Abstract

Chemometrics with AI: From Classical Models to AI-Enhanced Models

Liangfeng Guo, Chemometrics Lead Scientist, Paeonia Innovations

Spectral analysis is evolving from classical chemometrics, such as PCA and PLS, toward AI-enhanced architectures better suited for the non-linearities and high-dimensional noise of lab-scale flow chemistry. This session examines how deep learning enables automated feature extraction and complex deconvolution, allowing for the precise identification of chemical signatures within the overlapping molecular fingerprints of complex synthesis. By transcending linear models, researchers can maintain analytical integrity during the critical transition from benchtop R&D to pilot-scale validation. This presentation highlights the synergy between computational spectroscopy and miniaturized Mid-IR hardware. By embedding neural networks into ultra-compact spectrometers, it is possible to compensate for reduced optical paths and achieve lab-grade accuracy in-line. For process intensification, these instruments facilitate a "Smart Lab" environment, providing the instantaneous kinetic feedback required to dynamically optimize reaction parameters and ensure rigorous quality control throughout the experimental and pilot phases.