Robusta coffee (Coffea canephora), which contributes around 40% of the world's coffee production, has traditionally been overshadowed by Arabica (Coffea arabica) in terms of sensory quality and market value. The higher caffeine content and bitter, earthy flavors of Robusta have confined it primarily to lower-grade commercial uses, such as instant coffee or espresso blends. However, the global coffee industry is facing major challenges from climate change, affecting yield, quality, and land suitability for both Arabica and Robusta cultivation in combination with an increased demand for higher-quality coffee. This has put pressure on the current old varieties cultivated in Arabica and Robusta, and new varieties with improved quality, productivity, and climate resilience are needed. Robusta, adapted to a hot and humid environment with higher productivity and resistance to pests and diseases, could potentially provide the solution. However, to do so, its lower sensory quality characteristics need to be improved. In order to achieve these quality standards in the face of climate change, the search for new varieties and genetic material could lie in the genetic resources of C. canephora. The Democratic Republic of the Congo (DRC) is a vital center of origin for C. canephora and contains a substantial genetic diversity, especially in its local cultivars and wild material. This thesis characterizes the genetic resources of the C. canephora field genebank from the INERA Coffee Collection in Yangambi, DRC. A comprehensive approach was taken, combining recent genotyping results with sensory quality analysis, phenotyping, and metabolite profiling of Robusta genotypes. Additionally, we evaluated the impact of different coffee processing methods on the sensory and metabolite profiles of selected Robusta genotypes. The first step was a detailed genetic screening of the INERA Coffee Collection. Leaf tissue samples were collected from 730 coffee shrubs in the collection and genotyping by sequencing and amplicon-based targeted DNA sequencing was undertaken to investigate the genetic structure of the collection. Reference material from local wild coffee shrubs, INERA Luki germplasm material, and sequencing data from samples from the Republic of Congo and Togo allowed us to place the genetic makeup of the INERA Yangambi germplasm in a broader context. Three genetic clusters were discovered in the INERA Coffee Collection of Yangami: Wild material, 'Lula' cultivars descendant from local breeding programs, and Congolese subgroup A material with origins in Luki. Population genetic analysis also revealed that around one-quarter of the unique genetic fingerprints in the collection had a hybrid identity namely 'Lula'- Wild hybrids and 'Lula' - subgroup A hybrids. Next, we evaluated the sensory attributes of 70 genotypes representing the genetic structure of the coffee collection, comprising Wild, 'Lula' - Wild hybrid, 'Lula', 'Lula' - subgroup A hybrid, and subgroup A material. Of those 70, 22 genotypes were evaluated for two consecutive years to assess the consistency of the sensory quality. Standard coffee cupping with the Fine Robusta Standards and Protocols was enhanced through sensory descriptors from the Coffee Taster's Flavor Wheel. Each genotype's sensory profile was constructed based on the total cupping score and the frequency of reported sensory descriptors. The Total cupping score ranged from 75.75 to 84.75, with a substantial variation in sensory profiles, even within a genetic cluster. Nutty/Cocoa was the most frequently reported descriptor class. The sensory profile ideotype exhibits a high frequency of Fruity, Sweet, and Sour/fermented descriptors and a low frequency of Green/Vegetative, Other, and Roasted descriptors. Evidence suggests that the sensory profile of a genotype is consistent over two harvest years. Genotypes with promising and unique sensory profiles were discovered within the cultivars and the wild - cultivar hybrids. We then quantified the morphological diversity and agronomic potential of the 70 genotypes. We measured 29 morphological traits, comprising vegetative, reproductive, tree architecture, and yield traits. Classification models were applied to establish whether these traits could accurately classify genotypes based on their background. Furthermore, the agronomic potential and green bean quality of the genotypes were studied. While significant variation in morphological traits was observed, no combination of traits could reliably predict the genetic background of different genotypes. Genotypes with promising traits for green beans were identified in both 'Lula' and 'Lula' - Wild hybrids, while promising yield traits were found in 'Lula' - Congolese subgroup A hybrids. Additionally, certain 'Lula' - Wild hybrids showed low specific leaf area and stomatal density, indicating potential fitness advantages in dry environments. Next, we studied the metabolite profiles of roasted coffee, green beans, and coffee leaves from a subset of 39 genotypes through LC-HRMS with an untargeted metabolomics approach. Our results showed that metabolite profiles of roasted coffee, green beans, and coffee leaves can be discriminated based on the coffee's sensory quality. The highest predictive power of coffee sensory quality was achieved with the metabolite profiles of the coffee leaves. Genotypes with varying genetic backgrounds could only be discriminated by the metabolite profiles of the coffee leaves. Metabolite marker compounds predictive of sensory quality were a putative quercetin derivate in green beans and likely physagulin E and argophyllin in coffee leaves. Estimated levels of caffeoylquinic acids, di-caffeoylquinic acids, and caffeoylquinic acid lactones were higher in roasted coffee with a lower sensory quality. These differences were not apparent in their respective green beans and coffee leaves. Lastly, four genotypes were subjected to different coffee processing methods to study the impact of coffee processing on the sensory and metabolite profile. The five processing methods were: Strip-picked, unsorted, sundried cherries; Overripe, sorted, sundried cherries; ripe, sorted, sundried (Natural) cherries; ripe, sorted, Pulped, sundried parchment; and ripe, sorted, wet fermented (Washed), sundried parchment. The resulting green beans underwent sensory descriptive cupping after roasting, seed germination tests, and metabolite profiling using LC-HRMS. The Pulped and Washed methods produced coffees with higher sensory attribute scores, while the Overripe method was associated with the sensory 'potato taste' defect. Washed coffee was characterized by smooth, fruity, cocoa notes and was negatively correlated with rough mouthfeel, tobacco, and leather flavors. The Pulped and Washed method had significantly higher germination success after four months of storage. The processing method influenced caffeine concentration in green beans, depending on the genotype, while trigonelline levels varied significantly between genotypes but not between processing methods. The grouping of the metabolite profiles of roasted coffee and green beans was consistent with their genetic background rather than the processing method. This PhD thesis highlights the sensory quality and agronomic potential of underexplored Robusta coffee genotypes from the Democratic Republic of the Congo and indicates the need for further screening to maximize their value. Our study also reveals the potential of the metabolite profile of coffee leaves as a predictive tool for coffee sensory quality and genetic background. We demonstrate the impact of coffee processing on the sensory profile of Robusta and underscore the chemical diversity within the species. Limitations of the research include uneven sampling of the genotypes and potential biases due to hybrid material. Environmental impact and heritability were not addressed in our research. The range and detection of metabolites in the samples were constrained by the extraction method and analytical technique used. Metabolite identification relied on putative identification through database matches. Future studies should include multi-location trials, heritability studies, and identification of the predictive markers for sensory quality. Ultimately, integrating the Congolese Robusta genetic resources into the commercial coffee market and into international breeding efforts could result in the development of high-yielding, climate-resilient, and high-quality Robusta varieties, supporting both local and global coffee industries.