## 3 The Role of Artificial Intelligence in Sensory Analysis and Food Innovation  
Integrating artificial intelligence (AI) into sensory analysis represents a transformative advance in technology and science, enabling a deeper understanding of sensory perception and consumer preferences. AI facilitates the analysis of large datasets, identifying complex patterns that traditional methods may miss. Machine learning algorithms make it possible to predict how individuals or groups of consumers perceive sensory attributes, such as taste, aroma, texture, and appearance. This predictive capability accelerates product development, reduces costs, and shortens iterative processes, making AI a highly efficient tool for the food industry [29]. AI enhances the prediction of sensory preferences by analyzing complex data. Researchers use machine learning models tailored to specific sensory datasets [30]. Different machine learning models are applied, depending on the sensory data being analyzed. For example, artificial neural networks [31; 32] predict taste and aroma profiles based on chemical composition, while convolutional neural networks [33; 34] assess food texture and visual appeal; recurrent neural networks [35] analyze time-series sensory data, such as changes during storage, fermentation, or aging; support vector machines [36; 37; 38] classify flavor profiles based on sensory attributes, and random forest [39] and decision trees [40; 41] identify key sensory attributes. Researchers can use these AI techniques to develop predictive models that enhance sensory evaluations. Traditionally, sensory evaluation relies on human panels, which can introduce subjectivity and variability. AI mitigates these challenges by standardizing sensory data interpretation through models trained on consumer preferences and sensory responses. For instance, neural networks can analyze chemical properties, flavor compounds, and consumer ratings to provide objective, quantified assessments of sensory appeal, leading to more consistent and reliable results [42]. However, high-quality training data are essential for the accuracy and reliability of AI-driven sensory research. A system providing descriptors (non-sensory measures) for predicting flavor or sensory properties must be appropriately defined. This means ensuring that its responses have a valid correlation with the parameters being predicted. Additionally, the algorithms must be precise enough to meet the system's requirements for the intended task. Furthermore, predictive models must undergo rigorous validation to confirm their accuracy beyond the training set and to prevent overfitting [43; 44]. Data preprocessing techniques like normalization, noise reduction, and feature selection refine the raw data to improve pattern recognition and minimize irrelevant variation [44; 45]. Diverse training samples and rigorous validation across consumer groups should be used to mitigate algorithmic bias. Critical analysis of the effectiveness of these models in different scenarios provides insight into the trade-offs between bias reduction and model performance [31; 44; 46; 47]. AI is critical to analyzing food composition, flavors, and chemical components. Researchers use it to study complex flavors, understand consumer preferences, and develop flavor molecule databases to identify volatile compounds [48; 49; 50]. Integrating targeted metabolomics with machine learning enhances flavor chemistry analysis, enabling precise sensory perception predictions in fruit flavors [51]. AI-driven taste profiling helps identify key flavor compounds, as demonstrated in studies of dry-aged beef, wet- and dry-aged mutton, and cheddar cheese at various stages of maturation [42; 52]. Machine learning further enhances flavor pattern recognition for food quality improvement, natural flavor development, and sensory perception prediction [49]. [PERSON] et al. [49] introduced a reinforcement learning framework for discovering new flavor compounds, demonstrating the potential of AI in flavor engineering.