Protein content prediction in single wheat kernels using hyperspectral imaging.
Protein content prediction in single wheat kernels using hyperspectral imaging.
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DOI：
10.1016/j.foodchem.2017.07.048
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发表时间：
2018-02-01
期刊：
影响因子：
8.8
通讯作者：
Fisk ID
中科院分区：
文献类型：
--
作者：
Caporaso N;Whitworth MB;Fisk ID
将HSI应用于小麦籽粒蛋白质含量的无损预测。从约200个批次中取出超过2100个小麦粒并单独分析。PLS回归模型的R2 = 0.82，预测误差小于0.93%。蛋白质分布范围较宽（6-20%），并通过应用校准进行可视化。HgGcTe的性能上级通过模拟InGaAs传感器构建的性能。高光谱成像（HSI）结合了近红外（NIR）光谱和数字成像，以提供有关物体化学性质及其空间分布的信息。蛋白质含量是小麦最重要的品质因子之一。已知其根据栽培品种、农艺和气候条件而变化很大。然而，很少有信息是已知的单个籽粒蛋白质的变化在批次内。本工作的目的是以单个籽粒为基础测量全麦籽粒中蛋白质含量的分布，并应用HSI来预测该分布。来自2013年和2014年收获的小麦样品来自英国米莱尔和小麦育种者，并通过HSI和大仲马燃烧法分析单个籽粒的总蛋白质含量。使用推扫法在反射模式下在光谱区域980-2500 nm中应用HSI。利用单粒籽粒光谱建立偏最小二乘（PLS）回归模型，预测整粒籽粒的蛋白质含量。蛋白质含量范围为6.2%至19.8%（“原样”基础），硬小麦的值明显更高。使用3250份用于校准的样品和868份用于外部验证的样品的决定系数（R2）和均方根误差（RMSE）评价校准模型的性能。单个籽粒蛋白质含量的校准性能为R2为0.82和0.79，校准和验证数据集的RMSE为0.86和0.94%，能够量化籽粒之间的蛋白质分布，甚至在同一籽粒内可视化。单个内核测量的性能比通常获得的散装样品差，但对于某些特定的应用是可以接受的。通过分离硬小麦和软小麦或放置在类似方向上的籽粒来建立单独的校准的使用并没有大大提高预测能力。我们模拟了低成本InGaAs探测器（1000-1700 nm）的使用，并报告说，在有限的光谱范围内使用拟议的HgCdTe探测器给出了较低的预测误差（RMSEC = 0.86%对1.06%，分别为HgCdTe和InGaAs），并增加了R2值（Rc 2 = 0.82对0.73）。
HSI was applied for non-destructive prediction of total protein content in wheat kernels. Above 2100 wheat kernels were taken from ~200 batches and individually analysed. PLS regression models had R2 = 0.82 and prediction error lower than 0.93%. Protein distribution had wide range (6–20%) and was visualised by applying the calibration. The performance of HgGcTe was superior to the one built by simulating InGaAs sensors. Hyperspectral imaging (HSI) combines Near-infrared (NIR) spectroscopy and digital imaging to give information about the chemical properties of objects and their spatial distribution. Protein content is one of the most important quality factors in wheat. It is known to vary widely depending on the cultivar, agronomic and climatic conditions. However, little information is known about single kernel protein variation within batches. The aim of the present work was to measure the distribution of protein content in whole wheat kernels on a single kernel basis, and to apply HSI to predict this distribution. Wheat samples from 2013 and 2014 harvests were sourced from UK millers and wheat breeders, and individual kernels were analysed by HSI and by the Dumas combustion method for total protein content. HSI was applied in the spectral region 980–2500 nm in reflectance mode using the push-broom approach. Single kernel spectra were used to develop partial least squares (PLS) regression models for protein prediction of intact single grains. The protein content ranged from 6.2 to 19.8% (“as-is” basis), with significantly higher values for hard wheats. The performance of the calibration model was evaluated using the coefficient of determination (R2) and the root mean square error (RMSE) from 3250 samples used for calibration and 868 used for external validation. The calibration performance for single kernel protein content was R2 of 0.82 and 0.79, and RMSE of 0.86 and 0.94% for the calibration and validation dataset, enabling quantification of the protein distribution between kernels and even visualisation within the same kernel. The performance of the single kernel measurement was poorer than that typically obtained for bulk samples, but is acceptable for some specific applications. The use of separate calibrations built by separating hard and soft wheat, or on kernels placed on similar orientation did not greatly improve the prediction ability. We simulated the use of the lower cost InGaAs detector (1000–1700 nm), and reported that the use of proposed HgCdTe detectors over a restricted spectral range gave a lower prediction error (RMSEC = 0.86% vs 1.06%, for HgCdTe and InGaAs, respectively), and increased R2 value (Rc2 = 0.82 vs 0.73).
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影响因子：
6.1
作者：
Agelet, Lidia Esteve;Armstrong, Paul R.;Hurburgh, Charles R.
通讯作者：
Hurburgh, Charles R.
影响因子：
3.8
作者：
Delwiche, SR
通讯作者：
Delwiche, SR
影响因子：
4.1
作者：
OSBORNE, BG
通讯作者：
OSBORNE, BG
影响因子：
2.4
作者：
Nielsen, JP;Pedersen, DK;Munck, L
通讯作者：
Munck, L
DOI：
10.1016/s0169-7439(98)00167-1
发表时间：
1998-12-14
影响因子：
3.9
作者：
Martens, HA;Dardenne, P
通讯作者：
Dardenne, P