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Abstract and Applied Analysis
Volume 2014, Article ID 740754, 7 pages
Research Article

Multivariate Methods Based Soft Measurement for Wine Quality Evaluation

1College of Engineering, Bohai University, Liaoning 121013, China
2College of Mathematics and Physics, Bohai University, Liaoning 121013, China
3Department of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway

Received 22 April 2014; Accepted 13 May 2014; Published 3 June 2014

Academic Editor: Josep M. Rossell

Copyright © 2014 Shen Yin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Soft measurement is a new, developing, and promising industry technology and has been widely used in the industry nowadays. This technology plays a significant role especially in the case where some key variables are difficult to be measured by traditional measurement methods. In this paper, the quality of the wine is evaluated given the wine physicochemical indexes according to multivariate methods based soft measurement. The multivariate methods used in this paper include ordinary least squares regression (OLSR), principal component regression (PCR), partial least squares regression (PLSR), and modified partial least squares regression (MPLSR). By comparing the performance of the four methods, the MPLSR prediction model shows superior results than the others. In general, to determine the quality of the wine, experienced wine tasters are hired to taste the wine and make a decision. However, since the physicochemical indexes of wine can to some extent reflect the quality of wine, the multivariate statistical methods based soft measure can help the oenologist in wine evaluation.