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In-line estimation of falling number using near-infrared diffuse reflectance spectroscopy on a combine harvester

Risius, Hilke, Hahn, Jürgen, Huth, Markus, Tölle, Rainer, Korte, Hubert
Precision agriculture 2015 v.16 no.3 pp. 261-274
Triticum aestivum, alpha-amylase, combine harvesters, falling number, field experimentation, grain protein, grain quality, growers, marketing, models, near-infrared spectroscopy, normal values, prediction, principal component analysis, reflectance, reflectance spectroscopy, water content, wheat
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 (R² = 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 T²-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.