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Proteomics coupled machine learning—innovative approach in geographical origin authentication of green Coffea arabica

ABSTRACT: The geographical authentication of green specialty coffee is an intriguing phenomenon that is not yet fully resolved. As one of few, this study is focused on the authentication of the geographical origin of green specialty coffee beans from well-known harvesting regions in Central America, South America, Africa, and Asia using proteomic profiling coupled with Linear Discriminant Analysis. Out of 1596 proteins, we identified 30 of the most significant target markers for the authentication of the geographical origin of specialty green coffee beans. The prediction performance of the model using leave-one-out cross-validation reached 85.3%, with the lowest accuracy in the prediction rate for Asian samples. Model performance and prediction sensitivity to random states were tested using 5-fold cross-validation. After 20 iterations, the model performance decreased to 84.0%.

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ORGANISM(S): Coffea Arabica (arabian Coffee)

TISSUE(S): Plant Cell

SUBMITTER: Maksym Danchenko

LAB HEAD: Peter Baráth

PROVIDER: PXD059982 | Pride | 2025-10-09

REPOSITORIES: Pride

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Project description:Epidemics of coffee leaf rust (CLR) lead to great yield losses and huge depreciation of coffee marketing values, if no control measures are applied. Societal expectations of a more sustainable coffee production are increasingly imposing the replacement of pesticide treatments by alternative solutions. A good protection strategy is to take advantage of the plant immune system by eliciting its constitutive defenses. Based on such concept, plant resistance inducers (PRIs) have been developed. The Greenforce CuCa formulation made by UFLA (Brazil) is, in addition to acibenzolar-S-methyl (ASM), showing promising results in the control of CLR (Hemileia vastatrix) in Coffea arabica. In order to improve our understanding of the molecular mechanisms of the PRIs, proteomic (2DE-MALDI/TOF/TOF-MS/MS), physiological (leaf gas-exchange) and biochemical (enzymatic) analyses of coffee leaves treated with Greenforce CuCa and ASM and inoculation with H. vastatrix were performed. Proteomic data showed metabolic adjustments mainly related with photosynthesis, protein metabolism and stress responses but, the proteins modulated by the two PRIs were different. Greenforce CuCa, on its own, increased photosynthesis and stomatal conductance, while ASM caused a decrease in these parameters. Upon H. vastratix infection, the Greenforce CuCa showed a higher protective effect on the leaf physiology than ASM. The enzymatic analyses indicated that Greenforce CuCa reinforces the redox homeostasis of the leaf, while ASM seems to increase the involvement of secondary metabolism. So, the PRIs prepare the plant to resist CLR but, inducing different defense mechanisms upon pathogen infection. The data also evidenced the existence of a link between the primary metabolism and defense responses. Furthermore, Greenforce CuCa emerged as a significant agent for CLR management. The identification of components of the plant primary metabolism, essential for plant growth and development that, simultaneously, participate in the plant defense responses can open new perspectives for plant breeding programs.

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Proteomics coupled machine learning—innovative approach in geographical origin authentication of green Coffea arabica

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