Corn Density Measurement by Near-infrared Transmittance

Siska, Juraj; Hurburgh, Charles R.

doi:10.13031/2013.28010

Cited by 17 publications

(19 citation statements)

References 2 publications

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“…Because near-infrared spectroscopy is in common use for rapid measurement of com composition (Hurburgh, 1988) and it will measure density (Siska and Hurburgh, 1993) …”

mentioning

confidence: 99%

Prediction of Wisconsin Tester Breakage Susceptibility of Corn from Bulk Density and NIRS Measurements of Composition

Siska¹,

Hurburgh²

1994

Transactions of the ASAE

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An equation was developed to predict corn breakage susceptibility based on the protein content, oil content, starch content, kernel density, and test weight. Reference values of breakage susceptibility were measured by Wisconsin Breakage Tester. Two statistical techniques were used to design the prediction equation, multiple linear regression (MLR) and principal factor method (PFM). KeywordsBreakage susceptibility, Corn, Near infrared, Principal factor method, Grades and standards

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“…Because near-infrared spectroscopy is in common use for rapid measurement of com composition (Hurburgh, 1988) and it will measure density (Siska and Hurburgh, 1993) …”

mentioning

confidence: 99%

Prediction of Wisconsin Tester Breakage Susceptibility of Corn from Bulk Density and NIRS Measurements of Composition

Siska¹,

Hurburgh²

1994

Transactions of the ASAE

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“…However, the change on size and density has no effect in accuracy of determination of soybean protein and oil content. The effect is very small for determining protein, oil, and starch contents for corn [83].…”

Section: A25 Structural Characteristicsmentioning

confidence: 97%

“…Cereal composition reveals the properties of inner structure. Siska and Hurburgh Jr. [83] applied NIR transmittance and PLS to obtain corn density which is related to starch and protein.…”

Section: A32 Structural Characteristicsmentioning

confidence: 99%

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

Alander

Bochko

Martinkauppi

et al. 2013

International Journal of Spectroscopy

129

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This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.

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“…A 500-g kernel aliquot per sub-subplot was analyzed by nearinfrared transmittance (Foss Infratec 1241 grain analyzer, Foss Tecator) to estimate kernel density (in kg hL −1 ) (Siska and Hurburgh, 1995). Afterward, kernel aliquot was homogenized (MAGyP, 2018) and used to measure bulk density (or test weight, in kg hL −1 ) using a Tripette & Renaud TR-77400 instrument.…”

Section: Kernel Density Bulk Density and Kernel Size Distributionmentioning

confidence: 99%

Kernel Hardness‐Related Traits in Response to Heat Stress during the Grain‐Filling Period of Maize Crops

2019

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Postflowering heat stress causes the arrest of kernel growth, increasing kernel protein concentration and the relative abundance of γ‐zeins, two biochemical traits contributing to maize (Zea mays L.) hardness. The impact of early and late postflowering heat stress on kernel physical traits related to hardness was studied on field‐grown maize hybrids differing in their prevailing endosperm texture (two hybrids with a vitreous texture, and two others with a floury texture). Kernel texture was softened by heat stress (P < 0.001), as indicated by decreases in traits that are usually positively related to hardness (thousand‐kernel weight [up to 185 g], proportion of large kernels [up to 50–65 percentage points], kernel or bulk density [up to 7 kg hL−1] and milling ratio [up to 1 g g−1]) and increases in those usually negatively related (proportion of the smaller kernels and floater percentage [up to 30 and 75 percentage points, respectively]). Most of these effects were larger (P < 0.01), as heat stress occurred earlier in the grain‐filling period. Kernel physical traits of the genotypes with a predominantly floury texture varied the most (P < 0.05) in response to heat stress. Genotypic and environmental variation effects in most hardness‐related traits could be accounted for by kernel density (r2 = 0.74–0.87) or bulk density (r2 = 0.79–0.93). Sowing date and genotype selections should be considered as crop management practices for reducing or preventing the potential impact of heat stress on maize hardness.

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Corn Density Measurement by Near-infrared Transmittance

Cited by 17 publications

References 2 publications

Prediction of Wisconsin Tester Breakage Susceptibility of Corn from Bulk Density and NIRS Measurements of Composition

Prediction of Wisconsin Tester Breakage Susceptibility of Corn from Bulk Density and NIRS Measurements of Composition

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

Kernel Hardness‐Related Traits in Response to Heat Stress during the Grain‐Filling Period of Maize Crops

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