chemical space matters more than model interpretability. These conditions are common in some analytical domains and rare in others.
The conditions where ML adds real value over classical chemometrics:
- Large, diverse datasets. When training data runs to thousands of samples spanning diverse chemical and physical variation, machine learning approaches, including neural networks and gradient boosting, can model the variability more flexibly than a PLS model's linear latent variable structure. The payoff is most visible when the prediction task is genuinely complex.
- Non-linear relationships. PLS assumes a linear relationship between spectral predictors and the response variable. For calibrations where the underlying chemistry is strongly non-linear, even after preprocessing, kernel PLS and ML alternatives deliver better accuracy with fewer compromises.
- High-dimensional, unstructured input. Mass spectrometry datasets, particularly in proteomics and metabolomics, and hyperspectral imaging data have a dimensionality and sparsity that make classical chemometric approaches slow and sometimes impractical. Deep learning was not built for column-vector spectra; it was built for exactly this kind of problem.
- Transfer across instruments and conditions. Certain ML architectures transfer calibrations between instruments more effectively than classical PLS, which may require recalibration when an instrument or sample presentation changes. This is an active area of research and commercial development.
A review published in Artificial Intelligence Review describes the complementary relationship well: chemometrics and machine learning solve overlapping, but distinct problems in analytical data analysis, and the most effective applications recognise this rather than treating them as substitutes. The shared challenge, explicit in both the chemometrics and the ML literature, is data quality and availability: neither approach compensates for poorly measured, insufficiently diverse, or inconsistently annotated training data.
The Interpretability Advantage in Regulated Environments
Interpretability is where the choice between chemometrics and machine learning has the sharpest practical edge for analytical scientists working in regulated contexts. A model that cannot be explained is not merely inelegant; in a regulated analytical environment, it is a compliance problem.
What interpretability provides in regulated analytical work:
- Demonstration that the model works for chemical reasons, not spurious correlations. A PLS loadings plot can show which spectral regions drive the prediction, confirming that they correspond to known absorptions of the analyte rather than a correlated interferent. A deep neural network provides no equivalent transparency by default.
- Change control and revalidation scoping. A PLS model built on interpretable spectral variables has a defined response to changes in sample matrix, instrument, or method. Knowing why the model works is how you predict when it will stop working and scope what revalidation a change requires.
- Defensibility to a reviewer or inspector. When a method is reviewed for regulatory submission or an audit, the reviewer may ask why the model produces a given prediction. 'A neural network determined it' is not an adequate answer; 'the absorbance at this wavelength is characteristic of the analyte and this loading captures that' is.
This does not mean machine learning is incompatible with regulated work, but it does mean that interpretability must be addressed rather than dismissed. Techniques including permutation importance, SHAP values, and gradient-based attribution methods can provide post-hoc explanation for ML models, though they add complexity and require validation in their own right. For many regulated analytical applications, the additional effort is not justified by the accuracy gain a more complex model offers over a well-built PLS calibration.
Hybrid Approaches: Using Chemometrics and Machine Learning Together
The most productive framing for most analytical scientists is not a binary choice but a complementary toolbox: chemometric methods for what they do best, machine learning for what chemometrics cannot reach, and deliberate integration where both contribute.
Productive hybrid patterns in analytical practice:
- Chemometric preprocessing for ML input. Spectral preprocessing, baseline correction, normalisation, and variable selection using PCA or SIMPLS is standard practice before applying any ML model to spectroscopic data. This is not a workaround; it is good data science that makes ML models more reliable by feeding them structured, relevant information.
- Chemometric variable selection feeding ML. Using PLS or PCA to identify informative spectral regions, then building a machine learning model on those regions rather than the full spectrum, produces models that are simultaneously more accurate and more interpretable than using either approach alone.
- Ensemble models. Combining PLS predictions with ML corrections can capture residual non-linearity that the PLS model misses without discarding the interpretable foundation the PLS model provides.
- Chemometrics for validation of ML outputs. PCA applied to the predictions and residuals of an ML model is a practical tool for identifying systematic failures and outliers in a way that the ML model itself may not surface directly.
The integration is also increasingly bidirectional at the research frontier: deep learning architectures are being developed that explicitly incorporate chemometric constraints, producing models that retain some of PLS's interpretability while accessing ML's flexibility. For practising analytical scientists, the more immediate and reliable hybrid is the practical one: chemometric preprocessing and variable selection, with ML where the accuracy gain is real and justified.
A Practical Decision Framework for Analytical Scientists
The decision between chemometrics, machine learning, and hybrid approaches comes down to answering a structured set of questions about the specific analytical problem. Working through them in order produces a defensible choice.
Question | If Yes, Lean Toward... | If No, Lean Toward... |
Is the dataset small to moderate in size (tens to hundreds of samples)? | Chemometrics (PLS, PCA): designed for this regime | ML (if thousands of samples available with good coverage) |
Is interpretability or regulatory defensibility a hard requirement? | Chemometrics or interpretable ML with post-hoc explanation | ML with performance-focused architecture |
Is the spectral relationship with the analyte expected to be linear? | PLS: designed for linear latent-variable structure | Kernel PLS, ML, or neural network approach for non-linearity |
Is the input high-dimensional, sparse, or structurally complex (MS, hyperspectral)? | ML (particularly deep learning): handles this data type more naturally | Chemometrics with dimensionality reduction preprocessing |
Is the model going into routine QC or a regulated method? | Chemometrics or ML with validated interpretation and change control | Less constrained; prioritise predictive performance |
Do you need to know why the model makes a given prediction? | Chemometrics or SHAP-annotated ML with model-specific explanation | Black-box ML may be acceptable |
Working through this table with a specific problem usually points clearly toward one of three conclusions: chemometrics is the right tool, machine learning is the right tool, or a hybrid approach combining both will be more effective than either alone. None of those outcomes is universally correct; the table is a structured way of surfacing which one fits the problem at hand.
What This Means for Your LabStart any data analysis decision by asking what the problem actually requires: interpretability, accuracy, regulatory defensibility, or scale. For most routine spectroscopic and chromatographic calibration tasks in regulated environments, a well-built PLS model remains the practical standard, with good reason. For genuinely complex, large-scale, or non-linear problems, machine learning earns its place. Hybrid approaches that use chemometric preprocessing and variable selection to structure the problem before applying ML are often more effective and more defensible than using either approach in isolation. Whatever you choose, treat data quality as the foundation: neither chemometrics nor machine learning compensates for insufficient, inconsistent, or poorly annotated training data, and that discipline applies regardless of which analytical technique generates the spectra. For the specific applications across MS, NMR, and spectroscopy where this choice plays out, the companion guides in this series cover the details, starting with the machine learning for MS and spectral data analysis overview and the AI in analytical science guide. |
This article was produced under Separation Science’s AI Editorial Guidelines
