Efficient membrane filtration requires the understanding of the membrane foulants and the functional properties of different membrane types in water purification. In this study, dead-end filtration of aquaculture system effluents was performed and the membrane foulants were investigated via nuclear magnetic resonance (NMR) spectroscopy. Several machine learning models (Random Forest; RF, Extreme Gradient Boosting; XGBoost, Support Vector Machine; SVM, and Neural Network; NN) were constructed, one to predict the maximum transmembrane pressure, for revealing the chemical compounds causing fouling, and the other to classify the membrane materials based on chemometric analysis of NMR spectra, for determining their effect on the properties of the different membrane types tested. Especially, RF models exhibited high accuracy; the important chemical shifts observed in both the regression and classification models suggested that the proportional patterns of sugars and proteins are key factors in the fouling progress and the classification of membrane types. Therefore, the proposed strategy of chemometric analysis of NMR spectra is suitable for membrane research, which aims at investigating comprehensively the fouling phenomenon and how the foulants and environmental conditions vary according to the filtration systems.

Understanding the causes of microbiome formation and its relationship to environmental conditions is important to properly maintain recirculating aquaculture systems (RASs). Although RAS has been applied to numerous fish types and environmental conditions (e.g., loading intensity), the effects of these environmental conditions (i.e., fish type and loading intensity) on microbiome composition are limitedly known. Therefore, we established three experimental aquarium tanks to explore the effects of fish type, loading intensity, filter pore size, and rearing day on microbiome compositions: (1) a tank for Acanthogobius flavimanus , (2) for Girella punctata , and (3) for G. punctata with higher loading intensity. Multivariate analysis showed that the microbial community composition differed among the tanks, indicating that the fish type and loading intensity significantly affected microbiome formation in rearing water. Some microbes, such as Sediminicola and Glaciecola , were detected at a higher loading intensity, indicating that these microbes might be an indicator of eutrophic conditions in the aquacultural systems. In addition, a partial correlation network revealed a connection between microbes and metabolites in the aquarium tanks. Such a microbe–metabolite network might be a clue to control the microbiome by adjusting the molecule abundance in the aquacultural environment.