Background Lemna species are cosmopolitan floating plants that have great application potential in the food/feed, pharmaceutical, phytoremediation, biofuel, and bioplastic industries. In this study, the effects of exogenous melatonin (0.1, 1, and 10 µM) on the growth and production of various bioactive metabolites and intact lipid species were investigated in Lemna aequinoctialis culture. Results Melatonin treatment significantly enhanced the growth (total dry weight) of the Lemna aequinoctialis culture. Melatonin treatment also increased cellular production of metabolites including β-alanine, ascorbic acid, aspartic acid, citric acid, chlorophyll, glutamic acid, phytosterols, serotonin, and sucrose, and intact lipid species; digalactosyldiacylglycerols, monogalactosyldiacylglycerols, phosphatidylinositols, and sulfoquinovosyldiacylglycerols. Among those metabolites, the productivity of campesterol (1.79 mg/L) and stigmasterol (10.94 mg/L) were the highest at day 28, when 10 µM melatonin was treated at day 7. Conclusion These results suggest that melatonin treatment could be employed for enhanced production of biomass or various bioactive metabolites and intact lipid species in large-scale L. aequinoctialis cultivation as a resource for food, feed, and pharmaceutical industries.
Rice (Oryza sativa L.) is a widely consumed food source, and its geographical origin has long been a subject of discussion. In our study, we collected 44 and 20 rice samples from different regions of the Republic of Korea and China, respectively, of which 35 and 29 samples were of white and brown rice, respectively. These samples were analyzed using nuclear magnetic resonance (NMR) spectroscopy, followed by analyses with various data normalization and scaling methods. Then, leave-one-out cross-validation (LOOCV) and external validation were employed to evaluate various machine learning algorithms. Total area normalization, with unit variance and Pareto scaling for white and brown rice samples, respectively, was determined as the best pre-processing method in orthogonal partial least squares–discriminant analysis. Among the various tested algorithms, support vector machine (SVM) was the best algorithm for predicting the geographical origin of white and brown rice, with an accuracy of 0.99 and 0.96, respectively. In external validation, the SVM-based prediction model for white and brown rice showed good performance, with an accuracy of 1.0. The results of this study suggest the potential application of machine learning techniques based on NMR data for the differentiation and prediction of diverse geographical origins of white and brown rice.
Spirodela polyrhiza (Araceae family) is a duckweed species that serves as a potential resource for feed, food, bioremediation, and pharmaceutical applications. In this study, we assessed the effects of different concentrations of melatonin (0, 0.1, 1, and 10 μM) on the growth of S. polyrhiza during in vitro culture and the metabolic profiles and productivities of useful metabolites using gas chromatography–mass spectrometry coupled with multivariable statistical analysis. We found that exogenous melatonin significantly improved the total dry weight and altered the metabolic profiles of S. polyrhiza cultures. Melatonin significantly enhanced the cellular production of useful metabolites, such as γ-aminobutyric acid, dopamine, threonine, valine, and phytosterols. The volumetric productivities (mg/L) of γ-aminobutyric acid, dopamine, campesterol, β-sitosterol, and stigmasterol were the highest in the presence of 10 μM melatonin on day 12. Moreover, the productivities of ascorbic acid and serotonin were the highest in the presence of 1 μM melatonin on day 12. Therefore, melatonin could be used to enhance the production of biomass and useful metabolites during large-scale S. polyrhiza cultivation in cosmetic, food/feed, and pharmaceutical industries.
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