Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.
Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality.
State Key Laboratory of Biological Fermentation Engineering of Beer (in preparation), Tsingtao Brewery Co Ltd, R&D Ctr, Qingdao 266101, Shandong, PR China; Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin University of Science & Technology, Tianjin 300457, PR China.
Jian-Jun
JJ
Dong
Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin University of Science & Technology, Tianjin 300457, PR China.
Qing-Liang
QL
Li
State Key Laboratory of Biological Fermentation Engineering of Beer (in preparation), Tsingtao Brewery Co Ltd, R&D Ctr, Qingdao 266101, Shandong, PR China.
Hua
H
Yin
Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin University of Science & Technology, Tianjin 300457, PR China; Key Laboratory of Systems Bioengineering, Ministry of Education, P.O. Box 6888, Tianjin University, Tianjin 300072, PR China. Electronic address: chzhong.tju@gmail.com.
Cheng
C
Zhong
State Key Laboratory of Biological Fermentation Engineering of Beer (in preparation), Tsingtao Brewery Co Ltd, R&D Ctr, Qingdao 266101, Shandong, PR China.
Jun-Guang
JG
Hao
Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin University of Science & Technology, Tianjin 300457, PR China.
Pan-Fei
PF
Yang
State Key Laboratory of Biological Fermentation Engineering of Beer (in preparation), Tsingtao Brewery Co Ltd, R&D Ctr, Qingdao 266101, Shandong, PR China.
Yu-Hong
YH
Tian
Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin University of Science & Technology, Tianjin 300457, PR China. Electronic address: jiashiru@tust.edu.cn.
Shi-Ru
SR
Jia
D000465:Algorithms; D001515:Beer / Q000032:analysis* / Q000592:standards; D000431:Ethanol / Q000032:analysis; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D060388:Support Vector Machine
D000431:Ethanol
Artificial neural networks; Beer quality; Beer sensory evaluation; Partial least squares; Support vector machine