NIR and PLS discriminant analysis for predicting the processability of malt during lautering

D. Krause, C. Holtz, M. Gastl, M. A. Hussein, T. Becker

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

This work is focused on a new strategy for quality analysis of brewing malt using near infrared (NIR) spectra taken from malt kernels in reflection as fingerprint to classify directly to processability of malt. One part of the study deals with calibrating a partial least squares discriminant analysis (PLS-DA) model with NIR spectra classifying malt into the three different classes resulting in a five-component model. Therefore, suitable pre-processing algorithms for spectra were tested. The target for calibration is given by an expert opinion on lautering runs (filtration step in brewing). The accuracy achieved using pilot plant data in relation to the expert classification “good”, “normal” and “bad” was 90.6 and 92.7 % in validation and calibration, respectively. The second part of the study is presenting the transfer of these analytical tools to industrial scale. This was established via adjustment to corresponding system conditions. The accuracy achieved using similar algorithms as mentioned before was 93.6 and 76.6 % in calibration and validation, respectively. Independent from this, two numerical possibilities were established for automatic process evaluation classifying the different processes in three categories (good, normal, bad): the first is calculating the residual standard deviation of a process based on multivariate statistical process control and the second is discretizing each process individually based on its single online trends. Both methods were compared to the expert opinion coinciding with 84 and 85 %, respectively.

Original languageEnglish
Pages (from-to)831-846
Number of pages16
JournalEuropean Food Research and Technology
Volume240
Issue number4
DOIs
StatePublished - Apr 2015

Keywords

  • Malt quality
  • Multivariate statistical process control
  • Near infrared spectroscopy
  • PLS discriminant analysis

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