Identification and Isolation Techniques for Plant Growth Inhibitors in Rice - Nguyen Thi Hai Anh; La Hoang Anh; Nguyen Phuong Mai; Nguyen Van Quan; Tran Dang Xuan (Separations, 2023)
Plant growth inhibitors (PGIs) in rice (Oryza sativa), or rice allelochemicals, are secondary metabolites that are either exudated by rice plants to cope with natural competitors or produced during the decomposition of rice by-products in the paddy fields. Of these, the major groups of rice PGIs include phenolics, flavonoids, terpenoids, alkaloids, steroids, and fatty acids, which also exhibit potential medicinal and pharmaceutical properties. Recently, the exploitation of rice PGIs has attracted considerable attention from scientists worldwide. The biosynthesis, exudation, and release of PGIs are dependent on environmental conditions, relevant gene expression, and biodiversity among rice varieties. Along with the mechanism clarification, numerous analytical methods have been improved to effectively support the identification and isolation of rice PGIs during the last few decades. This paper provides an overview of rice PGIs and techniques used for determining and extracting those compounds from rice. In particular, the features, advantages, and limitations of conventional and upgraded extraction methods are comprehensively reported and discussed. The conventional extraction methods have been gradually replaced by advanced techniques consisting of pressurized liquid extraction (PLE), microwave-assisted extraction (MAE), and solid-phase extraction (SPE). Meanwhile, thin-layer chromatography (TLC), liquid chromatography (LC), gas chromatography (GC), mass spectrometry (MS), nuclear magnetic resonance (NMR), high-resolution mass spectrometry (HR-MS), infrared spectroscopy (IR), near-infrared spectroscopy (NIRS), and X-ray crystallography are major tools for rice PGI identification and confirmation. With smart agriculture becoming more prevalent, the statistics of rice PGIs and extraction methods will help to provide useful datasets for building an autonomous model for safer weed control. Conceivably, the efficient exploitation of rice PGIs will not only help to increase the yield and economic value of rice but may also pave the way for research directions on the development of smart and sustainable rice farming methods.