Key research themes
1. How do artificial sensory technologies (e-nose, e-tongue, and computer vision) emulate human sensory perception for objective food quality and sensory analysis?
This research area focuses on developing and applying artificial sensor systems—such as electronic noses, electronic tongues, and computer vision systems—that mimic human olfactory, gustatory, and visual senses to provide rapid, objective, and reproducible evaluations of food sensory characteristics. These technologies aim to overcome limitations of traditional sensory panels, including subjectivity, resource intensity, and low throughput, by delivering standardized measurements related to odor, taste, color, texture, and other quality attributes. Their integration and fusion also promise enhanced discrimination power and robustness, supporting food quality control, authenticity assessment, and product development.
2. What are comparative methodologies and innovations in consumer-based sensory characterization approaches for food products?
This theme investigates consumer-driven sensory evaluation methodologies, including holistic (projective mapping, polarized sensory positioning) and attribute-based approaches (check-all-that-apply, CATA), as alternatives to traditional descriptive analysis by trained panels. Multiple consumer-based techniques are compared in terms of discrimination power, repeatability, dimensionality of sensory space, and similarity to trained panel results. The focus is on methodological applicability, reliability, and integration of consumer perception in sensory profiling, enabling cost-effective, scalable, and ecologically valid product evaluations aligned with market preferences.
3. How can data fusion and integration of human sensory panels with electronic sensing enhance the discrimination and classification of complex food matrices such as monovarietal olive oils?
This research theme explores advanced multivariate data fusion techniques that combine human sensory evaluations with electronic tongue measurements to improve discrimination and classification of complex food products, exemplified by monovarietal extra-virgin olive oils. Through the application of chemometric models (LDA, PLS-DA) and variable selection algorithms, the integrated sensory data demonstrate superior predictive ability over individual sensory or instrumental methods. This synergy leverages complementary information from subjective and objective sensory sources to mitigate variability, enhance classification accuracy, and support authentication and quality control in high-value food products.