Description

Coffee quality assessment is a major scientific, economic, and societal challenge, as coffee is one of the most widely traded agricultural commodities worldwide. The quality of green and roasted coffee has a direct influence on market value, consumer perception, and the overall sustainability of the coffee supply chain. Quality variability arises from a complex interplay of factors, including plant physiology, storage conditions, microbial contamination, and environmental stress conditions. The global coffee industry represents a market of several hundred billion euros annually, with quality grading playing a central role in price determination. Producing regions are increasingly exposed to climate variability, which affects bean composition and aroma profiles, while consumers demand higher and more consistent sensory quality. Consequently, there is a growing need for rapid, objective, and valuable intelligent tools capable of assessing coffee quality throughout the production chain, from farm to cup.
The main objective of the ORBIT project is the development of a multi-sensory platform capable of simultaneously discriminating multiple chemical markers indicative related to coffee quality, defect signatures, and authenticity, including the identification of blends and non-authentic samples. These markers are related to VOCs associated with aroma, freshness, processing methods, contamination, and aging. The project focuses on the design, fabrication, and
characterization of flexible impedimetric sensor arrays using low-cost methods and organic functional materials. A key challenge lies in achieving sensitive and reproducible detection of low concentrations of VOCs within complex chemical environments. Coffee VOCs originate from diverse biochemical pathways and include aldehydes, alcohols, esters, ketones, furans, phenolic compounds, and sulfur-containing species. Their relative concentration evolves dynamically depending on processing and storage conditions. The project therefore aims to develop a sensory system capable not only of detecting individual compounds, but also of capturing multivariate signatures that reflect overall quality states. Another major challenge concerns data interpretation. The large volume of data generated by multi-sensor arrays requires advanced signal processing and machine learning strategies to extract relevant features, cluster responses, and quantify quality-related patterns. The integration of embedded intelligence is essential to enable real-time decisionmaking and autonomous operation.
The study will focus in particular on wetprocessing deposition techniques for organic materials. Two multi-material cointegration approaches will be considered: a top-down approach (via drop casting) and a bottom-up approach (via electro-grafting). The materials of interest will mainly
involve conductive polymers formulated with additives designed to optimize the chemo-specificity of the material through the incorporation of complexing compounds. These compounds will rely on fast, reversible chemistry, enabling sensor self-regeneration under operating conditions. They will include electron-acceptor species (Lewis’s acids). Particular attention will be paid to the
environmental footprint of the materials employed, guiding their selection for integration into a functional detection platform (e.g., porphyrins and metalloporphyrins will be considered as key functional materials due to their strong molecular recognition capabilities). The electrical characterization of the sensors under various conditions (laboratory and real-world environments), together with the implementation of machine-learning algorithms for data processing, clustering, and quantification of target molecules, will be investigated within the scope of this project.

Compétences requises

Electronique Organique Systèmes électroniques Programmation Python Réalisation de cartes électroniques PCB Capteurs

Bibliographie

[1] N. Núñez, E. Moret, P. Lucci, S. Moret, J. Saurina, et O. Núñez, « SPME-GC–MS and chemometrics for coffee characterization, classification and authentication », Microchemical Journal, vol. 213, p. 113771, juin 2025, doi: 10.1016/j.microc.2025.113771.
[2] J. J. van Mullem, J. S. de Sousa Bueno Filho, D. R. Dias, et R. F. Schwan, « Chemical and sensory characterization of coffee from Coffea arabica cv. Mundo Novo and cv. Catuai Vermelho obtained by four different post-harvest processing methods », Journal of the Science of Food and Agriculture, vol. 102, no 14, p. 6687‑6695, nov. 2022, doi: 10.1002/jsfa.12036.
[3] C. Taiti, G. Vivaldo, S. Mancuso, D. Comparini, et C. Pandolfi, « Volatile organic compounds (VOCs) fingerprinting combined with complex network analysis as a forecasting tool for tracing the origin and genetic lineage of Arabica specialty coffees », Scientific Reports, vol. 15, no 1, p. 13709, avr. 2025, doi: 10.1038/s41598-025-97162-5.
[4] « Yang, Si et al. “Determination of the Geographical Origin of Coffee Beans Using Terahertz Spectroscopy Combined With Machine Learning Methods.” Frontiers in nutrition vol. 8 680627. 17 Jun. 2021, doi:10.3389/fnut.2021.680627 ».
[5] « Moon JK, Shibamoto T. Role of roasting conditions in the profile of volatile flavor chemicals formed from coffee beans. J Agric Food Chem. 2009 Jul 8;57(13):5823-31. doi: 10.1021/jf901136e.
[6] L. Routier et al., « Single-point calibration process based integrated electrical impedance analyzer for multi-selective gas detection », Discov Appl Sci, vol. 6, no 8, p. 403, juill. 2024, doi: 10.1007/s42452-024-06102-x.

Mots clés

capteurs, Impedance, COVs, café, électronique organique