The fusion of artificial instruments and machine learning within the realm of food science and technology has heralded a transformative era characterized by precision and depth in food analysis.1 The advent of artificial technologies like electronic noses, electronic eyes, and electronic tongues has fundamentally reshaped our approach to evaluating food samples, as underscored by their ability to capture intricate sensory attributes.2 These cutting-edge instruments, synergistically harnessed with machine learning capabilities, empower us to delve into the nuanced characteristics of foods, spanning the spectrum of aroma, color, and taste profiles.
The development of electronic noses has advanced beyond conventional sensory assessments and made it possible to identify the volatile substances that create odors. These tools analyze complicated odor patterns when combined with machine learning, improving our understanding of the sensory experiences associated with food. The machine learning-enhanced electronic eye quickly evaluates visual cues including color and texture, enabling fair evaluations of food quality. The electronic tongue, which mimics human taste perception, differentiates taste qualities more precisely and so expands the utility of the device for classification and predictions.