Combination of machine learning and intelligent sensors in real-time quality control of alcoholic beverages

Abstract

Machine learning (ML) featured on its ability of learning and extracting features from a large set of data and automatically building statistical models. Through cooperation with intelligent sensors, which is designed to imitate human organs to analyze the sensory characteristics of foods, ML-based intelligent sensory systems such as electronic nose (E-nose) and electronic tongue (E-tongue) are developed for sensing applications in food industry. Consumption of alcohol beverages keep growing worldwide in recent years and fraudulent activities are stimulated due to the high price of alcoholic drinks, which motivates the application of intelligent sensory technology with high efficiency and accuracy for real-time quality control. Thus, this paper firstly summarizes the novel intelligent sensors that is suitable for sensory evaluation and the advanced ML algorithms used to create intelligent systems. Then the paper describes the mechanism of commercial ML-enabled intelligent devices and summarizes their practical sensing applications on the real-time quality control of a variety of alcoholic beverages, in term of detection of frauds and adulterations, aroma analysis, monitoring of the production process, and correlation with human sensory perception. Finally, the potential applications and future opportunities of ML-enabled intelligent sensor systems in the alcohol industry are discussed.

Keywords:
machine learning; intelligent sensory; electronic nose; electronic tongue; alcoholic beverages; quality control