Protein Content of Single Kernels of Wheat by Near-Infrared Reflectance Spectroscopy

Delwiche, Stephen R.

doi:10.1006/jcrs.1997.0165

Cited by 119 publications

(101 citation statements)

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“…A number of applications have been reported using SKNIR in wheat and maize for quality and fungal infection [4][5][6][7][8]14,21,22,25 , as well as for protein 4,26 and hardness 4,17 in wheat. In barley, there have been a number of recent reports on SK measurements for grain hardness 2,13,[15][16][17]19 .…”

Section: Introductionmentioning

confidence: 99%

Development of a Single Kernel NIR Barley Protein Calibration and Assessment of Variation in Protein on Grain Quality

Fox¹,

Kelly²,

Sweeney³

et al. 2011

Journal of the Institute of Brewing

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In this paper, we describe results from a preliminary experiment on the development of a near infrared reflectance (NIR) calibration for single kernel (SK) % protein content in barley. The SKNIR calibration was developed using kernels from breeding lines and commercial barley varieties with a range in protein content of 7.3% to 16.6% "as is" . The calibration model produced an R 2 = 0.903, while the validation set had a R 2 = 0.837 with a standard error of cross validation and a standard error of prediction of 0.8% for both the calibration and validation sets respectively. The calibration was then used to estimate the variation in % protein of 4,000 single kernels from a commercial variety (Gairdner at 9.3% protein) by segregating kernels into six sub-groups (<7.8%, 7.9-8.3%, 8.4-9.0%, 9.1-9.7%, 9.8-10.4%, >10.5% "as is" ). These sub-groups then had additional grain quality tests carried out including grain size, thousand kernel weight and NIR estimates of % protein, starch, hardness and barley hot water extract (HWE). The results showed an increase in grain size, and a decrease in HWE from the low to high % protein sub-groups. While only a single variety was used in the SKNIR protein segregation study, the results suggested SKNIR could be used to screen for the variation in grain quality traits based on variation in protein content.

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Section: Introductionmentioning

confidence: 99%

Development of a Single Kernel NIR Barley Protein Calibration and Assessment of Variation in Protein on Grain Quality

Fox¹,

Kelly²,

Sweeney³

et al. 2011

Journal of the Institute of Brewing

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“…3 Furthermore, higher wavelengths sometimes involved in this measurement mode may not produce linear responses between spectra and compound concentrations in single-kernel analysis. 4 Some research has reported NIRS calibrations for predicting the major attributes of single soybeans. The first NIRS study analyzing single soybeans determined moisture by transmittance (standard error of prediction (SEP) = 0.65− 0.69%).…”

Section: ■ Introductionmentioning

confidence: 99%

Measurement of Single Soybean Seed Attributes by Near-Infrared Technologies. A Comparative Study

Agelet

Armstrong

Clariana

et al. 2012

J. Agric. Food Chem.

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Four near-infrared spectrophotometers, and their associated spectral collection methods, were tested and compared for measuring three soybean single-seed attributes: weight (g), protein (%), and oil (%). Using partial least-squares (PLS) and four preprocessing methods, the attribute that was significantly most easily predicted was seed weight (RPD > 3 on average) and protein the least. The performance of all instruments differed from each other. Performances for oil and protein predictions were correlated with the instrument sampling system, with the best predictions using spectra taken from more than one seed angle. This was facilitated by the seed spinning or tumbling during spectral collection as opposed to static sampling methods. From the preprocessing methods utilized, no single one gave the best overall performances but weight measurements were often more successful with raw spectra, whereas protein and oil predictions were often enhanced by SNV and SNV + detrending. ABSTRACT: Four near-infrared spectrophotometers, and their associated spectral collection methods, were tested and compared for measuring three soybean single-seed attributes: weight (g), protein (%), and oil (%). Using partial least-squares (PLS) and four preprocessing methods, the attribute that was significantly most easily predicted was seed weight (RPD > 3 on average) and protein the least. The performance of all instruments differed from each other. Performances for oil and protein predictions were correlated with the instrument sampling system, with the best predictions using spectra taken from more than one seed angle. This was facilitated by the seed spinning or tumbling during spectral collection as opposed to static sampling methods. From the preprocessing methods utilized, no single one gave the best overall performances but weight measurements were often more successful with raw spectra, whereas protein and oil predictions were often enhanced by SNV and SNV + detrending.

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“…Standard analytical approaches used in analysis of reflectance data include (see [9] for review): discriminant analysis [10,11,26], principal component analysis [3,21,[26][27][28], multi-regression approaches, like partial least square (PLS) [12,29,30], use of spectral band ratios (indices) [3,[31][32][33], decision trees [34], artificial neural networks [17,28,34], and support vector machines [8,35]. One common denominator in all of these analytical approaches is that they do not incorporate-or take advantage of -the spatial information available in a HI data.…”

Section: Introductionmentioning

confidence: 99%

Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment

et al. 2010

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Many food products are subjected to quality control analyses for detection of surface residue/contaminants, and there is a trend of requiring more and more documentation and reporting by farmers regarding their use of pesticides. Recent outbreaks of food borne illnesses have been a major contributor to this trend. With a growing need for food safety measures and -smart applications‖ of insecticides, it is important to develop methods for rapid and accurate assessments of surface residues on food and feed items. As a model system, we investigated detection of a miticide applied to maize leaves and its miticidal bioactivity over time, and we compared two types of reflectance data: fourier transformed infrared (FTIR) data and hyperspectral imaging (HI) data. The miticide (bifenazate) was applied at a commercial field rate to maize leaves in the field, with or without application of a surfactant, and with or without application of a simulated -rain event‖. In addition, we collected FTIR and HI from untreated control leaves (total of five treatments). Maize leaf data were collected at seven time intervals from 0 to 48 hours after application. FTIR data were analyzed using conventional analysis of variance of miticide-specific vibration peaks. Two unique FTIR vibration peaks were associated with miticide application (1,700 cm −1 and 763 cm −1 ). The integrated intensities of these two OPEN ACCESSRemote Sensing 2010, 2 909 peaks, miticide application, surfactant, rain event, time between miticide application, and rain event were used as explanatory variables in a linear multi-regression fit to spider mite mortality. The same linear multi-regression approach was applied to variogram parameters derived from HI data in five selected spectral bands (664, 683, 706, 740, and 747 nm). For each spectral band, we conducted a spatial structure analysis, and the three standard variogram parameters (-sill‖, -range‖, and -nugget‖) were examined as possible -indicators‖ of miticide bioactivity. We demonstrated that both FTIR peaks and standard variogram parameters could be used to accurately predict spider mite mortality, but linear multi-regression fits based on standard variogram parameters had the highest accuracy and were successfully validated with independent data. Based on experimental manipulation of HI data, the use of spatial structure analysis in classification of HI data was discussed.

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Protein Content of Single Kernels of Wheat by Near-Infrared Reflectance Spectroscopy

Cited by 119 publications

References 0 publications

Development of a Single Kernel NIR Barley Protein Calibration and Assessment of Variation in Protein on Grain Quality

Development of a Single Kernel NIR Barley Protein Calibration and Assessment of Variation in Protein on Grain Quality

Measurement of Single Soybean Seed Attributes by Near-Infrared Technologies. A Comparative Study

Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment

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