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| Content Provider | Springer Nature Link |
|---|---|
| Author | Risius, Hilke Hahn, Jürgen Huth, Markus Tölle, Rainer Korte, Hubert |
| Copyright Year | 2014 |
| Abstract | Quality is an essential attribute of agricultural products and production processes. Wheat (Triticum aestivum L.) quality is primarily classified according to protein concentration and sub-classified depending on additional parameters, such as moisture content, sedimentation value and Hagberg falling number (HFN). Real-time sensing of grain protein concentration by means of near-infrared reflectance spectroscopy (NIRS) is an established method of assessing cereal grain quality during harvest. The objective of this study was to obtain NIRS calibration models for determining α-amylase activity of wheat and to identify changes of wheat quality. Performance characteristics were obtained during field trials in 2011 and 2012. HFN predictions correlated with reference measurements (R$^{2}$ = 0.70). The standard deviation of differences between the NIR-predicted and reference values denoted as standard error of prediction was 37 s. Processed data were classified using principal component analysis, the prediction range of HFN and Hotelling T$^{2}$-statistics. The average difference of NIR HFN estimation and HFN laboratory analysis was 34 s. The results obtained indicated that the use of near-infrared reflectance inline spectroscopy on combine harvesters can provide information for grain growers to optimize grain processing and marketing. |
| Starting Page | 261 |
| Ending Page | 274 |
| Page Count | 14 |
| File Format | |
| ISSN | 13852256 |
| Journal | Precision Agriculture |
| Volume Number | 16 |
| Issue Number | 3 |
| e-ISSN | 15731618 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2014-09-10 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Near-infrared reflectance spectroscopy On-combine sensing Winter wheat Hagberg falling number (HFN) α-amylase activity Grain quality assessment Agriculture Soil Science & Conservation Remote Sensing/Photogrammetry Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Atmospheric Sciences |
| Content Type | Text |
| Resource Type | Article |
| Subject | Agricultural and Biological Sciences |
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