Food fraud generates great economic losses for the industry and causes distrust in the consumer and traders. Black pepper is one of the most valued spices in the world, being susceptible to economically motivated adulteration. The objective of this study was to investigate the potential of near infrared hyperspectral imaging (NIR-HSI) combined with multivariate analysis to identify black pepper adulterated with common adulterant papaya seeds. Classification models based on principal component analyses (PCA) and soft independent modelling of class analogy (SIMCA) achieved 100% accuracy for classification of berry samples and sensitivity higher than 90% for ground samples. Partial least squares regression (PLSR) preprocessed by SNV + 2nd derivate presented the best prediction capability. A multispectral model using only 7 wavelengths presented RMSEP = 3.65% and RPD = 2.67. The adulteration maps show the distribution of ground papaya seeds in black pepper, which suggests NIR-HSI as a reliable analytical method for prediction of black pepper adulteration. This study demonstrates that NIR-HSI is a potential non-invasive and non-destructive technique to identify authentic black pepper and samples adulterated with papaya seeds.