An enhanced method for the calibration of Near Infra Red (NIR) reflectance spectra to wort fermentability is proposed using a signal pre-processing algorithm called orthogonal signal correction (OSC). Pre-processing NIR spectra prior to partial least squares Project to Latent Structures (PLS) regression modelling is becoming commonplace in multivariate calibration. A set of twenty wort samples subjected to a replicated 2 2 factorial design with a centre point and nine production samples were used to construct multivariate prediction models. The experimental design factors were the mash tun saccharification temperature and time used to purposely provide a sample set with significant leverage in the fermentability responses. Calibration PLS models for both wort apparent degree of fermentation (ADF) and final attenuation apparent extract (Final AE) values with and without OSC corrected spectra were compared demonstrating significant improvements in prediction capability with the prior (Q 2 = 0.90 versus Q 2 = 0.28). The OSC algorithm removed almost 60% of the variance in the NIR spectra, which was independent or orthogonal to the fermentability measures. By cleaning up the spectra, the standard errors of prediction (SEP) for ADF and Final AE were improved by 50 and 90%, respectively, illustrating not only the enhancement in calibration but also the aptness for process control applications. Various model validation tests, including an external validation example and random response permutation, verify the validity of the models using OSC. Furthermore, interpretation of the important wavelengths related to wort fermentability is provided and demonstrates that some key wavelengths are related to both carbohydrate overtones as well as nitrogen functional groups. The application of OSC prior to developing calibration models with NIR demonstrates promising results for brewers interested in real time control of wort fermentability.

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