Based on the activities developed so far, CHEMOSENS aims to increase the search for excellence, internationalization and knowledge transfer, promoting interaction between group members and external collaborators. The scientific challenges foreseen for the near future are the following:
- QUIMIOMETRIA I QUALIMETRIA
Smart analytical methods using miniaturized near-infrared (NIR) spectrometers: The main objectives of this line of research are the development of analytical methods using miniaturized NIR instruments, the characterization of their performance in application to real samples, and the application of chemometric techniques to their daily performance.
Development and validation of analytical methodologies to predict diesel properties: This study focuses on the development, validation, and application of multivariate calibration methods based on near-infrared (NIR) spectroscopy to predict various diesel properties.
Chemometric applications for optimal poultry feed formulation: This research focuses on the development and application of multivariate calibration models based on NIR spectra of raw materials, feed and excreta, to determine key nutritional properties, metabolizable energy and available nutrients (digestible protein, digestible fat, digestible amino acids).
Study of new raw materials as alternative protein sources for use in feed formulations: This research aims to develop NIR calibration and classification models to determine the nutritional properties of algae and insects as new raw materials.
- SENSOMETRIA INSTRUMENTAL
Characterization of the aroma of olive oil and its relationship with specific descriptors of the Catalan Protected Designations of Origin (PDO): This line of research aims to determine the differentiating aromatic notes of specific varieties and/or specific Catalan PDOs as well as to achieve the specification of the aromatic descriptor "ripe fruity" in olives and define it objectively.
Development of multivariate models for monitoring and controlling wine production: This study will evaluate infrared spectroscopy (FT-MIR) as a rapid instrumental technique to monitor both grape ripening and the must fermentation process. The final objective is to develop a tool that allows predicting quality parameters so that the winegrower can decide on the best use of his grapes.
Development of multivariate models for the monitoring and control of stone fruit derived products: This study aims to apply infrared spectroscopy (FT-MIR and NIR) to determine the physicochemical parameters of some stone fruits and control their ripening process. This information will be correlated with the characteristics of the final derived products obtained from different starting materials, and will help to allocate each harvest to the best end use according to its real properties.
- NANOSENSORS
Development of new iontronic platforms for the development of multiplexed electrochemical sensors: Ionic devices are systems in which an electronic current can be modulated by taking advantage of the electric field generated by ions. The main advantage is the amplification of the signal, i.e. several orders of magnitude higher than conventional systems. However, this technology is conventionally performed using the thin film methodology, which requires expertise at a high cost. In contrast, our group develops electrochemical organic transistors using the "thick film" technology. The sensors are printed on paper, thus reducing cost and complexity, with high sensitivity and are easily integrated into arrays for the simultaneous detection of several analytes in fields such as health and wellness.
Development of multiplexed iontronic devices for the determination of relevant analytical parameters in fermented beverages of mass consumption: This research combines iontronic systems with advanced statistical analysis techniques to obtain biochemical information in beverages. Therefore, we seek to promote active interaction with industry through fruitful technology transfers. The ultimate goal is the generation of functional prototypes for field evaluation.