The authenticity and quality of productions is an area of priority interest that involves safety of consumers and potential economic damages deriving from frauds on origin, adulteration and labeling of products. Several investigation techniques are currently used to characterize food matrices from physical-chemical-biological point of view using different methods in order to limit possible adulterations. In this work, we have developed an experimental and computational framework to improve the potentialities of sensitive crystallization: an experimental technique known since 1936, but never used for quantitative assessment of food quality. As a test case, it has been applied to investigate the geographical traceability and quality of coffee samples. An extensive statistical analysis associated with a careful choice of advanced image descriptors allows gathering quantitative information about the samples, which can constitute a digital fingerprint of their composition. With this new tool we are able to distinguish with blind tests high-quality coffee brands from low-quality mixtures, different coffee species, green from toasted condition of beans and, to a lesser extent, the macro-geographical provenience. A powder X-ray diffraction analysis reinforces the results obtained by sensitive crystallization for the case where crystalline domains are present in the coffee sample.