Artificial Taste Perception of Tea Beverage Using Machine Learning

Nowadays, an artificial perception of beverages is in high demand as working hours increase, and people depend on readymade food and beverages. An assurance of quality, safety, and edibility of food and drink products is essential both for food producers and consumers. Assurance of unique beverage taste and consistent taste uniformity creates a distinct identity in the market. India is the second largest tea producer country in the world. Based on geographic location, the tea has a specific flavor and aroma. Artificial Intelligence (AI) can contribute to the feature identification and grading of tea species. The taste, aroma, and color are the three main attributes that can be sensed with the help of E-tongue, E-nose and E-vision, and can be processed further for automatic tea grading. The various potentiometric, voltammetric, Metal Oxide Semiconductor (MOS) and acoustic sensors are available with Principal Component Analysis (PCA). For tea analysis, various reviews are mentioned, like User Experience (UX evaluation, literature review, bibliometric review, and patent review. An in-depth analysis of artificial taste perception using machine learning has been described in the chapter. The topic covered almost all possible approaches to the artificial perception of tea with various interesting facts. 

Keywords: Artificial taste perception, Acoustic wave sensors, Color, Flavor and odor detection, MOS sensors, Machine learning algorithms, Tea.