## 4 Electronic Nose, Electronic Tongue, and Electronic Eye in Sensory Food Sciences
### Electronic Eye  
Color is a critical factor in food quality, significantly influencing perceptions of freshness, desirability, ripeness, and safety and, ultimately, influencing consumer choices [107]. It results from the interaction of light with retinal receptors, which transmit signals to the brain via the optic nerve. However, perception is also affected by lighting conditions [108]. The E-eye is a low-cost, portable technology designed for food quality assessment. It differentiates colors by detecting wavelengths and utilizes various color spaces, including HSI (hue, saturation, intensity), HSV (hue, saturation, value), HSL (hue, saturation, lightness), and HSB (hue, saturation, brightness) [79]. Techniques such as colorimetry, spectrophotometry, and computer vision provide precise color analysis, making the E-eye suitable for large-scale applications [109]. An E-eye is a computer vision system that employs an image sensor to convert optical images into digital data, eliminating the subjectivity of human vision [110]. Combined with machine learning, it can process complex sensory information, develop predictive models based on training data, and establish relationships between input and output data--providing valuable insights for manufacturers [110]. This technology is widely used in food quality assessment, offering rapid, precise, and non-invasive evaluation of key attributes such as shape, size, color, and texture [111]. This innovative system integrates multiple disciplines, including mechanics, optics, electromagnetic detection, colorimetry, spectrophotometry, digital video, and image processing. These capabilities make it essential for tracking changes in visual quality during production [112]. The E-eye ensures accurate and consistent monitoring, as appearance and color influence consumers' perceptions of quality. Its advantages include objective and reproducible measurements, data storage for traceability, and the ability to analyze products without altering their texture or consistency. Furthermore, it complements sensory panel evaluations by providing detailed analytical data, thereby increasing the depth of quality assessment [113]. E-eyes are widely applied in the food industry to evaluate sensory attributes, from monitoring ripening and harvesting stages to assessing raw materials and processed foods [111]. The color of liquid foods can be difficult to assess visually, yet it plays a crucial role in consumer preference. Studies have demonstrated a strong correlation between beverage color and consumer perception. For instance, [PERSON] et al. [114] investigated the color of orange juice using trained panelists and instrumental measurements and found a significant relationship between sensory evaluations and instrumental data. Similarly, visual color estimates correlated with instrumental measurements for red wine, although discrepancies in hue perception were observed [115]. In the honey industry, color is an important quality indicator associated with the floral origin and chemical composition. [PERSON] et al. [116] used machine vision to classify honey by floral origin and predict chemical parameters but found it insufficient for comprehensive characterization. Integrating multiple technologies, such as E-tongue and E-nose with the E-eye, could improve honey characterization, offering a more reliable and robust assessment method. [PERSON] et al. [117] investigated using an E-eye to predict ripening parameters. This process typically involves capturing images with a flatbed scanner and analyzing color levels using the RGB scale. A satisfactory calibration model was developed to correlate image color data with the phenolic composition of grapes, allowing real-time monitoring of the ripening process [118]. Similarly, [PERSON] et al. [113] investigated the physical properties and sensory analysis of citrus oil-based nanoemulsions using a digital imaging system. Their study demonstrated how the electronic eye technology effectively detects color variations in emulsion samples.