## 4 Electronic Nose, Electronic Tongue, and Electronic Eye in Sensory Food Sciences
### Electronic-Nose  
Until the 1990s, sensory analysis was commonly used to determine the olfactory characteristics of food products. However, it was often inconsistent, imprecise, and highly subjective, as human senses, including smell, are influenced by physical, mental, and external factors. Sensory analysis is typically combined with analytical tools such as gas chromatography to improve accuracy. While effective, these methods require significant time, expertise, and expensive equipment [64]. The E-nose has emerged as a widely used alternative to address the food industry's need for faster and more efficient quality assessment. The E-nose detects volatile compounds in food matrices and converts them into electronic signals using a sensor array. A key advantage of the E-nose technology is its ability to leverage machine learning for data processing and interpretation. Advanced algorithms enhance their predictive capabilities, including support vector machines, artificial neural networks, and deep learning. Boosting techniques, such as AdaBoost, gradient boosting, XGBoost, and LightGBM, improve predictive accuracy by combining weak models into a strong learner. Compared to traditional sensory analysis, E-nose technology offers greater accuracy, reliability, and efficiency while being less affected by environmental factors. Additionally, unlike analytical methods such as gas chromatography, it is easier to use and interpret and requires less specialized expertise [65; 66]. The E-nose consists of three main components: a sample delivery system, an array of gas or chemical sensors, and a pattern recognition system. This technology is widely used to detect simple and complex volatile organic compounds, making it an invaluable tool in the food industry [65]. Since the introduction of the first E-nose in 1982, which incorporated chemical sensors and pattern recognition, significant technological advances have been made [67]. The E-nose effectively complements human sensory panels by providing fast, reliable, and consistent results [68]. Like the human nose, the E-nose operates through sensors that detect odors and generate specific patterns based on the type of odor detected. These patterns are analyzed and trained to discriminate between different odors, enabling the system to recognize new patterns tailored to the needs of the food industry [69]. [PERSON] et al. [70] evaluated cheese aroma using a gas chromatography-based E-nose and compared its performance with sensory analysis. Their results demonstrated that the E-nose is well suited for monitoring cheese aroma quality and successfully identified significant differences between smoked cheese treatments. E-nose systems have been applied in the bakery industry to analyze volatile compounds in flours, ingredients, processing steps, and storage conditions [71]. Many studies focus on detecting volatile compounds to identify spoilage and fungal growth. For example, a non-destructive E-nose was developed to monitor bread spoilage by analyzing changes in volatile compounds and fungal growth during storage [72]. [PERSON] and [PERSON] [73] developed a low-cost, wireless E-nose equipped with nine gas sensors to evaluate volatile compounds in beer. Similarly, [PERSON] et al. [74] applied this technology to assess smoke-tainted red wine treatments. They also developed an artificial neural network (ANN) model to predict the acceptability of sensory attributes, such as appearance, overall aroma, smoke aroma, bitterness, sweetness, acidity, warming mouthfeel, overall liking, perceived quality, and smoke aroma intensity. Additionally, the model assessed emotions using a 0-100 FaceScale. [PERSON] et al. [75] used an E-nose with ten different sensors to monitor changes in the aroma of banana juice over time. Similarly, [PERSON] et al. [76] utilized an E-nose to complement sensory analysis in evaluating the aroma of sourdough.