Combined machine learning and biomolecular analysis for stability assessment of anaerobic ammonium oxidation under salt stress

Junbeom Jeon; Kyungjin Cho; Jinkyu Kang; Suin Park; Okpete Uchenna Esther Ada; Jihye Park; Minsu Song; Quang Viet Ly; Hyokwan Bae

doi:10.1016/j.biortech.2022.127206

Combined machine learning and biomolecular analysis for stability assessment of anaerobic ammonium oxidation under salt stress

Bioresour Technol. 2022 Jul:355:127206. doi: 10.1016/j.biortech.2022.127206. Epub 2022 Apr 26.

Authors

Junbeom Jeon¹, Kyungjin Cho², Jinkyu Kang³, Suin Park⁴, Okpete Uchenna Esther Ada⁵, Jihye Park⁶, Minsu Song⁷, Quang Viet Ly⁸, Hyokwan Bae⁹

Affiliations

¹ Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea. Electronic address: swc7852@pusan.ac.kr.
² Center for Water Cycle Research, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea; Division of Energy and Environment Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Republic of Korea. Electronic address: faith@kist.re.kr.
³ Institute for Environment and Energy, Pusan National University, Busan 46241, Republic of Korea. Electronic address: jinkyu.kang@pusan.ac.kr.
⁴ Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea. Electronic address: starsinee@pusan.ac.kr.
⁵ Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea; Department of Precision Medicine, Step Forward Personalized Medicine (SPMED), Busan 46508, Republic of Korea.
⁶ Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea. Electronic address: wlgp917@pusan.ac.kr.
⁷ Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea. Electronic address: sms0310@pusan.ac.kr.
⁸ Institute of Environmental Engineering & Nano-Technology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
⁹ Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea; Institute for Environment and Energy, Pusan National University, Busan 46241, Republic of Korea. Electronic address: hyokwan.bae@pusan.ac.kr.

PMID: 35477105
DOI: 10.1016/j.biortech.2022.127206

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 × 10⁸ and 2.0 ± 2.2 × 10⁵ 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.

Keywords: Anammox; Artificial-neural network; Real-time qPCR; Salinity effect; T-RFLP.

MeSH terms

Ammonium Compounds*
Anaerobiosis
Bioreactors
Machine Learning
Nitrogen*
Oxidation-Reduction
Salt Stress

Substances

Ammonium Compounds
Nitrogen