Design and application of 3D-printed device for smartphone image analysis: Simultaneous detection of multiple adulterants in Specialty Canephora coffees

Coffee purity and the growing incidence of adulteration have been a constant concern. Depending on the adulterant, visual inspection is unreliable in roasted and ground coffee due to the similarity in color and texture of the materials used. An innovative device was created to couple a smartphone and perform the acquisition of images to detect pure and adulterated specialty Canephora coffees from three Brazilian origins (indigenous producers from Rondônia, non-indigenous producers from Rondônia, and Espírito Santo). Pure coffees were adulterated with Arabica, spent coffee ground, low-quality Canephora, coffee husks, açaí, corn, and soybean in increasing proportions of 10, 20, 30, 40, 50, 60, and 70%. The compact device had controlled lighting conditions and imaged the samples by means of a common smartphone. The images were converted into RGB-colorgrams signals and used to calculate models with partial least squares with discriminant analysis in order to predict and discriminate pure and adulterated coffees. The models shown that 100% from the pure Canephora from the non-indigenous, and 91% from the pure Canephora from the indigenous and Espírito Santo were correctly identified when discriminating pure from adulterated coffees separately. In an attempt to discriminate the samples according to the type of adulterant present in the mixtures, the values varied according to the adulterant. For indigenous coffee, only the samples adulterated with coffee husks were 100% identified; for non-indigenous coffee, only the samples adulterated with low-quality Canephora were 100% identified; For Espírito Santo samples, only the samples adulterated with corn were 100% identified. The other adulterated samples were detected in slightly lower proportions, ranging from 50 to 97%, with a prevalence of detection above 70% for all groups. The proposed method is a new alternative for detecting adulteration in coffee samples and could identify these adulterants when in higher proportions.