The monitoring and control of bioprocesses represent a crucial challenge in modern biotechnology. Bioreactors are inherently nonlinear and highly sensitive to operational and environmental variations, while also being subject to intrinsic biological variability. Moreover, many of their most relevant internal variables cannot be directly measured in real time, and those that are available are often indirect or noisy, which altogether complicates effective monitoring and control. In particular, the concentration of recombinant products remains inaccessible to current online sensing technologies, compromising the ability to ensure consistent and reproducible process performance. To overcome this limitation, developing mathematical tools capable of reconstructing these hidden states is indispensable for strengthening process supervision, improving controllability, and ultimately ensuring product quality in industrial biomanufacturing.