Near-infrared reflectance models for the rapid prediction of quality of brewing raw materials

Calibration models for quickly and reliably predicting moisture content and total nitrogen, both “as is” and “dry matter” on malt, as well as moisture content and total lipids, both “as is” and “dry matter”, on maize by means of near-infrared (NIR) spectroscopy were developed. The FT-NIR spectra recorded on the finely ground cereals were correlated to the analytical data by means of the multivariate PLS algorithm. In particular, these models were developed on the raw materials, which are used by the main Italian brewing industries. Validation was carried out both by means of cross-validation and test set validation. Regression coefficients (R2) were higher than 97 for both malt and maize moisture content and higher than 85 and 88 for malt total nitrogen and maize total lipids, respectively. The RMSE values (both RMSECV and RMSEP) were lower than 0.1% m/m for both malt and maize moisture contents, whereas they ranged from 0.024 to 0.042% m/m for malt total nitrogen and from 0.042 to 0.055% m/m for maize total lipids. Repeatability was tested by taking into account more than one sample for each calibration and compared, when possible, to those of the standard methods. Repeatability (r95) ranged from 0.060 to 0.158% m/m and from 0.020 to 0.055% m/m for malt moisture and total nitrogen contents, respectively, and from 0.094 to 0.160% m/m and from 0.076 to 0.208% m/m for maize moisture and total lipids contents, respectively.