Recent Research in Chemometrics and AI for Spectroscopy, Part I: Foundations, Definitions, and the Integration of Artificial Intelligence in Chemometric Analysis

Abstract

Artificial intelligence and chemometrics together represent a paradigm shift in spectroscopy. Classical chemometrics methods such as principal component analysis (PCA) and partial least squares (PLS) regression remain vital but are now complemented by advanced AI frameworks that automate feature extraction, nonlinear calibration, and data fusion methods. This article defines key concepts, such as generative AI, machine learning (ML), deep learning, and model interpretability, and provides a short primer on data types, ML paradigms, and core model architectures such as linear and logistic regression, decision trees, random forest, and XGBoost. The goal is to establish a clear conceptual foundation for understanding how AI is transforming the practice of chemometrics in spectroscopy.

Introduction

Chemometrics may be defined as the mathematical extraction of relevant chemical information from measured analytical data to identify, quantify, classify, or monitor physical or chemical characteristics of samples (1). In spectroscopy, chemometrics transforms complex multivariate datasets, often containing thousands of correlated wavelength intensities, into actionable insights to understand the chemical and physical properties of sample materials. Chemometrics is advancing rapidly through the integration of artificial intelligence (AI). Modern AI and machine learning (ML) techniques, including supervised, unsupervised, and reinforcement learning, are now applied across spectroscopic and imaging methods using near-infrared (NIR), infrared (IR), Raman, and atomic spectroscopy.

Traditional chemometric methods such as PCA, PLS regression, and multivariate curve resolution have formed the basis of calibration and quantitative modeling for decades (1,2). However, the advent of AI and ML has dramatically expanded this analytical capability, enabling data-driven pattern recognition, nonlinear modeling, and automated feature discovery from unstructured data sources such as hyperspectral images and high-throughput sensor arrays (3,4,5).

The integration of AI with spectroscopy facilitates rapid, non-destructive, and high-throughput chemical analysis across domains ranging from food authentication (3,6,7) to biomedical diagnostics (8,9). AI-assisted algorithms improve classification, regression, and feature selection performance and are increasingly embedded in sensor systems for real-time analytical decision support (4).

Defining Artificial Intelligence and Its Subfields

The following definitions are useful for understanding AI in chemometrics:

• Artificial Intelligence (AI) is the engineering of systems capable of producing intelligent outputs as content, predictions, or decisions based on human-defined objectives (4).
• Machine Learning (ML) is a subfield of AI that develops models capable of learning from data without explicit programming. ML algorithms identify structure in data and improve their analysis performance over time as they are exposed to more examples (10).
• Deep Learning (DL) is a specialized subset of ML employing multi-layered neural networks capable of hierarchical feature extraction. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models are among the most widely used architectures in spectroscopic applications (81,11).
• Generative AI (GenAI) extends deep learning by enabling models to create new data, spectra, molecular structures, or spectral augmentations, based on learned distributions. In spectroscopy, generative models can produce synthetic data to balance datasets, enhance calibration robustness, or simulate missing spectra or property data (11).

Types of Machine Learning

ML methods are generally categorized into three paradigms:

• Supervised Learning: Models are trained on labeled data to perform regression or classification (for example, PLS, support vector machine (SVMs), and Random Forest. Examples include spectral quantification and compositional analysis.
• Unsupervised Learning: Algorithms discover latent structures in unlabeled data (for example, PCA, clustering, or manifold learning), commonly used for exploratory spectral analysis and outlier detection.
• Reinforcement Learning: Algorithms learn optimal actions by maximizing cumulative rewards in dynamic environments. Though less common, reinforcement methods are being explored for adaptive calibration and autonomous spectral optimization (10,12).

Structured vs. Unstructured Data

In chemometrics, structured data refers to well-organized matrices of spectral intensities, calibration targets, or metadata suitable for tabular analysis. Unstructured data are images, text, free-form spectra, or sensor arrays, and require advanced feature extraction techniques, often handled via deep learning (13,14). The ability of modern AI to process unstructured spectroscopic data is one of its most transformative contributions.

Core ML Model Types

The following algorithm types are most common in ML:

Linear Regression (LR)

Linear Regression models the quantitative relationship between measured spectral variables (such as absorbance or reflectance at different wavelengths) and a target analyte concentration or property. In spectroscopy, LR forms the foundation of classical multivariate calibration, where spectral intensities serve as predictor variables, and the model estimates a best-fit linear function minimizing the sum of squared residuals. Despite its simplicity, LR provides fundamental insight into spectral–chemical correlations and underlies more advanced methods such as principal component regression (PCR) and partial least squares (PLS). Linear regression assumes linearity, homoscedasticity, and independence among predictors, assumptions often challenged by collinear and noisy spectral data, yet conceptually essential to multivariate model design (15).

Logistic Regression (LogR)

Logistic regression is a probabilistic classification model that predicts the likelihood that a sample belongs to a specific class, commonly used when the outcome is binary or categorical (for example, authentic vs. adulterated, Group 1 vs. 2, or species A vs. B). Instead of modeling continuous analyte concentrations, LogR applies a sigmoid (logistic) function to constrain predictions between 0 and 1, representing class probabilities. In spectroscopic analysis, LogR is employed for pattern recognition, authentication, and qualitative screening, where spectra encode diagnostic information about compositional or structural differences. It also provides interpretable coefficients indicating which wavelengths contribute most to class discrimination.

Decision Trees (DT)

Decision trees are hierarchical classifiers that iteratively partition spectral data based on threshold rules applied to individual spectral features or derived variables. Each internal node represents a decision criterion (for example, “absorbance at 1200 nm > 0.45”), and terminal leaves correspond to predicted classes or ranges. Decision trees are valued for their intuitive interpretability as they effectively mimic human reasoning in classification tasks such as sample type identification, contamination detection, or quality grading. However, a single decision tree can be unstable or prone to overfitting, as small spectral variations can alter the tree structure (17).

Random Forest (RF)

Random forest is an ensemble learning method that constructs a large number of decision trees using bootstrap-resampled spectral subsets and randomly selected wavelength features. Each tree votes on the outcome, and the ensemble majority defines the final prediction. In spectroscopy, RF offers strong generalization capability, reduced overfitting, and robustness against spectral noise, baseline shifts, and collinearity. RF models are widely applied in spectral classification, authentication, and process monitoring, and can output feature importance rankings, helping spectroscopists identify diagnostic wavelengths or informative regions in the spectra useful for selective and accurate predictive modeling of the data (16,17).

Extreme Gradient Boosting (XGBoost)

Extreme Gradient Boosting (XGBoost) is an advanced boosting algorithm that builds an ensemble of decision trees in a sequential, gradient-based manner, each new tree focuses on correcting the residual errors of prior trees. XGBoost includes regularization, parallel computation, and optimized gradient descent, offering high computational efficiency and predictive accuracy. In spectroscopy, XGBoost excels in complex, nonlinear relationships typical of food quality, pharmaceutical composition, and environmental analysis. It often achieves state-of-the-art performance in both regression and classification tasks, outperforming traditional chemometric models when sufficient labeled spectral data are available. Despite its power, XGBoost’s models are less transparent, motivating the use of explainable AI techniques to interpret wavelength contributions (16,17).

Support Vector Machine (SVM)

Support vector machines are supervised learning algorithms that find the optimal decision boundary (hyperplane) separating classes or predicting quantitative values in high-dimensional spectral space. For classification, SVM seeks the hyperplane that maximizes the margin between the nearest data points of different classes (called support vectors), providing robust discrimination even with noisy, overlapping, or nonlinear spectral data.

Through the use of kernel functions, such as linear, polynomial, or radial basis function (RBF) kernels, SVM can transform spectral data into higher-dimensional feature spaces, enabling nonlinear classification or regression. In spectroscopy, SVR is used for quantitative analyte prediction from NIR, IR, or Raman spectra when relationships deviate from strict linearity.

SVMs perform well with limited training samples but many correlated wavelengths, making them highly suited for spectroscopic datasets. They have been successfully applied to food authenticity, pharmaceutical quality control, process monitoring, and disease diagnosis based on vibrational spectral patterns. Parameter tuning (regularization C, kernel width γ) and preprocessing (scatter correction, normalization) are key to achieving optimal performance.

Neural Networks (NN) and Deep Neural Networks (DNN)

Neural Networks (NNs) are computational models inspired by the structure of the human brain, consisting of interconnected layers of “neurons” that learn nonlinear relationships between spectral inputs and target outputs. In spectroscopy, simple feed-forward NNs can approximate complex, nonlinear calibration functions, while Deep Neural Networks (DNNs)—with many hidden layers—can automatically extract hierarchical spectral features from raw or minimally preprocessed data.

NNs excel in pattern recognition tasks such as spectral classification, component quantification, and anomaly detection, and are particularly powerful when large spectral datasets are available. Variants include:

• Convolutional Neural Networks (CNNs), which learn localized spectral features (useful for vibrational band analysis or imaging spectroscopy).
• Recurrent Neural Networks (RNNs) and Transformers, which capture sequential dependencies across wavelengths or time-resolved spectra.

In chemometrics, DNNs often outperform traditional linear methods (like PLS) when dealing with nonlinearities, scattering effects, or complex mixtures. However, they require significant training data, regularization, and interpretability tools (for example, SHAP, Grad-CAM, or spectral sensitivity maps) to ensure reliable physical insight.

Neural networks are increasingly integrated with explainable AI (XAI) frameworks to identify informative wavelength regions and preserve chemical interpretability—a central goal for spectroscopists seeking both accuracy and understanding (1,2,8,15,19).

Conclusion

The convergence of chemometrics and AI represents a revolution in spectroscopic analysis. While traditional methods remain essential, new AI-driven frameworks bring interpretability, automation, and predictive power to unprecedented levels. Understanding fundamental AI concepts—model types, learning paradigms, and data structures—is crucial for scientists seeking to implement these methods responsibly.

Part II of this series will explore recent real-world applications of AI-driven chemometrics in spectroscopy, including explainable AI (XAI), generative models, and deep learning frameworks across food, biomedical, and environmental applications.

References

(1) Workman, J. Jr.; Mark, H. From Classical Regression to AI and Beyond: The Chronicles of Calibration in Spectroscopy: Part I. Spectroscopy 2025, 40 (2), 13–18. DOI: 10.56530/spectroscopy.pu3090t7

(2) Workman, J. Jr.; Mark, H. From Classical Regression to AI and Beyond: The Chronicles of Calibration in Spectroscopy: Part II. Spectroscopy 2025, 40 (7), 6–10. DOI: 10.56530/spectroscopy.fc1076p9

(3) Li, Q.; Wang, Z.; Wang, M.; Zhao, J.; Tu, K.; Lan, W.; Liu, J.; Pan, L. Next‐Generation Optical Imaging and Spectroscopy: AI and Chemometrics in Assessing Authenticity, Nutrition, and Hazard Factors in Cereals. Compr. Rev. Food Sci. Food Saf. 2025, 24 (5), e70248. DOI: 10.1111/1541-4337.70248

(4) Kumar, M.; Nandi, A.; Yadav, R. L.; Das Gupta, G.; Sharma, K. AI-Enhanced Prediction Tools and Sensor Integration in Advanced Analytical Chemistry Techniques. Curr. Anal. Chem. 2025. DOI: 10.2174/0115734110373957250516113853

(5) Varghese, R.; Shringi, H.; Efferth, T.; et al. Artificial Intelligence Driven Approaches in Phytochemical Research: Trends and Prospects. Phytochem. Rev. 2025. DOI: 10.1007/s11101-025-10096-8

(6) Heryanto, C. M.; Phan, C. W.; Tan, Y. S.; Saw, S. N.; Seow, E. K. Current Knowledge on Mushroom and Mushroom-Based Product Authentication: From DNA Barcoding, Chemometrics, to Artificial Intelligence. Food Rev. Int. 2025, 1–16. DOI: 10.1080/87559129.2025.2480233

(7) Gu, C.; Wang, G.; Zhuang, W.; Hu, J.; He, X.; Zhang, L.; Du, Z.; Xu, X.; Yin, M.; Yao, Y.; Sun, X. Artificial Intelligence–Enabled Analysis Methods and Their Applications in Food Chemistry. Crit. Rev. Food Sci. Nutr. 2025, 1–22. DOI: 10.1080/10408398.2025.2521648

(8) Liu, Y.; Chen, S.; Xiong, X.; et al. Artificial Intelligence Guided Raman Spectroscopy in Biomedicine: Applications and Prospects. J. Pharm. Anal. 2025, 101271. DOI: 10.1016/j.jpha.2025.101271

(9) Savelieva, T.; Romanishkin, I.; Ospanov, A.; et al. Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-Oncology. Photonics 2025, 12 (1), 37. https://ui.adsabs.harvard.edu/abs/2025Photo..12...37S/abstract (accessed 2025-10-29).

(10) Guo, K.; Shen, Y.; Gonzalez-Montiel, G. A.; et al. Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond. arXiv Preprint 2025, arXiv:2502.09897. DOI: 10.48550/arXiv.2502.09897

(11) Flanagan, A. R.; Dalal, D.; Glavin, F. G. Exploring Generative Artificial Intelligence and Data Augmentation Techniques for Spectroscopy Analysis. Chem. Rev. 2025, 125 (13), 6130–6155. DOI: 10.1021/acs.chemrev.4c00815

(12) Zhou, J.; Liu, X.; Zhou, H.; et al. Artificial‐Intelligence‐Enhanced Mid‐Infrared Lab‐on‐a‐Chip for Mixture Spectroscopy Analysis. Laser Photonics Rev. 2025, 19 (1), 2400754. DOI: 10.1002/lpor.202400754

(13) Yang, Z.; Xie, J.; Shen, S.; et al. Spectrumworld: Artificial Intelligence Foundation for Spectroscopy. arXiv Preprint 2025, arXiv:2508.01188. DOI: 10.48550/arXiv.2508.01188

(14) de Moraes, I. A.; Arrighi, L.; Junior, S. B.; et al. Explainable Artificial Intelligence Applied to Deep Computer Vision of Microscopy Imaging and Spectroscopy for Assessment of Oleogel Stability. J. Food Eng. 2025, 394, 112515. DOI: 10.1016/j.jfoodeng.2025.112515

(15) Ezenarro, J.; Schorn-García, D. How Are Chemometric Models Validated? A Systematic Review of Linear Regression Models for NIRS Data in Food Analysis. J. Chemom. 2025, 39 (6), e70036. DOI: 10.1002/cem.70036

(16) Ali, Z.; Jamil, Y.; Anwar, H.; Sarfraz, R. A. Classification of E-Waste Using Machine Learning-Assisted Laser-Induced Breakdown Spectroscopy. Waste Manag. Res. 2025, 43 (3), 408–420. DOI: 10.1177/0734242X241248730

(17) Lim, H.; Lee, S. Y.; Kim, J. Y.; et al. Comparison of Machine Learning Models for Classifying Edible Oils Using Fourier‐Transform Infrared Spectroscopy. Bull. Korean Chem. Soc. 2025, 46 (2), 131–137. DOI: 10.1002/bkcs.12932

(18) Contreras, J.; Bocklitz, T. Explainable Artificial Intelligence for Spectroscopy Data: A Review. Pflügers Arch. Eur. J. Physiol. 2025, 477, 603–615. DOI: 10.1007/s00424-024-02997-y

(19) Ahmed, M. T.; Ahmed, M. W.; Kamruzzaman, M. A Systematic Review of Explainable Artificial Intelligence for Spectroscopic Agricultural Quality Assessment. Comput. Electron. Agric. 2025, 235, 110354. DOI: 10.1016/j.compag.2025.110354

(20) Smith, R.; Spano, T. L.; McDonnell, M.; et al. Interpretable Machine Learning Models Classify Minerals via Spectroscopy. Sci. Rep. 2025, 15, 15807. DOI: 10.1038/s41598-025-92686-2