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Assessments in biocides with omics approaches to ecosystem
Ma, Seohee,Yoon, Dahye,Kim, Hyunsu,Lee, Hyangjin,Kim, Seonghye,Lee, Huichan,Kim, Jieun,Lee, Soojin,Lee, Yunsuk,Lee, Yujin,Kim, Suhkmann Korean Magnetic Resonance Society 2018 Journal of the Korean Magnetic Resonance Society Vol.22 No.4
Benzisothiazolinone (BIT) is the preservative that is widely used in industrial and household products. In this study, zebrafish (Danio rerio) was exposed to BIT at different concentrations (control, 0.5 g/L, 1.0 g/L and 2.0 g/L) for 72 hours. The techniques of nuclear magnetic resonance (NMR) spectroscopy were applied to analyze the effects of BIT on zebrafish. The advantages of NMR are the minimal sample preparation and high reproducibility of experimental results. With the multivariate statistical analysis, dimethylamine, N-acetylaspartate, glycine and histidine were identified as an important metabolite in differentiating between the control and BIT-exposed group. This study will improve the understanding the metabolite changes in the zebrafish in response to BIT exposure.
Assessments in biocides with omics approaches to ecosystem
Seohee Ma,Dahye Yoon,Hyunsu Kim,Hyangjin Lee,Seonghye Kim,Huichan Lee,Jieun Kim,Soojin Lee,Yunsuk Lee,Yujin Lee,김석만 한국자기공명학회 2018 Journal of the Korean Magnetic Resonance Society Vol.22 No.4
Benzisothiazolinone (BIT) is the preservative that is widely used in industrial and household products. In this study, zebrafish (Danio rerio) was exposed to BIT at different concentrations (control, 0.5 g/L, 1.0 g/L and 2.0 g/L) for 72 hours. The techniques of nuclear magnetic resonance (NMR) spectroscopy were applied to analyze the effects of BIT on zebrafish. The advantages of NMR are the minimal sample preparation and high reproducibility of experimental results. With the multivariate statistical analysis, dimethylamine, N-acetylaspartate, glycine and histidine were identified as an important metabolite in differentiating between the control and BIT-exposed group. This study will improve the understanding the metabolite changes in the zebrafish in response to BIT exposure.
Investigation of Germicide and Growth Enhancer Effects on Bean Sprout using NMR-based Metabolomics
Yoon, Dahye,Ma, Seohee,Choi, Hyeonsoo,Noh, Hyeonkyung,Ok, Youngjun,Kim, Suhkmann Korean Magnetic Resonance Society 2016 Journal of the Korean Magnetic Resonance Society Vol.20 No.4
Bean sprouts are often cultivated in the circumstances prevailing in the improper using of germicide and growth enhancer. The influence of ingestion those bean sprouts are unknown. The components of the bean sprouts are needed to evaluate for food safety. The extracts of the control, 0.5 g/L germicide, 1 g/L germicide, 12.5 mL/L growth enhancer and 25 mL/L growth enhancer were used to compare the components in the experiment. Nuclear Magnetic Resonance spectroscopy (NMR) was used to analyze the extracts. Statistical analysis of metabolomics showed significant changes between the control and head and the stem of the bean sprouts. Significant changes in metabolites were identified with the bean sprouts cultivated with germicide and growth enhancer by applying qualitative and quantitative analysis. Similar changes in the area of the bean sprouts were observed after treated to germicide and growth enhancer. Although treating germicide and growth enhancer showed no particular harmful metabolites changes to human, it made significant changes in the morphological and the metabolites of the bean sprouts. These changes indicate that the germicide and growth enhancer has substantially potential to influence the growth of the bean sprouts.
Machine learning for a rapid discrimination of ginseng cultivation age using 1H-NMR spectra
Lee Wonho,Yoon Dahye,Ma Seohee,Lee Dae Young,Lee Jae Won,Jo Ick-Hyun,Kim Taekwang,김석만 한국응용생명화학회 2020 Applied Biological Chemistry (Appl Biol Chem) Vol.63 No.5
The scientific and systematic classification of cultivation age is important for preventing age falsification and ensuring the quality of ginseng. Therefore, we applied deep learning to classify the cultivation age of ginseng. Deep learning, which is based on an artificial neural network, is one of the new class of models for machine learning, and is state-of-the-art. It is a powerful tool and has been used to solve complex problems in many fields. In the present study, powdered samples of 4-, 5-, and 6-year-old ginseng were measured using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy. NMR data were analyzed with deep learning and partial least-squares discriminant analysis (PLS-DA) to improve accuracy. The accuracy of the PLS-DA was 87.1% and the accuracy of the deep learning model was 93.9%. NMR spectroscopy with deep learning can be a useful tool for discrimination of ginseng cultivation age.