This study proposes that images of stains from 100-nanolitre drops can be automatically identified as signatures of fluid composition and substrate chemistry, for e.g. rapid biological testing. Two datasets of stain images are produced and made available online, one with consumable fluids, and the other with biological fluids. Classification algorithms are used to identify an unknown stain by measuring its similarity to representative examples of predefined categories. The accuracy ranges from 80 to 94%, compared to an accuracy by random assignment of 3 to 4%. Clustering algorithms are also applied to group unknown stain images into a number of clusters each likely to correspond to similar combinations of fluids and substrates. The clustering accuracy ranges from 62 to 80%, compared to an accuracy by random assignment of 3 or 4%. The algorithms were also remarkably accurate at determining the presence or absence of
