Fast nutritional characterization of different pigmented rice grains using a combination of NMR and decision tree analysis

Du, Hongying; Huo, Yinqiang; Liu, Huili; Kamal, Ghulam Mustafa; Yang, Jiaren; Zeng, Yongchao; Zhao, Siming; Liu, Youming

doi:10.1080/19476337.2018.1545800

Cited by 4 publications

(2 citation statements)

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“…SVMs, as with the classical supervised statistical methods, are, however, widely used for the discrimination of two different classes and, due to their limitations, they are rarely used for multi-class analysis. Hence, ML algorithms such as decision trees (DTs) have been developed and applied for the analysis of plant complex multifactorial characteristics [ 167 , 173 , 174 ].…”

Section: 4ir Technologies and Plant Metabolomicsmentioning

confidence: 99%

“…In plant metabolomics, DTs have been applied in the extraction of metabolite features that discriminate different rice cultivars [ 173 ], in the identification of 11 anti-inflammatory biomarkers from 57 Asteraceae species leaf extracts [ 153 ] and in the prediction of the most frequent substructures based on the mass spectral features and retention index ( Figure 3 ) [ 177 ]. A DT based on the fragmentation patterns of metabolites approach was used for the annotation of 28 unique avenanthramides in oat seedling extracts [ 178 ].…”

Section: 4ir Technologies and Plant Metabolomicsmentioning

confidence: 99%

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Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

et al. 2021

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Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

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Section: 4ir Technologies and Plant Metabolomicsmentioning

confidence: 99%