A new graphically oriented local modeling procedure called interval partial least-squares (i PLS) is presented for use on spectral data. The i PLS method is compared to full-spectrum partial least-squares and the variable selection methods principal variables (PV), forward stepwise selection (FSS), and recursively weighted regression (RWR). The methods are tested on a near-infrared (NIR) spectral data set recorded on 60 beer samples correlated to original extract concentration. The error of the full-spectrum correlation model between NIR and original extract concentration was reduced by a factor of 4 with the use of i PLS (r=0.998, and root mean square error of prediction equal to 0.17% plato), and the graphic output contributed to the interpretation of the chemical system under observation. The other methods tested gave a comparable reduction in the prediction error but suffered from the interpretation advantage of the graphic interface. The intervals chosen by i PLS cover both the variables found by FSS and all possible combinations as well as the variables found by PV and RWR, and i PLS is still able to utilize the first-order advantage. Index Headings: Interval PLS; Variable selection; NIR, Principal variables; Forward stepwise selection; Recursively weighted regression; Beer; Extract.

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