engleski

Prediction of Chemical Composition from Semi- natural Grassland by NIR Spectroscopy

Near-infrared (NIR) spectroscopy (1100 – 2500 nm) was used to predict the chemical composition from semi-natural grassland. Modified partial least square (MPLS), principal component regression (PCR) and partial least square (PLS) techniques were used. Standard errors of calibration (SEC) for crude proteins (CP) were 6.52, 4.87 and 6.94 for MPLS, PLS and PCR, while standard errors of cross validation (SECV) were 8.16, 6.13 and 7.56 respectively. SEC for organic matter (OM) were 7.69, 7.61 and 7.37 for MPLS, PLS and PCR, while SECV were 8.08, 8.27 and 7.57 respectively. Higher SEC and SECV were reported for neutral detergent fibre (NDF) and acid detergent fibre (ADF) content than reported for CP and OM content. Hyperspectral analysis by PLS resulted in the highest accuracy for the estimation of crude protein, organic matter and neutral detergent fibre and acid detergent fibre while MPLS was the best in predicting acid detergent fibre. The greates accuracy in this research was achieved for CP, than NDF, OM and finally ADF content. Prediction for NDF, OM and especially ADF content should be improved in the future by involving specific semi-grassland samples.

seminatural grassland; chemical composition; NIR spectroscopy; PLS; MPLS; PCR

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