From optical architectures to actual deployment a review of online process spectroscopy in industrial environments

Real-time quantitative analysis of complex matrices (e.g., moisture and multiple components) is a critical bottleneck in modern Process Analytical Chemistry (PAC). While online near- and mid-infrared spectroscopy are widely deployed, achieving high-fidelity measurements in dynamic industrial environments remains highly challenging. Severe matrix effects coupled with industrial multi-stress interferences, including continuous detector window pollution and light source drive voltage fluctuations, often cause significant baseline drift and non-linear spectral distortion, challenging both physical optical limits and traditional linear calibration models. To address these challenges, this review systematically evaluates the optical architectures and analytical boundaries of four mainstream online infrared spectrometers: filter-based, grating dispersion, Fourier Transform Infrared (FTIR), and Acousto-Optic Tunable Filters (AOTF). Crucially, we comprehensively review the evolution of chemometric compensation strategies designed to overcome these complex spectral interferences. By comparing traditional multivariate linear models, such as Partial Least Squares (PLS), with cutting-edge deep learning frameworks, notably 1D Convolutional Neural Networks (1D-CNN), we discuss the potential of AI-driven soft-calibration for robust feature extraction and precise quantitative prediction under extreme operational stresses. We emphasize that the advantages of such approaches are contingent upon data availability, the degree of spectral nonlinearity, and the specific process environment; they are not a universal replacement for conventional models, which remain effective under stable and linear conditions. Furthermore, the synergistic evolution of miniaturized solid-state hardware is discussed. Ultimately, this review provides a robust theoretical framework for selecting analytical instruments and developing advanced, data-driven chemometric methodologies in dynamic environments, while acknowledging the practical constraints and trade-offs involved in real-world deployment.