Using an interdisciplinary approach that combines advanced technologies in analytical chemistry, food science, and data science, the project aims to conduct comprehensive analyses of key chemical and morphological markers specific to various categories of high-value Italian foods. This work will establish chemical profiles of high-quality Italian foods, identifying and quantifying key compounds responsible for flavor, aroma and nutritional value, as well as food components with beneficial effects on human health. The adoption of sophisticated modeling approaches and machine learning algorithms will enable the development of predictive models for assessing food quality and authenticity, improving the ability to discern variations in product composition. In addition, the study aims to develop portable and rapid detection methods for assessing food quality on-site, facilitating real-time monitoring along the production and distribution chain. This approach will promote more effective and timely control of food quality, contributing to greater environmental sustainability through optimized resource management.

The candidate should be a dynamic person (traveling is required) and have good communication skills. Fluent in Italian and English (at least B2 level). Good knowledge of analytical chemistry applied to food science is required. Good knowledge and practical experience with at least one among sample preparation techniques, gas chromatography, liquid chromatography, and/or mass spectrometry techniques is an important requirement. Familiarity with data handling, analysis, and interpretation and intermediate programming skills (e.g., Python, R, or Matlab) are also preferred.

The research will be carried out in different institutes, both academies and industries. Each institute will have a scientific leader who will mentor the candidate in accordance with the project themes. The research team's knowledge ranges from sampling and sample preparation techniques, analytical analysis and data processing to industrial knowledge of raw materials (collection, storage, etc.). The PhD candidate will be trained in all these aspects of the research.