Development of predictive models for total phenolics and free p-coumaric acid contents in barley grain by near-infrared spectroscopy

Zhao, Han; Cai, Shengguan; Zhang, Xuelei; Qian, Qiufeng; Huang, Yuqing; Dai, Fei; Zhang, Guoping

doi:10.1016/j.foodchem.2017.01.063

Cited by 29 publications

(18 citation statements)

References 31 publications

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“…[15][16][17] LS-SVM is an alternate formulation of SVM that simpli¯es the training process of SVM and signi¯cantly reduces the computation time while maintaining similar performance to SVM. 18 The implementation of SVM requires three tuning parameters (, , Þ, while LS-SVM requires only two parameters (, Þ. 19 In this paper, LS-SVM was implemented to establish a calibration model to correlate the mean spectra and the moisture content of the rubber sheets.…”

Section: Least Square Support Vector Machine (Ls-svm)mentioning

confidence: 99%

Development of calibration models for rapid determination of moisture content in rubber sheets using portable near-infrared spectrometers

Puttipipatkajorn

2020

J. Innov. Opt. Health Sci.

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Rubber sheets are one of the primary products of natural rubber and are the main raw material in various rubber industries. The quality of a rubber sheet can be visually examined by holding it against clear light to inspect for any specks and impurities inside, but its moisture content is difficult to evaluate based on a visual inspection and this might lead to unfair trading. Herein, we developed a rapid, robust and nondestructive near-infrared spectroscopy (NIRS)-based method for moisture content determination in rubber sheets. A set of 300 rubber sheets were divided into a calibration (200 samples) and prediction groups (100 samples). The calibration set was used to develop NIRS calibration equation using different calibration models, Partial Least Square Regression (PLSR), Least Square Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN). Among the models investigated, the ANN model with the first derivative of spectral preprocessing presented the best prediction with a coefficient of determination ([Formula: see text] of 0.993, root mean square error of calibration (RMSEC) of 0.126% and root mean square error of prediction (RMSEP) of 0.179%. The results indicated that the proposed NIRS-ANN model will be able to reduce human error and provide a highly accurate estimate of the moisture content in a rubber sheet compared to traditional wet chemistry estimation methods according to AOAC standards.

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Section: Least Square Support Vector Machine (Ls-svm)mentioning

confidence: 99%

Development of calibration models for rapid determination of moisture content in rubber sheets using portable near-infrared spectrometers

Puttipipatkajorn

2020

J. Innov. Opt. Health Sci.

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“…With the improvement on measurement throughout, speed and accuracy by Near-infrared reflectance spectroscopy (NIRS) analyzer, NIRS has been widely used in the non-destructive analysis of grain components, such as grain protein content, oil, fatty acids, dietary fiber, moisture, seed physical traits ( Cen and He, 2007 ). Compared with traditional laboratory methods, the NIRS methods have the great advantages of non-destructive, less time, low cost, and fast speed, and have been used for rapid phenotyping of quality traits in maize, soybean, rice, barley, oat and triticale ( Delwiche et al., 1996 ; Windham et al., 1997 ; Fox et al., 2011 ; Tarr et al., 2012 ; Han et al., 2017 ; Xu et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-Locus GWAS of Quality Traits in Bread Wheat: Mining More Candidate Genes and Possible Regulatory Network

et al. 2020

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In wheat breeding, improved quality traits, including grain quality and dough rheological properties, have long been a critical goal. To understand the genetic basis of key quality traits of wheat, two single-locus and five multi-locus GWAS models were performed for six grain quality traits and three dough rheological properties based on 19, 254 SNPs in 267 bread wheat accessions. As a result, 299 quantitative trait nucleotides (QTNs) within 105 regions were identified to be associated with these quality traits in four environments. Of which, 40 core QTN regions were stably detected in at least three environments, 19 of which were novel. Compared with the previous studies, these novel QTN regions explained smaller phenotypic variation, which verified the advantages of the multi-locus GWAS models in detecting important small effect QTNs associated with complex traits. After characterization of the function and expression in-depth, 67 core candidate genes involved in protein/sugar synthesis, histone modification and the regulation of transcription factor were observed to be associated with the formation of grain quality, which showed that multi-level regulations influenced wheat grain quality. Finally, a preliminary network of gene regulation that may affect wheat quality formation was inferred. This study verified the power and reliability of multi-locus GWAS methods in wheat quality trait research, and increased the understanding of wheat quality formation mechanisms. The detected QTN regions and candidate genes in this study could be further used for gene cloning and marker-assisted selection in highquality breeding of bread wheat.

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“…Barley ( Hordeum vulgare L.) is mainly used as animal feed and raw material for malt and beer production. In recent decades, its use in the production of functional or healthy food has been increasingly focused because of its rich phytochemicals [ 1 , 2 ]. Some phytochemicals in cereal grains, including phenolic acids, flavonoids and anthocyanins, can reduce risk of human chronic inflammation, cardiovascular diseases, certain concers, and diabetes, and they are also involved in cell wall formation by forming bridges between polysaccharide and lignin [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Association mapping for total polyphenol content, total flavonoid content and antioxidant activity in barley

et al. 2018

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BackgroundThe interest has been increasing on the phenolic compounds in plants because of their nutritive function as food and the roles regulating plant growth. However, their underlying genetic mechanism in barley is still not clear.ResultsA genome-wide association study (GWAS) was conducted for total phenolic content (TPC), total flavonoid content (FLC) and antioxidant activity (AOA) in 67 cultivated and 156 Tibetan wild barley genotypes. Most markers associated with phenolic content were different in cultivated and wild barleys. The markers bPb-0572 and bPb-4531 were identified as the major QTLs controlling phenolic compounds in Tibetan wild barley. Moreover, the marker bPb-4531 was co-located with the UDP- glycosyltransferase gene (HvUGT), which is a homolog to Arabidopsis UGTs and involved in biosynthesis of flavonoid glycosides .ConclusionsGWAS is an efficient tool for exploring the genetic architecture of phenolic compounds in the cultivated and Tibetan wild barleys. The DArT markers applied in this study can be used in barley breeding for developing new barley cultivars with higher phenolics content. The candidate gene (HvUGT) provides a potential route for deep understanding of the molecular mechanism of flavonoid synthesis.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4483-6) contains supplementary material, which is available to authorized users.

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Development of predictive models for total phenolics and free p-coumaric acid contents in barley grain by near-infrared spectroscopy

Cited by 29 publications

References 31 publications

Development of calibration models for rapid determination of moisture content in rubber sheets using portable near-infrared spectrometers

Development of calibration models for rapid determination of moisture content in rubber sheets using portable near-infrared spectrometers

Multi-Locus GWAS of Quality Traits in Bread Wheat: Mining More Candidate Genes and Possible Regulatory Network

Association mapping for total polyphenol content, total flavonoid content and antioxidant activity in barley

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