Predictive ability of attenuated total reflectance (ATR) Fourier transform (FT) infrared (IR) spectroscopy to determine nutrient contents of sunflower meal (SFM) high in fibre and ash was investigated by testing the effect of potassium chloride (KCl) spiking. The partial least square regression (PLSR) models were generated using original IR spectrum, and its first and second derivate data followed by the normalisation, smoothing and multiplicative scatter correction in order to predict nutrient contents of spiked and non-spiked SFM samples. The results showed that the best model for the prediction was the one derived from the second derivate IR data with high degree of precision and accuracy (R2 = 0.99, r2 = 0.95, residual mean square error of cross-validation (RMSECV) = 0.66 for dry matter and 1.30 for crude ash). Furthermore, the precision and accuracy of all three models were improved by the KCl levels spiked at 27.5 and 57.0%, while no effects of further spiking with KCl was observed. In conclusion, the model generated from the second derivate IR data was highly recommended to predict the SFM nutrient contents, and the KCl dilution up to 57% improved the prediction accuracy and precision irrespective to the models used.
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