Genetic and environmental analysis of NIR feed quality predictions on genotypes of barley (Hordeum vulgare L.)

Fox, Glen; Borgognone, M. Gabriela; Flinn, Peter; Poulsen, David Me

doi:10.1016/j.fcr.2010.11.007

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…Almost similar performed model was also reported in the literature with parameters value (R 2 c=0.91; RPD=3.33). Therefore, the friability calibration model was not as such challenging like in dry matter and beta-glucan as observed from similar model reported by different authors [15]. Additionally, Figure 5 expressed the calibration curve obtained from chemistry/reference data and NIR predicted spectrum data of friability content.…”

Section: Friability Modelsupporting

confidence: 57%

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Developing Calibration Model for Prediction of Malt Barley Genotypes Quality Traits using Fourier Transform near Infrared Spectroscopy

Abeshu

Zewdu

2020

Agriculture and Food Sciences Research

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Ethiopia is the second largest barley producer in Africa. But the quality traits are always influenced by the cultivar itself and growing environment. Thus, the study was targeted on developing calibration model for predicting malt barley quality traits of genotypes grown at different locations using near infra-red spectroscopy for selection purposes in barley breeding program. For this purpose, 60 barley samples were collected from Holeta, Debre-Birhan and Bekoji. Samples were chemically analyzed in duplicate for 5 barley traits. The calibration model was developed based on 120 samples spectral data and 60 chemistry data results using the calibration software of the FT-Near Infrared Spectroscopy. The barley Protein calibration model having (R²c= 0.97; RPD=5.7 and R 2 c=0.94; RPD=4.16) respectively, can be regarded as broadly applicable; Extract and Friability (R²c= 0.96; RPD=4.54 and R 2 c=0.95; RPD=4.36) respectively were accepted as useable with good prediction capability; whereas β-Glucan calibration model (R²c= 0.90; RPD=3.18) allowed only for screening purpose in some applications. Barley grain dry matter with model parameters result (R 2 c=0.86; RPD=2.69)shown usable with caution only for rough screening purposes. Hence near infrared spectroscopy is fast and cost-efficient, the breeding program can increase the intensity of variety selection using calibration models reflected good predicting performances except models for dry matter and β-glucan.

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Section: Friability Modelsupporting

confidence: 57%

“…With a higher RPDv value the calibration will more likely be able to predict the right sample values. According to the generally used classification in agriculture based on RPDv-values and R 2 c-values, the calibration for protein can be regarded as successful calibration [15].…”

Section: Calibration Modelmentioning

confidence: 99%

Developing Calibration Model for Prediction of Malt Barley Genotypes Quality Traits using Fourier Transform near Infrared Spectroscopy

Abeshu

Zewdu

2020

Agriculture and Food Sciences Research

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“…There are several Nitrogen-Hydrogen (NH) absorbance regions and the use of spectra alone can be used for selection [8,13]. It has been shown that spectra can differ between growing regions and to a less extent underlying genetics [11,14]. Thus with a NIR spectrophotometer with a full spectra region it could be possible to do selections for protein and other traits without needing the support of any chemistry to develop calibrations.…”

Section: Proteinmentioning

confidence: 99%

The Brewing Industry and the Opportunities for Real-Time Quality Analysis Using Infrared Spectroscopy

Fox

2020

Applied Sciences

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Brewing is an ancient process which started in the middle east over 10,000 years ago. The style of beer varies across the globe but modern brewing is very much the same regardless of the style. While there are thousands of compounds in beer, current methods of analysis rely mostly on the content of only several important processing parameters such as gravity, bitterness, or alcohol. Near infrared and mid infrared spectroscopy offer opportunities to predict dozens to hundreds of compounds simultaneously at different stages of the brewing process. Importantly, this is an opportunity to move deeper into quality through measuring wort and beer composition, rather than just content. This includes measuring individual sugars and amino acids prior to fermentation, rather than total °Plato or free amino acids content. Portable devices and in-line probes, coupled with more complex algorithms can provide real time measurements, allowing brewers more control of the process, resulting in more consistent quality, reduced production costs and greater confidence for the future.

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“…Zhao et al (2013) revealed that the NIR spectra of wheat of the same species cultivated from four areas in China significantly differ; therefore, wheat-producing regions can be identified on the basis of NIRS [ 9 ]. In practice, genetic and environmental factors greatly influence the NIR spectra of crop seeds [ 10 , 11 ]. Zhao et al (2014) analyzed the seeds of 10 wheat varieties harvested from three origins and found that origins and production year, along with genetic factor, greatly affect the NIR spectra of wheat [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Effects of Varieties, Producing Areas, Ears, and Ear Positions of Single Maize Kernels on Near-Infrared Spectra for Identification and Traceability

Cui

Liu

et al. 2016

PLoS ONE

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The effects of varieties, producing areas, ears, and ear positions of maize on near-infrared (NIR) spectra were investigated to determine the factors causing the differences in NIR fingerprints of maize varieties. A total of 130 inbred lines were grown in two regions in China, and 12,350 kernel samples were analyzed through NIR spectroscopy. Spectral differences among varieties, producing areas, ears, and ear positions were determined and compared on the basis of pretreated spectra. The bands at 1300–1470, 1768–1949, 2010–2064, and 2235–2311 nm were mainly affected by the producing area. Band selection and principal component analysis were applied to improve the influence of variety on NIR spectra by processing the pretreated spectra. The degrees of the influence of varieties, producing areas, ears, and ear positions were calculated, and the percentages of the influence of varieties, producing areas, ears, and ear positions were 45.40%, 42.66%, 8.22%, and 3.72%, respectively. Therefore, genetic differences among maize inbred lines are the main factors accounted for NIR spectral differences. Producing area is a secondary factor. These results could provide a reference for researchers who authenticate varieties, perform geographical origin traceabilities, and conduct maize seed breeding.

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Genetic and environmental analysis of NIR feed quality predictions on genotypes of barley (Hordeum vulgare L.)

Cited by 3 publications

References 13 publications

Developing Calibration Model for Prediction of Malt Barley Genotypes Quality Traits using Fourier Transform near Infrared Spectroscopy

Developing Calibration Model for Prediction of Malt Barley Genotypes Quality Traits using Fourier Transform near Infrared Spectroscopy

The Brewing Industry and the Opportunities for Real-Time Quality Analysis Using Infrared Spectroscopy

Effects of Varieties, Producing Areas, Ears, and Ear Positions of Single Maize Kernels on Near-Infrared Spectra for Identification and Traceability

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