Selection of representative calibration sample sets for near-infrared reflectance spectroscopy to predict nitrogen concentration in grasses

The effect of using representative calibration sets with fewer samples was explored and discussed. The data set consisted of near-infrared reflectance (NIR) spectra of grass samples. The grass samples were taken from different years covering a wide range of species and cultivars. Partial least squares regression (PLSR), a chemometric method, has been applied on NIR spectroscopy data for the determination of the nitrogen (N) concentration in these grass samples. The sample selection method based on NIR spectral data proposed by Puchwein and the CADEX (computer aided design of experiments) algorithm were used and compared. Both Puchwein and CADEX methods provide a calibration set equally distributed in space, and both methods require a minimum prior of knowledge. The samples were also selected randomly using complete random, cultivar random (year fixed), year random (cultivar fixed) and interaction (cultivar × year fixed) random procedures to see the influence of different factors on sample selection. Puchwein's method performed best with lowest RMSEP followed by CADEX, interaction random, year random, cultivar random and complete random. Out of 118 samples of the complete calibration set, 19 samples were selected as minimal number of representative samples. RMSEP values obtained for subsets selected using Puchwein, CADEX and using full calibration set were 0.099% N, 0.109% N and 0.092% N respectively. The result indicated that the selection of representative calibration samples can effectively enhance the cost-effectiveness of NIR spectral analysis by reducing the number of analyzed samples in the calibration set by more than 80%, which substantially reduces the effort of laboratory analyses with no significant loss in prediction accuracy.

► The effect of using reduced representative calibration sets is described.
► Quantification of nitrogen concentration in grasses using NIR and PLSR.
► Puchwein, CADEX and random sample selection methods were compared.
► Reduction of calibration set by 80% with no significant loss in prediction accuracy.