Combined machine learning and biomolecular analysis for stability assessment of anaerobic ammonium oxidation under salt stress. (July 2022)

• Record Type: Journal Article
• Authors: Jeon, Junbeom; Cho, Kyungjin; Kang, Jinkyu; Park, Suin; Uchenna Esther Ada, Okpete; Park, Jihye; Song, Minsu; Viet Ly, Quang; Bae, Hyokwan
• Is Part Of: Bioresource technology. Volume 355(2022)
• Publication Date: 2022-07
• Subjects: Anammox -- Salinity effect -- Artificial-neural network -- T-RFLP -- Real-time qPCR; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.127206
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: Stability of the AMX process under saline conditions was evaluated. Increase in salinity significantly affected the AMX bacteria community. Classification model was developed to predict the stability of the AMX process. Real-time qPCR and T-RFLP enhanced the prediction performance of the model. Abstract: In this study, the stability of the total nitrogen removal efficiency (TNRE) was modeled using an artificial neural network (ANN)-based binary classification model for the anaerobic ammonium oxidation (AMX) process under saline conditions. The TNRE was stabilized to 80.2 ± 11.4% at the final phase under the salinity of 1.0 ± 0.02%. The results of terminal restriction fragment length polymorphism (T-RFLP) analysis showed the predominance of Candidatus Jettenia genus. Real-time quantitative PCR analysis revealed the average abundance of Ca . Jettenia and Kuenenia spp. increased in 3.2 ± 5.4 × 10 8 and 2.0 ± 2.2 × 10 5 copies/mL, respectively. The prediction accuracy using operational parameters with data augmentation was 88.2%. However, integration with T-RFLP and real-time qPCR signals improved the prediction accuracy by 97.1%. This study revealed the feasible application of machine learning and biomolecular signals to the stability prediction of the AMX process under increased salinity.