LOCAL Prediction with near Infrared Multi-Product Databases

This study evaluated the use of an algorithm (LOCAL) for local calibration using multi-product databases. Four different databases were used: forages (hay, corn silage, haylage, small grain silage and total mixed ration; n=2924), grain (barley, corn, oats and wheat; n=1464), meat (meat and bone meal, fish meal and poultry meal; n=693) and feed (bakery products, mixed feed, poultry feed and soya products; n=1518). One-tenth of the samples were selected for validation from each database. Predictions of validation samples using generic and specific global calibrations were compared to the predictions generated by LOCAL. Standard errors of prediction for LOCAL calibrations were always lower than those of generic global calibrations and similar to those of specific global calibrations. However, LOCAL predictions were further improved by using different settings for each constituent. The analysis of the samples selected by LOCAL showed that for heterogeneous products such as total mixed rations and corn silage, LOCAL optimised predictions by choosing samples from different products. LOCAL calibration was then used with one database (n=6599) comprising all the samples. Standard errors of prediction were similar to those obtained with the four different databases. LOCAL can accurately predict the composition of different products using multi-product databases. Routine analysis can be simplified by using LOCAL calibration combined with large databases. In addition, LOCAL can provide accurate predictions of spectra from remote standardised instrument without the operator identifying the sample.