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

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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.

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Abbreviations

µ:

Centroid of class S

A:

Number of components

ABOF:

Angle-based outlier factor

B :

Matrix of regression parameters

b 0 :

Vector of intercepts

d():

Distance

e i :

Vector of residuals

FAN:

Free amino nitrogen

hi :

Leverage

MLR:

Multilinear regression

MSC:

Multiplicative scatter correction

MSPC:

Multivariate statistical process control

n:

Number of sampling points

NAS:

Net analyte signals

NIR:

Near infrared

p:

Polynomial degree

p, P :

Vector/matrix of X-loadings

PC:

Principal components

PCA:

Principal component analysis

PLS-DA:

Partial least squares discriminant analysis

PLS(R):

Partial least squares (regression)

q, Q :

Vector/matrix of Y-loadings

RMSE:

Root mean squared error

RSD:

Residual standard deviation

S:

Classes

s P , b, c :

Vectors representing data point in multidimensional space

SSE:

Sum of squares

STA:

Single trend analysis

sVAST:

Supervised variable stability scaling

SVD:

Singular value decomposition

S X , S Y :

Diagonal matrix with standard deviation

t, T :

Vector/matrix of X-scores

VAST:

Variable stability scaling

VIP:

Variable importance in the projection

Vt :

Ratio of variance

w :

Vector of weight factors

w, W :

Weighted loading vector/matrix

x :

Vector of input values/NIR spectrum

X :

Matrix of input data

X T :

Matrix of restructured PLS scores

Y :

Matrix of targets

σ:

Variance

τ :

Vector of scaled time frames

^:

Predicted

¯:

Mean

a, i, j, k, n, p:

Counter

s:

Scaled

turb:

Turbidity

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Acknowledgments

This work was partially funded by the “Bundesministerium fuer Wirtschaft und Technologie” (via AiF) through the research project AIF 16529 N/1 in cooperation with “Wissenschaftsförderung der Deutschen Brauwirtschaft e.V”.

Conflict of interest

None.

Compliance with Ethics Requirements

This article does not contain any studies with human or animal subjects.

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Authors and Affiliations

  1. Research Group Bio-Process Analysis Technology, Center of Life and Food Sciences Weihenstephan, Technische Universität München, Weihenstephaner Steig 20, 85354, Freising, Germany

    D. Krause, M. A. Hussein & T. Becker

  2. Research Group Raw Materials, Center of Life and Food Sciences Weihenstephan, Technische Universität München, Weihenstephaner Steig 20, 85354, Freising, Germany

    C. Holtz & M. Gastl

Authors
  1. D. Krause
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  2. C. Holtz
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  3. M. Gastl
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  4. M. A. Hussein
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  5. T. Becker
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Corresponding author

Correspondence to D. Krause.

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Krause, D., Holtz, C., Gastl, M. et al. NIR and PLS discriminant analysis for predicting the processability of malt during lautering. Eur Food Res Technol 240, 831–846 (2015). https://doi.org/10.1007/s00217-014-2389-3

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  • DOI: https://doi.org/10.1007/s00217-014-2389-3

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