With Industry 4.0 and the proliferation of sensors and data systems, modern manufacturing generates unprecedented volumes of process data. The rapid advancement of data-driven methodologies for process analytics underscores the significance of multivariate analysis. Yet, ongoing innovation is essential to address emerging challenges in process analytics amid the digital revolution. Our group focuses on developing and applying chemometric tools for Process Analytical Technology (PAT), encompassing monitoring, modeling, and control of batch processes. These tools are adeptly applied to processes monitored via sensors, spectroscopic probes, and hyperspectral images.

Outstanding publications:

• Jaumot, J., Igne, B., Anderson, C.A., Drennen, J.K., de Juan, A. (2013). Blending process modeling and control by multivariate curve resolution. Talanta, 117, 492-504;
• de Oliveira, R. R., Avila, C., Bourne, R., Muller, F., de Juan, A. (2020). Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control. Anal. Bioanal. Chem., 412, 2151-2163;
• Rocha de Oliveira, R., de Juan, A. (2020). Design of heterogeneity indices for blending quality assessment based on hyperspectral images and variographic analysis. Anal. Chem., 92(24), 15880-15889;
• de Oliveira, R. R., de Juan, A. (2021). SWiVIA–Sliding window variographic image analysis for real-time assessment of heterogeneity indices in blending processes monitored with hyperspectral imaging. Anal. Chim. Acta, 1180, 338852.

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