Coffee is one of the most consumed beverages in the world. This has two main consequences: a high level of competitiveness among the players operating in the sector and an increasing pressure from the supply chain on the environment. These two aspects have to be supported by scientific research to foster innovation and reduce the negative impact of the coffee market on the environment. In this paper, we describe a mathematical model for espresso coffee extraction that is able to predict the chemical characterisation of the coffee in the cup. Such a model has been tested through a wide campaign of chemical laboratory analyses on espresso coffee samples extracted under different conditions. The results of such laboratory analyses are compared with the simulation results obtained using the aforementioned model. The comparison shows a close agreement between the real and in silico extractions, revealing that the model is a very promising scientific tool to take on the challenges of the coffee market.

Increasing evidence implicates mitochondrial dysfunction in the etiology of Parkinson’s disease (PD). Mitochondrial DNA (mtDNA) mutations are considered a possible cause and this mechanism might be shared with the aging process and with other age-related neurodegenerative disorders such as Alzheimer’s disease (AD). We have recently proposed a computerized method for mutated mtDNA characterization able to discriminate between AD and aging. The present study deals with mtDNA mutation-based profiling of PD. Peripheral blood mtDNA sequences from late-onset PD patients and age-matched controls were analyzed and compared to the revised Cambridge Reference Sequence (rCRS). The chaos game representation (CGR) method, modified to visualize heteroplasmic mutations, was used to display fractal properties of mtDNA sequences and fractal lacunarity analysis was applied to quantitatively characterize PD based on mtDNA mutations. Parameter β, from the hyperbola model function of our lacunarity method, was statistically different between PD and control groups when comparing mtDNA sequence frames corresponding to GenBank np 5713-9713. Our original method, based on CGR and lacunarity analysis, represents a useful tool to analyze mtDNA mutations. Lacunarity parameter β is able to characterize individual mutation profile of mitochondrial genome and could represent a promising index to discriminate between PD and aging.