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 ca
librations were always lower than those of generic global calibrations and
similar to those of specific global calibrations. However, LOCAL prediction
s were further improved by using different settings for each constituent. T
he analysis of the samples selected by LOCAL showed that for heterogeneous
products such as total mixed rations and corn silage, LOCAL optimised predi
ctions by choosing samples from different products. LOCAL calibration was t
hen 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 produ
cts using multi-product databases. Routine analysis can be simplified by us
ing LOCAL calibration combined with large databases. in addition, LOCAL can
provide accurate predictions of spectra from remote standardised instrumen
t without the operator identifying the sample.