Moisture Effects on the Prediction Performance of a Single‐Kernel Near‐Infrared Deoxynivalenol Calibration

Peiris, Kamaranga H. S.; Dong, Yanhong; Bockus, W. W.; Dowell, Floyd E.

doi:10.1094/cchem-04-16-0120-r

Cited by 8 publications

(9 citation statements)

References 48 publications

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“…However, studies on the moisture effects on the performance of NIR spectroscopy calibrations for cereal grain traits analysis are limited. Peiris, Dong, Bockus, and Dowell (2016) showed that grain moisture content influences the NIR spectroscopy prediction of deoxynivalenol levels in wheat grains. Since water is a strong NIR absorber (Büning-Pfaue, 2003), moisture level variability in grains may significantly influence the spectral features.…”

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Moisture effects on robustness of sorghum grain protein near‐infrared spectroscopy calibration

et al. 2019

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Background and objectives A near‐infrared (NIR) spectroscopy method was developed for rapid and nondestructive evaluation of protein content of intact sorghum grains. Effect of grain sample moisture variation on the robustness of protein calibration was investigated. Findings An initial NIR protein calibration model with sorghum grains in 7.28%–11.57% moisture content range with coefficient of determination (R2) of 0.94 and standard error of cross‐validation (SECV) of 0.41%, predicted protein content of an external validation set of different varieties with R2 = 0.90, root‐mean‐square error of prediction (RMSEP) = 0.42% and bias = −0.02%. However, when grains with a wider range of moisture content (7.50%–17.75%) were used for validation, prediction errors increased with R2 = 0.72 RMSEP = 0.87% and bias = −0.32%. Inclusion of grains with a wider moisture range to the calibration set improved the performance of the calibration model with a R2 = 0.83, RMSEP = 0.67% with a bias of −0.04%. Conclusions Variation of moisture content in grains affected the performance of the NIR protein calibration model. Likewise, inclusion of moisture variation in the calibration sample set improved the robustness of the model. Significance and novelty In addition to the traits of interest, variation of other physical or chemical traits should also be considered for inclusion into the calibration sample set to improve the robustness of NIR calibration models. This is especially important when NIR spectroscopy methods are developed for evaluation of breeding populations as the future grain samples from numerous crosses may be substantially diverse.

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Moisture effects on robustness of sorghum grain protein near‐infrared spectroscopy calibration

et al. 2019

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“…Furthermore, NIR spectroscopy can be used as an online analysis tool capable of testing a large amount of sample during processing. Finally, various factors other than the quality parameters of the sample, for example, sample shape, moisture, particle size, color, texture, and density, affect the NIR spectra, which may add some complications to the interpretation of the NIR data (Peiris, Dong, Bockus, & Dowell, 2016;Weir, Omielan, Lee, & Rajcan, 2005;Williams, 2007). NIR spectroscopy is also a nondestructive, safe, environmentally clean (no chemicals), and easy-to-use technique which requires simple or no sample preparation.…”

Section: Near-infrared (Nir) Spectroscopymentioning

confidence: 99%

“…NIR spectroscopy can be calibrated and validated against highly accurate techniques, with the flexibility to analyze more than 30 characteristic factors of cereals and pulses in a single test (Williams, 2007). For example, NIR calibration for deoxynivalenol (DON) levels in wheat kernel was shown to be significantly affected by moisture content (MC) leading to over-and underestimation of DON levels at decreased and increased MC, respectively (Peiris et al, 2016). On the other hand, there is always a need to calibrate the NIR instrument for each type of cereal and for varying environments as this method requires a large database in order for the data to be considered reliable (Akkaya et al, 2016).…”

Section: Near-infrared (Nir) Spectroscopymentioning

confidence: 99%

“…Additionally, the precision and accuracy of this method should be frequently checked to avoid deviations of NIR spectroscopy results from the results of accurate chemical and biochemical tests, such as Dumas and HPLC methods (Poji c, Mastilovi c, & Majcen, 2012). Finally, various factors other than the quality parameters of the sample, for example, sample shape, moisture, particle size, color, texture, and density, affect the NIR spectra, which may add some complications to the interpretation of the NIR data (Peiris, Dong, Bockus, & Dowell, 2016;Weir, Omielan, Lee, & Rajcan, 2005;Williams, 2007). For example, NIR calibration for deoxynivalenol (DON) levels in wheat kernel was shown to be significantly affected by moisture content (MC) leading to over-and underestimation of DON levels at decreased and increased MC, respectively (Peiris et al, 2016).…”

Section: Near-infrared (Nir) Spectroscopymentioning

confidence: 99%

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Advancing the science of wheat quality evaluation using nuclear magnetic resonance (NMR) and ultrasound‐based techniques

et al. 2018

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Background and objectives The need for cost‐effective, fast, and reliable techniques to evaluate wheat protein content and quality has led to the modification of available methods as well as the development of new methods. This review provides information on two emerging methods, that is, low‐field nuclear magnetic resonance (LF‐NMR) and low‐intensity ultrasound, with potential for evaluating wheat protein content and quality. Findings New techniques with additional capabilities have been developed over the years to provide wheat researchers with information that could not be acquired by the use of conventional methods. LF‐NMR and low‐intensity ultrasound are two emerging techniques in the world of cereal science whose speed, ease of operation, and reliability could promote their applications to wheat screening research studies. This review of these two potential wheat protein quality evaluation methods highlights the necessity of developing such cutting‐edge tools which are advantageous over the conventional tools. Conclusions Easy‐to‐use LF‐NMR and low‐intensity ultrasound seem to have the potential to provide nondestructive, fast evaluation methods that are superior to the currently used techniques in terms of accuracy and cost, respectively. However, further investigations to achieve complete development of such methods are still required. Significance and novelty The potential of LF‐NMR and low‐intensity ultrasound as fast, accurate, and cost‐effective wheat screening tools was confirmed through critical discussions and comparisons of a variety of most widely used wheat quality evaluation techniques.

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“…The moisture content of grain samples affects the accuracy of DON estimation by NIR spectroscopy; the estimated DON levels are over-and underestimated at lower and higher equilibrium moisture contents, respectively (Peiris et al 2016). Additionally, grain samples may differ in moisture level at the time of analysis.…”

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Estimation of the Deoxynivalenol and Moisture Contents of Bulk Wheat Grain Samples by FT‐NIR Spectroscopy

et al. 2017

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Cereal Chem. 94(4):677-682 Deoxynivalenol (DON) levels in harvested grain samples are used to evaluate the Fusarium head blight (FHB) resistance of wheat cultivars and breeding lines. Fourier transform near-infrared (FT-NIR) calibrations were developed to estimate the DON level and moisture content (MC) of bulk wheat grain samples harvested from FHB screening trials. Grains in a rotating glass petri dish were scanned in the 10,000-4,000 cm _ 1(1,000-2,500 nm) spectral range using a Perkin Elmer Spectrum 400 FT-IR/FT-NIR spectrometer. The DON calibration predicted the DON levels in test samples with R 2 = 0.62 and root mean square error of prediction (RMSEP) = 8.01 ppm. When 5-25 ppm of DON was used as the cut-off to classify samples into low-and high-DON groups, 60.8-82.3% of the low-DON samples were correctly classified, whereas the classification accuracy of the high-DON group was 82.3-94.0%. The MC calibration predicted the MC in grain samples with R 2 = 0.98 and RMSEP = 0.19%. Therefore, these FT-NIR calibrations can be used to rapidly prescreen wheat lines to identify low-DON lines for further evaluation using standard laboratory methods, thereby reducing the time and costs of analyzing samples from FHB screening trials. † Corresponding

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Moisture Effects on the Prediction Performance of a Single‐Kernel Near‐Infrared Deoxynivalenol Calibration

Cited by 8 publications

References 48 publications

Moisture effects on robustness of sorghum grain protein near‐infrared spectroscopy calibration

Moisture effects on robustness of sorghum grain protein near‐infrared spectroscopy calibration

Advancing the science of wheat quality evaluation using nuclear magnetic resonance (NMR) and ultrasound‐based techniques

Estimation of the Deoxynivalenol and Moisture Contents of Bulk Wheat Grain Samples by FT‐NIR Spectroscopy

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