Artificial intelligence has quickly become one of the most discussed technologies in analytical science. Spectroscopy is particularly attractive for AI and machine learning because a single measurement can contain hundreds or thousands of wavelength-dependent variables describing the chemical or physical properties of a sample. Yet before there can be artificial intelligence, there must be reliable analytical data.
A predictive model does not interact with the sample; the spectrometer does. Integration time, detector response, optical configuration, wavelength stability, reference measurements, sampling geometry, signal-to-noise ratio, and sample presentation can all affect the spectrum entering a model. Changes in these variables can ultimately affect the prediction produced at the end of the analytical workflow. The challenge is therefore no longer simply collecting a spectrum. It is creating a reliable path from sample to spectrum to model to decision.
Not Every Problem Requires AI
The growing availability of machine-learning tools can create the impression that increasingly complicated models necessarily produce better analytical results. In practice, the best analytical approach is generally the simplest one capable of producing a reliable, validated answer.
For some measurements, traditional univariate analysis is sufficient. More complicated spectra often benefit from chemometrics. Partial least squares (PLS) regression, principal component analysis (PCA), k-nearest-neighbor methods (kNN), and other multivariate approaches can use information distributed across many wavelengths
simultaneously. These techniques are particularly valuable in near-infrared spectroscopy, where broad overlapping absorption bands often make single-wavelength analysis impractical.1,2
More advanced machine-learning methods may become valuable when relationships are strongly nonlinear, datasets are large, sample matrices are highly variable, or traditional models cannot adequately separate complex classes. The question laboratories should therefore ask is not, “How can we use AI on our spectra?” It is, “What analytical method produces the most reliable answer for this measurement?”
The Calibration Set Matters More Than the Algorithm
Whether a laboratory uses PLS regression, classification, or a more sophisticated machine-learning algorithm, the model can only learn from the data it receives. Consider an NIR model designed to predict moisture in a powdered material. If all calibration samples are collected from one manufacturing batch, at one temperature, using one sample presentation, the resulting model may perform extremely well against similar samples but fail when raw-material properties, particle size, ambient conditions, or suppliers change.
A robust calibration set should represent the variation the model will encounter during actual use. This is also where overfitting becomes dangerous. A sufficiently flexible algorithm can produce impressive statistics by learning characteristics specific to the calibration dataset rather than relationships that generalize to future samples. Conversely, underfitting can occur when a model is too simple to capture the relevant relationships within the data. Independent validation therefore remains essential regardless of how sophisticated the algorithm becomes. AI does not eliminate the fundamentals of analytical chemistry; in many ways, it makes them more important.2,3
Why Instrument and Software Integration Matters
Traditional spectroscopy workflows have often involved several separate steps. A spectrum is acquired in instrument-control software, exported as a file, imported into another program for preprocessing or chemometric analysis, used to develop a model, and potentially transferred again for routine predictions. This approach can work well, particularly in research environments requiring specialized analytical tools, but it also introduces opportunities for inconsistency.
Which preprocessing method was applied? Were the same wavelength ranges used? Were acquisition settings identical? Which version of the calibration model is deployed? Did the operator perform the correct reference procedure before collecting the unknown?
Increasingly, spectroscopy platforms are bringing more of these steps into a common software environment. Instrument control, spectral acquisition, preprocessing, model development, classification, and prediction can occur within the same workflow. This provides a benefit that goes beyond convenience: it can reduce the distance between how calibration data are collected and how future unknown samples are measured. A predictive model should not be considered separately from the measurement system that generates its input.
Preprocessing: Helpful Tool or Hidden Variable?
Baseline correction, smoothing, derivatives, normalization, and scatter-correction techniques can make underlying spectral relationships easier for multivariate models to identify. In NIR spectroscopy, preprocessing can be especially valuable for reducing variations associated with scattering and physical sample characteristics. Without appropriate preprocessing, nonchemical sources of spectral variation can obscure relevant spectral features and reduce the model’s ability to resolve the underlying chemical relationships. However, preprocessing should not become an automatic attempt to make a model's statistics look better.3,4
Every transformation changes the data supplied to the model. Excessive preprocessing can remove useful information, emphasize noise, or create a calibration that performs well on the development dataset but poorly on independent samples. An integrated workflow makes it easier to preserve the preprocessing choices associated with a calibration and apply them consistently during routine analysis. Preprocessing should solve an identifiable analytical problem, not simply improve a number on a calibration report.
From Expert Tool to Routine Measurement
Integration also affects who can use advanced spectral analysis. Historically, developing a chemometric model might require a spectroscopy specialist to collect the data and a separate chemometrics package - or another specialist - to build and deploy the calibration. That remains appropriate for complex research applications, but many routine applications do not require that level of separation.
Modern spectroscopy software can increasingly guide users through collecting calibration spectra, associating reference values with those spectra, preprocessing the dataset, developing a multivariate model, evaluating its performance, and using the model to predict unknown samples. This can make chemometrics accessible to laboratories that previously considered multivariate spectroscopy too specialized or expensive to implement.
Advanced analytical capabilities have traditionally been offered through separate chemometrics packages or premium software modules. Some instrument platforms are now integrating model-building tools directly into their primary spectroscopy environments. For example, StellarNet's StellarPro software incorporates PLS calibration for quantitative prediction and classification capabilities such as kNN, along with spectral preprocessing, and runtime prediction in the same software environment used for spectral acquisition. Other platforms may provide similar capabilities through separate or advanced software packages. The larger trend is more important than any individual implementation: the boundary between the spectrometer and the analytical model is becoming less distinct.
Where Machine Learning Adds Value
None of this diminishes the potential of machine learning. AI-based spectral analysis is likely to become increasingly useful as laboratories collect larger datasets and combine spectroscopy with additional information such as process conditions, images, environmental measurements, or historical manufacturing data. Machine learning may also identify nonlinear relationships or subtle classification boundaries that are difficult to capture with conventional approaches.4
Laboratories evaluating these techniques should continue asking fundamental questions: Is the improvement meaningful on independent samples? Does the calibration dataset represent real-world variation? Can the result be reproduced when instruments, operators, or sample conditions change? Is the more complex model materially better than a simpler chemometric approach? Can the laboratory understand and document how the model is being used? The most sophisticated algorithm is not necessarily the most useful analytical method.
The Instrument Still Matters
As software becomes more capable, it can be tempting to think of the spectrometer primarily as a data source feeding an algorithm. Better algorithms can extract more information from good spectra, but they cannot reliably recreate analytical information that was never measured. Detector sensitivity, optical resolution, wavelength range, signal-to-noise performance, stray light, stability, and sampling configuration continue to determine the quality and type of information available to the analytical model.
A recent example in computational spectroscopy illustrates this relationship between measurement and algorithm. Researchers demonstrated that an RGB camera could be extended into a hyperspectral imaging system by combining programmable liquid-crystal spectral encoding with artificial-intelligence-based reconstruction. Importantly, the approach did not rely on AI alone: additional spectral information was first encoded optically, with reference spectrometer measurements used for training and verification.5
The future of spectroscopy therefore may not be defined by choosing between better instrumentation and better algorithms. It will increasingly depend on integrating the two. A well-designed analytical system begins with a reproducible measurement, applies appropriate spectral processing, selects a model suited to the problem, validates that model against representative samples, and ultimately converts the spectrum into an actionable answer. AI can make that process more powerful, but intelligence still begins with the measurement.
About the author
Jason Pierce is President of StellarNet, Inc., a U.S. manufacturer of compact spectrometers and optical measurement systems. During his 20 years with StellarNet, including more than a decade leading the company, Pierce has worked across a broad range of spectroscopy and optical sensing applications, including UV-VIS-NIR, Raman spectroscopy, radiometry, thin-film measurement, chemometrics, food analysis, advanced materials, and industrial quality control. His work focuses on translating optical measurement technologies from the laboratory into practical research and industrial applications. He is particularly interested in emerging uses of spectroscopy, miniaturized optical sensors, applied chemometrics, and making advanced analytical tools more accessible to researchers and manufacturers. Pierce is a Labcompare Editoral Advisory Board Member.