Polyhydroxyalkanoates (PHA) are biopolymers with potential to replace conventional oil-based plastics. However, PHA high production costs limit their scope of commercial applications. Downstream processing is currently the major cost factor for PHA production but one of the least investigated aspects of the PHA production chain. In this study, the extraction of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) produced at pilot scale by a mixed microbial culture was performed using sodium hydroxide (NaOH) or sodium hypochlorite (NaClO) as digestion agents of non-PHA cellular mass. Optimal conditions for digestion with NaOH (0.3 M, 4.8 h) and NaClO (9.0%, 3.4 h) resulted in polymers with a PHA purity and recovery of ca. 100%, in the case of the former and ca. 99% and 90%, respectively, in the case of the latter. These methods presented higher PHA recoveries than extraction by soxhlet with chloroform, the benchmark protocol for PHA extraction. The polymers extracted by the three methods presented similar PHA purities, molecular weights and polydispersity indices. Using the optimized conditions for NaOH and NaClO digestions, this study analyzed the effect of the initial intracellular PHA content (40–70%), biomass concentration (20–100 g/L) and biomass pre-treatment (fresh vs. dried vs. lyophilized) on the performance of PHA extraction by these two methods.

Real-time bioprocess monitoring is crucial for efficient operation and effective bioprocess control. Aiming to develop an online monitoring strategy for facilitating optimization, fault detection and decision-making during wastewater treatment in a photo-biological nutrient removal (photo-BNR) process, this study investigated the application of Raman spectroscopy for the quantification of total organic content (TOC), volatile fatty acids (VFAs), carbon dioxide (CO₂), ammonia (NH₃), nitrate (NO₃), phosphate (PO₄), total phosphorus (total P), polyhydroxyalkanoates (PHAs), total carbohydrates, total and volatile suspended solids (TSSs and VSSs, respectively). Specifically, partial least squares (PLS) regression models were developed to predict these parameters based on Raman spectra, and evaluated based on a full cross-validation. Through the optimization of spectral pre-processing, Raman shift regions and latent variables, 8 out of the 11 parameters that were investigated—namely TOC, VFAs, CO₂, NO₃, total P, PHAs, TSSs and VSSs—could be predicted with good quality by the respective Raman-based PLS calibration models, as shown by the high coefficient of determination (R² > 90.0%) and residual prediction deviation (RPD > 5.0), and relatively low root mean square error of cross-validation. This study showed for the first time the high potential of Raman spectroscopy for the online monitoring of TOC, VFAs, CO₂, NO₃, total P, PHAs, TSSs and VSSs in a photo-BNR reactor.