Development of NIR calibration models to assess year-to-year variation in total non-structural carbohydrates in grasses using PLSR

Near-infrared (NIR) spectroscopy was used in combination with chemometrics to quantify total non-structural carbohydrates (TNC) in grass samples in order to overcome year-to-year variation. A total of 1103 above-ground plant and root samples were collected from different field and pot experiments and with various experimental designs in the period from 2001 to 2005. A calibration model was developed using partial least squares regression (PLSR). The calibration model on a large data set spanning five years demonstrated that quantification of TNC using NIR spectroscopy was possible with an acceptable low root mean square of prediction error (RMSEP) of 1.30. However, for some years the estimated RMSEP was too optimistic as year-to-year variation for new years was not included in the model. Interval partial least squares (iPLS) regression was applied to remove non-relevant spectral regions and in order to improve model performance, but still it was not possible to avoid year-to-year variation using iPLS, however iPLS simplified the interpretation of the regression model. The best option was to expand the database with samples from a new year, to include these samples in the calibration model and to apply this on the remaining samples from the future year.

► NIR calibration model to quantify total non-structural carbohydrates in grasses. ► Assessment of year-to-year variation. ► Expand the calibration set with samples from a new year to reduce year-wise variation. ► Outlier warnings will aid in deciding when the model is robust for application.