## 4 Electronic Nose, Electronic Tongue, and Electronic Eye in Sensory Food Sciences
### Electronic Tongue
E-tongue systems have been widely used for food analysis, offering a wide range of applications by minimizing interference from external substances and effectively differentiating highly complex samples [60]. These systems employ chemometric methods and artificial intelligence to achieve key objectives, such as sample discrimination, identification, and quantification [58; 93]. Applications of E-tongue systems include various areas, such as quality control [94], food identification [95; 96; 97], taste evaluation [98; 99], and process monitoring [100; 101]. [PERSON] et al. [102] used a potentiometric E-tongue to detect spicy compounds in solutions, providing a potential method for evaluating spicy foods while reducing sensory panelist fatigue. [PERSON] and [PERSON] [103] successfully developed a paper-based potentiometric E-tongue capable of discriminating between beer and wine samples. Similarly, [PERSON] et al. [104] developed a paper-based disposable E-tongue connected to an impedance analyzer for chemical compound detection. This system was used to identify sugar types and predict the brand of apple juice samples using principal component analysis. Although paper-based E-tongues have demonstrated effectiveness and accuracy in analyzing various products, no commercial applications have been developed. A significant challenge in their implementation is ensuring consistent electrical responses from sensing units, which requires recalibration each time a unit is replaced. However, studies have shown that an E-tongue maintained stability over 90 samples without recalibration. Storage stability was assessed over four weeks by comparing each electrode's standard deviations (N = 3) using single-factor ANOVA, showing no significant differences between measurements and high reproducibility [103]. Data reproducibility was unsatisfactory in triplicate measurements using three independent E-tongues in complex samples like apple juice. Achieving precise fabrication and functionalization of twelve consistently sensitive units for triplicate measurements remains a significant challenge [104]. E-tongues are widely used in the wine industry to classify alcohols, detect adulterations, classify wines by geographic origin, and differentiate wines based on specific fingerprints [105]. Voltammetric E-tongues have also been applied to detect organ oil adulteration, differentiate honey by flower type, assess pasteurized milk quality, and analyze spring water quality [106]. Integrating multimodal sensor data requires combining hybrid sensor technologies, machine learning calibration, data fusion strategies, and AI-driven predictive modeling. These advances effectively address challenges such as sensor drift, cross-reactivity, and the complexity of real-world samples, resulting in a more precise and reliable sensory analysis platform. In this context, hybrid E-tongue systems that incorporate potentiometric, voltammetry, and enzymatic biosensors enhance the accuracy and reliability of sensory analysis. As mentioned above, these systems improve selectivity using chemometric techniques, e.g., PCA and LDA, to differentiate complex sample matrices and mitigate cross-reactivity issues [80; 86]. By processing data from multiple sensor modalities, a hybrid E-tongue provides a more comprehensive representation of taste-related properties, improving classifiable accuracy in food and beverage applications. To address sensor drift, real-time calibration models based on machine learning can detect and compensate for sensor response drift over time. Adaptive machine learning techniques such as support vector machines (SVMs) and artificial neural networks (ANNs) allow for continuous recalibration, ensuring long-term sensor stability with minimal human intervention [44]. These models are trained on large datasets that include various environmental conditions and product-specific attributes, making the sensors more robust.