No abstract

This study was implemented to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) technology to estimate chemical composition of dried press cake samples characterised by a wide range of parent materials. A total of 210 samples, derived from two studies on production of solid fuels from agricultural crops by application of the IFBB technology (Integrated production of solid fuels and biogas from biomass), were analysed to determine their chemical composition. A Foss XDSspectrometer was used to obtain near-infrared spectra (400-2,500 nm). Prediction equations, developed for chemical components, showed that NIRS technology could predict N, inorganic ash (ash), crude fiber (CF), ether extract (EE) and nitrogen free extracts (NFE) accurately (RSQ cal and SECV of 0.93 and 0.04 % DM, 0.89 and 0.48 % DM, 0.93 and 1.67 % DM, 0.87 and 0.28 % DM and 0.93 and 1.72 % DM, respectively). Mineral components could also be predicted with a moderate degree of accuracy using NIRS technology (RSQ cal and SECV of 0.85 and 0.10 % DM (K), 0.77 and 0.01 % DM (P) and 0.84 and 0.02 % DM (Cl), respectively), whereas calibration of gross energy (GE) did not succeed. Subsequent, external validation confirmed these results. Regression of mass flows with measured and NIRS-predicted values showed accurate results (RSQ 0.72-0.99) and promise an accelerated quality management in working biogas plants.

Sensor-based methods of analysis to assess dry matter yield and quality constituents of crops are time-and labour-saving, and can facilitate site-specific management. Nevertheless, standard nadir measurements of maize (Zea mays cv. Ambrosius), based on top-of-canopy reflectance, are difficult due to plant heights of more than three metres. This study was conducted to explore the potential of off-nadir field spectral measurements for the non-destructive prediction of dry matter yield (DM), metabolisable energy (ME) and crude protein (CP) in total biomass in a maize canopy. Plants were measured at five different heights (0-50, 50-100, 100-50, 150-200 and 200-250 cm above the soil) at three zenith view angles (60°, 75°and 90°, respectively). Modified partial least squares regression was used for analysis of the hyperspectral data (355-2300 nm and 620-1000 nm). Optimum combinations of angle and height as well as an optimum one-sensorstrategy were determined for DM yield, CP and ME in total biomass. Coefficients of determination for off-nadir measurements were compared to nadir measurements; the results showed improved prediction accuracies for DM yield and ME using off-nadir measurements, but not for CP for which nadir measurements were better.