A predictive model of the effects of genotypic, pre‐ and postharvest stages on barley β‐glucan levels

Tiwari, Uma; Cummins, Enda

doi:10.1002/jsfa.3346

Cited by 25 publications

(23 citation statements)

References 31 publications

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“…The results of this investigation demonstrated that varieties ' Ansis' and ' Austris' were the richest in β-glucans, where the amount of β-glucans was determined at 4.23±0.43% and 4.97±0.24%, respectively. After genotype excessive precipitation, heat-stress and nitrogen fertilizer regimes affected the β-glucans of barley [Guler, 2003;Hang et al, 2007;Tiwari & Cummins, 2008]. The results of investigation showed a positive, medium close correlation (r=0.505) between β-glucans and nitrogen fertilizer rate and the content of β-glucans signifi cantly differed by year, because environmental conditions were different.…”

Section: Resultsmentioning

confidence: 95%

“…The impact of factors infl uencing grain protein content was calculated as: The equation confi rms that the highest impact on grain protein content had the nitrogen fertilizer rate (factor of investment 56.3), followed by a variety (factor 33.7) and year (factor 25.1). Tiwari & Cummins [2008] evaluated the impact of pre-and postharvest stages (including genotypic factors, environmental conditions, agronomic factors and storage) on β-glucan levels in both covered and naked barley genotypes and concluded that the genotype was the most important parameter in determining the fi nal β-glucan content; it was far more important than any of agronomic factors analysed. Results of analysis highlighted the importance of harvest date and storage conditions with a potential decrease in β-glucan if harvesting is carried out early during physiological maturity and a potential 20.1% and 19.5% increase in β-glucan for covered and naked barley, respectively if the storage time is minimised [Tiwari & Cummins, 2008].…”

Section: Resultsmentioning

confidence: 99%

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Chemical Composition of Covered and Naked Spring Barley Varieties and Their Potential for Food Production

Šterna¹,

Zute²,

Jansone³

et al. 2017

Pol. J. Food Nutr. Sci.

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Key words: covered and naked barley, dietary fi ber, β-glucans, amino acids, α-tocopherol By developing new varieties suitable for the production of healthy products, given the greater consumer and manufacturer focus on the functional ingredients and nutritional properties of barley, new opportunities to incorporate barley into human foods are created. Therefore, the aim of investigation was to analyze grain composition of barley varieties and perspective breeding lines bred in Latvia and to evaluate its functional ingredients depending on varieties, year and nitrogen fertilizer rates. The content of protein, starch, β-glucans, total dietary fi ber, composition of amino acids and α-tocopherol were determined in the studied samples. The results of two-year analysis showed that the protein content in barley grain samples ranged from 10.5 to 13.9%, total dietary fi ber -from 18.74 to 20.82%, but the content of β-glucans ranged from 3.44 to 4.97%. The content of α-tocopherol was determined to range from 7.21 to 8.58 mg/kg, and the sum of essential amino acids -from 31.5 to 38.9 g/kg. Although covered barley varieties demonstrated a higher content of such functional ingredients as α-tocopherol, total dietary fi ber and β-glucans, naked barley grains had a higher protein content, the sum of essential amino acids, and, particularly lysine, was not far behind the content recommended by nutrition experts.

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Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

Chemical Composition of Covered and Naked Spring Barley Varieties and Their Potential for Food Production

Šterna¹,

Zute²,

Jansone³

et al. 2017

Pol. J. Food Nutr. Sci.

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“…A sensitivity analysis was carried out for a mathematical model developed with various cultivation and farm-level factors influencing the level of β-glucan content in harvested barley (HB and HLB) and oat (HO, NO) grains (Tiwari and Cummins 2008;2009b). Seven key parameters for barley (cultivar, harvest delay, germination time, storage days, precipitation and temperature) and six key parameters (cultivar, sowing delay days, germination time, storage days and storage temperature) for oat cultivars were collated (Tiwari and Cummins 2008;2009b) to perform the advanced sensitivity analysis.…”

Section: Model Developmentmentioning

confidence: 99%

“…Furthermore, several factors including cultivar, environmental conditions, agronomic practices and storage factors are reported to influence the β-glucan content of oats and barley grains (Fontana et al 2009;Tiwari and Cummins 2009a). Recently, a farm-level model was developed using a probabilistic approach showing the effect of various cultivar, agronomic and storage factors on the level of β-glucan in harvested barley and oat grains (Tiwari and Cummins 2008;2009b). A schematic of the model is shown in Fig.…”

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confidence: 99%

Sensitivity Analysis in Predictive Models for assessing the Level of β-Glucan in Oats and Barley Cultivars Using Meta-Models

Tiwari

Cummins

2010

Food Bioprocess Technol

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Oats and barley β-glucans are well-known for their many health benefits; this has encouraged the food industry to develop new functional foods containing oats and barley. This study aims to develop an advanced sensitivity analysis to analyse and evaluate the most significant model inputs contributing to uncertainty in assessing the level of β-glucan content in harvested oat and barley grains. Two methodologies, nominal value and regression method sensitivity analysis, were adopted. The nominal sensitivity analysis highlighted that cultivar selection is the predominant factor with a correlation coefficient 0.66 for hulled oats and barley cultivars, whereas the correlation was 0.80 and 0.77 for naked oats and hull-less barley, respectively. Advanced sensitivity analysis using regression modelling highlighted that cultivar selection, storage days and germination time (days) were the most important parameters in both the oats and barley model. Regression analysis using the response surface methodology shows that prediction models were found to be significant (P< 0.0001) with low standard errors and high coefficients of determination (R 2 >0.94). This study shows that regression modelling is an effective tool to highlight the effect of key input variables and their interactive effects on the predictive response of β-glucan in harvested oats and barley cultivars.

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“…According other authors, beta-glucan content in grain is aff ected by the course of weather; hot and dry weather during formation of caryopses is subsequently refl ected in higher beta-glucan content in grain (Güler, 2003;Ehrenbergerová et al, 2008;Tiwari & Cummins, 2008;Dickin et al, 2011).…”

Section: Main Eff Ectsmentioning

confidence: 99%

The Influence of Genotype and Environment on Arabinoxylan and Beta-glucan Contents in Grain of Spring Barley (Hordeum vulgare L.)

Macháň¹,

Ehrenbergerová²,

Cerkal³

et al. 2014

Acta Univ. Agric. Silvic. Mendelianae Brun.

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Arabinoxylan and beta-glucan contents are limiting factors for a wider use of barley production. Arabinoxylan and beta-glucan contents were assessed in grain samples in sets of seven malting hulled varieties, three hull-less lines and one hull-less spring variety grown in the localities of Branišovice, Žabčice, and Kroměříž in 2009 to 2011. Further, the eff ect of growing technologies on the level of these non-starch polysaccharides was studied. Variability of arabinoxylan contents was aff ected most signifi cantly by a genotype and growing technology whereas variability of beta-glucan contents was mostly aff ected by a genotype and growing environment (interaction of year with locality). The highest values of arabinoxylans and beta-glucans were determined in the grain samples of hullless lines (KM 1057: 6.16% of arabinoxylans and KM 2084: 6.41% of beta-glucans) and on the contrary, the lowest values of arabinoxylans were found in the grain of hull-less variety AF Lucius (3.85%) and lowest amounts of beta-glucans were found in malting variety Radegast (3.92%). The samples of the growing technology without fungicide treatment had on average more arabinoxylans and betaglucans than the fungicide non-treated ones.

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A predictive model of the effects of genotypic, pre‐ and postharvest stages on barley β‐glucan levels

Cited by 25 publications

References 31 publications

Chemical Composition of Covered and Naked Spring Barley Varieties and Their Potential for Food Production

Chemical Composition of Covered and Naked Spring Barley Varieties and Their Potential for Food Production

Sensitivity Analysis in Predictive Models for assessing the Level of β-Glucan in Oats and Barley Cultivars Using Meta-Models

The Influence of Genotype and Environment on Arabinoxylan and Beta-glucan Contents in Grain of Spring Barley (Hordeum vulgare L.)

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