Large calibration matrices are usually needed to cover all the variability of forage samples. The present work applies experimental design techniques to reduce the number of samples necessary to calibrate quality properties of pellets of forage. Near infrared spectra were recorded from the raw material which consisted of dehydrated alfalfa milled, pressed and packed in cylindrical pellets, 5 mm in diameter. Partial least squares calibration models for moisture and crude protein, built up from 771 samples, were used as reference. Full factorial, central composite and Box-Behnken designs, using the principal components of the raw spectra as design factors were tested for sample reduction. The effect of increasing the number of replicates for each design point was also studied. Comparisons between models have been made in terms of prediction accuracy, but also looking at the model stability by means of Martens' uncertainty test. Results obtained indicate that the prediction accuracy is similar in all assayed designs and similar to that of the reference model, although the stability of the models obtained using a reduced sample set is lower. Increasing the number of replicates of the design points increases stability.
There is an increasing need for using a set of near infrared (NIR) instruments in industrial activities. In this study, three methodologies of working with a set of NIR instruments are reviewed: the standardisation of the predicted values, the standardisation of the absorbances and the modelling of the instrument differences. Modelling the NIR differences is performed by including in the calibration/validation sets spectra recorded in any of the different instruments of the set without any pre-treatment or after transfer by orthogonal projection (TOP) pre-transformation. In the latter case, TOP calculations from different sets of products are assayed. The methods are briefly presented and discussed through the results of a set of four InfraAlyzer 2000 instruments, 19-filter spectrometers which are used in the quality control of fresh alfalfa. Although the optimal procedure depends on the particular conditions of the industrial work (number of products, number of properties, number of instruments, etc.), all the above methods have been found to be suitable for operating with a set of NIR instruments. The present results also indicate an independence of the TOP results from the product used to compute the TOP factors, at least for the present set of NIR instruments which all belong to the same model and company.
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