TY - JOUR

T1 - Interval ANOVA simultaneous component analysis (i-ASCA) applied to spectroscopic data to study the effect of fundamental fermentation variables in beer fermentation metabolites

AU - Grassi, Silvia

AU - Lyndgaard, Christian Bøge

AU - Rasmussen, Morten Arendt

AU - Amigo Rubio, Jose Manuel

PY - 2017

Y1 - 2017

N2 - This study explores the effect of different settings on beer fermentation process applying an interval-based version of ASCA on FT-IR data. Three main factors (yeast type, temperature, fermentation time) are included in the experimental design, being high sources of variation in brewing and strictly interdependent; thus, difficult to be studied through a univariate approach. The three-factor full factorial design leads to a spectral multi-set data, with a total of 12 independent fermentations, which is explored combining ASCA and an interval adaptation of ASCA (interval-ASCA or i-ASCA). The ASCA models calculated on two separate regions (2900–2250 cm−1 and 1500–980 cm−1) shows differences for average time levels and the interaction between yeast types and time linked to carbon dioxide, maltose consumption and ethanol production, respectively. To better investigate the punctual influence of the studied factors on the so-called IR fingerprint region, permutation testing of ASCA in variable intervals is investigated. The analysis highlights the significant effect not only of the fermentation in all intervals considered, but also the role of other factors, such as time × yeast, yeast and temperature, in smaller variable regions. The proposed approach demonstrates how interval-ASCA on FT-IR data, isolating the variation in the data according to the experimental design used, allows a rapid and accurate test for parameter control in beer manufacturing.

AB - This study explores the effect of different settings on beer fermentation process applying an interval-based version of ASCA on FT-IR data. Three main factors (yeast type, temperature, fermentation time) are included in the experimental design, being high sources of variation in brewing and strictly interdependent; thus, difficult to be studied through a univariate approach. The three-factor full factorial design leads to a spectral multi-set data, with a total of 12 independent fermentations, which is explored combining ASCA and an interval adaptation of ASCA (interval-ASCA or i-ASCA). The ASCA models calculated on two separate regions (2900–2250 cm−1 and 1500–980 cm−1) shows differences for average time levels and the interaction between yeast types and time linked to carbon dioxide, maltose consumption and ethanol production, respectively. To better investigate the punctual influence of the studied factors on the so-called IR fingerprint region, permutation testing of ASCA in variable intervals is investigated. The analysis highlights the significant effect not only of the fermentation in all intervals considered, but also the role of other factors, such as time × yeast, yeast and temperature, in smaller variable regions. The proposed approach demonstrates how interval-ASCA on FT-IR data, isolating the variation in the data according to the experimental design used, allows a rapid and accurate test for parameter control in beer manufacturing.

KW - Beer fermentation

KW - Chemometrics

KW - FT-IR

KW - Interval ANOVA simultaneous component analysis

KW - Interval-ASCA

U2 - 10.1016/j.chemolab.2017.02.010

DO - 10.1016/j.chemolab.2017.02.010

M3 - Journal article

AN - SCOPUS:85014474046

VL - 163

SP - 86

EP - 93

JO - Chemometrics and Intelligent Laboratory Systems

JF - Chemometrics and Intelligent Laboratory Systems

SN - 0169-7439

ER -