Advanced Chemometrics and Data Fusion for Food and Beverage Quality Assessment

The increasing complexity of food and beverage systems, together with the rapid development of advanced analytical platforms, is generating large and highly multidimensional datasets. Spectroscopic, chromatographic, sensory, imaging, omics, and sensor-based techniques can provide detailed information on food composition and properties. However, extracting meaningful, reliable, and actionable information from these data requires robust chemometric approaches and effective strategies for integrating complementary analytical outputs.Chemometrics plays a central role in transforming complex analytical signals into interpretable information for food classification, authentication, quality prediction, process monitoring, and the evaluation of sensory and nutritional characteristics. At the same time, data fusion strategies offer new opportunities to combine information obtained from different analytical platforms, allowing researchers to examine food and beverage quality from complementary perspectives rather than relying on a single analytical technique. Nevertheless, challenges remain regarding data preprocessing, model validation, variable selection, interpretability, reproducibility, and the transfer of predictive models from controlled laboratory conditions to real-world applications.This Research Topic aims to collect recent advances in chemometric modelling and data integration for the assessment of food and beverage quality. Particular attention will be given to studies that translate complex analytical data into reliable tools for quality control, product characterization, authentication, traceability, process optimization, and the prediction of sensory and nutritional properties.We welcome Original Research, Reviews, Methods, Perspectives, and other relevant article types addressing, but not limited to, the following topics:• Advanced chemometric methods for food and beverage analysis;• Multivariate classification, regression, pattern recognition, and feature selection;• Machine learning and interpretable artificial intelligence applied to analytical food data;• Low-, mid-, and high-level data fusion strategies;• Integration of spectroscopic, chromatographic, imaging, sensory, metabolomic, and other omics data;• Correlation between instrumental measurements and sensory, consumer, compositional, or nutritional outcomes;• Rapid and non-destructive methods based on NIR, MIR, Raman, hyperspectral imaging, electronic noses, electronic tongues, sensors, and biosensors;• Prediction of quality attributes, shelf life, nutritional composition, and sensory properties;• Chemometric tools for monitoring food processing, fermentation, storage, and product development;• Model validation, transferability, robustness, interpretability, and implementation in industrial settings.Contributions based on a single analytical platform are also encouraged when they demonstrate a significant methodological advancement or a rigorous application of chemometric modelling. Studies integrating multiple sources of data are particularly welcome, as are investigations demonstrating the applicability, scalability, and practical relevance of the proposed approaches.By bringing together developments in analytical science, chemometrics, data fusion, and food quality assessment, this Research Topic seeks to promote more reliable and comprehensive strategies for understanding and controlling the complex characteristics of foods and beverages. Topic editors: Margherita Modesti, Andrea Bellincontro, Jose Luis Aleixandre-Tudo Source: https://www.frontiersin.org/research-topics/86082/advanced-chemometrics-and-data-fusion-for-food-and-beverage-quality-assessment