Abstract
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.
Highlights
The evaluation of wine quality is highly significant due to their effects on wine classification and target marketing
We make a comparison among the models established by using ordinary least squares regression (OLSR), principal component regression (PCR), partial least squares regression (PLSR), and modified partial least squares regression (MPLSR), respectively, and select out the best model in order to improve the model accuracy [9]
We use three multivariate statistical methods based on soft measurement, including principal component regression (PCR), partial least squares regression (PLSR), and modified partial least squares regression (MPLSR) to find which is the best method according to the relationship between the red wine physicochemical indexes and the wine quality
Summary
The evaluation of wine quality is highly significant due to their effects on wine classification and target marketing. It is imperative to evaluate wine quality for both the food industry and wine science community [2, 3]. In view of the evaluation of wine quality, the traditional method is manual inspection or analysis of the chemical compounds. These methods cost huge financial inputs and time. The studies of wine quality evaluation are abundant internationally. The support vector machine (SVM) can build the wine quality classification model. In this paper, the multivariate methods based on soft measurement are the appropriate tools for chemical and physical measurements based on the wine physicochemical indexes
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