Traditional rice is gaining popularity worldwide due to its high nutritional and pharmaceutical value, as well as its high resistance to abiotic and biotic stresses. This has attracted significant attention from breeders, nutritionists, and plant protection scientists in recent years. Hence, it is critical to investigate the grain metabolome to reveal germination and nutritional importance. This research aimed to explore non-targeted metabolites of five traditional rice varieties, ., Chinnar, Chithiraikar, Karunguruvai, Kichili samba, and Thooyamalli, for their nutritional and therapeutic properties. Approximately 149 metabolites were identified using the National Institute of Standards and Technology (NIST) library and Human Metabolome Database (HMDB) and were grouped into 34 chemical classes. Major classes include fatty acids (31.1-56.3%), steroids and their derivatives (1.80-22.4%), dihydrofurans (8.98-11.6%), prenol lipids (0.66-4.44%), organooxygen compounds (0.12-6.45%), benzene and substituted derivatives (0.53-3.73%), glycerolipids (0.36-2.28%), and hydroxy acids and derivatives (0.03-2.70%). Significant variations in metabolite composition among the rice varieties were also observed through the combination of univariate and multivariate statistical analyses. Principal component analysis (PCA) reduced the dimensionality of 149 metabolites into five principle components (PCs), which explained 96% of the total variance. Two clusters were revealed by hierarchical cluster analysis, indicating the distinctiveness of the traditional varieties. Additionally, a partial least squares-discriminant analysis (PLS-DA) found 17 variables important in the projection (VIP) scores of metabolites. The findings of this study reveal the biochemical intricate and distinctive metabolomes of the traditional therapeutic rice varieties. This will serve as the foundation for future research on developing new rice varieties with traditional rice grain metabolisms to increase grain quality and production with various nutritional and therapeutic benefits.
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http://dx.doi.org/10.3389/fnut.2023.1254624 | DOI Listing |
Pest Manag Sci
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School of Pharmacy, Lanzhou University, Lanzhou, China.
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View Article and Find Full Text PDFJ Sci Food Agric
December 2024
Workshop Machinery and Maintenance Division, Bangladesh Rice Research Institute, Gazipur, Bangladesh.
Rice, a staple food in Bangladesh, produces substantial quantities of rice bran as a by-product, offering considerable potential for advancing agriculture. While rice bran holds value, traditional uses like poultry feed and landfilling remain prevalent despite growing interest in its broader applications. This article explores the current status, stabilization methods, oil extraction techniques, and opportunities and challenges associated with rice bran utilization in Bangladesh.
View Article and Find Full Text PDFBMC Plant Biol
December 2024
Zhejiang Province Key Laboratory of Plant Secondary Metabolism and Regulation, College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, 310018, China.
Tetrastigma hemsleyanum, a traditional Chinese medicinal plant with anti-inflammatory, anti-cancer, and anti-tumor properties, faces increasing abiotic stress due to climate change, agricultural chemicals, and industrialization. This study investigated how three abiotic stress factors influence antioxidant enzyme activity, MDA levels, DPPH free radical scavenging capacity, chlorophyll, carotenoids, active compounds, and gene expression in different T. hemsleyanum strains.
View Article and Find Full Text PDFSci Rep
December 2024
Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.
The rice plant is one of the most significant crops in the world, and it suffers from various diseases. The traditional methods for rice disease detection are complex and time-consuming, mainly depending on the expert's experience. The explosive growth in image processing, computer vision, and deep learning techniques provides effective and innovative agriculture solutions for automatically detecting and classifying these diseases.
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